Data Summaries in R

Introduction

An essential part of any data analysis project is to understand the data at hand. For this task, we will create a function that takes as input a variable from the data, a categorical variable to describe by, and returns summary tables and plots.

This tutorial uses the R programming language (R Core Team 2019). All of the files needed to reproduce these results can be downloaded from the Git repository https://github.com/wkingc/data-summaries-r.

Required Libraries

The libraries knitr, bookdown, and kableExtra are used to generate the HTML output (Xie 2019, 2018; Zhu 2019). The ggplot2 library is loaded for the example data set that is used in this tutorial (Wickham 2016). The Hmisc library provides functionality needed to create variable labels (Harrell Jr, Charles Dupont, and others. 2019). The libraries reshape2 and dplyr are loaded for their data manipulation funtions (Wickham et al. 2019; Wickham 2007).

library("knitr")
library("bookdown")
library("kableExtra")
library("ggplot2")
library("Hmisc")
library("reshape2")
library("dplyr")

Example Data Setup

The data set used in this tutorial is mpg from the ggplot2 package. From the description in the manual:

This dataset contains a subset of the fuel economy data that the EPA makes available here. It contains only models which had a new release every year between 1999 and 2008 - this was used as a proxy for the popularity of the car.

set.seed(123)
data(mpg)
mpg <- data.frame(mpg)

colnames(mpg)[which(colnames(mpg) == "manufacturer")] <- "manu"

mpg$manu <- factor(mpg$manu)
mpg$model <- factor(mpg$model)
mpg$displ <- as.numeric(mpg$displ)
mpg$year <- factor(mpg$year, levels = c("1999", "2008"), ordered = TRUE)

mpg$dp <- as.Date(NA, origin = "1970-01-01")
mpg$dp[which(mpg$year == "1999")] <- sample(seq(as.Date('1999-01-01', format = "%Y-%m-%d", origin = "1970-01-01"), as.Date('1999-12-25', format = "%Y-%m-%d", origin = "1970-01-01"), by="day"), dim(mpg)[1]/2)
mpg$dp[which(mpg$year == "2008")] <- sample(seq(as.Date('2008-01-01', format = "%Y-%m-%d", origin = "1970-01-01"), as.Date('2008-12-25', format = "%Y-%m-%d", origin = "1970-01-01"), by="day"), dim(mpg)[1]/2)
mpg$dp[sample(1:length(mpg$dp), size = 20)] <- NA
mpg$dp[10] <- as.Date('1000-05-02', format = "%Y-%m-%d", origin = "1970-01-01")

mpg$dplt <- as.POSIXlt(NA, origin = "1970-01-01 0:0:0")
mpg$dplt[which(mpg$year == "1999")] <- sample(seq(as.POSIXlt('1999-01-01 0:0:0', format = "%Y-%m-%d %H:%M:%S", origin = "1970-01-01 0:0:0"), as.POSIXlt('1999-12-25 0:0:0', format = "%Y-%m-%d %H:%M:%S", origin = "1970-01-01 0:0:0"), by="min"), dim(mpg)[1]/2)
mpg$dplt[which(mpg$year == "2008")] <- sample(seq(as.POSIXlt('2008-01-01 0:0:0', format = "%Y-%m-%d %H:%M:%S", origin = "1970-01-01 0:0:0"), as.POSIXlt('2008-12-25 0:0:0', format = "%Y-%m-%d %H:%M:%S", origin = "1970-01-01 0:0:0"), by="sec"), dim(mpg)[1]/2)
mpg$dplt[sample(1:length(mpg$dplt), size = 20)] <- NA

mpg$dpct <- as.POSIXct(NA, origin = "1970-01-01 0:0:0")
mpg$dpct[which(mpg$year == "1999")] <- sample(seq(as.POSIXct('1999-01-01 0:0:0', format = "%Y-%m-%d %H:%M:%S", origin = "1970-01-01 0:0:0"), as.POSIXct('1999-12-25 0:0:0', format = "%Y-%m-%d %H:%M:%S", origin = "1970-01-01 0:0:0"), by="min"), dim(mpg)[1]/2)
mpg$dpct[which(mpg$year == "2008")] <- sample(seq(as.POSIXct('2008-01-01 0:0:0', format = "%Y-%m-%d %H:%M:%S", origin = "1970-01-01 0:0:0"), as.POSIXct('2008-12-25 0:0:0', format = "%Y-%m-%d %H:%M:%S", origin = "1970-01-01 0:0:0"), by="sec"), dim(mpg)[1]/2)
mpg$dpct[sample(1:length(mpg$dpct), size = 20)] <- NA

mpg$cyl <- factor(mpg$cyl, levels = c(4, 5, 6, 8), ordered = TRUE)

mpg$trans <- factor(mpg$trans)
mpg$drv <- factor(mpg$drv, levels = c("f", "r", "4"), labels = c("front-wheel drive", "rear wheel drive", "4wd"))
mpg$fl <- factor(mpg$fl)
mpg$class <- factor(mpg$class)

mpg$rn <- rnorm(dim(mpg)[1], mean = 10, sd = 5)
mpg$rn[sample(1:length(mpg$rn), size = 50)] <- NA

mpg$rdifftime <- rnorm(dim(mpg)[1], mean = 10, sd = 5)
mpg$rdifftime[sample(1:length(mpg$rdifftime), size = 50)] <- NA
mpg$rdifftime <- as.difftime(mpg$rdifftime, units = "weeks")
mpg$rdifftime[which(mpg$rdifftime < 0)] <- 0

mpg$logical <- mpg$rdifftime >= 10

mpg$party <- factor(sample(c("republican", "democrat", "independent", NA), dim(mpg)[1], replace = TRUE), levels = c("republican", "democrat", "independent"))

mpg$comments <- sample(c("I like this car!", "Meh.", "This is the worst car ever!", "Does it come in green?", "want cheese flavoured cars.", "Does it also fly?", "Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah", "Missing", ".", NA), dim(mpg)[1], replace = TRUE)

mpg$miss <- NA

label(mpg$manu) <- "manufacturer"
label(mpg$model) <- "model name"
label(mpg$displ) <- "engine displacement, in litres"
label(mpg$year) <- "year of manufacture"
label(mpg$dp) <- "date of purchase (Date class)"
label(mpg$dplt) <- "date of purchase (POSIXlt class)"
label(mpg$dpct) <- "date of purchase (POSIXct class)"
label(mpg$cyl) <- "number of cylinders"
label(mpg$trans) <- "type of transmission"
label(mpg$drv) <- "drive type"
label(mpg$cty) <- "city miles per gallon"
label(mpg$hwy) <- "highway miles per gallon"
label(mpg$fl) <- "fuel type"
label(mpg$class) <- "type of car"
label(mpg$rn) <- "some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5"
label(mpg$rdifftime) <- "some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks"
label(mpg$logical) <- "some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, and then set to TRUE if the difference is greater than 10"
label(mpg$party) <- "some random political parties"
label(mpg$comments) <- "some random comments"
label(mpg$miss) <- "an all missing variable"

kable(head(mpg), caption = "Header of <b>mpg</b>.", booktabs = TRUE, escape = FALSE) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Header of mpg.

manu	model	displ	year	cyl	trans	drv	cty	hwy	fl	class	dp	dplt	dpct	rn	rdifftime	logical	party	comments	miss
audi	a4	1.8	1999	4	auto(l5)	front-wheel drive	18	29	p	compact	1999-06-28	1999-10-07 08:18:00	1999-10-27 08:00:00	8.935759	9.675375 weeks	FALSE	NA	Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	NA
audi	a4	1.8	1999	4	manual(m5)	front-wheel drive	21	29	p	compact	1999-01-14	1999-04-28 07:00:00	1999-01-25 04:26:00	9.531816	13.782912 weeks	TRUE	democrat	Does it also fly?	NA
audi	a4	2.0	2008	4	manual(m6)	front-wheel drive	20	31	p	compact	2008-02-08	2008-05-04 14:32:00	2008-01-06 09:57:35	9.566429	4.928852 weeks	FALSE	independent	.	NA
audi	a4	2.0	2008	4	auto(av)	front-wheel drive	21	30	p	compact	2008-07-14	2008-02-11 12:43:49	2008-01-30 06:40:31	17.207309	6.539646 weeks	FALSE	democrat	Does it come in green?	NA
audi	a4	2.8	1999	6	auto(l5)	front-wheel drive	16	26	p	compact	1999-07-14	1999-07-22 13:22:00	1999-03-02 01:18:00	NA	NA weeks	NA	NA	.	NA
audi	a4	2.8	1999	6	manual(m5)	front-wheel drive	18	26	p	compact	1999-11-02	1999-08-20 08:26:00	1999-04-03 22:19:00	14.172008	8.202642 weeks	FALSE	NA	This is the worst car ever!	NA

Data Summary Function

Below are a set of functions I wrote to using S4 (see https://www.cyclismo.org/tutorial/R/s4Classes.html for a gentle introduction to object oriented programming in R), culminating into a single function called data_summary. The basic structure uses an object of class dataSummaries and then, based on the class of x, the dataSummariesSetup method applied to the dataSummaries class, returns an object of class dataSummariesCharacter, dataSummariesNumeric, dataSummariesDate, or dataSummariesDifftime. Each of these four output classes inherits from the dataSummaries class; thus any method written for dataSummaries also applies to the four classes that inherit from it.

As input the data_summary function takes a variable to summarize (x), an optional variable or variables (as a character string) to summarize by (by), the data (data), and the units to use for difftime if x refers to a Date, POSIXlt, POSIXct, or difftime object in the data.

As output, the function returns an object of class dataSummaries. The function has a show method and a method called make_output that generates knitr friendly output. The summary table and plot can also be accessed individually through their accessor functions, data_summary_table, and data_summary_plot, respectively.

setOldClass(c("ggplot", "gg"))

dataSummaries <- setClass(
    "dataSummaries",
    
    slots = c(
        x = "character",
        by = "character",
        data = "data.frame",
        difftime_units = "character",
        xLab = "character",
        byLab = "character",
        table = "data.frame",
        plot = "ANY"
    ),
    
    prototype = list(
        x = character(0),
        by = character(0),
        data = data.frame(),
        difftime_units = character(0),
        xLab = character(0),
        byLab = character(0),
        table = data.frame(),
        plot = NULL
    ),
)

invisible(setValidity("dataSummaries", function(object) {
  if (!is.null(object@plot) && !inherits(object@plot, "ggplot")) {
    return("The 'plot' slot must be a ggplot object or NULL.")
  }
  TRUE
}))

dataSummariesCharacter <- setClass(
    "dataSummariesCharacter",
    
    slots = c(
        type = "character"
    ),
    
    prototype = list(
        type = character(0)
    ),
    
    contains = "dataSummaries"
)

dataSummariesNumeric <- setClass(
    "dataSummariesNumeric",
    
    slots = c(
        type = "character"
    ),
    
    prototype = list(
        type = character(0)
    ),
    
    contains = "dataSummaries"
)

dataSummariesDate <- setClass(
    "dataSummariesDate",
    
    slots = c(
        type = "character"
    ),
    
    prototype = list(
        type = character(0)
    ),
    
    contains = "dataSummaries"
)

dataSummariesDifftime <- setClass(
    "dataSummariesDifftime",
    
    slots = c(
        type = "character"
    ),
    
    prototype = list(
        type = character(0)
    ),
    
    contains = "dataSummaries"
)

invisible(setGeneric(name = "dataSummariesSetup", def = function(object) standardGeneric("dataSummariesSetup")))
setMethod(
    f = "dataSummariesSetup",
    signature = "dataSummaries", 
    definition = function(object) 
    {
        x = object@x
        by = object@by
        data = object@data
        
        xLab <- label(data[, x])
        colnames(data)[which(colnames(data) == x)] <- "var"
        
        if (length(by) == 0) {
            data$by <- factor(data$by <- "")
            label(data$by) <- ""
            byLab <- label(data$by)
        } else {
            data$by <- interaction(data[, by], sep = ", ")
            byLab <- paste(label(data[, by]), collapse = " by ")
            
            overall <- data
            overall$by <- "Overall"
            data <- rbind(data, overall)
        }
        
        data <- data[, c("var", "by")]
        
        if ("labelled" %in% class(data$var)) {
            class(data$var) <- class(data$var)[(-1)*which(class(data$var) == "labelled")]
        }
        
        object@xLab <- xLab
        object@byLab <- byLab
        object@data <- data
        
        if (any(c("character", "factor", "logical") %in% class(data$var))) { 
            return(dataSummariesCharacter(object, type = class(data$var)))
        } else if (any(c("numeric", "integer") %in% class(data$var))) {
            return(dataSummariesNumeric(object, type = class(data$var))) 
        } else if (any(c("Date", "POSIXlt", "POSIXct", "POSIXt") %in% class(data$var))) {
            if (length(object@difftime_units) == 0) stop("You need to specify the units for the difference in time.  See help(difftime) for additional information.")
            return(dataSummariesDate(object, type = class(data$var))) 
        } else if ("difftime" %in% class(data$var)) {
            if (length(object@difftime_units) == 0) stop("You need to specify the units for the difference in time.  See help(difftime) for additional information.")
            return(dataSummariesDifftime(object, type = class(data$var))) 
        } else {
            stop("x is an unsupported class")
        }
    }
)

invisible(setGeneric(name = "data_summary_switch", def = function(object) standardGeneric("data_summary_switch")))

setMethod(
    f = "data_summary_switch",
    signature = "dataSummariesCharacter", 
    definition = function(object) 
    {
        xLab <- object@xLab
        byLab <- object@byLab
        data <- object@data
        
        freqs <- table(data$var, data$by, useNA = "ifany", dnn = c(xLab, byLab))
        
        rownames(freqs)[which(is.na(rownames(freqs)))] <- "R NA Value"
        colnames(freqs)[which(is.na(colnames(freqs)))] <- "R NA Value" 
        
        props <- round(100*prop.table(freqs, 2), 2)
        
        res <- freqs
        for (i in 1:dim(freqs)[2]) {
            res[, i] <- paste(freqs[, i], " (", props[, i], "%)", sep = "")
        }
        res <- as.data.frame(res)
        colnames(res) <- c("var", "by", "freq")
        res <- dcast(res, var ~ by, value.var = "freq")
        colnames(res)[1] <- xLab
        if (byLab == "") colnames(res)[2] <- "n (%)"
        
        pData <- as.data.frame(props)
        colnames(pData) <- c("var", "by", "freq")
        
        levs <- as.character(pData$var)
        tmp <- nchar(levs)
        strCombRes <- list()
        for (k in 1:length(levs)) {
            strRes <- list()
            j = 0
            for (i in 1:ceiling(max(tmp)/30)) {
                strRes[[i]] <- substr(levs[k], j, 30*i)
                j = 30*i + 1
            }
            strCombRes[[k]] <- unlist(strRes)    
        }
        
        foo <- function(x) {
            if (!(length(which(x == "")) == 0)) x <- x[-1*which(x == "")]
            x <- paste(x, collapse = "\n")
            
            return(x)
        }
        
        levs <- unlist(lapply(strCombRes, foo))
        
        pData$names <- factor(rownames(pData), levels = rownames(pData), labels = levs)
        pData <- pData[, -1]
        
        colfunc <- colorRampPalette(c("#e41a1c","#377eb8","#4daf4a","#984ea3","#ff7f00"))
        colors <- colfunc(length(levels(pData$names)))
        
        p = ggplot(data = pData, aes(x = by, y = freq, fill = names)) +
            scale_fill_manual(values = colors) + 
            geom_bar(stat = "identity") + 
            xlab(paste(strwrap(xLab, width = 60), collapse = "\n")) +
            ylab("Percent") +
            theme(
                axis.line = element_line(colour = "black"),
                panel.border = element_rect(colour = "black", fill = NA, size = 1),
                axis.text = element_text(size = 12),
                axis.text.x = element_text(angle = 50, hjust = 1),
                axis.title = element_text(size = 12),
                legend.title = element_blank(),
                legend.position = "right",
                panel.grid = element_line(color = "lightgray"),
                panel.background = element_rect(fill = "white", colour = "white"))
        
        object@table <- res
        object@plot <- p
        
        return(object)
    }
)

setMethod(
    f = "data_summary_switch",
    signature = "dataSummariesNumeric", 
    definition = function(object) 
    {
        xLab <- object@xLab
        byLab <- object@byLab
        data <- object@data
        
        if (any(is.na(data$by))) {
            byLevs <- levels(data$by)
            data$by <- as.character(data$by)
            
            data$by[which(is.na(as.character(data$by)))] <- "R NA Value"
            data$by <- factor(data$by, levels = c(byLevs, "R NA Value"))
        }
        
        percMiss <- function(x) res <- round((length(which(is.na(x)))/length(x))*100, 2)
        
        res <- data %>%
            group_by(by) %>%
            summarize(
                label = xLab,
                n = length(na.omit(var)),
                miss = percMiss(var),
                mean = round(mean(var, na.rm = TRUE), 2),
                sd = round(sd(var, na.rm = TRUE), 2),
                median = round(median(var, na.rm = TRUE), 2),
                mad = round(mad(var, na.rm = TRUE), 2),
                q25 = round(quantile(var, probs = 0.25, na.rm = TRUE, type = 1), 2),
                q75 = round(quantile(var, probs = 0.75, na.rm = TRUE, type = 1), 2),
                IQR = round(IQR(var, na.rm = TRUE), 2),
                min = round(min(var, na.rm = TRUE), 2),
                max = round(max(var, na.rm = TRUE), 2)
            )
        
        res <- data.frame(res)
        
        colnames(res) <- c(byLab, "Label", "N", "P NA", "Mean", "S Dev", "Med", "MAD", "25th P", "75th P", "IQR", "Min", "Max")
        
        pData <- na.omit(data.frame(data[, c("var", "by")]))
        
        p = ggplot(data = pData, aes(x = by, y = var)) +
            geom_boxplot(position = position_dodge(1), fill = "#2c7bb6") +
            xlab(byLab) +
            ylab(paste(strwrap(xLab, width = 40), collapse = "\n")) +
            theme(
                axis.line = element_line(colour = "black"),
                panel.border = element_rect(colour = "black", fill = NA, size = 1),
                legend.position = "none",
                axis.text = element_text(size = 12),
                axis.text.x = element_text(angle = 50, hjust = 1),
                axis.title = element_text(size = 12), 
                panel.grid = element_line(color = "lightgray"),
                panel.background = element_rect(fill = "white", colour = "white"))
        
        object@table <- res
        object@plot <- p
        
        return(object)
    }
)

setMethod(
    f = "data_summary_switch",
    signature = "dataSummariesDate", 
    definition = function(object) 
    {
        xLab <- object@xLab
        byLab <- object@byLab
        data <- object@data
        difftime_units <- object@difftime_units
        
        if (any(is.na(data$by))) {
            byLevs <- levels(data$by)
            data$by <- as.character(data$by)
            
            data$by[which(is.na(as.character(data$by)))] <- "R NA Value"
            data$by <- factor(data$by, levels = c(byLevs, "R NA Value"))
        }
        
        percMiss <- function(x) round((length(which(is.na(x)))/length(x))*100, 2)
        
        sdDate <- function(x) {
            res <- difftime(x, mean(x, na.rm = TRUE), units = "secs")
            res <- as.numeric(as.character(res))
            res <- sd(res, na.rm = TRUE)
            res <- as.difftime(res, units = "secs")
            units(res) <- difftime_units
            
            return(res)
        }
        
        sdDate(data$var)
        
        madDate <- function(x) {
            res <- difftime(x, mean(x, na.rm = TRUE), units = "secs")
            res <- as.numeric(as.character(res))
            res <- mad(res, na.rm = TRUE)
            res <- as.difftime(res, units = "secs")
            units(res) <- difftime_units
            
            return(res)
        }
        
        dquantile <- function(x, probs){
            sx <- sort(x)
            pos <- round(probs*length(x))
            return(sx[pos])
        }
        
        q25Date <- function(x) dquantile(x, probs = 0.25)
        
        q75Date <- function(x) dquantile(x, probs = 0.75)
        
        IQRdate <- function(x) {
            res <- difftime(dquantile(x, probs = 0.75), dquantile(x, probs = 0.25), units = "secs")
            units(res) <- difftime_units
            return(res)
        }
        
        res <- data %>%
            group_by(by) %>%
            summarize(
                label = xLab,
                n = length(na.omit(var)),
                miss = percMiss(var),
                mean = mean(var, na.rm = TRUE),
                sd = round(sdDate(var), 2),
                median = median(var, na.rm = TRUE),
                mad = round(madDate(var), 2),
                q25 = q25Date(var),
                q75 = q75Date(var),
                IQR = IQRdate(var),
                min = min(var, na.rm = TRUE),
                max = max(var, na.rm = TRUE)
            )
        
        res <- data.frame(res)
        
        colnames(res) <- c(byLab, "Label", "N", "P NA", "Mean", "S Dev", "Med", "MAD", "25th P", "75th P", "IQR", "Min", "Max")
        
        pData <- na.omit(data.frame(data[, c("var", "by")]))
        
        if ("POSIXlt" %in% class(pData$var)) pData$var <- as.POSIXct(pData$var)
        
        p = ggplot(data = pData, aes(x = by, y = var)) +
            geom_boxplot(position = position_dodge(1), fill = "#2c7bb6") +
            xlab(byLab) +
            ylab(paste(strwrap(xLab, width = 40), collapse = "\n")) +
            theme(
                axis.line = element_line(colour = "black"),
                panel.border = element_rect(colour = "black", fill = NA, size = 1),
                legend.position = "none",
                axis.text = element_text(size = 12),
                axis.text.x = element_text(angle = 50, hjust = 1),
                axis.title = element_text(size = 12), 
                panel.grid = element_line(color = "lightgray"),
                panel.background = element_rect(fill = "white", colour = "white"))
        
        object@table <- res
        object@plot <- p
        
        return(object)
    }
)

setMethod(
    f = "data_summary_switch",
    signature = "dataSummariesDifftime", 
    definition = function(object) 
    {
        xLab <- object@xLab
        byLab <- object@byLab
        data <- object@data
        difftime_units <- object@difftime_units
        
        if (any(is.na(data$by))) {
            byLevs <- levels(data$by)
            data$by <- as.character(data$by)
            
            data$by[which(is.na(as.character(data$by)))] <- "R NA Value"
            data$by <- factor(data$by, levels = c(byLevs, "R NA Value"))
        }
        
        percMiss <- function(x) res <- round((length(which(is.na(x)))/length(x))*100, 2)
        
        units(data$var) <- "days"
        
        meanDate <- function(x) {
            res <- mean(x, na.rm = TRUE)
            units(res) <- difftime_units
            return(res)
        }
        
        medianDate <- function(x) {
            res <- median(x, na.rm = TRUE)
            units(res) <- difftime_units
            return(res)
        }
        
        sdDate <- function(x) {
            res <- as.difftime(sd(as.numeric(x), na.rm = TRUE), format = "%X", units = "days")
            units(res) <- difftime_units
            return(res)
        }
        
        madDate <- function(x) {
            res <- as.difftime(mad(as.numeric(x), na.rm = TRUE), format = "%X", units = "days")
            units(res) <- difftime_units
            return(res)
        }
        
        q25Date <- function(x) {
            res <- as.difftime(quantile(as.numeric(x), probs = 0.25, na.rm = TRUE, type = 1), units = "days")
            units(res) <- difftime_units
            return(res)
        }
        
        q75Date <- function(x) {
            res <- as.difftime(quantile(as.numeric(x), probs = 0.75, na.rm = TRUE, type = 1), units = "days")
            units(res) <- difftime_units
            return(res)
        }
        
        IQRdate <- function(x) {
            res <- as.difftime(IQR(as.numeric(x), na.rm = TRUE), format = "%X", units = "days")
            units(res) <- difftime_units
            return(res)
        }
        
        minDate <- function(x) {
            res <- as.difftime(min(as.numeric(x), na.rm = TRUE), format = "%X", units = "days")
            units(res) <- difftime_units
            return(res)
        }
        
        maxDate <- function(x) {
            res <- as.difftime(max(as.numeric(x), na.rm = TRUE), format = "%X", units = "days")
            units(res) <- difftime_units
            return(res)
        }  
        
        res <- data %>%
            group_by(by) %>%
            summarize(
                label = xLab,
                n = length(na.omit(var)),
                miss = percMiss(var),
                mean = round(meanDate(var), 2),
                sd = round(sdDate(var), 2),
                median = round(medianDate(var), 2),
                mad = round(madDate(var), 2),
                q25 = round(q25Date(var), 2),
                q75 = round(q75Date(var), 2),
                IQR = round(IQRdate(var), 2),
                min = round(minDate(var), 2),
                max = round(maxDate(var), 2)
            )
        
        res <- data.frame(res)
        
        colnames(res) <- c(byLab, "Label", "N", "P NA", "Mean", "S Dev", "Med", "MAD", "25th P", "75th P", "IQR", "Min", "Max")
        
        pData <- na.omit(data.frame(data[, c("var", "by")]))
        units(pData$var) <- difftime_units
        
        p = ggplot(data = pData, aes(x = by, y = var)) +
            geom_boxplot(position = position_dodge(1), fill = "#2c7bb6") +
            xlab(byLab) +
            ylab(paste(strwrap(xLab, width = 40), collapse = "\n")) +
            theme(
                axis.line = element_line(colour = "black"),
                panel.border = element_rect(colour = "black", fill = NA, size = 1),
                legend.position = "none",
                axis.text = element_text(size = 12),
                axis.text.x = element_text(angle = 50, hjust = 1),
                axis.title = element_text(size = 12), 
                panel.grid = element_line(color = "lightgray"),
                panel.background = element_rect(fill = "white", colour = "white"))
        
        object@table <- res
        object@plot <- p
        
        return(object)
    }
)

setMethod(
    f = "show",
    signature = "dataSummaries", 
    definition = function(object) 
    {
        print(object@table)
        print(object@plot)
    }
)

invisible(setGeneric(name = "make_kable_output", def = function(object) standardGeneric("make_kable_output")))
setMethod(
    f = "make_kable_output",
    signature = "dataSummaries", 
    definition = function(object) 
    {
        if (object@byLab == "") {
            print(
                kable(
                    object@table, caption = paste("Summary statistics of ", object@xLab, ".", sep = ""), booktabs = TRUE, table.attr = "data-quarto-disable-processing=true") %>% 
                    kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE, font_size = 14))
        } else {
            print(
                kable(
                    object@table, caption = paste("Summary statistics of ", object@xLab, " by ", object@byLab, ".", sep = ""), booktabs = TRUE, table.attr = "data-quarto-disable-processing=true") %>% 
                    kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE, font_size = 14))
        }
    }
)

invisible(setGeneric(name = "make_complete_output", def = function(object) standardGeneric("make_complete_output")))
setMethod(
    f = "make_complete_output",
    signature = "dataSummaries", 
    definition = function(object) 
    {
        if (object@byLab == "") {
            print(
                kable(
                    object@table, caption = paste("Summary statistics of ", object@xLab, ".", sep = ""), booktabs = TRUE, table.attr = "data-quarto-disable-processing=true") %>% 
                    kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE, font_size = 14))
        } else {
            print(
                kable(
                    object@table, caption = paste("Summary statistics of ", object@xLab, " by ", object@byLab, ".", sep = ""), booktabs = TRUE, table.attr = "data-quarto-disable-processing=true") %>% 
                    kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE, font_size = 14))
        }
        
        print(object@plot)
    }
)

invisible(setGeneric(name = "data_summary_table", def = function(object) standardGeneric("data_summary_table")))
setMethod(
    f = "data_summary_table",
    signature = "dataSummaries", 
    definition = function(object) 
    {
        object@table
    }
)

invisible(setGeneric(name = "data_summary_plot", def = function(object) standardGeneric("data_summary_plot")))
setMethod(
    f = "data_summary_plot",
    signature = "dataSummaries", 
    definition = function(object) 
    {
        object@plot
    }
)

data_summary <- function(x, by = character(0), data, difftime_units = character(0)) {
    object = dataSummaries(x = x, data = data, by = by, difftime_units = difftime_units)
    object = dataSummariesSetup(object)
    object = data_summary_switch(object)
}

Examples

Categorical

Show method to output table and plot

show(cylSummaryExample)

  number of cylinders       n (%)
1                   4 81 (34.62%)
2                   5   4 (1.71%)
3                   6 79 (33.76%)
4                   8 70 (29.91%)

Output the summary table

data_summary_table(cylSummaryExample)

  number of cylinders       n (%)
1                   4 81 (34.62%)
2                   5   4 (1.71%)
3                   6 79 (33.76%)
4                   8 70 (29.91%)

Output the plot

data_summary_plot(cylSummaryExample)

Generate knitr friendly summary table

make_kable_output(cylSummaryExample)

Summary statistics of number of cylinders.

number of cylinders	n (%)
4	81 (34.62%)
5	4 (1.71%)
6	79 (33.76%)
8	70 (29.91%)

Generate knitr friendly output

make_complete_output(cylSummaryExample)

Summary statistics of number of cylinders.

number of cylinders	n (%)
4	81 (34.62%)
5	4 (1.71%)
6	79 (33.76%)
8	70 (29.91%)

Stacked barplot of number of cylinders.

Categorical By

Show method to output table and plot

show(cylByYearSummaryExample)

  number of cylinders        1999        2008     Overall
1                   4 45 (38.46%) 36 (30.77%) 81 (34.62%)
2                   5      0 (0%)   4 (3.42%)   4 (1.71%)
3                   6 45 (38.46%) 34 (29.06%) 79 (33.76%)
4                   8 27 (23.08%) 43 (36.75%) 70 (29.91%)

Output the summary table

data_summary_table(cylByYearSummaryExample)

  number of cylinders        1999        2008     Overall
1                   4 45 (38.46%) 36 (30.77%) 81 (34.62%)
2                   5      0 (0%)   4 (3.42%)   4 (1.71%)
3                   6 45 (38.46%) 34 (29.06%) 79 (33.76%)
4                   8 27 (23.08%) 43 (36.75%) 70 (29.91%)

Output the plot

data_summary_plot(cylByYearSummaryExample)

Generate a knitr friendly summary table

make_kable_output(cylByYearSummaryExample)

Summary statistics of number of cylinders by year of manufacture.

number of cylinders	1999	2008	Overall
4	45 (38.46%)	36 (30.77%)	81 (34.62%)
5	0 (0%)	4 (3.42%)	4 (1.71%)
6	45 (38.46%)	34 (29.06%)	79 (33.76%)
8	27 (23.08%)	43 (36.75%)	70 (29.91%)

Generate knitr friendly output

make_complete_output(cylByYearSummaryExample)

Summary statistics of number of cylinders by year of manufacture.

number of cylinders	1999	2008	Overall
4	45 (38.46%)	36 (30.77%)	81 (34.62%)
5	0 (0%)	4 (3.42%)	4 (1.71%)
6	45 (38.46%)	34 (29.06%)	79 (33.76%)
8	27 (23.08%)	43 (36.75%)	70 (29.91%)

Stacked barplot of number of cylinders by year of manufacture.

Categorical By By

Show method to output table and plot

show(cylByYearByPartySummaryExample)

  number of cylinders 1999, republican 2008, republican 1999, democrat
1                   4      14 (45.16%)          9 (36%)       12 (40%)
2                   5           0 (0%)           1 (4%)         0 (0%)
3                   6      12 (38.71%)          5 (20%)     7 (23.33%)
4                   8       5 (16.13%)         10 (40%)    11 (36.67%)
  2008, democrat 1999, independent 2008, independent     Overall  R NA Value
1     8 (25.81%)        9 (32.14%)       12 (35.29%) 81 (34.62%) 17 (30.91%)
2      2 (6.45%)            0 (0%)            0 (0%)   4 (1.71%)   1 (1.82%)
3     6 (19.35%)       13 (46.43%)       12 (35.29%) 79 (33.76%) 24 (43.64%)
4    15 (48.39%)        6 (21.43%)       10 (29.41%) 70 (29.91%) 13 (23.64%)

Output the summary table

data_summary_table(cylByYearByPartySummaryExample)

  number of cylinders 1999, republican 2008, republican 1999, democrat
1                   4      14 (45.16%)          9 (36%)       12 (40%)
2                   5           0 (0%)           1 (4%)         0 (0%)
3                   6      12 (38.71%)          5 (20%)     7 (23.33%)
4                   8       5 (16.13%)         10 (40%)    11 (36.67%)
  2008, democrat 1999, independent 2008, independent     Overall  R NA Value
1     8 (25.81%)        9 (32.14%)       12 (35.29%) 81 (34.62%) 17 (30.91%)
2      2 (6.45%)            0 (0%)            0 (0%)   4 (1.71%)   1 (1.82%)
3     6 (19.35%)       13 (46.43%)       12 (35.29%) 79 (33.76%) 24 (43.64%)
4    15 (48.39%)        6 (21.43%)       10 (29.41%) 70 (29.91%) 13 (23.64%)

Output the plot

data_summary_plot(cylByYearByPartySummaryExample)

Generate a knitr friendly summary table

make_kable_output(cylByYearByPartySummaryExample)

Summary statistics of number of cylinders by year of manufacture by some random political parties.

number of cylinders	1999, republican	2008, republican	1999, democrat	2008, democrat	1999, independent	2008, independent	Overall	R NA Value
4	14 (45.16%)	9 (36%)	12 (40%)	8 (25.81%)	9 (32.14%)	12 (35.29%)	81 (34.62%)	17 (30.91%)
5	0 (0%)	1 (4%)	0 (0%)	2 (6.45%)	0 (0%)	0 (0%)	4 (1.71%)	1 (1.82%)
6	12 (38.71%)	5 (20%)	7 (23.33%)	6 (19.35%)	13 (46.43%)	12 (35.29%)	79 (33.76%)	24 (43.64%)
8	5 (16.13%)	10 (40%)	11 (36.67%)	15 (48.39%)	6 (21.43%)	10 (29.41%)	70 (29.91%)	13 (23.64%)

Generate knitr friendly output

make_complete_output(cylByYearByPartySummaryExample)

Summary statistics of number of cylinders by year of manufacture by some random political parties.

number of cylinders	1999, republican	2008, republican	1999, democrat	2008, democrat	1999, independent	2008, independent	Overall	R NA Value
4	14 (45.16%)	9 (36%)	12 (40%)	8 (25.81%)	9 (32.14%)	12 (35.29%)	81 (34.62%)	17 (30.91%)
5	0 (0%)	1 (4%)	0 (0%)	2 (6.45%)	0 (0%)	0 (0%)	4 (1.71%)	1 (1.82%)
6	12 (38.71%)	5 (20%)	7 (23.33%)	6 (19.35%)	13 (46.43%)	12 (35.29%)	79 (33.76%)	24 (43.64%)
8	5 (16.13%)	10 (40%)	11 (36.67%)	15 (48.39%)	6 (21.43%)	10 (29.41%)	70 (29.91%)	13 (23.64%)

Stacked barplot of number of cylinders by year of manufacture by some random political parties.

Continuous

Show method to output table and plot

show(ctySummaryExample)

                   Label   N P NA  Mean S Dev Med  MAD 25th P 75th P IQR Min
1  city miles per gallon 234    0 16.86  4.26  17 4.45     14     19   5   9
  Max
1  35

Output the summary table

data_summary_table(ctySummaryExample)

                   Label   N P NA  Mean S Dev Med  MAD 25th P 75th P IQR Min
1  city miles per gallon 234    0 16.86  4.26  17 4.45     14     19   5   9
  Max
1  35

Output the plot

data_summary_plot(ctySummaryExample)

Generate knitr friendly summary table

make_kable_output(ctySummaryExample)

Summary statistics of city miles per gallon.

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5	9	35

Generate knitr friendly output

make_complete_output(ctySummaryExample)

Summary statistics of city miles per gallon.

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5	9	35

Stacked barplot of city miles per gallon.

Continuous By

Show method to output table and plot

show(ctyByCylSummaryExample)

  number of cylinders                 Label   N P NA  Mean S Dev  Med  MAD
1                   4 city miles per gallon  81    0 21.01  3.50 21.0 2.97
2                   5 city miles per gallon   4    0 20.50  0.58 20.5 0.74
3                   6 city miles per gallon  79    0 16.22  1.77 16.0 1.48
4                   8 city miles per gallon  70    0 12.57  1.81 13.0 2.22
5             Overall city miles per gallon 234    0 16.86  4.26 17.0 4.45
  25th P 75th P IQR Min Max
1     19     22   3  15  35
2     20     21   1  20  21
3     15     18   3  11  19
4     11     14   3   9  16
5     14     19   5   9  35

Output the summary table

data_summary_table(ctyByCylSummaryExample)

  number of cylinders                 Label   N P NA  Mean S Dev  Med  MAD
1                   4 city miles per gallon  81    0 21.01  3.50 21.0 2.97
2                   5 city miles per gallon   4    0 20.50  0.58 20.5 0.74
3                   6 city miles per gallon  79    0 16.22  1.77 16.0 1.48
4                   8 city miles per gallon  70    0 12.57  1.81 13.0 2.22
5             Overall city miles per gallon 234    0 16.86  4.26 17.0 4.45
  25th P 75th P IQR Min Max
1     19     22   3  15  35
2     20     21   1  20  21
3     15     18   3  11  19
4     11     14   3   9  16
5     14     19   5   9  35

Output the plot

data_summary_plot(ctyByCylSummaryExample)

Generate a knitr friendly summary table

make_kable_output(ctyByCylSummaryExample)

Summary statistics of city miles per gallon by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	city miles per gallon	81	0	21.01	3.50	21.0	2.97	19	22	3	15	35
5	city miles per gallon	4	0	20.50	0.58	20.5	0.74	20	21	1	20	21
6	city miles per gallon	79	0	16.22	1.77	16.0	1.48	15	18	3	11	19
8	city miles per gallon	70	0	12.57	1.81	13.0	2.22	11	14	3	9	16
Overall	city miles per gallon	234	0	16.86	4.26	17.0	4.45	14	19	5	9	35

Generate knitr friendly output

make_complete_output(ctyByCylSummaryExample)

Summary statistics of city miles per gallon by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	city miles per gallon	81	0	21.01	3.50	21.0	2.97	19	22	3	15	35
5	city miles per gallon	4	0	20.50	0.58	20.5	0.74	20	21	1	20	21
6	city miles per gallon	79	0	16.22	1.77	16.0	1.48	15	18	3	11	19
8	city miles per gallon	70	0	12.57	1.81	13.0	2.22	11	14	3	9	16
Overall	city miles per gallon	234	0	16.86	4.26	17.0	4.45	14	19	5	9	35

Stacked barplot of city miles per gallon by number of cylinders.

Continuous By By

Show method to output table and plot

show(ctyByCylByYearSummaryExample)

  number of cylinders by year of manufacture                 Label   N P NA
1                                    4, 1999 city miles per gallon  45    0
2                                    6, 1999 city miles per gallon  45    0
3                                    8, 1999 city miles per gallon  27    0
4                                    4, 2008 city miles per gallon  36    0
5                                    5, 2008 city miles per gallon   4    0
6                                    6, 2008 city miles per gallon  34    0
7                                    8, 2008 city miles per gallon  43    0
8                                    Overall city miles per gallon 234    0
   Mean S Dev  Med  MAD 25th P 75th P IQR Min Max
1 20.84  4.24 19.0 2.97     18     21 3.0  15  35
2 16.07  1.67 16.0 2.97     15     18 3.0  13  19
3 12.22  1.65 11.0 0.00     11     13 2.0  11  16
4 21.22  2.29 21.0 1.48     20     22 2.0  17  28
5 20.50  0.58 20.5 0.74     20     21 1.0  20  21
6 16.41  1.91 17.0 1.48     15     18 2.5  11  19
7 12.79  1.88 13.0 1.48     12     14 2.0   9  16
8 16.86  4.26 17.0 4.45     14     19 5.0   9  35

Output the summary table

data_summary_table(ctyByCylByYearSummaryExample)

  number of cylinders by year of manufacture                 Label   N P NA
1                                    4, 1999 city miles per gallon  45    0
2                                    6, 1999 city miles per gallon  45    0
3                                    8, 1999 city miles per gallon  27    0
4                                    4, 2008 city miles per gallon  36    0
5                                    5, 2008 city miles per gallon   4    0
6                                    6, 2008 city miles per gallon  34    0
7                                    8, 2008 city miles per gallon  43    0
8                                    Overall city miles per gallon 234    0
   Mean S Dev  Med  MAD 25th P 75th P IQR Min Max
1 20.84  4.24 19.0 2.97     18     21 3.0  15  35
2 16.07  1.67 16.0 2.97     15     18 3.0  13  19
3 12.22  1.65 11.0 0.00     11     13 2.0  11  16
4 21.22  2.29 21.0 1.48     20     22 2.0  17  28
5 20.50  0.58 20.5 0.74     20     21 1.0  20  21
6 16.41  1.91 17.0 1.48     15     18 2.5  11  19
7 12.79  1.88 13.0 1.48     12     14 2.0   9  16
8 16.86  4.26 17.0 4.45     14     19 5.0   9  35

Output the plot

data_summary_plot(ctyByCylByYearSummaryExample)

Generate a knitr friendly summary table

make_kable_output(ctyByCylByYearSummaryExample)

Summary statistics of city miles per gallon by number of cylinders by year of manufacture.

number of cylinders by year of manufacture	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4, 1999	city miles per gallon	45	0	20.84	4.24	19.0	2.97	18	21	3.0	15	35
6, 1999	city miles per gallon	45	0	16.07	1.67	16.0	2.97	15	18	3.0	13	19
8, 1999	city miles per gallon	27	0	12.22	1.65	11.0	0.00	11	13	2.0	11	16
4, 2008	city miles per gallon	36	0	21.22	2.29	21.0	1.48	20	22	2.0	17	28
5, 2008	city miles per gallon	4	0	20.50	0.58	20.5	0.74	20	21	1.0	20	21
6, 2008	city miles per gallon	34	0	16.41	1.91	17.0	1.48	15	18	2.5	11	19
8, 2008	city miles per gallon	43	0	12.79	1.88	13.0	1.48	12	14	2.0	9	16
Overall	city miles per gallon	234	0	16.86	4.26	17.0	4.45	14	19	5.0	9	35

Generate knitr friendly output

make_complete_output(ctyByCylByYearSummaryExample)

Summary statistics of city miles per gallon by number of cylinders by year of manufacture.

number of cylinders by year of manufacture	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4, 1999	city miles per gallon	45	0	20.84	4.24	19.0	2.97	18	21	3.0	15	35
6, 1999	city miles per gallon	45	0	16.07	1.67	16.0	2.97	15	18	3.0	13	19
8, 1999	city miles per gallon	27	0	12.22	1.65	11.0	0.00	11	13	2.0	11	16
4, 2008	city miles per gallon	36	0	21.22	2.29	21.0	1.48	20	22	2.0	17	28
5, 2008	city miles per gallon	4	0	20.50	0.58	20.5	0.74	20	21	1.0	20	21
6, 2008	city miles per gallon	34	0	16.41	1.91	17.0	1.48	15	18	2.5	11	19
8, 2008	city miles per gallon	43	0	12.79	1.88	13.0	1.48	12	14	2.0	9	16
Overall	city miles per gallon	234	0	16.86	4.26	17.0	4.45	14	19	5.0	9	35

Stacked barplot of city miles per gallon by number of cylinders by year of manufacture.

Date

Show method to output table and plot

show(dpSummaryExample)

                           Label   N P NA       Mean        S Dev        Med
1  date of purchase (Date class) 213 8.58 2003-12-21 236.59 weeks 1999-12-24
          MAD     25th P     75th P            IQR        Min        Max
1 74.98 weeks 1999-07-14 2008-09-01 476.7143 weeks 1999-01-04 2008-12-23

Output the summary table

data_summary_table(dpSummaryExample)

                           Label   N P NA       Mean        S Dev        Med
1  date of purchase (Date class) 213 8.58 2003-12-21 236.59 weeks 1999-12-24
          MAD     25th P     75th P            IQR        Min        Max
1 74.98 weeks 1999-07-14 2008-09-01 476.7143 weeks 1999-01-04 2008-12-23

Output the plot

data_summary_plot(dpSummaryExample)

Generate knitr friendly summary table

make_kable_output(dpSummaryExample)

Summary statistics of date of purchase (Date class).

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.7143 weeks	1999-01-04	2008-12-23

Generate knitr friendly output

make_complete_output(dpSummaryExample)

Summary statistics of date of purchase (Date class).

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.7143 weeks	1999-01-04	2008-12-23

Stacked barplot of date of purchase (Date class).

Date By

Show method to output table and plot

show(dpByCylSummaryExample)

  number of cylinders                         Label   N  P NA       Mean
1                   4 date of purchase (Date class)  73  8.75 2003-03-03
2                   5 date of purchase (Date class)   3 25.00 2008-09-25
3                   6 date of purchase (Date class)  71 10.13 2003-06-14
4                   8 date of purchase (Date class)  66  5.71 2005-03-13
5             Overall date of purchase (Date class) 213  8.58 2003-12-21
         S Dev        Med         MAD     25th P     75th P             IQR
1 234.04 weeks 1999-10-11 49.35 weeks 1999-06-03 2008-07-28 477.57143 weeks
2  16.08 weeks 2008-11-13  6.78 weeks 2008-05-20 2008-12-15  29.85714 weeks
3 235.29 weeks 1999-11-02 50.20 weeks 1999-07-14 2008-08-02 472.42857 weeks
4 229.06 weeks 2008-02-10 52.42 weeks 1999-10-04 2008-09-08 466.00000 weeks
5 236.59 weeks 1999-12-24 74.98 weeks 1999-07-14 2008-09-01 476.71429 weeks
         Min        Max
1 1999-01-14 2008-12-23
2 2008-05-20 2008-12-15
3 1999-01-05 2008-12-09
4 1999-01-04 2008-12-14
5 1999-01-04 2008-12-23

Output the summary table

data_summary_table(dpByCylSummaryExample)

  number of cylinders                         Label   N  P NA       Mean
1                   4 date of purchase (Date class)  73  8.75 2003-03-03
2                   5 date of purchase (Date class)   3 25.00 2008-09-25
3                   6 date of purchase (Date class)  71 10.13 2003-06-14
4                   8 date of purchase (Date class)  66  5.71 2005-03-13
5             Overall date of purchase (Date class) 213  8.58 2003-12-21
         S Dev        Med         MAD     25th P     75th P             IQR
1 234.04 weeks 1999-10-11 49.35 weeks 1999-06-03 2008-07-28 477.57143 weeks
2  16.08 weeks 2008-11-13  6.78 weeks 2008-05-20 2008-12-15  29.85714 weeks
3 235.29 weeks 1999-11-02 50.20 weeks 1999-07-14 2008-08-02 472.42857 weeks
4 229.06 weeks 2008-02-10 52.42 weeks 1999-10-04 2008-09-08 466.00000 weeks
5 236.59 weeks 1999-12-24 74.98 weeks 1999-07-14 2008-09-01 476.71429 weeks
         Min        Max
1 1999-01-14 2008-12-23
2 2008-05-20 2008-12-15
3 1999-01-05 2008-12-09
4 1999-01-04 2008-12-14
5 1999-01-04 2008-12-23

Output the plot

data_summary_plot(dpByCylSummaryExample)

Generate a knitr friendly summary table

make_kable_output(dpByCylSummaryExample)

Summary statistics of date of purchase (Date class) by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	date of purchase (Date class)	73	8.75	2003-03-03	234.04 weeks	1999-10-11	49.35 weeks	1999-06-03	2008-07-28	477.57143 weeks	1999-01-14	2008-12-23
5	date of purchase (Date class)	3	25.00	2008-09-25	16.08 weeks	2008-11-13	6.78 weeks	2008-05-20	2008-12-15	29.85714 weeks	2008-05-20	2008-12-15
6	date of purchase (Date class)	71	10.13	2003-06-14	235.29 weeks	1999-11-02	50.20 weeks	1999-07-14	2008-08-02	472.42857 weeks	1999-01-05	2008-12-09
8	date of purchase (Date class)	66	5.71	2005-03-13	229.06 weeks	2008-02-10	52.42 weeks	1999-10-04	2008-09-08	466.00000 weeks	1999-01-04	2008-12-14
Overall	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.71429 weeks	1999-01-04	2008-12-23

Generate knitr friendly output

make_complete_output(dpByCylSummaryExample)

Summary statistics of date of purchase (Date class) by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	date of purchase (Date class)	73	8.75	2003-03-03	234.04 weeks	1999-10-11	49.35 weeks	1999-06-03	2008-07-28	477.57143 weeks	1999-01-14	2008-12-23
5	date of purchase (Date class)	3	25.00	2008-09-25	16.08 weeks	2008-11-13	6.78 weeks	2008-05-20	2008-12-15	29.85714 weeks	2008-05-20	2008-12-15
6	date of purchase (Date class)	71	10.13	2003-06-14	235.29 weeks	1999-11-02	50.20 weeks	1999-07-14	2008-08-02	472.42857 weeks	1999-01-05	2008-12-09
8	date of purchase (Date class)	66	5.71	2005-03-13	229.06 weeks	2008-02-10	52.42 weeks	1999-10-04	2008-09-08	466.00000 weeks	1999-01-04	2008-12-14
Overall	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.71429 weeks	1999-01-04	2008-12-23

Stacked barplot of date of purchase (Date class) by number of cylinders.

Date By By

Show method to output table and plot

show(dpByCylByCommentsSummaryExample)

         number of cylinders by some random comments
1                                               4, .
2                                               6, .
3                                               8, .
4  4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
5  6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
6  8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
7                               4, Does it also fly?
8                               6, Does it also fly?
9                               8, Does it also fly?
10                         4, Does it come in green?
11                         6, Does it come in green?
12                         8, Does it come in green?
13                               4, I like this car!
14                               6, I like this car!
15                               8, I like this car!
16                                           4, Meh.
17                                           6, Meh.
18                                           8, Meh.
19                                        4, Missing
20                                        6, Missing
21                                        8, Missing
22                    4, This is the worst car ever!
23                    6, This is the worst car ever!
24                    8, This is the worst car ever!
25                    4, want cheese flavoured cars.
26                    6, want cheese flavoured cars.
27                    8, want cheese flavoured cars.
28                                           Overall
29                                        R NA Value
                           Label   N  P NA       Mean        S Dev        Med
1  date of purchase (Date class)   5  0.00 2003-01-27 252.39 weeks 1999-10-26
2  date of purchase (Date class)   9  0.00 2004-08-21 241.15 weeks 2008-04-02
3  date of purchase (Date class)  10  9.09 2004-11-28 246.03 weeks 2008-02-07
4  date of purchase (Date class)   9  0.00 2002-08-10 241.71 weeks 1999-09-12
5  date of purchase (Date class)   7 12.50 2004-08-16 254.96 weeks 2008-05-13
6  date of purchase (Date class)   4  0.00 2004-01-07 274.68 weeks 2004-02-23
7  date of purchase (Date class)   5  0.00 2002-12-14 265.47 weeks 1999-08-06
8  date of purchase (Date class)   4 42.86 2001-09-28 225.84 weeks 1999-08-23
9  date of purchase (Date class)   4  0.00 2004-03-25 272.68 weeks 2004-04-06
10 date of purchase (Date class)  15  0.00 2003-07-23 243.88 weeks 1999-08-26
11 date of purchase (Date class)   3  0.00 2002-06-23 268.42 weeks 1999-09-09
12 date of purchase (Date class)   5  0.00 2006-07-09 217.62 weeks 2008-02-09
13 date of purchase (Date class)   8 11.11 2005-04-06 247.72 weeks 2008-07-29
14 date of purchase (Date class)   7 22.22 2002-01-30 232.78 weeks 1999-08-23
15 date of purchase (Date class)   4  0.00 2001-11-20 246.39 weeks 1999-09-27
16 date of purchase (Date class)   6  0.00 1999-05-01   9.86 weeks 1999-04-25
17 date of purchase (Date class)   6  0.00 2005-06-05 248.14 weeks 2008-04-27
18 date of purchase (Date class)   5 16.67 2008-04-14   9.80 weeks 2008-04-15
19 date of purchase (Date class)   3 50.00 2002-08-26 280.92 weeks 1999-10-09
20 date of purchase (Date class)   5  0.00 2004-12-23 255.73 weeks 2008-05-24
21 date of purchase (Date class)  14  0.00 2005-11-13 226.66 weeks 2008-04-02
22 date of purchase (Date class)   7  0.00 2003-05-28 247.48 weeks 1999-11-24
23 date of purchase (Date class)   9 10.00 2002-08-03 237.70 weeks 1999-10-18
24 date of purchase (Date class)   5  0.00 2006-09-25 213.60 weeks 2008-07-04
25 date of purchase (Date class)  10  9.09 2003-02-13 238.91 weeks 1999-12-10
26 date of purchase (Date class)  13  0.00 2002-04-09 231.98 weeks 1999-08-31
27 date of purchase (Date class)   8 11.11 2005-01-02 240.53 weeks 2008-02-06
28 date of purchase (Date class) 213  8.58 2003-12-21 236.59 weeks 1999-12-24
29 date of purchase (Date class)  20 16.67 2003-12-06 236.95 weeks 2003-12-24
            MAD     25th P     75th P              IQR        Min        Max
1   54.64 weeks 1999-02-10 2008-02-08 469.285714 weeks 1999-02-10 2008-08-12
2   44.27 weeks 1999-08-28 2008-06-18 459.571429 weeks 1999-07-14 2008-10-28
3   61.53 weeks 1999-10-05 2008-09-06 465.571429 weeks 1999-01-13 2008-11-27
4   38.34 weeks 1999-03-15 2008-05-25 479.857143 weeks 1999-03-08 2008-12-23
5   31.13 weeks 1999-06-07 2008-08-02 477.714286 weeks 1999-03-19 2008-10-07
6  342.16 weeks 1999-02-03 2008-06-12 488.142857 weeks 1999-02-03 2008-09-09
7   43.21 weeks 1999-01-14 2008-02-14 474.000000 weeks 1999-01-14 2008-11-26
8   12.71 weeks 1999-07-03       <NA>         NA weeks 1999-06-15 2008-03-25
9  347.46 weeks 1999-07-16 2008-08-25 475.428571 weeks 1999-07-16 2008-11-10
10  47.02 weeks 1999-03-24 2008-02-26 465.857143 weeks 1999-01-16 2008-10-13
11  27.75 weeks 1999-05-01 1999-09-09  18.714286 weeks 1999-05-01 2008-05-31
12  24.15 weeks 1999-02-01 2008-06-02 487.000000 weeks 1999-02-01 2008-12-08
13  20.33 weeks 1999-06-23 2008-09-23 482.857143 weeks 1999-06-08 2008-12-12
14  25.84 weeks 1999-04-23 2008-09-05 489.000000 weeks 1999-03-13 2008-09-05
15  30.61 weeks 1999-02-12 1999-11-28  41.285714 weeks 1999-02-12 2008-12-14
16   8.47 weeks 1999-03-26 1999-05-16   7.285714 weeks 1999-01-25 1999-08-11
17  26.58 weeks 1999-07-31 2008-05-08 457.714286 weeks 1999-01-05 2008-09-26
18  11.44 weeks 2008-02-21 2008-05-27  13.714286 weeks 2008-01-27 2008-07-13
19  34.74 weeks 1999-10-09       <NA>         NA weeks 1999-04-28 2008-11-11
20  21.18 weeks 1999-07-30 2008-08-09 471.142857 weeks 1999-07-30 2008-09-01
21  38.55 weeks 1999-10-04 2008-09-08 466.000000 weeks 1999-01-04 2008-11-15
22  50.62 weeks 1999-06-03 2008-02-10 453.428571 weeks 1999-03-30 2008-09-29
23  38.34 weeks 1999-04-20 2008-09-13 490.571429 weeks 1999-03-22 2008-10-26
24  16.10 weeks 1999-06-02 2008-09-18 485.142857 weeks 1999-06-02 2008-09-19
25  57.50 weeks 1999-06-09 2008-05-15 466.142857 weeks 1999-02-17 2008-10-31
26  36.85 weeks 1999-03-10 2008-06-24 484.857143 weeks 1999-01-26 2008-12-09
27  44.16 weeks 1999-05-21 2008-07-18 478.000000 weeks 1999-04-01 2008-10-18
28  74.98 weeks 1999-07-14 2008-09-01 476.714286 weeks 1999-01-04 2008-12-23
29 337.72 weeks 1999-08-14 2008-06-25 462.571429 weeks 1999-01-07 2008-12-03

Output the summary table

data_summary_table(dpByCylByCommentsSummaryExample)

         number of cylinders by some random comments
1                                               4, .
2                                               6, .
3                                               8, .
4  4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
5  6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
6  8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
7                               4, Does it also fly?
8                               6, Does it also fly?
9                               8, Does it also fly?
10                         4, Does it come in green?
11                         6, Does it come in green?
12                         8, Does it come in green?
13                               4, I like this car!
14                               6, I like this car!
15                               8, I like this car!
16                                           4, Meh.
17                                           6, Meh.
18                                           8, Meh.
19                                        4, Missing
20                                        6, Missing
21                                        8, Missing
22                    4, This is the worst car ever!
23                    6, This is the worst car ever!
24                    8, This is the worst car ever!
25                    4, want cheese flavoured cars.
26                    6, want cheese flavoured cars.
27                    8, want cheese flavoured cars.
28                                           Overall
29                                        R NA Value
                           Label   N  P NA       Mean        S Dev        Med
1  date of purchase (Date class)   5  0.00 2003-01-27 252.39 weeks 1999-10-26
2  date of purchase (Date class)   9  0.00 2004-08-21 241.15 weeks 2008-04-02
3  date of purchase (Date class)  10  9.09 2004-11-28 246.03 weeks 2008-02-07
4  date of purchase (Date class)   9  0.00 2002-08-10 241.71 weeks 1999-09-12
5  date of purchase (Date class)   7 12.50 2004-08-16 254.96 weeks 2008-05-13
6  date of purchase (Date class)   4  0.00 2004-01-07 274.68 weeks 2004-02-23
7  date of purchase (Date class)   5  0.00 2002-12-14 265.47 weeks 1999-08-06
8  date of purchase (Date class)   4 42.86 2001-09-28 225.84 weeks 1999-08-23
9  date of purchase (Date class)   4  0.00 2004-03-25 272.68 weeks 2004-04-06
10 date of purchase (Date class)  15  0.00 2003-07-23 243.88 weeks 1999-08-26
11 date of purchase (Date class)   3  0.00 2002-06-23 268.42 weeks 1999-09-09
12 date of purchase (Date class)   5  0.00 2006-07-09 217.62 weeks 2008-02-09
13 date of purchase (Date class)   8 11.11 2005-04-06 247.72 weeks 2008-07-29
14 date of purchase (Date class)   7 22.22 2002-01-30 232.78 weeks 1999-08-23
15 date of purchase (Date class)   4  0.00 2001-11-20 246.39 weeks 1999-09-27
16 date of purchase (Date class)   6  0.00 1999-05-01   9.86 weeks 1999-04-25
17 date of purchase (Date class)   6  0.00 2005-06-05 248.14 weeks 2008-04-27
18 date of purchase (Date class)   5 16.67 2008-04-14   9.80 weeks 2008-04-15
19 date of purchase (Date class)   3 50.00 2002-08-26 280.92 weeks 1999-10-09
20 date of purchase (Date class)   5  0.00 2004-12-23 255.73 weeks 2008-05-24
21 date of purchase (Date class)  14  0.00 2005-11-13 226.66 weeks 2008-04-02
22 date of purchase (Date class)   7  0.00 2003-05-28 247.48 weeks 1999-11-24
23 date of purchase (Date class)   9 10.00 2002-08-03 237.70 weeks 1999-10-18
24 date of purchase (Date class)   5  0.00 2006-09-25 213.60 weeks 2008-07-04
25 date of purchase (Date class)  10  9.09 2003-02-13 238.91 weeks 1999-12-10
26 date of purchase (Date class)  13  0.00 2002-04-09 231.98 weeks 1999-08-31
27 date of purchase (Date class)   8 11.11 2005-01-02 240.53 weeks 2008-02-06
28 date of purchase (Date class) 213  8.58 2003-12-21 236.59 weeks 1999-12-24
29 date of purchase (Date class)  20 16.67 2003-12-06 236.95 weeks 2003-12-24
            MAD     25th P     75th P              IQR        Min        Max
1   54.64 weeks 1999-02-10 2008-02-08 469.285714 weeks 1999-02-10 2008-08-12
2   44.27 weeks 1999-08-28 2008-06-18 459.571429 weeks 1999-07-14 2008-10-28
3   61.53 weeks 1999-10-05 2008-09-06 465.571429 weeks 1999-01-13 2008-11-27
4   38.34 weeks 1999-03-15 2008-05-25 479.857143 weeks 1999-03-08 2008-12-23
5   31.13 weeks 1999-06-07 2008-08-02 477.714286 weeks 1999-03-19 2008-10-07
6  342.16 weeks 1999-02-03 2008-06-12 488.142857 weeks 1999-02-03 2008-09-09
7   43.21 weeks 1999-01-14 2008-02-14 474.000000 weeks 1999-01-14 2008-11-26
8   12.71 weeks 1999-07-03       <NA>         NA weeks 1999-06-15 2008-03-25
9  347.46 weeks 1999-07-16 2008-08-25 475.428571 weeks 1999-07-16 2008-11-10
10  47.02 weeks 1999-03-24 2008-02-26 465.857143 weeks 1999-01-16 2008-10-13
11  27.75 weeks 1999-05-01 1999-09-09  18.714286 weeks 1999-05-01 2008-05-31
12  24.15 weeks 1999-02-01 2008-06-02 487.000000 weeks 1999-02-01 2008-12-08
13  20.33 weeks 1999-06-23 2008-09-23 482.857143 weeks 1999-06-08 2008-12-12
14  25.84 weeks 1999-04-23 2008-09-05 489.000000 weeks 1999-03-13 2008-09-05
15  30.61 weeks 1999-02-12 1999-11-28  41.285714 weeks 1999-02-12 2008-12-14
16   8.47 weeks 1999-03-26 1999-05-16   7.285714 weeks 1999-01-25 1999-08-11
17  26.58 weeks 1999-07-31 2008-05-08 457.714286 weeks 1999-01-05 2008-09-26
18  11.44 weeks 2008-02-21 2008-05-27  13.714286 weeks 2008-01-27 2008-07-13
19  34.74 weeks 1999-10-09       <NA>         NA weeks 1999-04-28 2008-11-11
20  21.18 weeks 1999-07-30 2008-08-09 471.142857 weeks 1999-07-30 2008-09-01
21  38.55 weeks 1999-10-04 2008-09-08 466.000000 weeks 1999-01-04 2008-11-15
22  50.62 weeks 1999-06-03 2008-02-10 453.428571 weeks 1999-03-30 2008-09-29
23  38.34 weeks 1999-04-20 2008-09-13 490.571429 weeks 1999-03-22 2008-10-26
24  16.10 weeks 1999-06-02 2008-09-18 485.142857 weeks 1999-06-02 2008-09-19
25  57.50 weeks 1999-06-09 2008-05-15 466.142857 weeks 1999-02-17 2008-10-31
26  36.85 weeks 1999-03-10 2008-06-24 484.857143 weeks 1999-01-26 2008-12-09
27  44.16 weeks 1999-05-21 2008-07-18 478.000000 weeks 1999-04-01 2008-10-18
28  74.98 weeks 1999-07-14 2008-09-01 476.714286 weeks 1999-01-04 2008-12-23
29 337.72 weeks 1999-08-14 2008-06-25 462.571429 weeks 1999-01-07 2008-12-03

Output the plot

data_summary_plot(dpByCylByCommentsSummaryExample)

Generate a knitr friendly summary table

make_kable_output(dpByCylByCommentsSummaryExample)

Summary statistics of date of purchase (Date class) by number of cylinders by some random comments.

number of cylinders by some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4, .	date of purchase (Date class)	5	0.00	2003-01-27	252.39 weeks	1999-10-26	54.64 weeks	1999-02-10	2008-02-08	469.285714 weeks	1999-02-10	2008-08-12
6, .	date of purchase (Date class)	9	0.00	2004-08-21	241.15 weeks	2008-04-02	44.27 weeks	1999-08-28	2008-06-18	459.571429 weeks	1999-07-14	2008-10-28
8, .	date of purchase (Date class)	10	9.09	2004-11-28	246.03 weeks	2008-02-07	61.53 weeks	1999-10-05	2008-09-06	465.571429 weeks	1999-01-13	2008-11-27
4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	9	0.00	2002-08-10	241.71 weeks	1999-09-12	38.34 weeks	1999-03-15	2008-05-25	479.857143 weeks	1999-03-08	2008-12-23
6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	7	12.50	2004-08-16	254.96 weeks	2008-05-13	31.13 weeks	1999-06-07	2008-08-02	477.714286 weeks	1999-03-19	2008-10-07
8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	4	0.00	2004-01-07	274.68 weeks	2004-02-23	342.16 weeks	1999-02-03	2008-06-12	488.142857 weeks	1999-02-03	2008-09-09
4, Does it also fly?	date of purchase (Date class)	5	0.00	2002-12-14	265.47 weeks	1999-08-06	43.21 weeks	1999-01-14	2008-02-14	474.000000 weeks	1999-01-14	2008-11-26
6, Does it also fly?	date of purchase (Date class)	4	42.86	2001-09-28	225.84 weeks	1999-08-23	12.71 weeks	1999-07-03	NA	NA weeks	1999-06-15	2008-03-25
8, Does it also fly?	date of purchase (Date class)	4	0.00	2004-03-25	272.68 weeks	2004-04-06	347.46 weeks	1999-07-16	2008-08-25	475.428571 weeks	1999-07-16	2008-11-10
4, Does it come in green?	date of purchase (Date class)	15	0.00	2003-07-23	243.88 weeks	1999-08-26	47.02 weeks	1999-03-24	2008-02-26	465.857143 weeks	1999-01-16	2008-10-13
6, Does it come in green?	date of purchase (Date class)	3	0.00	2002-06-23	268.42 weeks	1999-09-09	27.75 weeks	1999-05-01	1999-09-09	18.714286 weeks	1999-05-01	2008-05-31
8, Does it come in green?	date of purchase (Date class)	5	0.00	2006-07-09	217.62 weeks	2008-02-09	24.15 weeks	1999-02-01	2008-06-02	487.000000 weeks	1999-02-01	2008-12-08
4, I like this car!	date of purchase (Date class)	8	11.11	2005-04-06	247.72 weeks	2008-07-29	20.33 weeks	1999-06-23	2008-09-23	482.857143 weeks	1999-06-08	2008-12-12
6, I like this car!	date of purchase (Date class)	7	22.22	2002-01-30	232.78 weeks	1999-08-23	25.84 weeks	1999-04-23	2008-09-05	489.000000 weeks	1999-03-13	2008-09-05
8, I like this car!	date of purchase (Date class)	4	0.00	2001-11-20	246.39 weeks	1999-09-27	30.61 weeks	1999-02-12	1999-11-28	41.285714 weeks	1999-02-12	2008-12-14
4, Meh.	date of purchase (Date class)	6	0.00	1999-05-01	9.86 weeks	1999-04-25	8.47 weeks	1999-03-26	1999-05-16	7.285714 weeks	1999-01-25	1999-08-11
6, Meh.	date of purchase (Date class)	6	0.00	2005-06-05	248.14 weeks	2008-04-27	26.58 weeks	1999-07-31	2008-05-08	457.714286 weeks	1999-01-05	2008-09-26
8, Meh.	date of purchase (Date class)	5	16.67	2008-04-14	9.80 weeks	2008-04-15	11.44 weeks	2008-02-21	2008-05-27	13.714286 weeks	2008-01-27	2008-07-13
4, Missing	date of purchase (Date class)	3	50.00	2002-08-26	280.92 weeks	1999-10-09	34.74 weeks	1999-10-09	NA	NA weeks	1999-04-28	2008-11-11
6, Missing	date of purchase (Date class)	5	0.00	2004-12-23	255.73 weeks	2008-05-24	21.18 weeks	1999-07-30	2008-08-09	471.142857 weeks	1999-07-30	2008-09-01
8, Missing	date of purchase (Date class)	14	0.00	2005-11-13	226.66 weeks	2008-04-02	38.55 weeks	1999-10-04	2008-09-08	466.000000 weeks	1999-01-04	2008-11-15
4, This is the worst car ever!	date of purchase (Date class)	7	0.00	2003-05-28	247.48 weeks	1999-11-24	50.62 weeks	1999-06-03	2008-02-10	453.428571 weeks	1999-03-30	2008-09-29
6, This is the worst car ever!	date of purchase (Date class)	9	10.00	2002-08-03	237.70 weeks	1999-10-18	38.34 weeks	1999-04-20	2008-09-13	490.571429 weeks	1999-03-22	2008-10-26
8, This is the worst car ever!	date of purchase (Date class)	5	0.00	2006-09-25	213.60 weeks	2008-07-04	16.10 weeks	1999-06-02	2008-09-18	485.142857 weeks	1999-06-02	2008-09-19
4, want cheese flavoured cars.	date of purchase (Date class)	10	9.09	2003-02-13	238.91 weeks	1999-12-10	57.50 weeks	1999-06-09	2008-05-15	466.142857 weeks	1999-02-17	2008-10-31
6, want cheese flavoured cars.	date of purchase (Date class)	13	0.00	2002-04-09	231.98 weeks	1999-08-31	36.85 weeks	1999-03-10	2008-06-24	484.857143 weeks	1999-01-26	2008-12-09
8, want cheese flavoured cars.	date of purchase (Date class)	8	11.11	2005-01-02	240.53 weeks	2008-02-06	44.16 weeks	1999-05-21	2008-07-18	478.000000 weeks	1999-04-01	2008-10-18
Overall	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.714286 weeks	1999-01-04	2008-12-23
R NA Value	date of purchase (Date class)	20	16.67	2003-12-06	236.95 weeks	2003-12-24	337.72 weeks	1999-08-14	2008-06-25	462.571429 weeks	1999-01-07	2008-12-03

Generate knitr friendly output

make_complete_output(dpByCylByCommentsSummaryExample)

Summary statistics of date of purchase (Date class) by number of cylinders by some random comments.

number of cylinders by some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4, .	date of purchase (Date class)	5	0.00	2003-01-27	252.39 weeks	1999-10-26	54.64 weeks	1999-02-10	2008-02-08	469.285714 weeks	1999-02-10	2008-08-12
6, .	date of purchase (Date class)	9	0.00	2004-08-21	241.15 weeks	2008-04-02	44.27 weeks	1999-08-28	2008-06-18	459.571429 weeks	1999-07-14	2008-10-28
8, .	date of purchase (Date class)	10	9.09	2004-11-28	246.03 weeks	2008-02-07	61.53 weeks	1999-10-05	2008-09-06	465.571429 weeks	1999-01-13	2008-11-27
4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	9	0.00	2002-08-10	241.71 weeks	1999-09-12	38.34 weeks	1999-03-15	2008-05-25	479.857143 weeks	1999-03-08	2008-12-23
6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	7	12.50	2004-08-16	254.96 weeks	2008-05-13	31.13 weeks	1999-06-07	2008-08-02	477.714286 weeks	1999-03-19	2008-10-07
8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	4	0.00	2004-01-07	274.68 weeks	2004-02-23	342.16 weeks	1999-02-03	2008-06-12	488.142857 weeks	1999-02-03	2008-09-09
4, Does it also fly?	date of purchase (Date class)	5	0.00	2002-12-14	265.47 weeks	1999-08-06	43.21 weeks	1999-01-14	2008-02-14	474.000000 weeks	1999-01-14	2008-11-26
6, Does it also fly?	date of purchase (Date class)	4	42.86	2001-09-28	225.84 weeks	1999-08-23	12.71 weeks	1999-07-03	NA	NA weeks	1999-06-15	2008-03-25
8, Does it also fly?	date of purchase (Date class)	4	0.00	2004-03-25	272.68 weeks	2004-04-06	347.46 weeks	1999-07-16	2008-08-25	475.428571 weeks	1999-07-16	2008-11-10
4, Does it come in green?	date of purchase (Date class)	15	0.00	2003-07-23	243.88 weeks	1999-08-26	47.02 weeks	1999-03-24	2008-02-26	465.857143 weeks	1999-01-16	2008-10-13
6, Does it come in green?	date of purchase (Date class)	3	0.00	2002-06-23	268.42 weeks	1999-09-09	27.75 weeks	1999-05-01	1999-09-09	18.714286 weeks	1999-05-01	2008-05-31
8, Does it come in green?	date of purchase (Date class)	5	0.00	2006-07-09	217.62 weeks	2008-02-09	24.15 weeks	1999-02-01	2008-06-02	487.000000 weeks	1999-02-01	2008-12-08
4, I like this car!	date of purchase (Date class)	8	11.11	2005-04-06	247.72 weeks	2008-07-29	20.33 weeks	1999-06-23	2008-09-23	482.857143 weeks	1999-06-08	2008-12-12
6, I like this car!	date of purchase (Date class)	7	22.22	2002-01-30	232.78 weeks	1999-08-23	25.84 weeks	1999-04-23	2008-09-05	489.000000 weeks	1999-03-13	2008-09-05
8, I like this car!	date of purchase (Date class)	4	0.00	2001-11-20	246.39 weeks	1999-09-27	30.61 weeks	1999-02-12	1999-11-28	41.285714 weeks	1999-02-12	2008-12-14
4, Meh.	date of purchase (Date class)	6	0.00	1999-05-01	9.86 weeks	1999-04-25	8.47 weeks	1999-03-26	1999-05-16	7.285714 weeks	1999-01-25	1999-08-11
6, Meh.	date of purchase (Date class)	6	0.00	2005-06-05	248.14 weeks	2008-04-27	26.58 weeks	1999-07-31	2008-05-08	457.714286 weeks	1999-01-05	2008-09-26
8, Meh.	date of purchase (Date class)	5	16.67	2008-04-14	9.80 weeks	2008-04-15	11.44 weeks	2008-02-21	2008-05-27	13.714286 weeks	2008-01-27	2008-07-13
4, Missing	date of purchase (Date class)	3	50.00	2002-08-26	280.92 weeks	1999-10-09	34.74 weeks	1999-10-09	NA	NA weeks	1999-04-28	2008-11-11
6, Missing	date of purchase (Date class)	5	0.00	2004-12-23	255.73 weeks	2008-05-24	21.18 weeks	1999-07-30	2008-08-09	471.142857 weeks	1999-07-30	2008-09-01
8, Missing	date of purchase (Date class)	14	0.00	2005-11-13	226.66 weeks	2008-04-02	38.55 weeks	1999-10-04	2008-09-08	466.000000 weeks	1999-01-04	2008-11-15
4, This is the worst car ever!	date of purchase (Date class)	7	0.00	2003-05-28	247.48 weeks	1999-11-24	50.62 weeks	1999-06-03	2008-02-10	453.428571 weeks	1999-03-30	2008-09-29
6, This is the worst car ever!	date of purchase (Date class)	9	10.00	2002-08-03	237.70 weeks	1999-10-18	38.34 weeks	1999-04-20	2008-09-13	490.571429 weeks	1999-03-22	2008-10-26
8, This is the worst car ever!	date of purchase (Date class)	5	0.00	2006-09-25	213.60 weeks	2008-07-04	16.10 weeks	1999-06-02	2008-09-18	485.142857 weeks	1999-06-02	2008-09-19
4, want cheese flavoured cars.	date of purchase (Date class)	10	9.09	2003-02-13	238.91 weeks	1999-12-10	57.50 weeks	1999-06-09	2008-05-15	466.142857 weeks	1999-02-17	2008-10-31
6, want cheese flavoured cars.	date of purchase (Date class)	13	0.00	2002-04-09	231.98 weeks	1999-08-31	36.85 weeks	1999-03-10	2008-06-24	484.857143 weeks	1999-01-26	2008-12-09
8, want cheese flavoured cars.	date of purchase (Date class)	8	11.11	2005-01-02	240.53 weeks	2008-02-06	44.16 weeks	1999-05-21	2008-07-18	478.000000 weeks	1999-04-01	2008-10-18
Overall	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.714286 weeks	1999-01-04	2008-12-23
R NA Value	date of purchase (Date class)	20	16.67	2003-12-06	236.95 weeks	2003-12-24	337.72 weeks	1999-08-14	2008-06-25	462.571429 weeks	1999-01-07	2008-12-03

Stacked barplot of date of purchase (Date class) by number of cylinders by some random comments.

POSIXlt Date

Show method to output table and plot

show(dpltSummaryExample)

                              Label   N P NA                Mean       S Dev
1  date of purchase (POSIXlt class) 234 8.55 2003-11-15 13:01:05 234.3 weeks
                  Med         MAD              25th P              75th P
1 1999-12-16 04:23:30 67.67 weeks 1999-07-17 10:42:00 2008-07-23 16:02:51
             IQR                 Min                 Max
1 470.6033 weeks 1999-01-04 04:59:00 2008-12-23 01:06:02

Output the summary table

data_summary_table(dpltSummaryExample)

                              Label   N P NA                Mean       S Dev
1  date of purchase (POSIXlt class) 234 8.55 2003-11-15 13:01:05 234.3 weeks
                  Med         MAD              25th P              75th P
1 1999-12-16 04:23:30 67.67 weeks 1999-07-17 10:42:00 2008-07-23 16:02:51
             IQR                 Min                 Max
1 470.6033 weeks 1999-01-04 04:59:00 2008-12-23 01:06:02

Output the plot

data_summary_plot(dpltSummaryExample)

Generate knitr friendly summary table

make_kable_output(dpltSummaryExample)

Summary statistics of date of purchase (POSIXlt class).

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	date of purchase (POSIXlt class)	234	8.55	2003-11-15 13:01:05	234.3 weeks	1999-12-16 04:23:30	67.67 weeks	1999-07-17 10:42:00	2008-07-23 16:02:51	470.6033 weeks	1999-01-04 04:59:00	2008-12-23 01:06:02

Generate knitr friendly output

make_complete_output(dpltSummaryExample)

Summary statistics of date of purchase (POSIXlt class).

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	date of purchase (POSIXlt class)	234	8.55	2003-11-15 13:01:05	234.3 weeks	1999-12-16 04:23:30	67.67 weeks	1999-07-17 10:42:00	2008-07-23 16:02:51	470.6033 weeks	1999-01-04 04:59:00	2008-12-23 01:06:02

Stacked barplot of date of purchase (Date class).

POSIXlt Date By

Show method to output table and plot

show(dpltByCylSummaryExample)

  number of cylinders                            Label   N  P NA
1                   4 date of purchase (POSIXlt class)  81  8.64
2                   5 date of purchase (POSIXlt class)   4  0.00
3                   6 date of purchase (POSIXlt class)  79  7.59
4                   8 date of purchase (POSIXlt class)  70 10.00
5             Overall date of purchase (POSIXlt class) 234  8.55
                 Mean        S Dev                 Med         MAD
1 2003-06-07 09:46:09 230.17 weeks 1999-11-21 23:21:30 44.34 weeks
2 2008-06-18 22:25:47  17.06 weeks 2008-06-19 02:08:01 21.71 weeks
3 2002-12-18 08:32:02 231.02 weeks 1999-09-23 08:05:00 38.02 weeks
4 2005-02-25 06:40:15 229.55 weeks 2008-02-28 14:28:40 52.72 weeks
5 2003-11-15 13:01:05 234.30 weeks 1999-12-16 04:23:30 67.67 weeks
               25th P              75th P             IQR                 Min
1 1999-08-17 07:59:00 2008-07-19 06:48:36 465.56444 weeks 1999-02-06 23:51:00
2 2008-02-24 08:01:04 2008-09-16 23:32:21  29.37215 weeks 2008-02-24 08:01:04
3 1999-06-01 05:12:00 2008-06-30 00:50:43 473.83122 weeks 1999-02-02 05:57:00
4 1999-08-03 00:23:00 2008-08-16 21:36:23 471.69776 weeks 1999-01-04 04:59:00
5 1999-07-17 10:42:00 2008-07-23 16:02:51 470.60326 weeks 1999-01-04 04:59:00
                  Max
1 2008-12-06 16:25:11
2 2008-10-12 04:26:02
3 2008-12-23 01:06:02
4 2008-12-15 06:26:36
5 2008-12-23 01:06:02

Output the summary table

data_summary_table(dpltByCylSummaryExample)

  number of cylinders                            Label   N  P NA
1                   4 date of purchase (POSIXlt class)  81  8.64
2                   5 date of purchase (POSIXlt class)   4  0.00
3                   6 date of purchase (POSIXlt class)  79  7.59
4                   8 date of purchase (POSIXlt class)  70 10.00
5             Overall date of purchase (POSIXlt class) 234  8.55
                 Mean        S Dev                 Med         MAD
1 2003-06-07 09:46:09 230.17 weeks 1999-11-21 23:21:30 44.34 weeks
2 2008-06-18 22:25:47  17.06 weeks 2008-06-19 02:08:01 21.71 weeks
3 2002-12-18 08:32:02 231.02 weeks 1999-09-23 08:05:00 38.02 weeks
4 2005-02-25 06:40:15 229.55 weeks 2008-02-28 14:28:40 52.72 weeks
5 2003-11-15 13:01:05 234.30 weeks 1999-12-16 04:23:30 67.67 weeks
               25th P              75th P             IQR                 Min
1 1999-08-17 07:59:00 2008-07-19 06:48:36 465.56444 weeks 1999-02-06 23:51:00
2 2008-02-24 08:01:04 2008-09-16 23:32:21  29.37215 weeks 2008-02-24 08:01:04
3 1999-06-01 05:12:00 2008-06-30 00:50:43 473.83122 weeks 1999-02-02 05:57:00
4 1999-08-03 00:23:00 2008-08-16 21:36:23 471.69776 weeks 1999-01-04 04:59:00
5 1999-07-17 10:42:00 2008-07-23 16:02:51 470.60326 weeks 1999-01-04 04:59:00
                  Max
1 2008-12-06 16:25:11
2 2008-10-12 04:26:02
3 2008-12-23 01:06:02
4 2008-12-15 06:26:36
5 2008-12-23 01:06:02

Output the plot

data_summary_plot(dpltByCylSummaryExample)

Generate a knitr friendly summary table

make_kable_output(dpltByCylSummaryExample)

Summary statistics of date of purchase (POSIXlt class) by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	date of purchase (POSIXlt class)	81	8.64	2003-06-07 09:46:09	230.17 weeks	1999-11-21 23:21:30	44.34 weeks	1999-08-17 07:59:00	2008-07-19 06:48:36	465.56444 weeks	1999-02-06 23:51:00	2008-12-06 16:25:11
5	date of purchase (POSIXlt class)	4	0.00	2008-06-18 22:25:47	17.06 weeks	2008-06-19 02:08:01	21.71 weeks	2008-02-24 08:01:04	2008-09-16 23:32:21	29.37215 weeks	2008-02-24 08:01:04	2008-10-12 04:26:02
6	date of purchase (POSIXlt class)	79	7.59	2002-12-18 08:32:02	231.02 weeks	1999-09-23 08:05:00	38.02 weeks	1999-06-01 05:12:00	2008-06-30 00:50:43	473.83122 weeks	1999-02-02 05:57:00	2008-12-23 01:06:02
8	date of purchase (POSIXlt class)	70	10.00	2005-02-25 06:40:15	229.55 weeks	2008-02-28 14:28:40	52.72 weeks	1999-08-03 00:23:00	2008-08-16 21:36:23	471.69776 weeks	1999-01-04 04:59:00	2008-12-15 06:26:36
Overall	date of purchase (POSIXlt class)	234	8.55	2003-11-15 13:01:05	234.30 weeks	1999-12-16 04:23:30	67.67 weeks	1999-07-17 10:42:00	2008-07-23 16:02:51	470.60326 weeks	1999-01-04 04:59:00	2008-12-23 01:06:02

Generate knitr friendly output

make_complete_output(dpltByCylSummaryExample)

Summary statistics of date of purchase (POSIXlt class) by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	date of purchase (POSIXlt class)	81	8.64	2003-06-07 09:46:09	230.17 weeks	1999-11-21 23:21:30	44.34 weeks	1999-08-17 07:59:00	2008-07-19 06:48:36	465.56444 weeks	1999-02-06 23:51:00	2008-12-06 16:25:11
5	date of purchase (POSIXlt class)	4	0.00	2008-06-18 22:25:47	17.06 weeks	2008-06-19 02:08:01	21.71 weeks	2008-02-24 08:01:04	2008-09-16 23:32:21	29.37215 weeks	2008-02-24 08:01:04	2008-10-12 04:26:02
6	date of purchase (POSIXlt class)	79	7.59	2002-12-18 08:32:02	231.02 weeks	1999-09-23 08:05:00	38.02 weeks	1999-06-01 05:12:00	2008-06-30 00:50:43	473.83122 weeks	1999-02-02 05:57:00	2008-12-23 01:06:02
8	date of purchase (POSIXlt class)	70	10.00	2005-02-25 06:40:15	229.55 weeks	2008-02-28 14:28:40	52.72 weeks	1999-08-03 00:23:00	2008-08-16 21:36:23	471.69776 weeks	1999-01-04 04:59:00	2008-12-15 06:26:36
Overall	date of purchase (POSIXlt class)	234	8.55	2003-11-15 13:01:05	234.30 weeks	1999-12-16 04:23:30	67.67 weeks	1999-07-17 10:42:00	2008-07-23 16:02:51	470.60326 weeks	1999-01-04 04:59:00	2008-12-23 01:06:02

Stacked barplot of date of purchase (Date class) by number of cylinders.

POSIXlt Date By By

Show method to output table and plot

show(dpltByCylByCommentsSummaryExample)

         number of cylinders by some random comments
1                                               4, .
2                                               6, .
3                                               8, .
4  4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
5  6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
6  8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
7                               4, Does it also fly?
8                               6, Does it also fly?
9                               8, Does it also fly?
10                         4, Does it come in green?
11                         6, Does it come in green?
12                         8, Does it come in green?
13                               4, I like this car!
14                               6, I like this car!
15                               8, I like this car!
16                                           4, Meh.
17                                           6, Meh.
18                                           8, Meh.
19                                        4, Missing
20                                        6, Missing
21                                        8, Missing
22                    4, This is the worst car ever!
23                    6, This is the worst car ever!
24                    8, This is the worst car ever!
25                    4, want cheese flavoured cars.
26                    6, want cheese flavoured cars.
27                    8, want cheese flavoured cars.
28                                           Overall
29                                        R NA Value
                              Label   N  P NA                Mean        S Dev
1  date of purchase (POSIXlt class)   5  0.00 2002-12-26 13:38:31 260.08 weeks
2  date of purchase (POSIXlt class)   9 11.11 2003-12-31 07:58:18 247.12 weeks
3  date of purchase (POSIXlt class)  11  0.00 2004-05-29 02:24:54 245.48 weeks
4  date of purchase (POSIXlt class)   9  0.00 2002-06-23 10:51:45 223.34 weeks
5  date of purchase (POSIXlt class)   8 12.50 2004-07-24 21:46:13 261.15 weeks
6  date of purchase (POSIXlt class)   4 50.00 2008-05-14 06:42:44   2.05 weeks
7  date of purchase (POSIXlt class)   5  0.00 2003-03-07 13:02:00 254.76 weeks
8  date of purchase (POSIXlt class)   7 14.29 2000-12-31 00:40:43 201.17 weeks
9  date of purchase (POSIXlt class)   4  0.00 2003-12-13 22:25:46 278.25 weeks
10 date of purchase (POSIXlt class)  15  6.67 2003-06-16 07:56:21 236.42 weeks
11 date of purchase (POSIXlt class)   3  0.00 2002-06-30 09:12:42 270.33 weeks
12 date of purchase (POSIXlt class)   5  0.00 2006-10-07 04:49:16 212.99 weeks
13 date of purchase (POSIXlt class)  10  0.00 2004-11-13 07:26:51 239.01 weeks
14 date of purchase (POSIXlt class)   9  0.00 2001-07-09 08:24:32 208.13 weeks
15 date of purchase (POSIXlt class)   4  0.00 2001-07-01 16:33:59 239.79 weeks
16 date of purchase (POSIXlt class)   6  0.00 1999-08-25 13:01:40   9.25 weeks
17 date of purchase (POSIXlt class)   6  0.00 2005-06-27 06:16:57 237.83 weeks
18 date of purchase (POSIXlt class)   6 16.67 2008-04-25 10:42:01   7.88 weeks
19 date of purchase (POSIXlt class)   6 33.33 2004-02-24 10:35:52 267.91 weeks
20 date of purchase (POSIXlt class)   5  0.00 2004-10-25 02:13:27 267.95 weeks
21 date of purchase (POSIXlt class)  14 14.29 2006-04-18 07:42:10 206.29 weeks
22 date of purchase (POSIXlt class)   7  0.00 2003-04-05 22:54:10 245.37 weeks
23 date of purchase (POSIXlt class)  10 10.00 2002-08-17 06:20:29 241.20 weeks
24 date of purchase (POSIXlt class)   5  0.00 2006-08-16 09:39:48 207.93 weeks
25 date of purchase (POSIXlt class)  11 36.36 2004-09-23 19:01:28 249.43 weeks
26 date of purchase (POSIXlt class)  13  7.69 2001-10-13 09:39:46 206.72 weeks
27 date of purchase (POSIXlt class)   9 22.22 2004-09-01 04:43:02 260.61 weeks
28 date of purchase (POSIXlt class) 234  8.55 2003-11-15 13:01:05 234.30 weeks
29 date of purchase (POSIXlt class)  24  4.17 2003-05-14 06:30:18 237.27 weeks
                   Med          MAD              25th P              75th P
1  1999-06-24 10:05:00  17.72 weeks 1999-04-01 16:49:00 2008-05-04 14:32:00
2  2004-01-11 07:48:34 340.15 weeks 1999-06-21 15:04:00 2008-05-07 12:07:04
3  2008-01-08 08:48:50  69.28 weeks 1999-06-17 20:51:00 2008-06-16 11:15:19
4  1999-11-10 03:37:00  28.85 weeks 1999-06-26 23:47:00 2008-01-01 21:17:41
5  2008-04-06 05:33:44  50.36 weeks 1999-03-18 09:04:00 2008-09-11 12:16:05
6  2008-05-14 06:42:44   2.15 weeks 2008-05-04 03:11:56                <NA>
7  1999-12-03 14:02:00  46.46 weeks 1999-04-28 07:00:00 2008-02-04 15:09:51
8  1999-05-30 17:24:30  12.40 weeks 1999-05-05 06:29:00 1999-10-26 18:15:00
9  2003-11-02 13:02:39 348.06 weeks 1999-04-03 04:24:00 2008-04-01 22:16:18
10 1999-11-24 15:28:30  39.39 weeks 1999-09-02 06:35:00 2008-07-23 16:02:51
11 1999-08-29 04:43:00  23.66 weeks 1999-05-09 12:09:00 1999-08-29 04:43:00
12 2008-04-16 10:43:47  42.60 weeks 1999-06-27 02:00:00 2008-11-03 12:36:31
13 2008-02-20 02:59:31  43.53 weeks 1999-07-19 02:45:00 2008-06-06 00:16:30
14 1999-09-01 03:08:00  21.11 weeks 1999-05-20 05:21:00 1999-10-17 20:26:00
15 1999-03-21 09:08:00   2.24 weeks 1999-03-03 10:20:00 1999-03-24 14:33:00
16 1999-08-29 00:33:00   6.94 weeks 1999-08-04 17:17:00 1999-09-09 17:07:00
17 2008-05-07 16:52:30  12.18 weeks 1999-10-01 22:39:00 2008-06-02 22:26:13
18 2008-04-22 04:39:45   4.67 weeks 2008-03-31 03:48:18 2008-05-06 11:35:30
19 2004-02-24 04:39:56 343.94 weeks 1999-09-16 13:56:00 2008-08-08 08:13:36
20 2008-07-21 14:26:31   2.15 weeks 1999-02-23 01:03:00 2008-07-24 04:53:54
21 2008-05-04 23:11:45  28.88 weeks 2008-01-15 10:13:33 2008-10-14 03:45:41
22 1999-10-03 20:59:00  40.91 weeks 1999-04-01 16:56:00 2008-02-06 14:44:36
23 1999-09-23 08:05:00  31.28 weeks 1999-04-28 15:16:00 2008-11-24 04:28:26
24 2008-05-16 15:45:50  18.25 weeks 1999-07-03 08:38:00 2008-06-23 14:12:36
25 2008-02-20 02:52:27  51.90 weeks 1999-09-24 23:41:00                <NA>
26 1999-09-17 07:54:30  16.31 weeks 1999-07-02 02:34:00 2008-04-01 22:06:18
27 2008-01-19 02:34:35  67.07 weeks 1999-06-20 14:17:00 2008-11-30 18:38:11
28 1999-12-16 04:23:30  67.67 weeks 1999-07-17 10:42:00 2008-07-23 16:02:51
29 1999-12-02 03:54:00  64.95 weeks 1999-04-07 12:51:00 2008-05-14 11:48:26
                IQR                 Min                 Max
1  474.409028 weeks 1999-04-01 16:49:00 2008-07-19 06:48:36
2  463.268161 weeks 1999-06-01 05:12:00 2008-11-12 17:56:39
3  469.514317 weeks 1999-01-31 21:39:00 2008-11-30 10:47:06
4  444.419711 weeks 1999-02-06 23:51:00 2008-06-22 02:19:06
5  495.013104 weeks 1999-02-02 05:57:00 2008-11-29 23:22:31
6          NA weeks 2008-05-04 03:11:56 2008-05-24 10:13:33
7  457.768834 weeks 1999-04-28 07:00:00 2008-12-06 16:25:11
8   24.927183 weeks 1999-02-28 00:08:00 2008-11-08 20:23:21
9  469.528998 weeks 1999-04-03 04:24:00 2008-11-15 11:13:47
10 463.913477 weeks 1999-05-08 12:54:00 2008-10-21 21:32:53
11  15.955754 weeks 1999-05-09 12:09:00 2008-06-22 10:46:07
12 488.211956 weeks 1999-06-27 02:00:00 2008-12-13 19:57:34
13 463.556696 weeks 1999-05-25 09:23:00 2008-09-27 23:41:44
14  21.518353 weeks 1999-04-06 05:38:00 2008-12-23 01:06:02
15   3.025099 weeks 1999-03-03 10:20:00 2008-05-23 10:39:59
16   5.141865 weeks 1999-05-07 21:04:00 1999-11-12 08:41:00
17 452.427303 weeks 1999-06-17 19:37:00 2008-07-19 03:28:08
18   5.189206 weeks 2008-02-19 04:00:08 2008-07-18 04:26:27
19 464.108889 weeks 1999-09-12 02:50:00 2008-08-08 08:13:36
20 491.302669 weeks 1999-02-23 01:03:00 2008-07-31 17:39:54
21  38.955569 weeks 1999-08-03 00:23:00 2008-12-15 06:26:36
22 461.844107 weeks 1999-03-24 16:04:00 2008-06-16 20:57:55
23 499.655995 weeks 1999-02-22 04:55:00 2008-11-29 02:51:30
24 468.318909 weeks 1999-07-03 08:38:00 2008-08-16 21:36:23
25         NA weeks 1999-07-17 10:42:00 2008-10-22 04:49:26
26 456.687728 weeks 1999-02-02 06:27:00 2008-06-30 00:50:43
27 493.031863 weeks 1999-01-06 16:15:00 2008-11-30 18:38:11
28 470.603259 weeks 1999-01-04 04:59:00 2008-12-23 01:06:02
29 474.993793 weeks 1999-01-04 04:59:00 2008-10-23 19:14:24

Output the summary table

data_summary_table(dpltByCylByCommentsSummaryExample)

         number of cylinders by some random comments
1                                               4, .
2                                               6, .
3                                               8, .
4  4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
5  6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
6  8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
7                               4, Does it also fly?
8                               6, Does it also fly?
9                               8, Does it also fly?
10                         4, Does it come in green?
11                         6, Does it come in green?
12                         8, Does it come in green?
13                               4, I like this car!
14                               6, I like this car!
15                               8, I like this car!
16                                           4, Meh.
17                                           6, Meh.
18                                           8, Meh.
19                                        4, Missing
20                                        6, Missing
21                                        8, Missing
22                    4, This is the worst car ever!
23                    6, This is the worst car ever!
24                    8, This is the worst car ever!
25                    4, want cheese flavoured cars.
26                    6, want cheese flavoured cars.
27                    8, want cheese flavoured cars.
28                                           Overall
29                                        R NA Value
                              Label   N  P NA                Mean        S Dev
1  date of purchase (POSIXlt class)   5  0.00 2002-12-26 13:38:31 260.08 weeks
2  date of purchase (POSIXlt class)   9 11.11 2003-12-31 07:58:18 247.12 weeks
3  date of purchase (POSIXlt class)  11  0.00 2004-05-29 02:24:54 245.48 weeks
4  date of purchase (POSIXlt class)   9  0.00 2002-06-23 10:51:45 223.34 weeks
5  date of purchase (POSIXlt class)   8 12.50 2004-07-24 21:46:13 261.15 weeks
6  date of purchase (POSIXlt class)   4 50.00 2008-05-14 06:42:44   2.05 weeks
7  date of purchase (POSIXlt class)   5  0.00 2003-03-07 13:02:00 254.76 weeks
8  date of purchase (POSIXlt class)   7 14.29 2000-12-31 00:40:43 201.17 weeks
9  date of purchase (POSIXlt class)   4  0.00 2003-12-13 22:25:46 278.25 weeks
10 date of purchase (POSIXlt class)  15  6.67 2003-06-16 07:56:21 236.42 weeks
11 date of purchase (POSIXlt class)   3  0.00 2002-06-30 09:12:42 270.33 weeks
12 date of purchase (POSIXlt class)   5  0.00 2006-10-07 04:49:16 212.99 weeks
13 date of purchase (POSIXlt class)  10  0.00 2004-11-13 07:26:51 239.01 weeks
14 date of purchase (POSIXlt class)   9  0.00 2001-07-09 08:24:32 208.13 weeks
15 date of purchase (POSIXlt class)   4  0.00 2001-07-01 16:33:59 239.79 weeks
16 date of purchase (POSIXlt class)   6  0.00 1999-08-25 13:01:40   9.25 weeks
17 date of purchase (POSIXlt class)   6  0.00 2005-06-27 06:16:57 237.83 weeks
18 date of purchase (POSIXlt class)   6 16.67 2008-04-25 10:42:01   7.88 weeks
19 date of purchase (POSIXlt class)   6 33.33 2004-02-24 10:35:52 267.91 weeks
20 date of purchase (POSIXlt class)   5  0.00 2004-10-25 02:13:27 267.95 weeks
21 date of purchase (POSIXlt class)  14 14.29 2006-04-18 07:42:10 206.29 weeks
22 date of purchase (POSIXlt class)   7  0.00 2003-04-05 22:54:10 245.37 weeks
23 date of purchase (POSIXlt class)  10 10.00 2002-08-17 06:20:29 241.20 weeks
24 date of purchase (POSIXlt class)   5  0.00 2006-08-16 09:39:48 207.93 weeks
25 date of purchase (POSIXlt class)  11 36.36 2004-09-23 19:01:28 249.43 weeks
26 date of purchase (POSIXlt class)  13  7.69 2001-10-13 09:39:46 206.72 weeks
27 date of purchase (POSIXlt class)   9 22.22 2004-09-01 04:43:02 260.61 weeks
28 date of purchase (POSIXlt class) 234  8.55 2003-11-15 13:01:05 234.30 weeks
29 date of purchase (POSIXlt class)  24  4.17 2003-05-14 06:30:18 237.27 weeks
                   Med          MAD              25th P              75th P
1  1999-06-24 10:05:00  17.72 weeks 1999-04-01 16:49:00 2008-05-04 14:32:00
2  2004-01-11 07:48:34 340.15 weeks 1999-06-21 15:04:00 2008-05-07 12:07:04
3  2008-01-08 08:48:50  69.28 weeks 1999-06-17 20:51:00 2008-06-16 11:15:19
4  1999-11-10 03:37:00  28.85 weeks 1999-06-26 23:47:00 2008-01-01 21:17:41
5  2008-04-06 05:33:44  50.36 weeks 1999-03-18 09:04:00 2008-09-11 12:16:05
6  2008-05-14 06:42:44   2.15 weeks 2008-05-04 03:11:56                <NA>
7  1999-12-03 14:02:00  46.46 weeks 1999-04-28 07:00:00 2008-02-04 15:09:51
8  1999-05-30 17:24:30  12.40 weeks 1999-05-05 06:29:00 1999-10-26 18:15:00
9  2003-11-02 13:02:39 348.06 weeks 1999-04-03 04:24:00 2008-04-01 22:16:18
10 1999-11-24 15:28:30  39.39 weeks 1999-09-02 06:35:00 2008-07-23 16:02:51
11 1999-08-29 04:43:00  23.66 weeks 1999-05-09 12:09:00 1999-08-29 04:43:00
12 2008-04-16 10:43:47  42.60 weeks 1999-06-27 02:00:00 2008-11-03 12:36:31
13 2008-02-20 02:59:31  43.53 weeks 1999-07-19 02:45:00 2008-06-06 00:16:30
14 1999-09-01 03:08:00  21.11 weeks 1999-05-20 05:21:00 1999-10-17 20:26:00
15 1999-03-21 09:08:00   2.24 weeks 1999-03-03 10:20:00 1999-03-24 14:33:00
16 1999-08-29 00:33:00   6.94 weeks 1999-08-04 17:17:00 1999-09-09 17:07:00
17 2008-05-07 16:52:30  12.18 weeks 1999-10-01 22:39:00 2008-06-02 22:26:13
18 2008-04-22 04:39:45   4.67 weeks 2008-03-31 03:48:18 2008-05-06 11:35:30
19 2004-02-24 04:39:56 343.94 weeks 1999-09-16 13:56:00 2008-08-08 08:13:36
20 2008-07-21 14:26:31   2.15 weeks 1999-02-23 01:03:00 2008-07-24 04:53:54
21 2008-05-04 23:11:45  28.88 weeks 2008-01-15 10:13:33 2008-10-14 03:45:41
22 1999-10-03 20:59:00  40.91 weeks 1999-04-01 16:56:00 2008-02-06 14:44:36
23 1999-09-23 08:05:00  31.28 weeks 1999-04-28 15:16:00 2008-11-24 04:28:26
24 2008-05-16 15:45:50  18.25 weeks 1999-07-03 08:38:00 2008-06-23 14:12:36
25 2008-02-20 02:52:27  51.90 weeks 1999-09-24 23:41:00                <NA>
26 1999-09-17 07:54:30  16.31 weeks 1999-07-02 02:34:00 2008-04-01 22:06:18
27 2008-01-19 02:34:35  67.07 weeks 1999-06-20 14:17:00 2008-11-30 18:38:11
28 1999-12-16 04:23:30  67.67 weeks 1999-07-17 10:42:00 2008-07-23 16:02:51
29 1999-12-02 03:54:00  64.95 weeks 1999-04-07 12:51:00 2008-05-14 11:48:26
                IQR                 Min                 Max
1  474.409028 weeks 1999-04-01 16:49:00 2008-07-19 06:48:36
2  463.268161 weeks 1999-06-01 05:12:00 2008-11-12 17:56:39
3  469.514317 weeks 1999-01-31 21:39:00 2008-11-30 10:47:06
4  444.419711 weeks 1999-02-06 23:51:00 2008-06-22 02:19:06
5  495.013104 weeks 1999-02-02 05:57:00 2008-11-29 23:22:31
6          NA weeks 2008-05-04 03:11:56 2008-05-24 10:13:33
7  457.768834 weeks 1999-04-28 07:00:00 2008-12-06 16:25:11
8   24.927183 weeks 1999-02-28 00:08:00 2008-11-08 20:23:21
9  469.528998 weeks 1999-04-03 04:24:00 2008-11-15 11:13:47
10 463.913477 weeks 1999-05-08 12:54:00 2008-10-21 21:32:53
11  15.955754 weeks 1999-05-09 12:09:00 2008-06-22 10:46:07
12 488.211956 weeks 1999-06-27 02:00:00 2008-12-13 19:57:34
13 463.556696 weeks 1999-05-25 09:23:00 2008-09-27 23:41:44
14  21.518353 weeks 1999-04-06 05:38:00 2008-12-23 01:06:02
15   3.025099 weeks 1999-03-03 10:20:00 2008-05-23 10:39:59
16   5.141865 weeks 1999-05-07 21:04:00 1999-11-12 08:41:00
17 452.427303 weeks 1999-06-17 19:37:00 2008-07-19 03:28:08
18   5.189206 weeks 2008-02-19 04:00:08 2008-07-18 04:26:27
19 464.108889 weeks 1999-09-12 02:50:00 2008-08-08 08:13:36
20 491.302669 weeks 1999-02-23 01:03:00 2008-07-31 17:39:54
21  38.955569 weeks 1999-08-03 00:23:00 2008-12-15 06:26:36
22 461.844107 weeks 1999-03-24 16:04:00 2008-06-16 20:57:55
23 499.655995 weeks 1999-02-22 04:55:00 2008-11-29 02:51:30
24 468.318909 weeks 1999-07-03 08:38:00 2008-08-16 21:36:23
25         NA weeks 1999-07-17 10:42:00 2008-10-22 04:49:26
26 456.687728 weeks 1999-02-02 06:27:00 2008-06-30 00:50:43
27 493.031863 weeks 1999-01-06 16:15:00 2008-11-30 18:38:11
28 470.603259 weeks 1999-01-04 04:59:00 2008-12-23 01:06:02
29 474.993793 weeks 1999-01-04 04:59:00 2008-10-23 19:14:24

Output the plot

data_summary_plot(dpltByCylByCommentsSummaryExample)

Generate a knitr friendly summary table

make_kable_output(dpltByCylByCommentsSummaryExample)

Summary statistics of date of purchase (POSIXlt class) by number of cylinders by some random comments.

number of cylinders by some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4, .	date of purchase (POSIXlt class)	5	0.00	2002-12-26 13:38:31	260.08 weeks	1999-06-24 10:05:00	17.72 weeks	1999-04-01 16:49:00	2008-05-04 14:32:00	474.409028 weeks	1999-04-01 16:49:00	2008-07-19 06:48:36
6, .	date of purchase (POSIXlt class)	9	11.11	2003-12-31 07:58:18	247.12 weeks	2004-01-11 07:48:34	340.15 weeks	1999-06-21 15:04:00	2008-05-07 12:07:04	463.268161 weeks	1999-06-01 05:12:00	2008-11-12 17:56:39
8, .	date of purchase (POSIXlt class)	11	0.00	2004-05-29 02:24:54	245.48 weeks	2008-01-08 08:48:50	69.28 weeks	1999-06-17 20:51:00	2008-06-16 11:15:19	469.514317 weeks	1999-01-31 21:39:00	2008-11-30 10:47:06
4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXlt class)	9	0.00	2002-06-23 10:51:45	223.34 weeks	1999-11-10 03:37:00	28.85 weeks	1999-06-26 23:47:00	2008-01-01 21:17:41	444.419711 weeks	1999-02-06 23:51:00	2008-06-22 02:19:06
6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXlt class)	8	12.50	2004-07-24 21:46:13	261.15 weeks	2008-04-06 05:33:44	50.36 weeks	1999-03-18 09:04:00	2008-09-11 12:16:05	495.013103 weeks	1999-02-02 05:57:00	2008-11-29 23:22:31
8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXlt class)	4	50.00	2008-05-14 06:42:44	2.05 weeks	2008-05-14 06:42:44	2.15 weeks	2008-05-04 03:11:56	NA	NA weeks	2008-05-04 03:11:56	2008-05-24 10:13:33
4, Does it also fly?	date of purchase (POSIXlt class)	5	0.00	2003-03-07 13:02:00	254.76 weeks	1999-12-03 14:02:00	46.46 weeks	1999-04-28 07:00:00	2008-02-04 15:09:51	457.768834 weeks	1999-04-28 07:00:00	2008-12-06 16:25:11
6, Does it also fly?	date of purchase (POSIXlt class)	7	14.29	2000-12-31 00:40:43	201.17 weeks	1999-05-30 17:24:30	12.40 weeks	1999-05-05 06:29:00	1999-10-26 18:15:00	24.927183 weeks	1999-02-28 00:08:00	2008-11-08 20:23:21
8, Does it also fly?	date of purchase (POSIXlt class)	4	0.00	2003-12-13 22:25:46	278.25 weeks	2003-11-02 13:02:39	348.06 weeks	1999-04-03 04:24:00	2008-04-01 22:16:18	469.528998 weeks	1999-04-03 04:24:00	2008-11-15 11:13:47
4, Does it come in green?	date of purchase (POSIXlt class)	15	6.67	2003-06-16 07:56:21	236.42 weeks	1999-11-24 15:28:30	39.39 weeks	1999-09-02 06:35:00	2008-07-23 16:02:51	463.913477 weeks	1999-05-08 12:54:00	2008-10-21 21:32:53
6, Does it come in green?	date of purchase (POSIXlt class)	3	0.00	2002-06-30 09:12:42	270.33 weeks	1999-08-29 04:43:00	23.66 weeks	1999-05-09 12:09:00	1999-08-29 04:43:00	15.955754 weeks	1999-05-09 12:09:00	2008-06-22 10:46:07
8, Does it come in green?	date of purchase (POSIXlt class)	5	0.00	2006-10-07 04:49:16	212.99 weeks	2008-04-16 10:43:47	42.60 weeks	1999-06-27 02:00:00	2008-11-03 12:36:31	488.211956 weeks	1999-06-27 02:00:00	2008-12-13 19:57:34
4, I like this car!	date of purchase (POSIXlt class)	10	0.00	2004-11-13 07:26:51	239.01 weeks	2008-02-20 02:59:31	43.53 weeks	1999-07-19 02:45:00	2008-06-06 00:16:30	463.556696 weeks	1999-05-25 09:23:00	2008-09-27 23:41:44
6, I like this car!	date of purchase (POSIXlt class)	9	0.00	2001-07-09 08:24:32	208.13 weeks	1999-09-01 03:08:00	21.11 weeks	1999-05-20 05:21:00	1999-10-17 20:26:00	21.518353 weeks	1999-04-06 05:38:00	2008-12-23 01:06:02
8, I like this car!	date of purchase (POSIXlt class)	4	0.00	2001-07-01 16:33:59	239.79 weeks	1999-03-21 09:08:00	2.24 weeks	1999-03-03 10:20:00	1999-03-24 14:33:00	3.025099 weeks	1999-03-03 10:20:00	2008-05-23 10:39:59
4, Meh.	date of purchase (POSIXlt class)	6	0.00	1999-08-25 13:01:40	9.25 weeks	1999-08-29 00:33:00	6.94 weeks	1999-08-04 17:17:00	1999-09-09 17:07:00	5.141865 weeks	1999-05-07 21:04:00	1999-11-12 08:41:00
6, Meh.	date of purchase (POSIXlt class)	6	0.00	2005-06-27 06:16:57	237.83 weeks	2008-05-07 16:52:30	12.18 weeks	1999-10-01 22:39:00	2008-06-02 22:26:13	452.427303 weeks	1999-06-17 19:37:00	2008-07-19 03:28:08
8, Meh.	date of purchase (POSIXlt class)	6	16.67	2008-04-25 10:42:01	7.88 weeks	2008-04-22 04:39:45	4.67 weeks	2008-03-31 03:48:18	2008-05-06 11:35:30	5.189206 weeks	2008-02-19 04:00:08	2008-07-18 04:26:27
4, Missing	date of purchase (POSIXlt class)	6	33.33	2004-02-24 10:35:52	267.91 weeks	2004-02-24 04:39:56	343.94 weeks	1999-09-16 13:56:00	2008-08-08 08:13:36	464.108889 weeks	1999-09-12 02:50:00	2008-08-08 08:13:36
6, Missing	date of purchase (POSIXlt class)	5	0.00	2004-10-25 02:13:27	267.95 weeks	2008-07-21 14:26:31	2.15 weeks	1999-02-23 01:03:00	2008-07-24 04:53:54	491.302669 weeks	1999-02-23 01:03:00	2008-07-31 17:39:54
8, Missing	date of purchase (POSIXlt class)	14	14.29	2006-04-18 07:42:10	206.29 weeks	2008-05-04 23:11:45	28.88 weeks	2008-01-15 10:13:33	2008-10-14 03:45:41	38.955569 weeks	1999-08-03 00:23:00	2008-12-15 06:26:36
4, This is the worst car ever!	date of purchase (POSIXlt class)	7	0.00	2003-04-05 22:54:10	245.37 weeks	1999-10-03 20:59:00	40.91 weeks	1999-04-01 16:56:00	2008-02-06 14:44:36	461.844107 weeks	1999-03-24 16:04:00	2008-06-16 20:57:55
6, This is the worst car ever!	date of purchase (POSIXlt class)	10	10.00	2002-08-17 06:20:29	241.20 weeks	1999-09-23 08:05:00	31.28 weeks	1999-04-28 15:16:00	2008-11-24 04:28:26	499.655995 weeks	1999-02-22 04:55:00	2008-11-29 02:51:30
8, This is the worst car ever!	date of purchase (POSIXlt class)	5	0.00	2006-08-16 09:39:48	207.93 weeks	2008-05-16 15:45:50	18.25 weeks	1999-07-03 08:38:00	2008-06-23 14:12:36	468.318909 weeks	1999-07-03 08:38:00	2008-08-16 21:36:23
4, want cheese flavoured cars.	date of purchase (POSIXlt class)	11	36.36	2004-09-23 19:01:28	249.43 weeks	2008-02-20 02:52:27	51.90 weeks	1999-09-24 23:41:00	NA	NA weeks	1999-07-17 10:42:00	2008-10-22 04:49:26
6, want cheese flavoured cars.	date of purchase (POSIXlt class)	13	7.69	2001-10-13 09:39:46	206.72 weeks	1999-09-17 07:54:30	16.31 weeks	1999-07-02 02:34:00	2008-04-01 22:06:18	456.687728 weeks	1999-02-02 06:27:00	2008-06-30 00:50:43
8, want cheese flavoured cars.	date of purchase (POSIXlt class)	9	22.22	2004-09-01 04:43:02	260.61 weeks	2008-01-19 02:34:35	67.07 weeks	1999-06-20 14:17:00	2008-11-30 18:38:11	493.031863 weeks	1999-01-06 16:15:00	2008-11-30 18:38:11
Overall	date of purchase (POSIXlt class)	234	8.55	2003-11-15 13:01:05	234.30 weeks	1999-12-16 04:23:30	67.67 weeks	1999-07-17 10:42:00	2008-07-23 16:02:51	470.603259 weeks	1999-01-04 04:59:00	2008-12-23 01:06:02
R NA Value	date of purchase (POSIXlt class)	24	4.17	2003-05-14 06:30:18	237.27 weeks	1999-12-02 03:54:00	64.95 weeks	1999-04-07 12:51:00	2008-05-14 11:48:26	474.993793 weeks	1999-01-04 04:59:00	2008-10-23 19:14:24

Generate knitr friendly output

make_complete_output(dpltByCylByCommentsSummaryExample)

Summary statistics of date of purchase (POSIXlt class) by number of cylinders by some random comments.

number of cylinders by some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4, .	date of purchase (POSIXlt class)	5	0.00	2002-12-26 13:38:31	260.08 weeks	1999-06-24 10:05:00	17.72 weeks	1999-04-01 16:49:00	2008-05-04 14:32:00	474.409028 weeks	1999-04-01 16:49:00	2008-07-19 06:48:36
6, .	date of purchase (POSIXlt class)	9	11.11	2003-12-31 07:58:18	247.12 weeks	2004-01-11 07:48:34	340.15 weeks	1999-06-21 15:04:00	2008-05-07 12:07:04	463.268161 weeks	1999-06-01 05:12:00	2008-11-12 17:56:39
8, .	date of purchase (POSIXlt class)	11	0.00	2004-05-29 02:24:54	245.48 weeks	2008-01-08 08:48:50	69.28 weeks	1999-06-17 20:51:00	2008-06-16 11:15:19	469.514317 weeks	1999-01-31 21:39:00	2008-11-30 10:47:06
4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXlt class)	9	0.00	2002-06-23 10:51:45	223.34 weeks	1999-11-10 03:37:00	28.85 weeks	1999-06-26 23:47:00	2008-01-01 21:17:41	444.419711 weeks	1999-02-06 23:51:00	2008-06-22 02:19:06
6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXlt class)	8	12.50	2004-07-24 21:46:13	261.15 weeks	2008-04-06 05:33:44	50.36 weeks	1999-03-18 09:04:00	2008-09-11 12:16:05	495.013103 weeks	1999-02-02 05:57:00	2008-11-29 23:22:31
8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXlt class)	4	50.00	2008-05-14 06:42:44	2.05 weeks	2008-05-14 06:42:44	2.15 weeks	2008-05-04 03:11:56	NA	NA weeks	2008-05-04 03:11:56	2008-05-24 10:13:33
4, Does it also fly?	date of purchase (POSIXlt class)	5	0.00	2003-03-07 13:02:00	254.76 weeks	1999-12-03 14:02:00	46.46 weeks	1999-04-28 07:00:00	2008-02-04 15:09:51	457.768834 weeks	1999-04-28 07:00:00	2008-12-06 16:25:11
6, Does it also fly?	date of purchase (POSIXlt class)	7	14.29	2000-12-31 00:40:43	201.17 weeks	1999-05-30 17:24:30	12.40 weeks	1999-05-05 06:29:00	1999-10-26 18:15:00	24.927183 weeks	1999-02-28 00:08:00	2008-11-08 20:23:21
8, Does it also fly?	date of purchase (POSIXlt class)	4	0.00	2003-12-13 22:25:46	278.25 weeks	2003-11-02 13:02:39	348.06 weeks	1999-04-03 04:24:00	2008-04-01 22:16:18	469.528998 weeks	1999-04-03 04:24:00	2008-11-15 11:13:47
4, Does it come in green?	date of purchase (POSIXlt class)	15	6.67	2003-06-16 07:56:21	236.42 weeks	1999-11-24 15:28:30	39.39 weeks	1999-09-02 06:35:00	2008-07-23 16:02:51	463.913477 weeks	1999-05-08 12:54:00	2008-10-21 21:32:53
6, Does it come in green?	date of purchase (POSIXlt class)	3	0.00	2002-06-30 09:12:42	270.33 weeks	1999-08-29 04:43:00	23.66 weeks	1999-05-09 12:09:00	1999-08-29 04:43:00	15.955754 weeks	1999-05-09 12:09:00	2008-06-22 10:46:07
8, Does it come in green?	date of purchase (POSIXlt class)	5	0.00	2006-10-07 04:49:16	212.99 weeks	2008-04-16 10:43:47	42.60 weeks	1999-06-27 02:00:00	2008-11-03 12:36:31	488.211956 weeks	1999-06-27 02:00:00	2008-12-13 19:57:34
4, I like this car!	date of purchase (POSIXlt class)	10	0.00	2004-11-13 07:26:51	239.01 weeks	2008-02-20 02:59:31	43.53 weeks	1999-07-19 02:45:00	2008-06-06 00:16:30	463.556696 weeks	1999-05-25 09:23:00	2008-09-27 23:41:44
6, I like this car!	date of purchase (POSIXlt class)	9	0.00	2001-07-09 08:24:32	208.13 weeks	1999-09-01 03:08:00	21.11 weeks	1999-05-20 05:21:00	1999-10-17 20:26:00	21.518353 weeks	1999-04-06 05:38:00	2008-12-23 01:06:02
8, I like this car!	date of purchase (POSIXlt class)	4	0.00	2001-07-01 16:33:59	239.79 weeks	1999-03-21 09:08:00	2.24 weeks	1999-03-03 10:20:00	1999-03-24 14:33:00	3.025099 weeks	1999-03-03 10:20:00	2008-05-23 10:39:59
4, Meh.	date of purchase (POSIXlt class)	6	0.00	1999-08-25 13:01:40	9.25 weeks	1999-08-29 00:33:00	6.94 weeks	1999-08-04 17:17:00	1999-09-09 17:07:00	5.141865 weeks	1999-05-07 21:04:00	1999-11-12 08:41:00
6, Meh.	date of purchase (POSIXlt class)	6	0.00	2005-06-27 06:16:57	237.83 weeks	2008-05-07 16:52:30	12.18 weeks	1999-10-01 22:39:00	2008-06-02 22:26:13	452.427303 weeks	1999-06-17 19:37:00	2008-07-19 03:28:08
8, Meh.	date of purchase (POSIXlt class)	6	16.67	2008-04-25 10:42:01	7.88 weeks	2008-04-22 04:39:45	4.67 weeks	2008-03-31 03:48:18	2008-05-06 11:35:30	5.189206 weeks	2008-02-19 04:00:08	2008-07-18 04:26:27
4, Missing	date of purchase (POSIXlt class)	6	33.33	2004-02-24 10:35:52	267.91 weeks	2004-02-24 04:39:56	343.94 weeks	1999-09-16 13:56:00	2008-08-08 08:13:36	464.108889 weeks	1999-09-12 02:50:00	2008-08-08 08:13:36
6, Missing	date of purchase (POSIXlt class)	5	0.00	2004-10-25 02:13:27	267.95 weeks	2008-07-21 14:26:31	2.15 weeks	1999-02-23 01:03:00	2008-07-24 04:53:54	491.302669 weeks	1999-02-23 01:03:00	2008-07-31 17:39:54
8, Missing	date of purchase (POSIXlt class)	14	14.29	2006-04-18 07:42:10	206.29 weeks	2008-05-04 23:11:45	28.88 weeks	2008-01-15 10:13:33	2008-10-14 03:45:41	38.955569 weeks	1999-08-03 00:23:00	2008-12-15 06:26:36
4, This is the worst car ever!	date of purchase (POSIXlt class)	7	0.00	2003-04-05 22:54:10	245.37 weeks	1999-10-03 20:59:00	40.91 weeks	1999-04-01 16:56:00	2008-02-06 14:44:36	461.844107 weeks	1999-03-24 16:04:00	2008-06-16 20:57:55
6, This is the worst car ever!	date of purchase (POSIXlt class)	10	10.00	2002-08-17 06:20:29	241.20 weeks	1999-09-23 08:05:00	31.28 weeks	1999-04-28 15:16:00	2008-11-24 04:28:26	499.655995 weeks	1999-02-22 04:55:00	2008-11-29 02:51:30
8, This is the worst car ever!	date of purchase (POSIXlt class)	5	0.00	2006-08-16 09:39:48	207.93 weeks	2008-05-16 15:45:50	18.25 weeks	1999-07-03 08:38:00	2008-06-23 14:12:36	468.318909 weeks	1999-07-03 08:38:00	2008-08-16 21:36:23
4, want cheese flavoured cars.	date of purchase (POSIXlt class)	11	36.36	2004-09-23 19:01:28	249.43 weeks	2008-02-20 02:52:27	51.90 weeks	1999-09-24 23:41:00	NA	NA weeks	1999-07-17 10:42:00	2008-10-22 04:49:26
6, want cheese flavoured cars.	date of purchase (POSIXlt class)	13	7.69	2001-10-13 09:39:46	206.72 weeks	1999-09-17 07:54:30	16.31 weeks	1999-07-02 02:34:00	2008-04-01 22:06:18	456.687728 weeks	1999-02-02 06:27:00	2008-06-30 00:50:43
8, want cheese flavoured cars.	date of purchase (POSIXlt class)	9	22.22	2004-09-01 04:43:02	260.61 weeks	2008-01-19 02:34:35	67.07 weeks	1999-06-20 14:17:00	2008-11-30 18:38:11	493.031863 weeks	1999-01-06 16:15:00	2008-11-30 18:38:11
Overall	date of purchase (POSIXlt class)	234	8.55	2003-11-15 13:01:05	234.30 weeks	1999-12-16 04:23:30	67.67 weeks	1999-07-17 10:42:00	2008-07-23 16:02:51	470.603259 weeks	1999-01-04 04:59:00	2008-12-23 01:06:02
R NA Value	date of purchase (POSIXlt class)	24	4.17	2003-05-14 06:30:18	237.27 weeks	1999-12-02 03:54:00	64.95 weeks	1999-04-07 12:51:00	2008-05-14 11:48:26	474.993793 weeks	1999-01-04 04:59:00	2008-10-23 19:14:24

Stacked barplot of date of purchase (Date class) by number of cylinders by some random comments.

POSIXct Date

Show method to output table and plot

show(dpctSummaryExample)

                              Label   N P NA                Mean        S Dev
1  date of purchase (POSIXct class) 214 8.55 2003-11-21 01:59:50 234.68 weeks
                  Med         MAD              25th P              75th P
1 1999-12-20 21:58:00 71.08 weeks 1999-07-12 06:29:00 2008-08-08 04:44:28
             IQR                 Min                 Max
1 473.5611 weeks 1999-01-14 10:39:00 2008-12-23 02:41:31

Output the summary table

data_summary_table(dpctSummaryExample)

                              Label   N P NA                Mean        S Dev
1  date of purchase (POSIXct class) 214 8.55 2003-11-21 01:59:50 234.68 weeks
                  Med         MAD              25th P              75th P
1 1999-12-20 21:58:00 71.08 weeks 1999-07-12 06:29:00 2008-08-08 04:44:28
             IQR                 Min                 Max
1 473.5611 weeks 1999-01-14 10:39:00 2008-12-23 02:41:31

Output the plot

data_summary_plot(dpctSummaryExample)

Generate knitr friendly summary table

make_kable_output(dpctSummaryExample)

Summary statistics of date of purchase (POSIXct class).

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	date of purchase (POSIXct class)	214	8.55	2003-11-21 01:59:50	234.68 weeks	1999-12-20 21:58:00	71.08 weeks	1999-07-12 06:29:00	2008-08-08 04:44:28	473.5611 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31

Generate knitr friendly output

make_complete_output(dpctSummaryExample)

Summary statistics of date of purchase (POSIXct class).

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	date of purchase (POSIXct class)	214	8.55	2003-11-21 01:59:50	234.68 weeks	1999-12-20 21:58:00	71.08 weeks	1999-07-12 06:29:00	2008-08-08 04:44:28	473.5611 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31

Stacked barplot of date of purchase (Date class).

POSIXct Date By

Show method to output table and plot

show(dpctByCylSummaryExample)

  number of cylinders                            Label   N  P NA
1                   4 date of purchase (POSIXct class)  75  7.41
2                   5 date of purchase (POSIXct class)   3 25.00
3                   6 date of purchase (POSIXct class)  72  8.86
4                   8 date of purchase (POSIXct class)  64  8.57
5             Overall date of purchase (POSIXct class) 214  8.55
                 Mean        S Dev                 Med         MAD
1 2003-07-18 01:05:46 234.79 weeks 1999-11-04 12:33:00 59.78 weeks
2 2008-05-03 16:56:21  22.58 weeks 2008-02-11 11:23:10  3.77 weeks
3 2003-02-19 22:01:34 232.07 weeks 1999-11-01 13:42:00 49.24 weeks
4 2004-12-05 01:39:52 230.29 weeks 2008-02-22 08:13:23 42.02 weeks
5 2003-11-21 01:59:50 234.68 weeks 1999-12-20 21:58:00 71.08 weeks
               25th P              75th P            IQR                 Min
1 1999-06-29 06:05:00 2008-06-13 12:42:47 467.4680 weeks 1999-01-19 10:45:00
2 2008-01-24 15:45:51 2008-11-01 21:40:02  40.3149 weeks 2008-01-24 15:45:51
3 1999-06-15 02:46:00 2008-07-22 21:44:19 475.1129 weeks 1999-01-19 05:05:00
4 1999-09-07 08:18:00 2008-07-30 23:21:17 464.2325 weeks 1999-01-14 10:39:00
5 1999-07-12 06:29:00 2008-08-08 04:44:28 473.5611 weeks 1999-01-14 10:39:00
                  Max
1 2008-12-10 14:15:29
2 2008-11-01 21:40:02
3 2008-12-19 04:14:10
4 2008-12-23 02:41:31
5 2008-12-23 02:41:31

Output the summary table

data_summary_table(dpctByCylSummaryExample)

  number of cylinders                            Label   N  P NA
1                   4 date of purchase (POSIXct class)  75  7.41
2                   5 date of purchase (POSIXct class)   3 25.00
3                   6 date of purchase (POSIXct class)  72  8.86
4                   8 date of purchase (POSIXct class)  64  8.57
5             Overall date of purchase (POSIXct class) 214  8.55
                 Mean        S Dev                 Med         MAD
1 2003-07-18 01:05:46 234.79 weeks 1999-11-04 12:33:00 59.78 weeks
2 2008-05-03 16:56:21  22.58 weeks 2008-02-11 11:23:10  3.77 weeks
3 2003-02-19 22:01:34 232.07 weeks 1999-11-01 13:42:00 49.24 weeks
4 2004-12-05 01:39:52 230.29 weeks 2008-02-22 08:13:23 42.02 weeks
5 2003-11-21 01:59:50 234.68 weeks 1999-12-20 21:58:00 71.08 weeks
               25th P              75th P            IQR                 Min
1 1999-06-29 06:05:00 2008-06-13 12:42:47 467.4680 weeks 1999-01-19 10:45:00
2 2008-01-24 15:45:51 2008-11-01 21:40:02  40.3149 weeks 2008-01-24 15:45:51
3 1999-06-15 02:46:00 2008-07-22 21:44:19 475.1129 weeks 1999-01-19 05:05:00
4 1999-09-07 08:18:00 2008-07-30 23:21:17 464.2325 weeks 1999-01-14 10:39:00
5 1999-07-12 06:29:00 2008-08-08 04:44:28 473.5611 weeks 1999-01-14 10:39:00
                  Max
1 2008-12-10 14:15:29
2 2008-11-01 21:40:02
3 2008-12-19 04:14:10
4 2008-12-23 02:41:31
5 2008-12-23 02:41:31

Output the plot

data_summary_plot(dpctByCylSummaryExample)

Generate a knitr friendly summary table

make_kable_output(dpctByCylSummaryExample)

Summary statistics of date of purchase (POSIXct class) by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	date of purchase (POSIXct class)	75	7.41	2003-07-18 01:05:46	234.79 weeks	1999-11-04 12:33:00	59.78 weeks	1999-06-29 06:05:00	2008-06-13 12:42:47	467.4680 weeks	1999-01-19 10:45:00	2008-12-10 14:15:29
5	date of purchase (POSIXct class)	3	25.00	2008-05-03 16:56:21	22.58 weeks	2008-02-11 11:23:10	3.77 weeks	2008-01-24 15:45:51	2008-11-01 21:40:02	40.3149 weeks	2008-01-24 15:45:51	2008-11-01 21:40:02
6	date of purchase (POSIXct class)	72	8.86	2003-02-19 22:01:34	232.07 weeks	1999-11-01 13:42:00	49.24 weeks	1999-06-15 02:46:00	2008-07-22 21:44:19	475.1129 weeks	1999-01-19 05:05:00	2008-12-19 04:14:10
8	date of purchase (POSIXct class)	64	8.57	2004-12-05 01:39:52	230.29 weeks	2008-02-22 08:13:23	42.02 weeks	1999-09-07 08:18:00	2008-07-30 23:21:17	464.2325 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31
Overall	date of purchase (POSIXct class)	214	8.55	2003-11-21 01:59:50	234.68 weeks	1999-12-20 21:58:00	71.08 weeks	1999-07-12 06:29:00	2008-08-08 04:44:28	473.5611 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31

Generate knitr friendly output

make_complete_output(dpctByCylSummaryExample)

Summary statistics of date of purchase (POSIXct class) by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	date of purchase (POSIXct class)	75	7.41	2003-07-18 01:05:46	234.79 weeks	1999-11-04 12:33:00	59.78 weeks	1999-06-29 06:05:00	2008-06-13 12:42:47	467.4680 weeks	1999-01-19 10:45:00	2008-12-10 14:15:29
5	date of purchase (POSIXct class)	3	25.00	2008-05-03 16:56:21	22.58 weeks	2008-02-11 11:23:10	3.77 weeks	2008-01-24 15:45:51	2008-11-01 21:40:02	40.3149 weeks	2008-01-24 15:45:51	2008-11-01 21:40:02
6	date of purchase (POSIXct class)	72	8.86	2003-02-19 22:01:34	232.07 weeks	1999-11-01 13:42:00	49.24 weeks	1999-06-15 02:46:00	2008-07-22 21:44:19	475.1129 weeks	1999-01-19 05:05:00	2008-12-19 04:14:10
8	date of purchase (POSIXct class)	64	8.57	2004-12-05 01:39:52	230.29 weeks	2008-02-22 08:13:23	42.02 weeks	1999-09-07 08:18:00	2008-07-30 23:21:17	464.2325 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31
Overall	date of purchase (POSIXct class)	214	8.55	2003-11-21 01:59:50	234.68 weeks	1999-12-20 21:58:00	71.08 weeks	1999-07-12 06:29:00	2008-08-08 04:44:28	473.5611 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31

Stacked barplot of date of purchase (Date class) by number of cylinders.

POSIXct Date By By

Show method to output table and plot

show(dpctByCylByCommentsSummaryExample)

         number of cylinders by some random comments
1                                               4, .
2                                               6, .
3                                               8, .
4  4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
5  6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
6  8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
7                               4, Does it also fly?
8                               6, Does it also fly?
9                               8, Does it also fly?
10                         4, Does it come in green?
11                         6, Does it come in green?
12                         8, Does it come in green?
13                               4, I like this car!
14                               6, I like this car!
15                               8, I like this car!
16                                           4, Meh.
17                                           6, Meh.
18                                           8, Meh.
19                                        4, Missing
20                                        6, Missing
21                                        8, Missing
22                    4, This is the worst car ever!
23                    6, This is the worst car ever!
24                    8, This is the worst car ever!
25                    4, want cheese flavoured cars.
26                    6, want cheese flavoured cars.
27                    8, want cheese flavoured cars.
28                                           Overall
29                                        R NA Value
                              Label   N  P NA                Mean        S Dev
1  date of purchase (POSIXct class)   4 20.00 2003-11-26 01:28:09 264.73 weeks
2  date of purchase (POSIXct class)   5 44.44 2002-12-17 07:22:57 270.27 weeks
3  date of purchase (POSIXct class)  11  0.00 2004-04-24 19:25:05 249.42 weeks
4  date of purchase (POSIXct class)   9  0.00 2002-07-15 12:07:25 241.61 weeks
5  date of purchase (POSIXct class)   8  0.00 2005-03-06 05:47:49 242.11 weeks
6  date of purchase (POSIXct class)   4  0.00 2004-02-11 02:10:30 276.28 weeks
7  date of purchase (POSIXct class)   4 20.00 2001-09-24 21:28:30 248.62 weeks
8  date of purchase (POSIXct class)   7  0.00 2002-01-07 00:42:09 233.05 weeks
9  date of purchase (POSIXct class)   4  0.00 2003-12-10 08:59:56 274.38 weeks
10 date of purchase (POSIXct class)  13 13.33 2004-04-26 18:00:18 242.32 weeks
11 date of purchase (POSIXct class)   3  0.00 2002-05-06 17:40:58 282.96 weeks
12 date of purchase (POSIXct class)   5  0.00 2006-07-04 19:28:09 202.85 weeks
13 date of purchase (POSIXct class)  10  0.00 2004-09-06 11:29:02 236.37 weeks
14 date of purchase (POSIXct class)   8 11.11 2001-09-26 15:59:45 221.03 weeks
15 date of purchase (POSIXct class)   4  0.00 2001-10-11 21:23:10 217.30 weeks
16 date of purchase (POSIXct class)   5 16.67 1999-09-07 16:47:00  10.58 weeks
17 date of purchase (POSIXct class)   6  0.00 2005-07-02 01:16:06 239.72 weeks
18 date of purchase (POSIXct class)   6  0.00 2008-06-20 07:15:14  15.40 weeks
19 date of purchase (POSIXct class)   6  0.00 2005-06-18 04:14:08 242.90 weeks
20 date of purchase (POSIXct class)   5  0.00 2004-11-20 04:13:04 261.49 weeks
21 date of purchase (POSIXct class)  10 28.57 2005-09-03 05:52:51 232.45 weeks
22 date of purchase (POSIXct class)   7  0.00 2003-04-25 22:09:50 251.79 weeks
23 date of purchase (POSIXct class)   9 10.00 2002-08-24 05:35:52 231.70 weeks
24 date of purchase (POSIXct class)   5  0.00 2006-10-12 10:56:39 199.59 weeks
25 date of purchase (POSIXct class)  11  0.00 2003-07-08 17:22:47 243.99 weeks
26 date of purchase (POSIXct class)  13  0.00 2002-04-20 13:43:21 220.53 weeks
27 date of purchase (POSIXct class)   7 22.22 2004-08-05 03:35:48 242.19 weeks
28 date of purchase (POSIXct class) 214  8.55 2003-11-21 01:59:50 234.68 weeks
29 date of purchase (POSIXct class)  22  8.33 2003-03-11 06:54:43 237.23 weeks
                   Med          MAD              25th P              75th P
1  2003-11-06 00:13:47 335.21 weeks 1999-05-08 03:24:00 2008-07-25 04:01:02
2  1999-03-12 05:37:00   2.16 weeks 1999-03-05 23:19:00                <NA>
3  2008-01-13 14:49:44  66.19 weeks 1999-03-07 05:25:00 2008-02-26 03:51:50
4  1999-10-27 08:00:00  52.44 weeks 1999-02-21 16:40:00 2008-06-08 19:04:52
5  2008-02-29 11:08:42  60.19 weeks 1999-07-29 02:43:00 2008-06-11 20:05:41
6  2004-03-28 00:37:14 344.49 weeks 1999-03-12 10:49:00 2008-08-08 04:44:28
7  1999-07-02 14:44:30  29.09 weeks 1999-01-25 04:26:00 2008-11-11 01:59:02
8  1999-06-15 04:11:00  20.52 weeks 1999-04-09 00:55:00 1999-09-20 01:32:00
9  2003-11-28 18:09:45 349.78 weeks 1999-05-04 19:30:00 2008-05-19 17:04:31
10 2008-01-30 06:40:31  65.27 weeks 1999-09-11 11:21:00 2008-08-21 12:07:05
11 1999-04-10 20:07:00   9.15 weeks 1999-02-26 14:09:00 1999-04-10 20:07:00
12 2008-02-18 12:34:57   9.89 weeks 1999-07-23 20:34:00 2008-04-05 06:03:33
13 2008-01-15 18:48:46  24.65 weeks 1999-02-03 02:54:00 2008-03-27 14:15:56
14 1999-09-15 13:26:30  40.56 weeks 1999-02-28 02:12:00 2008-04-21 15:55:24
15 1999-11-08 23:10:00  21.40 weeks 1999-05-23 12:06:00 1999-12-11 11:51:00
16 1999-09-08 16:06:00   4.96 weeks 1999-08-16 06:16:00 1999-09-24 05:34:00
17 2008-03-20 18:16:55  37.37 weeks 1999-10-04 16:53:00 2008-04-10 15:49:22
18 2008-06-07 04:14:04  18.68 weeks 2008-03-11 18:07:43 2008-07-30 23:21:17
19 2008-02-16 06:07:01  51.03 weeks 1999-11-09 01:13:00 2008-03-08 08:49:18
20 2008-03-11 20:09:42  44.33 weeks 1999-04-04 20:26:00 2008-09-01 08:38:40
21 2008-05-08 08:56:16  16.38 weeks 2008-01-19 10:45:49 2008-12-23 02:41:31
22 1999-09-09 16:35:00  40.37 weeks 1999-05-18 04:07:00 2008-04-02 22:50:26
23 1999-11-23 09:40:00  33.31 weeks 1999-06-19 03:37:00 2008-08-22 23:14:44
24 2008-06-06 11:58:47  19.12 weeks 1999-12-12 17:15:00 2008-08-31 05:08:21
25 1999-12-19 19:44:00  70.82 weeks 1999-05-29 22:46:00 2008-03-17 01:58:41
26 1999-10-22 23:01:00  29.77 weeks 1999-06-04 09:07:00 2008-01-23 02:49:05
27 2008-02-12 08:03:24  34.18 weeks 1999-11-17 16:08:00 2008-07-22 17:39:40
28 1999-12-20 21:58:00  71.08 weeks 1999-07-12 06:29:00 2008-08-08 04:44:28
29 1999-10-23 07:22:30  46.90 weeks 1999-06-05 15:30:00 2008-06-03 02:59:13
                IQR                 Min                 Max
1  480.860817 weeks 1999-05-08 03:24:00 2008-07-25 04:01:02
2          NA weeks 1999-03-02 01:18:00 2008-09-16 10:56:27
3  468.276472 weeks 1999-01-21 09:02:00 2008-11-21 02:41:49
4  485.008419 weeks 1999-01-20 00:04:00 2008-11-13 01:54:34
5  462.960584 weeks 1999-04-21 10:44:00 2008-12-19 04:14:10
6  490.957884 weeks 1999-03-12 10:49:00 2008-10-11 21:38:33
7  511.128277 weeks 1999-01-25 04:26:00 2008-11-11 01:59:02
8   23.432242 weeks 1999-02-22 21:00:00 2008-09-26 00:11:30
9  471.842710 weeks 1999-05-04 19:30:00 2008-08-09 06:10:15
10 466.718857 weeks 1999-02-13 09:45:00 2008-12-03 10:17:51
11   6.172421 weeks 1999-02-26 14:09:00 2008-08-09 17:46:55
12 454.056503 weeks 1999-07-23 20:34:00 2008-06-29 10:41:03
13 477.204557 weeks 1999-01-26 06:24:00 2008-06-13 12:42:47
14 477.218591 weeks 1999-01-19 05:05:00 2008-11-05 13:24:43
15  28.861607 weeks 1999-05-23 12:06:00 2008-01-07 00:06:43
16   5.567262 weeks 1999-05-30 06:47:00 1999-12-22 00:12:00
17 444.422259 weeks 1999-05-28 16:38:00 2008-12-15 14:11:55
18  20.173965 weeks 2008-03-05 18:36:33 2008-11-24 17:31:18
19 434.616696 weeks 1999-02-03 20:54:00 2008-12-10 14:15:29
20 491.072685 weeks 1999-04-04 20:26:00 2008-10-07 03:05:02
21  48.380526 weeks 1999-01-14 10:39:00 2008-12-23 02:41:31
22 463.254309 weeks 1999-03-03 01:37:00 2008-10-25 00:08:02
23 478.973981 weeks 1999-04-03 22:19:00 2008-09-22 01:07:21
24 454.921959 weeks 1999-12-12 17:15:00 2008-09-04 18:09:48
25 459.161973 weeks 1999-01-19 10:45:00 2008-09-23 15:04:14
26 450.682746 weeks 1999-04-04 00:33:00 2008-10-30 15:10:27
27 452.860284 weeks 1999-02-27 18:31:00 2008-07-22 17:39:40
28 473.561058 weeks 1999-01-14 10:39:00 2008-12-23 02:41:31
29 469.354089 weeks 1999-02-12 16:08:00 2008-12-04 06:19:25

Output the summary table

data_summary_table(dpctByCylByCommentsSummaryExample)

         number of cylinders by some random comments
1                                               4, .
2                                               6, .
3                                               8, .
4  4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
5  6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
6  8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah
7                               4, Does it also fly?
8                               6, Does it also fly?
9                               8, Does it also fly?
10                         4, Does it come in green?
11                         6, Does it come in green?
12                         8, Does it come in green?
13                               4, I like this car!
14                               6, I like this car!
15                               8, I like this car!
16                                           4, Meh.
17                                           6, Meh.
18                                           8, Meh.
19                                        4, Missing
20                                        6, Missing
21                                        8, Missing
22                    4, This is the worst car ever!
23                    6, This is the worst car ever!
24                    8, This is the worst car ever!
25                    4, want cheese flavoured cars.
26                    6, want cheese flavoured cars.
27                    8, want cheese flavoured cars.
28                                           Overall
29                                        R NA Value
                              Label   N  P NA                Mean        S Dev
1  date of purchase (POSIXct class)   4 20.00 2003-11-26 01:28:09 264.73 weeks
2  date of purchase (POSIXct class)   5 44.44 2002-12-17 07:22:57 270.27 weeks
3  date of purchase (POSIXct class)  11  0.00 2004-04-24 19:25:05 249.42 weeks
4  date of purchase (POSIXct class)   9  0.00 2002-07-15 12:07:25 241.61 weeks
5  date of purchase (POSIXct class)   8  0.00 2005-03-06 05:47:49 242.11 weeks
6  date of purchase (POSIXct class)   4  0.00 2004-02-11 02:10:30 276.28 weeks
7  date of purchase (POSIXct class)   4 20.00 2001-09-24 21:28:30 248.62 weeks
8  date of purchase (POSIXct class)   7  0.00 2002-01-07 00:42:09 233.05 weeks
9  date of purchase (POSIXct class)   4  0.00 2003-12-10 08:59:56 274.38 weeks
10 date of purchase (POSIXct class)  13 13.33 2004-04-26 18:00:18 242.32 weeks
11 date of purchase (POSIXct class)   3  0.00 2002-05-06 17:40:58 282.96 weeks
12 date of purchase (POSIXct class)   5  0.00 2006-07-04 19:28:09 202.85 weeks
13 date of purchase (POSIXct class)  10  0.00 2004-09-06 11:29:02 236.37 weeks
14 date of purchase (POSIXct class)   8 11.11 2001-09-26 15:59:45 221.03 weeks
15 date of purchase (POSIXct class)   4  0.00 2001-10-11 21:23:10 217.30 weeks
16 date of purchase (POSIXct class)   5 16.67 1999-09-07 16:47:00  10.58 weeks
17 date of purchase (POSIXct class)   6  0.00 2005-07-02 01:16:06 239.72 weeks
18 date of purchase (POSIXct class)   6  0.00 2008-06-20 07:15:14  15.40 weeks
19 date of purchase (POSIXct class)   6  0.00 2005-06-18 04:14:08 242.90 weeks
20 date of purchase (POSIXct class)   5  0.00 2004-11-20 04:13:04 261.49 weeks
21 date of purchase (POSIXct class)  10 28.57 2005-09-03 05:52:51 232.45 weeks
22 date of purchase (POSIXct class)   7  0.00 2003-04-25 22:09:50 251.79 weeks
23 date of purchase (POSIXct class)   9 10.00 2002-08-24 05:35:52 231.70 weeks
24 date of purchase (POSIXct class)   5  0.00 2006-10-12 10:56:39 199.59 weeks
25 date of purchase (POSIXct class)  11  0.00 2003-07-08 17:22:47 243.99 weeks
26 date of purchase (POSIXct class)  13  0.00 2002-04-20 13:43:21 220.53 weeks
27 date of purchase (POSIXct class)   7 22.22 2004-08-05 03:35:48 242.19 weeks
28 date of purchase (POSIXct class) 214  8.55 2003-11-21 01:59:50 234.68 weeks
29 date of purchase (POSIXct class)  22  8.33 2003-03-11 06:54:43 237.23 weeks
                   Med          MAD              25th P              75th P
1  2003-11-06 00:13:47 335.21 weeks 1999-05-08 03:24:00 2008-07-25 04:01:02
2  1999-03-12 05:37:00   2.16 weeks 1999-03-05 23:19:00                <NA>
3  2008-01-13 14:49:44  66.19 weeks 1999-03-07 05:25:00 2008-02-26 03:51:50
4  1999-10-27 08:00:00  52.44 weeks 1999-02-21 16:40:00 2008-06-08 19:04:52
5  2008-02-29 11:08:42  60.19 weeks 1999-07-29 02:43:00 2008-06-11 20:05:41
6  2004-03-28 00:37:14 344.49 weeks 1999-03-12 10:49:00 2008-08-08 04:44:28
7  1999-07-02 14:44:30  29.09 weeks 1999-01-25 04:26:00 2008-11-11 01:59:02
8  1999-06-15 04:11:00  20.52 weeks 1999-04-09 00:55:00 1999-09-20 01:32:00
9  2003-11-28 18:09:45 349.78 weeks 1999-05-04 19:30:00 2008-05-19 17:04:31
10 2008-01-30 06:40:31  65.27 weeks 1999-09-11 11:21:00 2008-08-21 12:07:05
11 1999-04-10 20:07:00   9.15 weeks 1999-02-26 14:09:00 1999-04-10 20:07:00
12 2008-02-18 12:34:57   9.89 weeks 1999-07-23 20:34:00 2008-04-05 06:03:33
13 2008-01-15 18:48:46  24.65 weeks 1999-02-03 02:54:00 2008-03-27 14:15:56
14 1999-09-15 13:26:30  40.56 weeks 1999-02-28 02:12:00 2008-04-21 15:55:24
15 1999-11-08 23:10:00  21.40 weeks 1999-05-23 12:06:00 1999-12-11 11:51:00
16 1999-09-08 16:06:00   4.96 weeks 1999-08-16 06:16:00 1999-09-24 05:34:00
17 2008-03-20 18:16:55  37.37 weeks 1999-10-04 16:53:00 2008-04-10 15:49:22
18 2008-06-07 04:14:04  18.68 weeks 2008-03-11 18:07:43 2008-07-30 23:21:17
19 2008-02-16 06:07:01  51.03 weeks 1999-11-09 01:13:00 2008-03-08 08:49:18
20 2008-03-11 20:09:42  44.33 weeks 1999-04-04 20:26:00 2008-09-01 08:38:40
21 2008-05-08 08:56:16  16.38 weeks 2008-01-19 10:45:49 2008-12-23 02:41:31
22 1999-09-09 16:35:00  40.37 weeks 1999-05-18 04:07:00 2008-04-02 22:50:26
23 1999-11-23 09:40:00  33.31 weeks 1999-06-19 03:37:00 2008-08-22 23:14:44
24 2008-06-06 11:58:47  19.12 weeks 1999-12-12 17:15:00 2008-08-31 05:08:21
25 1999-12-19 19:44:00  70.82 weeks 1999-05-29 22:46:00 2008-03-17 01:58:41
26 1999-10-22 23:01:00  29.77 weeks 1999-06-04 09:07:00 2008-01-23 02:49:05
27 2008-02-12 08:03:24  34.18 weeks 1999-11-17 16:08:00 2008-07-22 17:39:40
28 1999-12-20 21:58:00  71.08 weeks 1999-07-12 06:29:00 2008-08-08 04:44:28
29 1999-10-23 07:22:30  46.90 weeks 1999-06-05 15:30:00 2008-06-03 02:59:13
                IQR                 Min                 Max
1  480.860817 weeks 1999-05-08 03:24:00 2008-07-25 04:01:02
2          NA weeks 1999-03-02 01:18:00 2008-09-16 10:56:27
3  468.276472 weeks 1999-01-21 09:02:00 2008-11-21 02:41:49
4  485.008419 weeks 1999-01-20 00:04:00 2008-11-13 01:54:34
5  462.960584 weeks 1999-04-21 10:44:00 2008-12-19 04:14:10
6  490.957884 weeks 1999-03-12 10:49:00 2008-10-11 21:38:33
7  511.128277 weeks 1999-01-25 04:26:00 2008-11-11 01:59:02
8   23.432242 weeks 1999-02-22 21:00:00 2008-09-26 00:11:30
9  471.842710 weeks 1999-05-04 19:30:00 2008-08-09 06:10:15
10 466.718857 weeks 1999-02-13 09:45:00 2008-12-03 10:17:51
11   6.172421 weeks 1999-02-26 14:09:00 2008-08-09 17:46:55
12 454.056503 weeks 1999-07-23 20:34:00 2008-06-29 10:41:03
13 477.204557 weeks 1999-01-26 06:24:00 2008-06-13 12:42:47
14 477.218591 weeks 1999-01-19 05:05:00 2008-11-05 13:24:43
15  28.861607 weeks 1999-05-23 12:06:00 2008-01-07 00:06:43
16   5.567262 weeks 1999-05-30 06:47:00 1999-12-22 00:12:00
17 444.422259 weeks 1999-05-28 16:38:00 2008-12-15 14:11:55
18  20.173965 weeks 2008-03-05 18:36:33 2008-11-24 17:31:18
19 434.616696 weeks 1999-02-03 20:54:00 2008-12-10 14:15:29
20 491.072685 weeks 1999-04-04 20:26:00 2008-10-07 03:05:02
21  48.380526 weeks 1999-01-14 10:39:00 2008-12-23 02:41:31
22 463.254309 weeks 1999-03-03 01:37:00 2008-10-25 00:08:02
23 478.973981 weeks 1999-04-03 22:19:00 2008-09-22 01:07:21
24 454.921959 weeks 1999-12-12 17:15:00 2008-09-04 18:09:48
25 459.161973 weeks 1999-01-19 10:45:00 2008-09-23 15:04:14
26 450.682746 weeks 1999-04-04 00:33:00 2008-10-30 15:10:27
27 452.860284 weeks 1999-02-27 18:31:00 2008-07-22 17:39:40
28 473.561058 weeks 1999-01-14 10:39:00 2008-12-23 02:41:31
29 469.354089 weeks 1999-02-12 16:08:00 2008-12-04 06:19:25

Output the plot

data_summary_plot(dpctByCylByCommentsSummaryExample)

Generate a knitr friendly summary table

make_kable_output(dpctByCylByCommentsSummaryExample)

Summary statistics of date of purchase (POSIXct class) by number of cylinders by some random comments.

number of cylinders by some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4, .	date of purchase (POSIXct class)	4	20.00	2003-11-26 01:28:09	264.73 weeks	2003-11-06 00:13:47	335.21 weeks	1999-05-08 03:24:00	2008-07-25 04:01:02	480.860817 weeks	1999-05-08 03:24:00	2008-07-25 04:01:02
6, .	date of purchase (POSIXct class)	5	44.44	2002-12-17 07:22:57	270.27 weeks	1999-03-12 05:37:00	2.16 weeks	1999-03-05 23:19:00	NA	NA weeks	1999-03-02 01:18:00	2008-09-16 10:56:27
8, .	date of purchase (POSIXct class)	11	0.00	2004-04-24 19:25:05	249.42 weeks	2008-01-13 14:49:44	66.19 weeks	1999-03-07 05:25:00	2008-02-26 03:51:50	468.276472 weeks	1999-01-21 09:02:00	2008-11-21 02:41:49
4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXct class)	9	0.00	2002-07-15 12:07:25	241.61 weeks	1999-10-27 08:00:00	52.44 weeks	1999-02-21 16:40:00	2008-06-08 19:04:52	485.008419 weeks	1999-01-20 00:04:00	2008-11-13 01:54:34
6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXct class)	8	0.00	2005-03-06 05:47:49	242.11 weeks	2008-02-29 11:08:42	60.19 weeks	1999-07-29 02:43:00	2008-06-11 20:05:41	462.960584 weeks	1999-04-21 10:44:00	2008-12-19 04:14:10
8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXct class)	4	0.00	2004-02-11 02:10:30	276.28 weeks	2004-03-28 00:37:14	344.49 weeks	1999-03-12 10:49:00	2008-08-08 04:44:28	490.957884 weeks	1999-03-12 10:49:00	2008-10-11 21:38:33
4, Does it also fly?	date of purchase (POSIXct class)	4	20.00	2001-09-24 21:28:30	248.62 weeks	1999-07-02 14:44:30	29.09 weeks	1999-01-25 04:26:00	2008-11-11 01:59:02	511.128277 weeks	1999-01-25 04:26:00	2008-11-11 01:59:02
6, Does it also fly?	date of purchase (POSIXct class)	7	0.00	2002-01-07 00:42:09	233.05 weeks	1999-06-15 04:11:00	20.52 weeks	1999-04-09 00:55:00	1999-09-20 01:32:00	23.432242 weeks	1999-02-22 21:00:00	2008-09-26 00:11:30
8, Does it also fly?	date of purchase (POSIXct class)	4	0.00	2003-12-10 08:59:56	274.38 weeks	2003-11-28 18:09:45	349.78 weeks	1999-05-04 19:30:00	2008-05-19 17:04:31	471.842710 weeks	1999-05-04 19:30:00	2008-08-09 06:10:15
4, Does it come in green?	date of purchase (POSIXct class)	13	13.33	2004-04-26 18:00:18	242.32 weeks	2008-01-30 06:40:31	65.27 weeks	1999-09-11 11:21:00	2008-08-21 12:07:05	466.718858 weeks	1999-02-13 09:45:00	2008-12-03 10:17:51
6, Does it come in green?	date of purchase (POSIXct class)	3	0.00	2002-05-06 17:40:58	282.96 weeks	1999-04-10 20:07:00	9.15 weeks	1999-02-26 14:09:00	1999-04-10 20:07:00	6.172421 weeks	1999-02-26 14:09:00	2008-08-09 17:46:55
8, Does it come in green?	date of purchase (POSIXct class)	5	0.00	2006-07-04 19:28:09	202.85 weeks	2008-02-18 12:34:57	9.89 weeks	1999-07-23 20:34:00	2008-04-05 06:03:33	454.056503 weeks	1999-07-23 20:34:00	2008-06-29 10:41:03
4, I like this car!	date of purchase (POSIXct class)	10	0.00	2004-09-06 11:29:02	236.37 weeks	2008-01-15 18:48:46	24.65 weeks	1999-02-03 02:54:00	2008-03-27 14:15:56	477.204557 weeks	1999-01-26 06:24:00	2008-06-13 12:42:47
6, I like this car!	date of purchase (POSIXct class)	8	11.11	2001-09-26 15:59:45	221.03 weeks	1999-09-15 13:26:30	40.56 weeks	1999-02-28 02:12:00	2008-04-21 15:55:24	477.218591 weeks	1999-01-19 05:05:00	2008-11-05 13:24:43
8, I like this car!	date of purchase (POSIXct class)	4	0.00	2001-10-11 21:23:10	217.30 weeks	1999-11-08 23:10:00	21.40 weeks	1999-05-23 12:06:00	1999-12-11 11:51:00	28.861607 weeks	1999-05-23 12:06:00	2008-01-07 00:06:43
4, Meh.	date of purchase (POSIXct class)	5	16.67	1999-09-07 16:47:00	10.58 weeks	1999-09-08 16:06:00	4.96 weeks	1999-08-16 06:16:00	1999-09-24 05:34:00	5.567262 weeks	1999-05-30 06:47:00	1999-12-22 00:12:00
6, Meh.	date of purchase (POSIXct class)	6	0.00	2005-07-02 01:16:06	239.72 weeks	2008-03-20 18:16:55	37.37 weeks	1999-10-04 16:53:00	2008-04-10 15:49:22	444.422259 weeks	1999-05-28 16:38:00	2008-12-15 14:11:55
8, Meh.	date of purchase (POSIXct class)	6	0.00	2008-06-20 07:15:14	15.40 weeks	2008-06-07 04:14:04	18.68 weeks	2008-03-11 18:07:43	2008-07-30 23:21:17	20.173965 weeks	2008-03-05 18:36:33	2008-11-24 17:31:18
4, Missing	date of purchase (POSIXct class)	6	0.00	2005-06-18 04:14:08	242.90 weeks	2008-02-16 06:07:01	51.03 weeks	1999-11-09 01:13:00	2008-03-08 08:49:18	434.616696 weeks	1999-02-03 20:54:00	2008-12-10 14:15:29
6, Missing	date of purchase (POSIXct class)	5	0.00	2004-11-20 04:13:04	261.49 weeks	2008-03-11 20:09:42	44.33 weeks	1999-04-04 20:26:00	2008-09-01 08:38:40	491.072685 weeks	1999-04-04 20:26:00	2008-10-07 03:05:02
8, Missing	date of purchase (POSIXct class)	10	28.57	2005-09-03 05:52:51	232.45 weeks	2008-05-08 08:56:16	16.38 weeks	2008-01-19 10:45:49	2008-12-23 02:41:31	48.380526 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31
4, This is the worst car ever!	date of purchase (POSIXct class)	7	0.00	2003-04-25 22:09:50	251.79 weeks	1999-09-09 16:35:00	40.37 weeks	1999-05-18 04:07:00	2008-04-02 22:50:26	463.254309 weeks	1999-03-03 01:37:00	2008-10-25 00:08:02
6, This is the worst car ever!	date of purchase (POSIXct class)	9	10.00	2002-08-24 05:35:52	231.70 weeks	1999-11-23 09:40:00	33.31 weeks	1999-06-19 03:37:00	2008-08-22 23:14:44	478.973981 weeks	1999-04-03 22:19:00	2008-09-22 01:07:21
8, This is the worst car ever!	date of purchase (POSIXct class)	5	0.00	2006-10-12 10:56:39	199.59 weeks	2008-06-06 11:58:47	19.12 weeks	1999-12-12 17:15:00	2008-08-31 05:08:21	454.921959 weeks	1999-12-12 17:15:00	2008-09-04 18:09:48
4, want cheese flavoured cars.	date of purchase (POSIXct class)	11	0.00	2003-07-08 17:22:47	243.99 weeks	1999-12-19 19:44:00	70.82 weeks	1999-05-29 22:46:00	2008-03-17 01:58:41	459.161973 weeks	1999-01-19 10:45:00	2008-09-23 15:04:14
6, want cheese flavoured cars.	date of purchase (POSIXct class)	13	0.00	2002-04-20 13:43:21	220.53 weeks	1999-10-22 23:01:00	29.77 weeks	1999-06-04 09:07:00	2008-01-23 02:49:05	450.682746 weeks	1999-04-04 00:33:00	2008-10-30 15:10:27
8, want cheese flavoured cars.	date of purchase (POSIXct class)	7	22.22	2004-08-05 03:35:48	242.19 weeks	2008-02-12 08:03:24	34.18 weeks	1999-11-17 16:08:00	2008-07-22 17:39:40	452.860284 weeks	1999-02-27 18:31:00	2008-07-22 17:39:40
Overall	date of purchase (POSIXct class)	214	8.55	2003-11-21 01:59:50	234.68 weeks	1999-12-20 21:58:00	71.08 weeks	1999-07-12 06:29:00	2008-08-08 04:44:28	473.561058 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31
R NA Value	date of purchase (POSIXct class)	22	8.33	2003-03-11 06:54:43	237.23 weeks	1999-10-23 07:22:30	46.90 weeks	1999-06-05 15:30:00	2008-06-03 02:59:13	469.354089 weeks	1999-02-12 16:08:00	2008-12-04 06:19:25

Generate knitr friendly output

make_complete_output(dpctByCylByCommentsSummaryExample)

Summary statistics of date of purchase (POSIXct class) by number of cylinders by some random comments.

number of cylinders by some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4, .	date of purchase (POSIXct class)	4	20.00	2003-11-26 01:28:09	264.73 weeks	2003-11-06 00:13:47	335.21 weeks	1999-05-08 03:24:00	2008-07-25 04:01:02	480.860817 weeks	1999-05-08 03:24:00	2008-07-25 04:01:02
6, .	date of purchase (POSIXct class)	5	44.44	2002-12-17 07:22:57	270.27 weeks	1999-03-12 05:37:00	2.16 weeks	1999-03-05 23:19:00	NA	NA weeks	1999-03-02 01:18:00	2008-09-16 10:56:27
8, .	date of purchase (POSIXct class)	11	0.00	2004-04-24 19:25:05	249.42 weeks	2008-01-13 14:49:44	66.19 weeks	1999-03-07 05:25:00	2008-02-26 03:51:50	468.276472 weeks	1999-01-21 09:02:00	2008-11-21 02:41:49
4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXct class)	9	0.00	2002-07-15 12:07:25	241.61 weeks	1999-10-27 08:00:00	52.44 weeks	1999-02-21 16:40:00	2008-06-08 19:04:52	485.008419 weeks	1999-01-20 00:04:00	2008-11-13 01:54:34
6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXct class)	8	0.00	2005-03-06 05:47:49	242.11 weeks	2008-02-29 11:08:42	60.19 weeks	1999-07-29 02:43:00	2008-06-11 20:05:41	462.960584 weeks	1999-04-21 10:44:00	2008-12-19 04:14:10
8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (POSIXct class)	4	0.00	2004-02-11 02:10:30	276.28 weeks	2004-03-28 00:37:14	344.49 weeks	1999-03-12 10:49:00	2008-08-08 04:44:28	490.957884 weeks	1999-03-12 10:49:00	2008-10-11 21:38:33
4, Does it also fly?	date of purchase (POSIXct class)	4	20.00	2001-09-24 21:28:30	248.62 weeks	1999-07-02 14:44:30	29.09 weeks	1999-01-25 04:26:00	2008-11-11 01:59:02	511.128277 weeks	1999-01-25 04:26:00	2008-11-11 01:59:02
6, Does it also fly?	date of purchase (POSIXct class)	7	0.00	2002-01-07 00:42:09	233.05 weeks	1999-06-15 04:11:00	20.52 weeks	1999-04-09 00:55:00	1999-09-20 01:32:00	23.432242 weeks	1999-02-22 21:00:00	2008-09-26 00:11:30
8, Does it also fly?	date of purchase (POSIXct class)	4	0.00	2003-12-10 08:59:56	274.38 weeks	2003-11-28 18:09:45	349.78 weeks	1999-05-04 19:30:00	2008-05-19 17:04:31	471.842710 weeks	1999-05-04 19:30:00	2008-08-09 06:10:15
4, Does it come in green?	date of purchase (POSIXct class)	13	13.33	2004-04-26 18:00:18	242.32 weeks	2008-01-30 06:40:31	65.27 weeks	1999-09-11 11:21:00	2008-08-21 12:07:05	466.718858 weeks	1999-02-13 09:45:00	2008-12-03 10:17:51
6, Does it come in green?	date of purchase (POSIXct class)	3	0.00	2002-05-06 17:40:58	282.96 weeks	1999-04-10 20:07:00	9.15 weeks	1999-02-26 14:09:00	1999-04-10 20:07:00	6.172421 weeks	1999-02-26 14:09:00	2008-08-09 17:46:55
8, Does it come in green?	date of purchase (POSIXct class)	5	0.00	2006-07-04 19:28:09	202.85 weeks	2008-02-18 12:34:57	9.89 weeks	1999-07-23 20:34:00	2008-04-05 06:03:33	454.056503 weeks	1999-07-23 20:34:00	2008-06-29 10:41:03
4, I like this car!	date of purchase (POSIXct class)	10	0.00	2004-09-06 11:29:02	236.37 weeks	2008-01-15 18:48:46	24.65 weeks	1999-02-03 02:54:00	2008-03-27 14:15:56	477.204557 weeks	1999-01-26 06:24:00	2008-06-13 12:42:47
6, I like this car!	date of purchase (POSIXct class)	8	11.11	2001-09-26 15:59:45	221.03 weeks	1999-09-15 13:26:30	40.56 weeks	1999-02-28 02:12:00	2008-04-21 15:55:24	477.218591 weeks	1999-01-19 05:05:00	2008-11-05 13:24:43
8, I like this car!	date of purchase (POSIXct class)	4	0.00	2001-10-11 21:23:10	217.30 weeks	1999-11-08 23:10:00	21.40 weeks	1999-05-23 12:06:00	1999-12-11 11:51:00	28.861607 weeks	1999-05-23 12:06:00	2008-01-07 00:06:43
4, Meh.	date of purchase (POSIXct class)	5	16.67	1999-09-07 16:47:00	10.58 weeks	1999-09-08 16:06:00	4.96 weeks	1999-08-16 06:16:00	1999-09-24 05:34:00	5.567262 weeks	1999-05-30 06:47:00	1999-12-22 00:12:00
6, Meh.	date of purchase (POSIXct class)	6	0.00	2005-07-02 01:16:06	239.72 weeks	2008-03-20 18:16:55	37.37 weeks	1999-10-04 16:53:00	2008-04-10 15:49:22	444.422259 weeks	1999-05-28 16:38:00	2008-12-15 14:11:55
8, Meh.	date of purchase (POSIXct class)	6	0.00	2008-06-20 07:15:14	15.40 weeks	2008-06-07 04:14:04	18.68 weeks	2008-03-11 18:07:43	2008-07-30 23:21:17	20.173965 weeks	2008-03-05 18:36:33	2008-11-24 17:31:18
4, Missing	date of purchase (POSIXct class)	6	0.00	2005-06-18 04:14:08	242.90 weeks	2008-02-16 06:07:01	51.03 weeks	1999-11-09 01:13:00	2008-03-08 08:49:18	434.616696 weeks	1999-02-03 20:54:00	2008-12-10 14:15:29
6, Missing	date of purchase (POSIXct class)	5	0.00	2004-11-20 04:13:04	261.49 weeks	2008-03-11 20:09:42	44.33 weeks	1999-04-04 20:26:00	2008-09-01 08:38:40	491.072685 weeks	1999-04-04 20:26:00	2008-10-07 03:05:02
8, Missing	date of purchase (POSIXct class)	10	28.57	2005-09-03 05:52:51	232.45 weeks	2008-05-08 08:56:16	16.38 weeks	2008-01-19 10:45:49	2008-12-23 02:41:31	48.380526 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31
4, This is the worst car ever!	date of purchase (POSIXct class)	7	0.00	2003-04-25 22:09:50	251.79 weeks	1999-09-09 16:35:00	40.37 weeks	1999-05-18 04:07:00	2008-04-02 22:50:26	463.254309 weeks	1999-03-03 01:37:00	2008-10-25 00:08:02
6, This is the worst car ever!	date of purchase (POSIXct class)	9	10.00	2002-08-24 05:35:52	231.70 weeks	1999-11-23 09:40:00	33.31 weeks	1999-06-19 03:37:00	2008-08-22 23:14:44	478.973981 weeks	1999-04-03 22:19:00	2008-09-22 01:07:21
8, This is the worst car ever!	date of purchase (POSIXct class)	5	0.00	2006-10-12 10:56:39	199.59 weeks	2008-06-06 11:58:47	19.12 weeks	1999-12-12 17:15:00	2008-08-31 05:08:21	454.921959 weeks	1999-12-12 17:15:00	2008-09-04 18:09:48
4, want cheese flavoured cars.	date of purchase (POSIXct class)	11	0.00	2003-07-08 17:22:47	243.99 weeks	1999-12-19 19:44:00	70.82 weeks	1999-05-29 22:46:00	2008-03-17 01:58:41	459.161973 weeks	1999-01-19 10:45:00	2008-09-23 15:04:14
6, want cheese flavoured cars.	date of purchase (POSIXct class)	13	0.00	2002-04-20 13:43:21	220.53 weeks	1999-10-22 23:01:00	29.77 weeks	1999-06-04 09:07:00	2008-01-23 02:49:05	450.682746 weeks	1999-04-04 00:33:00	2008-10-30 15:10:27
8, want cheese flavoured cars.	date of purchase (POSIXct class)	7	22.22	2004-08-05 03:35:48	242.19 weeks	2008-02-12 08:03:24	34.18 weeks	1999-11-17 16:08:00	2008-07-22 17:39:40	452.860284 weeks	1999-02-27 18:31:00	2008-07-22 17:39:40
Overall	date of purchase (POSIXct class)	214	8.55	2003-11-21 01:59:50	234.68 weeks	1999-12-20 21:58:00	71.08 weeks	1999-07-12 06:29:00	2008-08-08 04:44:28	473.561058 weeks	1999-01-14 10:39:00	2008-12-23 02:41:31
R NA Value	date of purchase (POSIXct class)	22	8.33	2003-03-11 06:54:43	237.23 weeks	1999-10-23 07:22:30	46.90 weeks	1999-06-05 15:30:00	2008-06-03 02:59:13	469.354089 weeks	1999-02-12 16:08:00	2008-12-04 06:19:25

Stacked barplot of date of purchase (Date class) by number of cylinders by some random comments.

Difftime

Show method to output table and plot

show(rdifftimeSummaryExample)

  
1 
                                                                                                                    Label
1 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
    N  P NA       Mean      S Dev       Med        MAD     25th P      75th P
1 184 21.37 9.76 weeks 5.07 weeks 9.6 weeks 5.12 weeks 6.11 weeks 13.05 weeks
         IQR     Min         Max
1 6.79 weeks 0 weeks 23.49 weeks

Output the summary table

data_summary_table(rdifftimeSummaryExample)

  
1 
                                                                                                                    Label
1 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
    N  P NA       Mean      S Dev       Med        MAD     25th P      75th P
1 184 21.37 9.76 weeks 5.07 weeks 9.6 weeks 5.12 weeks 6.11 weeks 13.05 weeks
         IQR     Min         Max
1 6.79 weeks 0 weeks 23.49 weeks

Output the plot

data_summary_plot(rdifftimeSummaryExample)

Generate knitr friendly summary table

make_kable_output(rdifftimeSummaryExample)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks.

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.6 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0 weeks	23.49 weeks

Generate knitr friendly output

make_complete_output(rdifftimeSummaryExample)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks.

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.6 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0 weeks	23.49 weeks

Stacked barplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks.

Difftime By

Show method to output table and plot

show(rdifftimeByDrvSummaryExample)

         drive type
1 front-wheel drive
2  rear wheel drive
3               4wd
4           Overall
                                                                                                                    Label
1 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
2 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
3 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
4 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
    N  P NA        Mean      S Dev         Med        MAD     25th P
1  86 18.87 10.57 weeks 5.24 weeks 10.79 weeks 4.80 weeks 6.59 weeks
2  19 24.00  8.43 weeks 5.17 weeks  8.57 weeks 5.74 weeks 4.70 weeks
3  79 23.30  9.19 weeks 4.77 weeks  9.02 weeks 4.77 weeks 6.11 weeks
4 184 21.37  9.76 weeks 5.07 weeks  9.60 weeks 5.12 weeks 6.11 weeks
       75th P        IQR     Min         Max
1 13.57 weeks 6.87 weeks 0 weeks 23.49 weeks
2 12.72 weeks 7.27 weeks 0 weeks 19.07 weeks
3 12.58 weeks 6.15 weeks 0 weeks 21.71 weeks
4 13.05 weeks 6.79 weeks 0 weeks 23.49 weeks

Output the summary table

data_summary_table(rdifftimeByDrvSummaryExample)

         drive type
1 front-wheel drive
2  rear wheel drive
3               4wd
4           Overall
                                                                                                                    Label
1 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
2 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
3 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
4 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
    N  P NA        Mean      S Dev         Med        MAD     25th P
1  86 18.87 10.57 weeks 5.24 weeks 10.79 weeks 4.80 weeks 6.59 weeks
2  19 24.00  8.43 weeks 5.17 weeks  8.57 weeks 5.74 weeks 4.70 weeks
3  79 23.30  9.19 weeks 4.77 weeks  9.02 weeks 4.77 weeks 6.11 weeks
4 184 21.37  9.76 weeks 5.07 weeks  9.60 weeks 5.12 weeks 6.11 weeks
       75th P        IQR     Min         Max
1 13.57 weeks 6.87 weeks 0 weeks 23.49 weeks
2 12.72 weeks 7.27 weeks 0 weeks 19.07 weeks
3 12.58 weeks 6.15 weeks 0 weeks 21.71 weeks
4 13.05 weeks 6.79 weeks 0 weeks 23.49 weeks

Output the plot

data_summary_plot(rdifftimeByDrvSummaryExample)

Generate a knitr friendly summary table

make_kable_output(rdifftimeByDrvSummaryExample)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by drive type.

drive type	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
front-wheel drive	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	86	18.87	10.57 weeks	5.24 weeks	10.79 weeks	4.80 weeks	6.59 weeks	13.57 weeks	6.87 weeks	0 weeks	23.49 weeks
rear wheel drive	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	19	24.00	8.43 weeks	5.17 weeks	8.57 weeks	5.74 weeks	4.70 weeks	12.72 weeks	7.27 weeks	0 weeks	19.07 weeks
4wd	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	79	23.30	9.19 weeks	4.77 weeks	9.02 weeks	4.77 weeks	6.11 weeks	12.58 weeks	6.15 weeks	0 weeks	21.71 weeks
Overall	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.60 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0 weeks	23.49 weeks

Generate knitr friendly output

make_complete_output(rdifftimeByDrvSummaryExample)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by drive type.

drive type	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
front-wheel drive	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	86	18.87	10.57 weeks	5.24 weeks	10.79 weeks	4.80 weeks	6.59 weeks	13.57 weeks	6.87 weeks	0 weeks	23.49 weeks
rear wheel drive	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	19	24.00	8.43 weeks	5.17 weeks	8.57 weeks	5.74 weeks	4.70 weeks	12.72 weeks	7.27 weeks	0 weeks	19.07 weeks
4wd	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	79	23.30	9.19 weeks	4.77 weeks	9.02 weeks	4.77 weeks	6.11 weeks	12.58 weeks	6.15 weeks	0 weeks	21.71 weeks
Overall	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.60 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0 weeks	23.49 weeks

Stacked barplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by drive type.

Difftime By By

Show method to output table and plot

show(rdifftimeByDrvBypartySummaryExample)

   drive type by some random political parties
1                front-wheel drive, republican
2                 rear wheel drive, republican
3                              4wd, republican
4                  front-wheel drive, democrat
5                   rear wheel drive, democrat
6                                4wd, democrat
7               front-wheel drive, independent
8                rear wheel drive, independent
9                             4wd, independent
10                                     Overall
11                                  R NA Value
                                                                                                                     Label
1  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
2  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
3  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
4  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
5  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
6  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
7  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
8  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
9  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
10 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
11 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
     N  P NA        Mean      S Dev         Med        MAD     25th P
1   18 30.77  8.95 weeks 6.26 weeks  9.97 weeks 5.64 weeks 4.95 weeks
2    4  0.00  8.75 weeks 4.34 weeks  8.31 weeks 4.84 weeks 4.96 weeks
3   23 11.54 10.07 weeks 6.02 weeks 11.25 weeks 6.73 weeks 4.81 weeks
4   17  5.56 11.37 weeks 4.11 weeks 12.03 weeks 4.84 weeks 7.55 weeks
5    5 16.67  8.26 weeks 3.99 weeks  9.35 weeks 5.25 weeks 4.70 weeks
6   30 18.92  8.31 weeks 4.50 weeks  7.60 weeks 5.60 weeks 6.41 weeks
7   26 18.75 10.75 weeks 4.96 weeks 10.31 weeks 4.84 weeks 5.99 weeks
8    6 14.29  8.80 weeks 5.07 weeks  8.18 weeks 5.40 weeks 5.44 weeks
9   15 34.78  9.86 weeks 4.04 weeks  9.58 weeks 5.87 weeks 5.61 weeks
10 184 21.37  9.76 weeks 5.07 weeks  9.60 weeks 5.12 weeks 6.11 weeks
11  40 27.27 10.08 weeks 5.38 weeks 10.06 weeks 4.12 weeks 6.76 weeks
        75th P        IQR        Min         Max
1  11.98 weeks 6.63 weeks 0.00 weeks 23.49 weeks
2  11.48 weeks 6.87 weeks 4.96 weeks 13.41 weeks
3  15.45 weeks 9.23 weeks 0.00 weeks 21.71 weeks
4  13.88 weeks 6.32 weeks 5.05 weeks 18.28 weeks
5  10.78 weeks 6.08 weeks 3.57 weeks 12.89 weeks
6  11.90 weeks 5.46 weeks 0.00 weeks 16.25 weeks
7  13.57 weeks 6.80 weeks 2.95 weeks 21.65 weeks
8  12.72 weeks 5.66 weeks 2.04 weeks 16.22 weeks
9  13.65 weeks 6.94 weeks 4.33 weeks 15.38 weeks
10 13.05 weeks 6.79 weeks 0.00 weeks 23.49 weeks
11 12.27 weeks 5.46 weeks 0.00 weeks 22.98 weeks

Output the summary table

data_summary_table(rdifftimeByDrvBypartySummaryExample)

   drive type by some random political parties
1                front-wheel drive, republican
2                 rear wheel drive, republican
3                              4wd, republican
4                  front-wheel drive, democrat
5                   rear wheel drive, democrat
6                                4wd, democrat
7               front-wheel drive, independent
8                rear wheel drive, independent
9                             4wd, independent
10                                     Overall
11                                  R NA Value
                                                                                                                     Label
1  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
2  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
3  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
4  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
5  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
6  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
7  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
8  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
9  some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
10 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
11 some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks
     N  P NA        Mean      S Dev         Med        MAD     25th P
1   18 30.77  8.95 weeks 6.26 weeks  9.97 weeks 5.64 weeks 4.95 weeks
2    4  0.00  8.75 weeks 4.34 weeks  8.31 weeks 4.84 weeks 4.96 weeks
3   23 11.54 10.07 weeks 6.02 weeks 11.25 weeks 6.73 weeks 4.81 weeks
4   17  5.56 11.37 weeks 4.11 weeks 12.03 weeks 4.84 weeks 7.55 weeks
5    5 16.67  8.26 weeks 3.99 weeks  9.35 weeks 5.25 weeks 4.70 weeks
6   30 18.92  8.31 weeks 4.50 weeks  7.60 weeks 5.60 weeks 6.41 weeks
7   26 18.75 10.75 weeks 4.96 weeks 10.31 weeks 4.84 weeks 5.99 weeks
8    6 14.29  8.80 weeks 5.07 weeks  8.18 weeks 5.40 weeks 5.44 weeks
9   15 34.78  9.86 weeks 4.04 weeks  9.58 weeks 5.87 weeks 5.61 weeks
10 184 21.37  9.76 weeks 5.07 weeks  9.60 weeks 5.12 weeks 6.11 weeks
11  40 27.27 10.08 weeks 5.38 weeks 10.06 weeks 4.12 weeks 6.76 weeks
        75th P        IQR        Min         Max
1  11.98 weeks 6.63 weeks 0.00 weeks 23.49 weeks
2  11.48 weeks 6.87 weeks 4.96 weeks 13.41 weeks
3  15.45 weeks 9.23 weeks 0.00 weeks 21.71 weeks
4  13.88 weeks 6.32 weeks 5.05 weeks 18.28 weeks
5  10.78 weeks 6.08 weeks 3.57 weeks 12.89 weeks
6  11.90 weeks 5.46 weeks 0.00 weeks 16.25 weeks
7  13.57 weeks 6.80 weeks 2.95 weeks 21.65 weeks
8  12.72 weeks 5.66 weeks 2.04 weeks 16.22 weeks
9  13.65 weeks 6.94 weeks 4.33 weeks 15.38 weeks
10 13.05 weeks 6.79 weeks 0.00 weeks 23.49 weeks
11 12.27 weeks 5.46 weeks 0.00 weeks 22.98 weeks

Output the plot

data_summary_plot(rdifftimeByDrvBypartySummaryExample)

Generate a knitr friendly summary table

make_kable_output(rdifftimeByDrvBypartySummaryExample)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by drive type by some random political parties.

drive type by some random political parties	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
front-wheel drive, republican	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	18	30.77	8.95 weeks	6.26 weeks	9.97 weeks	5.64 weeks	4.95 weeks	11.98 weeks	6.63 weeks	0.00 weeks	23.49 weeks
rear wheel drive, republican	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	4	0.00	8.75 weeks	4.34 weeks	8.31 weeks	4.84 weeks	4.96 weeks	11.48 weeks	6.87 weeks	4.96 weeks	13.41 weeks
4wd, republican	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	23	11.54	10.07 weeks	6.02 weeks	11.25 weeks	6.73 weeks	4.81 weeks	15.45 weeks	9.23 weeks	0.00 weeks	21.71 weeks
front-wheel drive, democrat	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	17	5.56	11.37 weeks	4.11 weeks	12.03 weeks	4.84 weeks	7.55 weeks	13.88 weeks	6.32 weeks	5.05 weeks	18.28 weeks
rear wheel drive, democrat	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	5	16.67	8.26 weeks	3.99 weeks	9.35 weeks	5.25 weeks	4.70 weeks	10.78 weeks	6.08 weeks	3.57 weeks	12.89 weeks
4wd, democrat	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	30	18.92	8.31 weeks	4.50 weeks	7.60 weeks	5.60 weeks	6.41 weeks	11.90 weeks	5.46 weeks	0.00 weeks	16.25 weeks
front-wheel drive, independent	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	26	18.75	10.75 weeks	4.96 weeks	10.31 weeks	4.84 weeks	5.99 weeks	13.57 weeks	6.80 weeks	2.95 weeks	21.65 weeks
rear wheel drive, independent	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	6	14.29	8.80 weeks	5.07 weeks	8.18 weeks	5.40 weeks	5.44 weeks	12.72 weeks	5.66 weeks	2.04 weeks	16.22 weeks
4wd, independent	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	15	34.78	9.86 weeks	4.04 weeks	9.58 weeks	5.87 weeks	5.61 weeks	13.65 weeks	6.94 weeks	4.33 weeks	15.38 weeks
Overall	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.60 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0.00 weeks	23.49 weeks
R NA Value	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	40	27.27	10.08 weeks	5.38 weeks	10.06 weeks	4.12 weeks	6.76 weeks	12.27 weeks	5.46 weeks	0.00 weeks	22.98 weeks

Generate knitr friendly output

make_complete_output(rdifftimeByDrvBypartySummaryExample)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by drive type by some random political parties.

drive type by some random political parties	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
front-wheel drive, republican	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	18	30.77	8.95 weeks	6.26 weeks	9.97 weeks	5.64 weeks	4.95 weeks	11.98 weeks	6.63 weeks	0.00 weeks	23.49 weeks
rear wheel drive, republican	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	4	0.00	8.75 weeks	4.34 weeks	8.31 weeks	4.84 weeks	4.96 weeks	11.48 weeks	6.87 weeks	4.96 weeks	13.41 weeks
4wd, republican	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	23	11.54	10.07 weeks	6.02 weeks	11.25 weeks	6.73 weeks	4.81 weeks	15.45 weeks	9.23 weeks	0.00 weeks	21.71 weeks
front-wheel drive, democrat	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	17	5.56	11.37 weeks	4.11 weeks	12.03 weeks	4.84 weeks	7.55 weeks	13.88 weeks	6.32 weeks	5.05 weeks	18.28 weeks
rear wheel drive, democrat	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	5	16.67	8.26 weeks	3.99 weeks	9.35 weeks	5.25 weeks	4.70 weeks	10.78 weeks	6.08 weeks	3.57 weeks	12.89 weeks
4wd, democrat	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	30	18.92	8.31 weeks	4.50 weeks	7.60 weeks	5.60 weeks	6.41 weeks	11.90 weeks	5.46 weeks	0.00 weeks	16.25 weeks
front-wheel drive, independent	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	26	18.75	10.75 weeks	4.96 weeks	10.31 weeks	4.84 weeks	5.99 weeks	13.57 weeks	6.80 weeks	2.95 weeks	21.65 weeks
rear wheel drive, independent	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	6	14.29	8.80 weeks	5.07 weeks	8.18 weeks	5.40 weeks	5.44 weeks	12.72 weeks	5.66 weeks	2.04 weeks	16.22 weeks
4wd, independent	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	15	34.78	9.86 weeks	4.04 weeks	9.58 weeks	5.87 weeks	5.61 weeks	13.65 weeks	6.94 weeks	4.33 weeks	15.38 weeks
Overall	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.60 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0.00 weeks	23.49 weeks
R NA Value	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	40	27.27	10.08 weeks	5.38 weeks	10.06 weeks	4.12 weeks	6.76 weeks	12.27 weeks	5.46 weeks	0.00 weeks	22.98 weeks

Stacked barplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by drive type by some random political parties.

Data Summaries

Manufacturer

manuSummary <- data_summary(x = "manu", data = mpg)

make_complete_output(manuSummary)

Summary statistics of manufacturer.

manufacturer	n (%)
audi	18 (7.69%)
chevrolet	19 (8.12%)
dodge	37 (15.81%)
ford	25 (10.68%)
honda	9 (3.85%)
hyundai	14 (5.98%)
jeep	8 (3.42%)
land rover	4 (1.71%)
lincoln	3 (1.28%)
mercury	4 (1.71%)
nissan	13 (5.56%)
pontiac	5 (2.14%)
subaru	14 (5.98%)
toyota	34 (14.53%)
volkswagen	27 (11.54%)

Stacked barplot of manufacturer.

Model Name

modelSummary <- data_summary(x = "model", data = mpg)

make_complete_output(modelSummary)

Summary statistics of model name.

model name	n (%)
4runner 4wd	6 (2.56%)
a4	7 (2.99%)
a4 quattro	8 (3.42%)
a6 quattro	3 (1.28%)
altima	6 (2.56%)
c1500 suburban 2wd	5 (2.14%)
camry	7 (2.99%)
camry solara	7 (2.99%)
caravan 2wd	11 (4.7%)
civic	9 (3.85%)
corolla	5 (2.14%)
corvette	5 (2.14%)
dakota pickup 4wd	9 (3.85%)
durango 4wd	7 (2.99%)
expedition 2wd	3 (1.28%)
explorer 4wd	6 (2.56%)
f150 pickup 4wd	7 (2.99%)
forester awd	6 (2.56%)
grand cherokee 4wd	8 (3.42%)
grand prix	5 (2.14%)
gti	5 (2.14%)
impreza awd	8 (3.42%)
jetta	9 (3.85%)
k1500 tahoe 4wd	4 (1.71%)
land cruiser wagon 4wd	2 (0.85%)
malibu	5 (2.14%)
maxima	3 (1.28%)
mountaineer 4wd	4 (1.71%)
mustang	9 (3.85%)
navigator 2wd	3 (1.28%)
new beetle	6 (2.56%)
passat	7 (2.99%)
pathfinder 4wd	4 (1.71%)
ram 1500 pickup 4wd	10 (4.27%)
range rover	4 (1.71%)
sonata	7 (2.99%)
tiburon	7 (2.99%)
toyota tacoma 4wd	7 (2.99%)

Stacked barplot of model name.

Engine Displacement (Litres)

displSummary <- data_summary(x = "displ", data = mpg)

make_complete_output(displSummary)

Summary statistics of engine displacement, in litres.

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	engine displacement, in litres	234	0	3.47	1.29	3.3	1.33	2.4	4.6	2.2	1.6	7

Boxplot of engine displacement, in litres.

Year of Manufacture

yearSummary <- data_summary(x = "year", data = mpg)

make_complete_output(yearSummary)

Summary statistics of year of manufacture.

year of manufacture	n (%)
1999	117 (50%)
2008	117 (50%)

Stacked barplot of year of manufacture.

Date of Purchase

dpSummary <- data_summary(x = "dp", data = mpg[which(mpg$dp != "1000-05-02" | is.na(mpg$dp)), ], difftime_units = "weeks")

make_complete_output(dpSummary)

Summary statistics of date of purchase (Date class).

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.7143 weeks	1999-01-04	2008-12-23

Boxplot of date of purchase (Date class).

Number of Cylinders

cylSummary <- data_summary(x = "cyl", data = mpg)

make_complete_output(cylSummary)

Summary statistics of number of cylinders.

number of cylinders	n (%)
4	81 (34.62%)
5	4 (1.71%)
6	79 (33.76%)
8	70 (29.91%)

Stacked barplot of number of cylinders.

Type of Transmission

transSummary <- data_summary(x = "trans", data = mpg)

make_complete_output(transSummary)

Summary statistics of type of transmission.

type of transmission	n (%)
auto(av)	5 (2.14%)
auto(l3)	2 (0.85%)
auto(l4)	83 (35.47%)
auto(l5)	39 (16.67%)
auto(l6)	6 (2.56%)
auto(s4)	3 (1.28%)
auto(s5)	3 (1.28%)
auto(s6)	16 (6.84%)
manual(m5)	58 (24.79%)
manual(m6)	19 (8.12%)

Stacked barplot of type of transmission.

Drive Type

drvSummary <- data_summary(x = "drv", data = mpg)

make_complete_output(drvSummary)

Summary statistics of drive type.

drive type	n (%)
front-wheel drive	106 (45.3%)
rear wheel drive	25 (10.68%)
4wd	103 (44.02%)

Stacked barplot of drive type.

City MPG

ctySummary <- data_summary(x = "cty", data = mpg)

make_complete_output(ctySummary)

Summary statistics of city miles per gallon.

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5	9	35

Boxplot of city miles per gallon.

Highway MPG

hwySummary <- data_summary(x = "hwy", data = mpg)

make_complete_output(hwySummary)

Summary statistics of highway miles per gallon.

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	highway miles per gallon	234	0	23.44	5.95	24	7.41	18	27	9	12	44

Boxplot of highway miles per gallon.

Fuel Type

flSummary <- data_summary(x = "fl", data = mpg)

make_complete_output(flSummary)

Summary statistics of fuel type.

fuel type	n (%)
c	1 (0.43%)
d	5 (2.14%)
e	8 (3.42%)
p	52 (22.22%)
r	168 (71.79%)

Stacked barplot of fuel type.

Type of Car

classSummary <- data_summary(x = "class", data = mpg)

make_complete_output(classSummary)

Summary statistics of type of car.

type of car	n (%)
2seater	5 (2.14%)
compact	47 (20.09%)
midsize	41 (17.52%)
minivan	11 (4.7%)
pickup	33 (14.1%)
subcompact	35 (14.96%)
suv	62 (26.5%)

Stacked barplot of type of car.

Random Numbers

rnSummary <- data_summary(x = "rn", data = mpg)

make_complete_output(rnSummary)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5.

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	184	21.37	10.53	5.09	10.73	4.72	7.12	13.32	6.12	-2.54	23.46

Boxplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5.

Random Difference in Time

rdifftimeSummary <- data_summary(x = "rdifftime", difftime_units = "weeks", data = mpg)

make_complete_output(rdifftimeSummary)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks.

	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.6 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0 weeks	23.49 weeks

Boxplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks.

Random Difference in Time Greater than 10

logicalSummary <- data_summary(x = "logical", difftime_units = "weeks", data = mpg)

make_complete_output(logicalSummary)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, and then set to TRUE if the difference is greater than 10.

some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, and then set to TRUE if the difference is greater than 10	n (%)
FALSE	96 (41.03%)
TRUE	88 (37.61%)
R NA Value	50 (21.37%)

Boxplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, and then set to TRUE if the difference is greater than 10.

Party

partySummary <- data_summary(x = "party", data = mpg)

make_complete_output(partySummary)

Summary statistics of some random political parties.

some random political parties	n (%)
republican	56 (23.93%)
democrat	61 (26.07%)
independent	62 (26.5%)
R NA Value	55 (23.5%)

Stacked barplot of some random political parties.

Comments

commentsSummary <- data_summary(x = "comments", data = mpg)

make_complete_output(commentsSummary)

Summary statistics of some random comments.

some random comments	n (%)
.	26 (11.11%)
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	23 (9.83%)
Does it also fly?	16 (6.84%)
Does it come in green?	23 (9.83%)
I like this car!	24 (10.26%)
Meh.	18 (7.69%)
Missing	25 (10.68%)
This is the worst car ever!	22 (9.4%)
want cheese flavoured cars.	33 (14.1%)
R NA Value	24 (10.26%)

Stacked barplot of some random comments.

All Missing

missSummary <- data_summary(x = "miss", data = mpg)

make_complete_output(missSummary)

Summary statistics of an all missing variable.

an all missing variable	n (%)
R NA Value	234 (100%)

Stacked barplot of an all missing variable.

By Data Summaries

By Drive Type

Number of Cylinders by Drive Type

cylByDrvSummary <- data_summary(x = "cyl", by = "drv", data = mpg)

make_complete_output(cylByDrvSummary)

Summary statistics of number of cylinders by drive type.

number of cylinders	front-wheel drive	rear wheel drive	4wd	Overall
4	58 (54.72%)	0 (0%)	23 (22.33%)	81 (34.62%)
5	4 (3.77%)	0 (0%)	0 (0%)	4 (1.71%)
6	43 (40.57%)	4 (16%)	32 (31.07%)	79 (33.76%)
8	1 (0.94%)	21 (84%)	48 (46.6%)	70 (29.91%)

Stacked barplot of number of cylinders by drive type.

Date of Purchase by Drive Type

dpByDrvSummary <- data_summary(x = "dp", by = "drv", data = mpg[which(mpg$dp != "1000-05-02" | is.na(mpg$dp)), ], difftime_units = "weeks")

make_complete_output(dpByDrvSummary)

Summary statistics of date of purchase (Date class) by drive type.

drive type	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
front-wheel drive	date of purchase (Date class)	94	11.32	2003-06-08	235.20 weeks	1999-11-21	60.47 weeks	1999-07-03	2008-08-25	477.2857 weeks	1999-01-07	2008-12-23
rear wheel drive	date of purchase (Date class)	24	4.00	2004-09-28	235.47 weeks	2008-01-21	60.57 weeks	1999-07-29	2008-07-13	467.4286 weeks	1999-01-13	2008-12-14
4wd	date of purchase (Date class)	95	6.86	2004-04-21	237.55 weeks	2008-01-26	64.81 weeks	1999-06-27	2008-09-01	479.1429 weeks	1999-01-04	2008-12-09
Overall	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.7143 weeks	1999-01-04	2008-12-23

Boxplot of date of purchase (Date class) by drive type.

Random Numbers by Drive Type

rnByDrvSummary <- data_summary(x = "rn", by = "drv", data = mpg)

make_complete_output(rnByDrvSummary)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5 by drive type.

drive type	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
front-wheel drive	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	86	18.87	10.50	5.32	9.88	4.68	7.36	13.95	6.53	-2.54	23.46
rear wheel drive	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	18	28.00	11.59	4.49	11.05	2.89	9.62	14.28	4.58	0.28	20.81
4wd	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	80	22.33	10.33	4.99	11.20	4.60	6.20	13.08	6.78	0.57	23.42
Overall	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	184	21.37	10.53	5.09	10.73	4.72	7.12	13.32	6.12	-2.54	23.46

Boxplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5 by drive type.

Random Difference in Time by Drive Type

rdifftimeByDrvSummary <- data_summary(x = "rdifftime", by = "drv", difftime_units = "weeks", data = mpg)

make_complete_output(rdifftimeByDrvSummary)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by drive type.

drive type	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
front-wheel drive	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	86	18.87	10.57 weeks	5.24 weeks	10.79 weeks	4.80 weeks	6.59 weeks	13.57 weeks	6.87 weeks	0 weeks	23.49 weeks
rear wheel drive	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	19	24.00	8.43 weeks	5.17 weeks	8.57 weeks	5.74 weeks	4.70 weeks	12.72 weeks	7.27 weeks	0 weeks	19.07 weeks
4wd	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	79	23.30	9.19 weeks	4.77 weeks	9.02 weeks	4.77 weeks	6.11 weeks	12.58 weeks	6.15 weeks	0 weeks	21.71 weeks
Overall	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.60 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0 weeks	23.49 weeks

Boxplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by drive type.

Random Difference in Time Greater than 10 by Drive Type

logicalByDrvSummary <- data_summary(x = "logical", by = "drv", difftime_units = "weeks", data = mpg)

make_complete_output(logicalByDrvSummary)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, and then set to TRUE if the difference is greater than 10 by drive type.

some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, and then set to TRUE if the difference is greater than 10	front-wheel drive	rear wheel drive	4wd	Overall
FALSE	40 (37.74%)	11 (44%)	45 (43.69%)	96 (41.03%)
TRUE	46 (43.4%)	8 (32%)	34 (33.01%)	88 (37.61%)
R NA Value	20 (18.87%)	6 (24%)	24 (23.3%)	50 (21.37%)

Stacked barplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, and then set to TRUE if the difference is greater than 10 by drive type.

Comments by Drive Type

commentsByDrvSummary <- data_summary(x = "comments", by = "drv", data = mpg)

make_complete_output(commentsByDrvSummary)

Summary statistics of some random comments by drive type.

some random comments	front-wheel drive	rear wheel drive	4wd	Overall
.	9 (8.49%)	5 (20%)	12 (11.65%)	26 (11.11%)
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	12 (11.32%)	3 (12%)	8 (7.77%)	23 (9.83%)
Does it also fly?	11 (10.38%)	1 (4%)	4 (3.88%)	16 (6.84%)
Does it come in green?	12 (11.32%)	1 (4%)	10 (9.71%)	23 (9.83%)
I like this car!	13 (12.26%)	1 (4%)	10 (9.71%)	24 (10.26%)
Meh.	6 (5.66%)	2 (8%)	10 (9.71%)	18 (7.69%)
Missing	8 (7.55%)	3 (12%)	14 (13.59%)	25 (10.68%)
This is the worst car ever!	14 (13.21%)	2 (8%)	6 (5.83%)	22 (9.4%)
want cheese flavoured cars.	13 (12.26%)	2 (8%)	18 (17.48%)	33 (14.1%)
R NA Value	8 (7.55%)	5 (20%)	11 (10.68%)	24 (10.26%)

Stacked barplot of some random comments by drive type.

All Missing by Drive Type

missByDrvSummary <- data_summary(x = "miss", by = "drv", data = mpg)

make_complete_output(missByDrvSummary)

Summary statistics of an all missing variable by drive type.

an all missing variable	front-wheel drive	rear wheel drive	4wd	Overall
R NA Value	106 (100%)	25 (100%)	103 (100%)	234 (100%)

Stacked barplot of an all missing variable by drive type.

City and Highway MPG

City MPG by Manufacturer

ctyBymanuSummary <- data_summary(x = "cty", by = "manu", data = mpg)

make_complete_output(ctyBymanuSummary)

Summary statistics of city miles per gallon by manufacturer.

manufacturer	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
audi	city miles per gallon	18	0	17.61	1.97	17.5	2.22	16	19	2.75	15	21
chevrolet	city miles per gallon	19	0	15.00	2.92	15.0	2.97	13	17	3.00	11	22
dodge	city miles per gallon	37	0	13.14	2.49	13.0	2.97	11	15	4.00	9	18
ford	city miles per gallon	25	0	14.00	1.91	14.0	1.48	13	15	2.00	11	18
honda	city miles per gallon	9	0	24.44	1.94	24.0	1.48	24	25	1.00	21	28
hyundai	city miles per gallon	14	0	18.64	1.50	18.5	1.48	18	20	1.75	16	21
jeep	city miles per gallon	8	0	13.50	2.51	14.0	1.48	11	15	2.50	9	17
land rover	city miles per gallon	4	0	11.50	0.58	11.5	0.74	11	12	1.00	11	12
lincoln	city miles per gallon	3	0	11.33	0.58	11.0	0.00	11	12	0.50	11	12
mercury	city miles per gallon	4	0	13.25	0.50	13.0	0.00	13	13	0.25	13	14
nissan	city miles per gallon	13	0	18.08	3.43	19.0	2.97	15	19	4.00	12	23
pontiac	city miles per gallon	5	0	17.00	1.00	17.0	1.48	16	18	2.00	16	18
subaru	city miles per gallon	14	0	19.29	0.91	19.0	1.48	19	20	1.00	18	21
toyota	city miles per gallon	34	0	18.53	4.05	18.0	4.45	15	21	6.00	11	28
volkswagen	city miles per gallon	27	0	20.93	4.56	21.0	2.97	18	21	2.50	16	35
Overall	city miles per gallon	234	0	16.86	4.26	17.0	4.45	14	19	5.00	9	35

Boxplot of city miles per gallon by manufacturer.

Highway MPG by Manufacturer

hwyBymanuSummary <- data_summary(x = "hwy", by = "manu", data = mpg)

make_complete_output(hwyBymanuSummary)

Summary statistics of highway miles per gallon by manufacturer.

manufacturer	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
audi	highway miles per gallon	18	0	26.44	2.18	26.0	1.48	25	28	2.75	23	31
chevrolet	highway miles per gallon	19	0	21.89	5.11	23.0	5.93	17	26	9.00	14	30
dodge	highway miles per gallon	37	0	17.95	3.57	17.0	2.97	16	21	5.00	12	24
ford	highway miles per gallon	25	0	19.36	3.33	18.0	1.48	17	22	5.00	15	26
honda	highway miles per gallon	9	0	32.56	2.55	32.0	2.97	32	34	2.00	29	36
hyundai	highway miles per gallon	14	0	26.86	2.18	26.5	2.22	26	28	2.00	24	31
jeep	highway miles per gallon	8	0	17.62	3.25	18.5	2.22	14	19	3.00	12	22
land rover	highway miles per gallon	4	0	16.50	1.73	16.5	2.22	15	18	3.00	15	18
lincoln	highway miles per gallon	3	0	17.00	1.00	17.0	1.48	16	18	1.00	16	18
mercury	highway miles per gallon	4	0	18.00	1.15	18.0	1.48	17	19	2.00	17	19
nissan	highway miles per gallon	13	0	24.62	5.09	26.0	4.45	20	27	7.00	17	32
pontiac	highway miles per gallon	5	0	26.40	1.14	26.0	1.48	26	27	1.00	25	28
subaru	highway miles per gallon	14	0	25.57	1.16	26.0	1.48	25	26	1.00	23	27
toyota	highway miles per gallon	34	0	24.91	6.17	26.0	8.90	20	30	9.75	15	37
volkswagen	highway miles per gallon	27	0	29.22	5.32	29.0	1.48	26	29	3.00	23	44
Overall	highway miles per gallon	234	0	23.44	5.95	24.0	7.41	18	27	9.00	12	44

Boxplot of highway miles per gallon by manufacturer.

City MPG by Model Name

ctyBymodelSummary <- data_summary(x = "cty", by = "model", data = mpg)

make_complete_output(ctyBymodelSummary)

Summary statistics of city miles per gallon by model name.

model name	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4runner 4wd	city miles per gallon	6	0	15.17	0.75	15.0	0.74	15	16	0.75	14	16
a4	city miles per gallon	7	0	18.86	1.86	18.0	2.97	18	21	2.50	16	21
a4 quattro	city miles per gallon	8	0	17.12	1.81	17.0	2.22	15	18	2.50	15	20
a6 quattro	city miles per gallon	3	0	16.00	1.00	16.0	1.48	15	17	1.00	15	17
altima	city miles per gallon	6	0	20.67	1.97	20.0	1.48	19	23	3.50	19	23
c1500 suburban 2wd	city miles per gallon	5	0	12.80	1.30	13.0	1.48	12	14	2.00	11	14
camry	city miles per gallon	7	0	19.86	1.46	21.0	0.00	18	21	2.50	18	21
camry solara	city miles per gallon	7	0	19.86	1.77	21.0	1.48	18	21	3.00	18	22
caravan 2wd	city miles per gallon	11	0	15.82	1.83	16.0	1.48	15	17	1.50	11	18
civic	city miles per gallon	9	0	24.44	1.94	24.0	1.48	24	25	1.00	21	28
corolla	city miles per gallon	5	0	25.60	1.67	26.0	2.97	24	26	2.00	24	28
corvette	city miles per gallon	5	0	15.40	0.55	15.0	0.00	15	16	1.00	15	16
dakota pickup 4wd	city miles per gallon	9	0	12.78	1.99	14.0	1.48	11	14	3.00	9	15
durango 4wd	city miles per gallon	7	0	11.86	1.57	13.0	0.00	11	13	2.00	9	13
expedition 2wd	city miles per gallon	3	0	11.33	0.58	11.0	0.00	11	12	0.50	11	12
explorer 4wd	city miles per gallon	6	0	13.67	0.82	13.5	0.74	13	14	1.00	13	15
f150 pickup 4wd	city miles per gallon	7	0	13.00	1.00	13.0	0.00	13	14	0.50	11	14
forester awd	city miles per gallon	6	0	18.83	0.98	18.5	0.74	18	20	1.75	18	20
grand cherokee 4wd	city miles per gallon	8	0	13.50	2.51	14.0	1.48	11	15	2.50	9	17
grand prix	city miles per gallon	5	0	17.00	1.00	17.0	1.48	16	18	2.00	16	18
gti	city miles per gallon	5	0	20.00	2.00	21.0	1.48	19	21	2.00	17	22
impreza awd	city miles per gallon	8	0	19.62	0.74	19.5	0.74	19	20	1.00	19	21
jetta	city miles per gallon	9	0	21.22	4.87	21.0	1.48	19	21	2.00	16	33
k1500 tahoe 4wd	city miles per gallon	4	0	12.50	1.73	12.5	2.22	11	14	3.00	11	14
land cruiser wagon 4wd	city miles per gallon	2	0	12.00	1.41	12.0	1.48	11	13	1.00	11	13
malibu	city miles per gallon	5	0	18.80	1.92	18.0	1.48	18	19	1.00	17	22
maxima	city miles per gallon	3	0	18.67	0.58	19.0	0.00	18	19	0.50	18	19
mountaineer 4wd	city miles per gallon	4	0	13.25	0.50	13.0	0.00	13	13	0.25	13	14
mustang	city miles per gallon	9	0	15.89	1.45	15.0	1.48	15	17	2.00	14	18
navigator 2wd	city miles per gallon	3	0	11.33	0.58	11.0	0.00	11	12	0.50	11	12
new beetle	city miles per gallon	6	0	24.00	6.51	20.5	1.48	20	29	7.00	19	35
passat	city miles per gallon	7	0	18.57	1.90	18.0	1.48	17	21	2.50	16	21
pathfinder 4wd	city miles per gallon	4	0	13.75	1.26	14.0	0.74	12	14	0.75	12	15
ram 1500 pickup 4wd	city miles per gallon	10	0	11.40	1.51	11.5	1.48	11	13	1.75	9	13
range rover	city miles per gallon	4	0	11.50	0.58	11.5	0.74	11	12	1.00	11	12
sonata	city miles per gallon	7	0	19.00	1.41	18.0	0.00	18	21	2.00	18	21
tiburon	city miles per gallon	7	0	18.29	1.60	19.0	1.48	17	20	2.50	16	20
toyota tacoma 4wd	city miles per gallon	7	0	15.57	0.79	15.0	0.00	15	16	1.00	15	17
Overall	city miles per gallon	234	0	16.86	4.26	17.0	4.45	14	19	5.00	9	35

Boxplot of city miles per gallon by model name.

Highway MPG by Model Name

hwyBymodelSummary <- data_summary(x = "hwy", by = "model", data = mpg)

make_complete_output(hwyBymodelSummary)

Summary statistics of highway miles per gallon by model name.

model name	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4runner 4wd	highway miles per gallon	6	0	18.83	1.47	19.5	0.74	17	20	2.50	17	20
a4	highway miles per gallon	7	0	28.29	1.98	29.0	2.97	26	30	3.00	26	31
a4 quattro	highway miles per gallon	8	0	25.75	1.16	25.0	0.00	25	26	1.25	25	28
a6 quattro	highway miles per gallon	3	0	24.00	1.00	24.0	1.48	23	25	1.00	23	25
altima	highway miles per gallon	6	0	28.67	2.42	28.0	2.22	27	31	3.50	26	32
c1500 suburban 2wd	highway miles per gallon	5	0	17.80	2.17	17.0	2.97	17	20	3.00	15	20
camry	highway miles per gallon	7	0	28.29	2.14	28.0	2.97	26	31	3.50	26	31
camry solara	highway miles per gallon	7	0	28.14	2.19	27.0	1.48	26	31	3.50	26	31
caravan 2wd	highway miles per gallon	11	0	22.36	2.06	23.0	1.48	22	24	2.00	17	24
civic	highway miles per gallon	9	0	32.56	2.55	32.0	2.97	32	34	2.00	29	36
corolla	highway miles per gallon	5	0	34.00	2.65	35.0	2.97	33	35	2.00	30	37
corvette	highway miles per gallon	5	0	24.80	1.30	25.0	1.48	24	26	2.00	23	26
dakota pickup 4wd	highway miles per gallon	9	0	17.00	2.29	17.0	2.97	17	19	2.00	12	19
durango 4wd	highway miles per gallon	7	0	16.00	2.00	17.0	1.48	15	17	1.50	12	18
expedition 2wd	highway miles per gallon	3	0	17.33	0.58	17.0	0.00	17	18	0.50	17	18
explorer 4wd	highway miles per gallon	6	0	18.00	1.10	18.0	1.48	17	19	2.00	17	19
f150 pickup 4wd	highway miles per gallon	7	0	16.43	0.79	17.0	0.00	16	17	1.00	15	17
forester awd	highway miles per gallon	6	0	25.00	1.41	25.0	1.48	24	26	1.50	23	27
grand cherokee 4wd	highway miles per gallon	8	0	17.62	3.25	18.5	2.22	14	19	3.00	12	22
grand prix	highway miles per gallon	5	0	26.40	1.14	26.0	1.48	26	27	1.00	25	28
gti	highway miles per gallon	5	0	27.40	2.30	29.0	0.00	26	29	3.00	24	29
impreza awd	highway miles per gallon	8	0	26.00	0.76	26.0	0.74	25	26	0.50	25	27
jetta	highway miles per gallon	9	0	29.11	6.07	29.0	0.00	26	29	3.00	23	44
k1500 tahoe 4wd	highway miles per gallon	4	0	16.25	2.22	16.0	2.22	14	17	2.75	14	19
land cruiser wagon 4wd	highway miles per gallon	2	0	16.50	2.12	16.5	2.22	15	18	1.50	15	18
malibu	highway miles per gallon	5	0	27.60	1.82	27.0	1.48	26	29	3.00	26	30
maxima	highway miles per gallon	3	0	25.33	0.58	25.0	0.00	25	26	0.50	25	26
mountaineer 4wd	highway miles per gallon	4	0	18.00	1.15	18.0	1.48	17	19	2.00	17	19
mustang	highway miles per gallon	9	0	23.22	2.17	23.0	2.97	22	25	3.00	20	26
navigator 2wd	highway miles per gallon	3	0	17.00	1.00	17.0	1.48	16	18	1.00	16	18
new beetle	highway miles per gallon	6	0	32.83	7.63	29.0	2.97	28	41	9.75	26	44
passat	highway miles per gallon	7	0	27.57	1.51	28.0	1.48	26	29	3.00	26	29
pathfinder 4wd	highway miles per gallon	4	0	18.00	1.41	17.5	0.74	17	18	1.50	17	20
ram 1500 pickup 4wd	highway miles per gallon	10	0	15.30	1.89	16.0	1.48	15	17	1.75	12	17
range rover	highway miles per gallon	4	0	16.50	1.73	16.5	2.22	15	18	3.00	15	18
sonata	highway miles per gallon	7	0	27.71	2.06	27.0	1.48	26	30	3.00	26	31
tiburon	highway miles per gallon	7	0	26.00	2.08	26.0	2.97	24	28	3.50	24	29
toyota tacoma 4wd	highway miles per gallon	7	0	19.43	1.62	20.0	1.48	18	20	1.50	17	22
Overall	highway miles per gallon	234	0	23.44	5.95	24.0	7.41	18	27	9.00	12	44

Boxplot of highway miles per gallon by model name.

City MPG by Year of Manufacture

ctyByYearSummary <- data_summary(x = "cty", by = "year", data = mpg)

make_complete_output(ctyByYearSummary)

Summary statistics of city miles per gallon by year of manufacture.

year of manufacture	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
1999	city miles per gallon	117	0	17.02	4.46	17	2.97	14	19	5	11	35
2008	city miles per gallon	117	0	16.70	4.06	17	4.45	13	20	7	9	28
Overall	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5	9	35

Boxplot of city miles per gallon by year of manufacture.

Highway MPG by Year of Manufacture

hwyByYearSummary <- data_summary(x = "hwy", by = "year", data = mpg)

make_complete_output(hwyByYearSummary)

Summary statistics of highway miles per gallon by year of manufacture.

year of manufacture	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
1999	highway miles per gallon	117	0	23.43	6.08	25	5.93	17	26	9	15	44
2008	highway miles per gallon	117	0	23.45	5.85	24	7.41	18	28	10	12	37
Overall	highway miles per gallon	234	0	23.44	5.95	24	7.41	18	27	9	12	44

Boxplot of highway miles per gallon by year of manufacture.

City MPG by Number of Cylinders

ctyByCylSummary <- data_summary(x = "cty", by = "cyl", data = mpg)

make_complete_output(ctyByCylSummary)

Summary statistics of city miles per gallon by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	city miles per gallon	81	0	21.01	3.50	21.0	2.97	19	22	3	15	35
5	city miles per gallon	4	0	20.50	0.58	20.5	0.74	20	21	1	20	21
6	city miles per gallon	79	0	16.22	1.77	16.0	1.48	15	18	3	11	19
8	city miles per gallon	70	0	12.57	1.81	13.0	2.22	11	14	3	9	16
Overall	city miles per gallon	234	0	16.86	4.26	17.0	4.45	14	19	5	9	35

Boxplot of city miles per gallon by number of cylinders.

Highway MPG by Number of Cylinders

hwyByCylSummary <- data_summary(x = "hwy", by = "cyl", data = mpg)

make_complete_output(hwyByCylSummary)

Summary statistics of highway miles per gallon by number of cylinders.

number of cylinders	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4	highway miles per gallon	81	0	28.80	4.52	29	2.97	26	31	5.00	20	44
5	highway miles per gallon	4	0	28.75	0.50	29	0.00	28	29	0.25	28	29
6	highway miles per gallon	79	0	22.82	3.69	24	2.97	19	26	7.00	17	29
8	highway miles per gallon	70	0	17.63	3.26	17	2.97	16	19	3.00	12	26
Overall	highway miles per gallon	234	0	23.44	5.95	24	7.41	18	27	9.00	12	44

Boxplot of highway miles per gallon by number of cylinders.

City MPG by Type of Transmission

ctyBytransSummary <- data_summary(x = "cty", by = "trans", data = mpg)

make_complete_output(ctyBytransSummary)

Summary statistics of city miles per gallon by type of transmission.

type of transmission	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
auto(av)	city miles per gallon	5	0	20.00	2.00	19	1.48	19	21	2.0	18	23
auto(l3)	city miles per gallon	2	0	21.00	4.24	21	4.45	18	24	3.0	18	24
auto(l4)	city miles per gallon	83	0	15.94	3.98	16	4.45	13	18	5.0	11	29
auto(l5)	city miles per gallon	39	0	14.72	3.49	14	1.48	13	16	3.0	9	25
auto(l6)	city miles per gallon	6	0	13.67	1.86	13	1.48	12	16	3.0	12	16
auto(s4)	city miles per gallon	3	0	18.67	2.31	20	0.00	16	20	2.0	16	20
auto(s5)	city miles per gallon	3	0	17.33	5.03	18	5.93	12	22	5.0	12	22
auto(s6)	city miles per gallon	16	0	17.38	3.22	17	2.97	15	19	3.5	12	22
manual(m5)	city miles per gallon	58	0	19.26	4.56	19	2.97	17	21	4.0	11	35
manual(m6)	city miles per gallon	19	0	16.89	3.83	16	5.93	15	21	5.5	9	23
Overall	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5.0	9	35

Boxplot of city miles per gallon by type of transmission.

Highway MPG by Type of Transmission

hwyBytransSummary <- data_summary(x = "hwy", by = "trans", data = mpg)

make_complete_output(hwyBytransSummary)

Summary statistics of highway miles per gallon by type of transmission.

type of transmission	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
auto(av)	highway miles per gallon	5	0	27.80	2.59	27	2.97	26	30	4.00	25	31
auto(l3)	highway miles per gallon	2	0	27.00	4.24	27	4.45	24	30	3.00	24	30
auto(l4)	highway miles per gallon	83	0	21.96	5.64	22	7.41	17	26	9.00	14	41
auto(l5)	highway miles per gallon	39	0	20.72	6.04	19	2.97	17	25	7.50	12	36
auto(l6)	highway miles per gallon	6	0	20.00	2.37	19	1.48	18	23	3.75	18	23
auto(s4)	highway miles per gallon	3	0	25.67	1.15	25	0.00	25	27	1.00	25	27
auto(s5)	highway miles per gallon	3	0	25.33	6.66	27	5.93	18	31	6.50	18	31
auto(s6)	highway miles per gallon	16	0	25.19	3.99	26	3.71	23	28	3.75	18	29
manual(m5)	highway miles per gallon	58	0	26.29	5.99	26	4.45	24	29	5.00	16	44
manual(m6)	highway miles per gallon	19	0	24.21	5.75	26	4.45	19	29	9.50	12	32
Overall	highway miles per gallon	234	0	23.44	5.95	24	7.41	18	27	9.00	12	44

Boxplot of highway miles per gallon by type of transmission.

City MPG by Drive Type

ctyByDrvSummary <- data_summary(x = "cty", by = "drv", data = mpg)

make_complete_output(ctyByDrvSummary)

Summary statistics of city miles per gallon by drive type.

drive type	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
front-wheel drive	city miles per gallon	106	0	19.97	3.63	19	2.97	18	21	3	11	35
rear wheel drive	city miles per gallon	25	0	14.08	2.22	15	1.48	12	15	3	11	18
4wd	city miles per gallon	103	0	14.33	2.87	14	2.97	13	16	3	9	21
Overall	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5	9	35

Boxplot of city miles per gallon by drive type.

Highway MPG by Drive Type

hwyByDrvSummary <- data_summary(x = "hwy", by = "drv", data = mpg)

make_complete_output(hwyByDrvSummary)

Summary statistics of highway miles per gallon by drive type.

drive type	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
front-wheel drive	highway miles per gallon	106	0	28.16	4.21	28	2.97	26	29	3	17	44
rear wheel drive	highway miles per gallon	25	0	21.00	3.66	21	5.93	17	24	7	15	26
4wd	highway miles per gallon	103	0	19.17	4.08	18	2.97	17	22	5	12	28
Overall	highway miles per gallon	234	0	23.44	5.95	24	7.41	18	27	9	12	44

Boxplot of highway miles per gallon by drive type.

City MPG by Fuel Type

ctyByflSummary <- data_summary(x = "cty", by = "fl", data = mpg)

make_complete_output(ctyByflSummary)

Summary statistics of city miles per gallon by fuel type.

fuel type	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
c	city miles per gallon	1	0	24.00	NA	24	0.00	24	24	0.0	24	24
d	city miles per gallon	5	0	25.60	9.53	29	8.90	17	33	16.0	14	35
e	city miles per gallon	8	0	9.75	1.04	9	0.00	9	11	2.0	9	11
p	city miles per gallon	52	0	17.37	3.04	18	2.97	15	19	3.5	11	23
r	city miles per gallon	168	0	16.74	3.89	16	4.45	14	19	5.0	11	28
Overall	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5.0	9	35

Boxplot of city miles per gallon by fuel type.

Highway MPG by Fuel Type

hwyByflSummary <- data_summary(x = "hwy", by = "fl", data = mpg)

make_complete_output(hwyByflSummary)

Summary statistics of highway miles per gallon by fuel type.

fuel type	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
c	highway miles per gallon	1	0	36.00	NA	36	0.00	36	36	0.00	36	36
d	highway miles per gallon	5	0	33.60	13.05	41	4.45	22	44	22.00	17	44
e	highway miles per gallon	8	0	13.25	1.91	12	0.00	12	14	2.25	12	17
p	highway miles per gallon	52	0	25.23	3.93	26	2.97	25	28	3.25	14	31
r	highway miles per gallon	168	0	22.99	5.51	23	7.41	17	27	9.25	15	37
Overall	highway miles per gallon	234	0	23.44	5.95	24	7.41	18	27	9.00	12	44

Boxplot of highway miles per gallon by fuel type.

City MPG by Type of Car

ctyByclassSummary <- data_summary(x = "cty", by = "class", data = mpg)

make_complete_output(ctyByclassSummary)

Summary statistics of city miles per gallon by type of car.

type of car	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
2seater	city miles per gallon	5	0	15.40	0.55	15	0.00	15	16	1.00	15	16
compact	city miles per gallon	47	0	20.13	3.39	20	2.97	18	21	3.00	15	33
midsize	city miles per gallon	41	0	18.76	1.95	18	1.48	18	21	3.00	15	23
minivan	city miles per gallon	11	0	15.82	1.83	16	1.48	15	17	1.50	11	18
pickup	city miles per gallon	33	0	13.00	2.05	13	2.97	11	14	3.00	9	17
subcompact	city miles per gallon	35	0	20.37	4.60	19	2.97	17	24	6.50	14	35
suv	city miles per gallon	62	0	13.50	2.42	13	2.22	12	15	2.75	9	20
Overall	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5.00	9	35

Boxplot of city miles per gallon by type of car.

Highway MPG by Type of Car

hwyByclassSummary <- data_summary(x = "hwy", by = "class", data = mpg)

make_complete_output(hwyByclassSummary)

Summary statistics of highway miles per gallon by type of car.

type of car	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
2seater	highway miles per gallon	5	0	24.80	1.30	25.0	1.48	24	26	2	23	26
compact	highway miles per gallon	47	0	28.30	3.78	27.0	2.97	26	29	3	23	44
midsize	highway miles per gallon	41	0	27.29	2.14	27.0	1.48	26	29	3	23	32
minivan	highway miles per gallon	11	0	22.36	2.06	23.0	1.48	22	24	2	17	24
pickup	highway miles per gallon	33	0	16.88	2.27	17.0	1.48	16	18	2	12	22
subcompact	highway miles per gallon	35	0	28.14	5.38	26.0	4.45	24	32	6	20	44
suv	highway miles per gallon	62	0	18.13	2.98	17.5	2.22	17	19	2	12	27
Overall	highway miles per gallon	234	0	23.44	5.95	24.0	7.41	18	27	9	12	44

Boxplot of highway miles per gallon by type of car.

City MPG by Comments

ctyByCommentsSummary <- data_summary(x = "cty", by = "comments", data = mpg)

make_complete_output(ctyByCommentsSummary)

Summary statistics of city miles per gallon by some random comments.

some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
.	city miles per gallon	26	0	15.42	3.94	15.0	2.97	13	17	4.0	9	28
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	city miles per gallon	23	0	17.04	3.23	17.0	2.97	15	19	3.5	11	22
Does it also fly?	city miles per gallon	16	0	17.94	5.52	18.0	3.71	14	19	4.5	11	35
Does it come in green?	city miles per gallon	23	0	18.39	4.31	19.0	2.97	14	21	6.5	11	28
I like this car!	city miles per gallon	24	0	17.92	5.16	18.0	5.93	14	21	7.0	11	33
Meh.	city miles per gallon	18	0	16.33	3.40	16.0	3.71	14	19	4.5	11	25
Missing	city miles per gallon	25	0	15.84	5.16	15.0	4.45	12	19	7.0	9	26
This is the worst car ever!	city miles per gallon	22	0	17.09	4.00	18.0	4.45	14	19	4.5	9	26
want cheese flavoured cars.	city miles per gallon	33	0	16.91	3.95	16.0	2.97	14	20	6.0	11	29
Overall	city miles per gallon	234	0	16.86	4.26	17.0	4.45	14	19	5.0	9	35
R NA Value	city miles per gallon	24	0	16.17	3.34	15.5	3.71	14	19	5.0	11	22

Boxplot of city miles per gallon by some random comments.

Highway MPG by Comments

hwyByCommentsSummary <- data_summary(x = "hwy", by = "comments", data = mpg)

make_complete_output(hwyByCommentsSummary)

Summary statistics of highway miles per gallon by some random comments.

some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
.	highway miles per gallon	26	0	22.08	5.60	23.5	6.67	17	26	9.00	12	33
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	highway miles per gallon	23	0	24.09	4.95	26.0	4.45	20	29	8.50	15	31
Does it also fly?	highway miles per gallon	16	0	24.75	7.33	25.5	5.19	17	28	9.75	14	44
Does it come in green?	highway miles per gallon	23	0	25.26	5.50	27.0	4.45	19	29	9.50	16	37
I like this car!	highway miles per gallon	24	0	24.75	7.25	26.0	7.41	17	29	12.00	15	44
Meh.	highway miles per gallon	18	0	22.61	4.58	23.5	5.19	19	26	7.00	15	32
Missing	highway miles per gallon	25	0	21.96	7.33	19.0	5.93	17	26	9.00	12	36
This is the worst car ever!	highway miles per gallon	22	0	23.82	5.78	25.0	5.93	19	28	8.50	12	35
want cheese flavoured cars.	highway miles per gallon	33	0	23.33	5.85	24.0	7.41	17	27	10.00	15	41
Overall	highway miles per gallon	234	0	23.44	5.95	24.0	7.41	18	27	9.00	12	44
R NA Value	highway miles per gallon	24	0	22.33	4.72	24.0	5.19	17	25	7.50	15	31

Boxplot of highway miles per gallon by some random comments.

City MPG by Party

ctyByPartySummary <- data_summary(x = "cty", by = "party", data = mpg)

make_complete_output(ctyByPartySummary)

Summary statistics of city miles per gallon by some random political parties.

some random political parties	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
republican	city miles per gallon	56	0	17.29	4.52	17	5.19	14	21	7	9	29
democrat	city miles per gallon	61	0	16.26	4.59	16	4.45	13	19	6	9	33
independent	city miles per gallon	62	0	16.84	3.39	17	3.71	14	19	5	9	24
Overall	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5	9	35
R NA Value	city miles per gallon	55	0	17.11	4.50	17	2.97	14	19	5	9	35

Boxplot of city miles per gallon by .

Highway MPG by Party

hwyByPartySummary <- data_summary(x = "hwy", by = "party", data = mpg)

make_complete_output(hwyByPartySummary)

Summary statistics of highway miles per gallon by some random political parties.

some random political parties	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
republican	highway miles per gallon	56	0	23.57	6.42	24	7.41	17	29	12	12	41
democrat	highway miles per gallon	61	0	22.38	6.25	21	7.41	17	27	10	12	44
independent	highway miles per gallon	62	0	23.68	4.83	25	4.45	19	27	8	12	36
Overall	highway miles per gallon	234	0	23.44	5.95	24	7.41	18	27	9	12	44
R NA Value	highway miles per gallon	55	0	24.22	6.26	26	4.45	18	28	10	12	44

Boxplot of highway miles per gallon by .

City MPG by All Missing

ctyByMissSummary <- data_summary(x = "cty", by = "miss", data = mpg)

make_complete_output(ctyByMissSummary)

Summary statistics of city miles per gallon by an all missing variable.

an all missing variable	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
Overall	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5	9	35
R NA Value	city miles per gallon	234	0	16.86	4.26	17	4.45	14	19	5	9	35

Boxplot of city miles per gallon by an all missing variable.

Highway MPG by All Missing

hwyByMissSummary <- data_summary(x = "hwy", by = "miss", data = mpg)

make_complete_output(hwyByMissSummary)

Summary statistics of highway miles per gallon by an all missing variable.

an all missing variable	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
Overall	highway miles per gallon	234	0	23.44	5.95	24	7.41	18	27	9	12	44
R NA Value	highway miles per gallon	234	0	23.44	5.95	24	7.41	18	27	9	12	44

Boxplot of highway miles per gallon by an all missing variable.

Other Examples

Date of Purchase by Year of Manufacture

dpByYearSummary <- data_summary(x = "dp", by = "year", data = mpg[which(mpg$dp != "1000-05-02" | is.na(mpg$dp)), ], difftime_units = "weeks")

make_complete_output(dpByYearSummary)

Summary statistics of date of purchase (Date class) by year of manufacture.

year of manufacture	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
1999	date of purchase (Date class)	107	8.55	1999-06-23	14.72 weeks	1999-06-23	18.64 weeks	1999-03-27	1999-10-13	28.57143 weeks	1999-01-04	1999-12-24
2008	date of purchase (Date class)	106	8.62	2008-07-03	14.96 weeks	2008-07-12	18.43 weeks	2008-04-16	2008-10-18	26.42857 weeks	2008-01-02	2008-12-23
Overall	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.71429 weeks	1999-01-04	2008-12-23

Boxplot of date of purchase (Date class) by year of manufacture.

Date of Purchase by Comments

dpByCommentsSummary <- data_summary(x = "dp", by = "comments", data = mpg[which(mpg$dp != "1000-05-02" | is.na(mpg$dp)), ], difftime_units = "weeks")

make_complete_output(dpByCommentsSummary)

Summary statistics of date of purchase (Date class) by some random comments.

some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
.	date of purchase (Date class)	25	3.85	2004-08-08	237.07 weeks	2008-02-08	60.57 weeks	1999-09-19	2008-09-06	467.8571 weeks	1999-01-13	2008-11-27
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	21	8.70	2003-10-28	243.86 weeks	1999-12-14	66.51 weeks	1999-06-28	2008-08-02	474.7143 weeks	1999-02-03	2008-12-23
Does it also fly?	date of purchase (Date class)	13	18.75	2002-12-21	240.09 weeks	1999-10-13	52.31 weeks	1999-07-03	2008-11-10	488.2857 weeks	1999-01-14	2008-11-26
Does it come in green?	date of purchase (Date class)	23	0.00	2004-01-22	241.15 weeks	2008-01-04	71.80 weeks	1999-04-26	2008-03-24	465.0000 weeks	1999-01-16	2008-12-08
I like this car!	date of purchase (Date class)	20	13.04	2003-08-29	246.12 weeks	1999-11-13	54.96 weeks	1999-06-23	2008-09-23	482.8571 weeks	1999-02-12	2008-12-15
Meh.	date of purchase (Date class)	17	5.56	2004-02-12	243.37 weeks	2008-01-27	51.47 weeks	1999-04-04	2008-05-27	477.2857 weeks	1999-01-05	2008-09-26
Missing	date of purchase (Date class)	22	12.00	2005-03-23	234.82 weeks	2008-03-25	44.90 weeks	1999-08-24	2008-10-11	476.5714 weeks	1999-01-04	2008-11-15
This is the worst car ever!	date of purchase (Date class)	21	4.55	2003-11-06	240.62 weeks	1999-11-24	52.31 weeks	1999-06-27	2008-07-17	472.5714 weeks	1999-03-22	2008-10-26
want cheese flavoured cars.	date of purchase (Date class)	31	6.06	2003-04-02	235.66 weeks	1999-12-06	58.46 weeks	1999-05-07	2008-06-24	476.5714 weeks	1999-01-26	2008-12-09
Overall	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.7143 weeks	1999-01-04	2008-12-23
R NA Value	date of purchase (Date class)	20	16.67	2003-12-06	236.95 weeks	2003-12-24	337.72 weeks	1999-08-14	2008-06-25	462.5714 weeks	1999-01-07	2008-12-03

Boxplot of date of purchase (Date class) by some random comments.

Random Number by Party

rnByPartySummary <- data_summary(x = "rn", by = "party", data = mpg)

make_complete_output(rnByPartySummary)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5 by some random political parties.

some random political parties	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
republican	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	41	26.79	10.39	6.15	11.78	4.28	5.67	13.95	8.28	-0.19	22.88
democrat	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	46	24.59	10.00	5.37	10.41	5.38	6.34	13.24	6.74	-2.54	23.46
independent	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	54	12.90	10.60	4.99	10.49	5.46	7.12	14.52	7.25	0.74	20.81
Overall	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	184	21.37	10.53	5.09	10.73	4.72	7.12	13.32	6.12	-2.54	23.46
R NA Value	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5	43	21.82	11.16	3.71	10.72	2.65	8.99	12.81	3.72	4.42	22.44

Boxplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5 by some random political parties.

Random Difference in Time by Party

rdifftimeByPartySummary <- data_summary(x = "rdifftime", by = "party", difftime_units = "weeks", data = mpg)

make_complete_output(rdifftimeByPartySummary)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by some random political parties.

some random political parties	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
republican	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	45	19.64	9.50 weeks	5.91 weeks	10.35 weeks	6.54 weeks	4.96 weeks	13.06 weeks	8.11 weeks	0.00 weeks	23.49 weeks
democrat	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	52	14.75	9.31 weeks	4.49 weeks	9.38 weeks	4.37 weeks	6.44 weeks	12.68 weeks	6.23 weeks	0.00 weeks	18.28 weeks
independent	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	47	24.19	10.22 weeks	4.64 weeks	9.58 weeks	5.87 weeks	5.61 weeks	13.57 weeks	7.64 weeks	2.04 weeks	21.65 weeks
Overall	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.60 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0.00 weeks	23.49 weeks
R NA Value	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	40	27.27	10.08 weeks	5.38 weeks	10.06 weeks	4.12 weeks	6.76 weeks	12.27 weeks	5.46 weeks	0.00 weeks	22.98 weeks

Boxplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by some random political parties.

Random Difference in Time by Comments

rdifftimeByCommentsSummary <- data_summary(x = "rdifftime", by = "comments", difftime_units = "weeks", data = mpg)

make_complete_output(rdifftimeByCommentsSummary)

Summary statistics of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by some random comments.

some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
.	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	21	19.23	8.21 weeks	5.55 weeks	9.07 weeks	5.97 weeks	4.81 weeks	10.97 weeks	6.16 weeks	0.00 weeks	19.07 weeks
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	19	17.39	8.32 weeks	4.50 weeks	9.57 weeks	3.78 weeks	4.33 weeks	11.54 weeks	6.86 weeks	0.00 weeks	14.76 weeks
Does it also fly?	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	14	12.50	9.49 weeks	5.54 weeks	10.25 weeks	4.60 weeks	6.51 weeks	13.29 weeks	6.34 weeks	0.00 weeks	21.14 weeks
Does it come in green?	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	19	17.39	8.51 weeks	4.28 weeks	8.76 weeks	4.49 weeks	5.27 weeks	11.79 weeks	5.44 weeks	0.00 weeks	16.31 weeks
I like this car!	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	22	8.33	10.44 weeks	6.03 weeks	9.78 weeks	6.78 weeks	5.32 weeks	16.22 weeks	10.30 weeks	1.79 weeks	21.65 weeks
Meh.	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	13	27.78	10.21 weeks	4.88 weeks	10.56 weeks	4.61 weeks	7.13 weeks	12.60 weeks	5.47 weeks	3.88 weeks	21.71 weeks
Missing	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	19	24.00	11.44 weeks	5.29 weeks	12.24 weeks	5.19 weeks	6.65 weeks	15.38 weeks	8.17 weeks	3.57 weeks	23.49 weeks
This is the worst car ever!	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	17	22.73	9.28 weeks	4.29 weeks	9.63 weeks	4.80 weeks	6.76 weeks	13.05 weeks	6.29 weeks	0.36 weeks	13.93 weeks
want cheese flavoured cars.	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	26	21.21	10.88 weeks	3.62 weeks	11.19 weeks	3.00 weeks	9.01 weeks	13.16 weeks	3.91 weeks	2.53 weeks	17.07 weeks
Overall	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	184	21.37	9.76 weeks	5.07 weeks	9.60 weeks	5.12 weeks	6.11 weeks	13.05 weeks	6.79 weeks	0.00 weeks	23.49 weeks
R NA Value	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks	14	41.67	10.69 weeks	6.75 weeks	11.80 weeks	7.16 weeks	5.23 weeks	15.02 weeks	9.42 weeks	0.00 weeks	22.98 weeks

Boxplot of some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks by some random comments.

Comments by Party

commentsByPartySummary <- data_summary(x = "comments", by = "party", data = mpg)

make_complete_output(commentsByPartySummary)

Summary statistics of some random comments by some random political parties.

some random comments	republican	democrat	independent	Overall	R NA Value
.	3 (5.36%)	7 (11.48%)	9 (14.52%)	26 (11.11%)	7 (12.73%)
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	5 (8.93%)	7 (11.48%)	6 (9.68%)	23 (9.83%)	5 (9.09%)
Does it also fly?	1 (1.79%)	6 (9.84%)	3 (4.84%)	16 (6.84%)	6 (10.91%)
Does it come in green?	8 (14.29%)	6 (9.84%)	6 (9.68%)	23 (9.83%)	3 (5.45%)
I like this car!	6 (10.71%)	6 (9.84%)	8 (12.9%)	24 (10.26%)	4 (7.27%)
Meh.	10 (17.86%)	5 (8.2%)	0 (0%)	18 (7.69%)	3 (5.45%)
Missing	5 (8.93%)	9 (14.75%)	6 (9.68%)	25 (10.68%)	5 (9.09%)
This is the worst car ever!	9 (16.07%)	4 (6.56%)	4 (6.45%)	22 (9.4%)	5 (9.09%)
want cheese flavoured cars.	7 (12.5%)	7 (11.48%)	10 (16.13%)	33 (14.1%)	9 (16.36%)
R NA Value	2 (3.57%)	4 (6.56%)	10 (16.13%)	24 (10.26%)	8 (14.55%)

Barplot of some random comments by some random political parties.

By By Data Summaries

City MPG by Number of Cylinders by Class

ctyByCylByClassSummary <- data_summary(x = "cty", by = c("cyl", "class"), data = mpg)

make_complete_output(ctyByCylByClassSummary)

Summary statistics of city miles per gallon by number of cylinders by type of car.

number of cylinders by type of car	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
8, 2seater	city miles per gallon	5	0	15.40	0.55	15.0	0.00	15	16	1.00	15	16
4, compact	city miles per gallon	32	0	21.38	3.25	21.0	1.48	19	22	2.25	16	33
5, compact	city miles per gallon	2	0	21.00	0.00	21.0	0.00	21	21	0.00	21	21
6, compact	city miles per gallon	13	0	16.92	1.12	17.0	1.48	16	18	2.00	15	18
4, midsize	city miles per gallon	16	0	20.50	1.63	21.0	0.74	19	21	2.00	18	23
6, midsize	city miles per gallon	23	0	17.78	1.09	18.0	1.48	17	19	1.50	15	19
8, midsize	city miles per gallon	2	0	16.00	0.00	16.0	0.00	16	16	0.00	16	16
4, minivan	city miles per gallon	1	0	18.00	NA	18.0	0.00	18	18	0.00	18	18
6, minivan	city miles per gallon	10	0	15.60	1.78	16.0	1.48	15	17	1.50	11	17
4, pickup	city miles per gallon	3	0	16.00	1.00	16.0	1.48	15	17	1.00	15	17
6, pickup	city miles per gallon	10	0	14.50	0.85	14.5	0.74	14	15	1.00	13	16
8, pickup	city miles per gallon	20	0	11.80	1.58	12.0	1.48	11	13	2.00	9	14
4, subcompact	city miles per gallon	21	0	22.86	4.19	21.0	2.97	19	25	6.00	19	35
5, subcompact	city miles per gallon	2	0	20.00	0.00	20.0	0.00	20	20	0.00	20	20
6, subcompact	city miles per gallon	7	0	17.00	0.82	17.0	1.48	16	18	1.00	16	18
8, subcompact	city miles per gallon	5	0	14.80	0.45	15.0	0.00	15	15	0.00	14	15
4, suv	city miles per gallon	8	0	18.00	1.77	18.0	2.22	16	19	1.75	15	20
6, suv	city miles per gallon	16	0	14.50	1.10	14.5	0.74	14	15	1.00	13	17
8, suv	city miles per gallon	38	0	12.13	1.36	12.0	1.48	11	13	2.00	9	14
Overall	city miles per gallon	234	0	16.86	4.26	17.0	4.45	14	19	5.00	9	35

Boxplot of city miles per gallon by number of cylinders by type of car.

Highway MPG by Number of Cylinders by Class

hwyByCylByClassSummary <- data_summary(x = "hwy", by = c("cyl", "class"), data = mpg)

make_complete_output(hwyByCylByClassSummary)

Summary statistics of highway miles per gallon by number of cylinders by type of car.

number of cylinders by type of car	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
8, 2seater	highway miles per gallon	5	0	24.80	1.30	25.0	1.48	24	26	2.00	23	26
4, compact	highway miles per gallon	32	0	29.47	3.93	29.0	2.97	27	30	3.25	25	44
5, compact	highway miles per gallon	2	0	29.00	0.00	29.0	0.00	29	29	0.00	29	29
6, compact	highway miles per gallon	13	0	25.31	1.18	25.0	1.48	25	26	1.00	23	27
4, midsize	highway miles per gallon	16	0	29.19	1.80	29.0	2.97	27	31	3.25	26	32
6, midsize	highway miles per gallon	23	0	26.26	1.14	26.0	0.00	26	27	0.50	24	29
8, midsize	highway miles per gallon	2	0	24.00	1.41	24.0	1.48	23	25	1.00	23	25
4, minivan	highway miles per gallon	1	0	24.00	NA	24.0	0.00	24	24	0.00	24	24
6, minivan	highway miles per gallon	10	0	22.20	2.10	22.5	1.48	22	24	1.75	17	24
4, pickup	highway miles per gallon	3	0	20.67	1.15	20.0	0.00	20	22	1.00	20	22
6, pickup	highway miles per gallon	10	0	17.90	1.10	17.5	0.74	17	19	1.75	17	20
8, pickup	highway miles per gallon	20	0	15.80	1.99	16.0	1.48	15	17	2.00	12	19
4, subcompact	highway miles per gallon	21	0	30.81	5.12	29.0	4.45	26	33	7.00	26	44
5, subcompact	highway miles per gallon	2	0	28.50	0.71	28.5	0.74	28	29	0.50	28	29
6, subcompact	highway miles per gallon	7	0	24.71	0.95	24.0	0.00	24	26	1.50	24	26
8, subcompact	highway miles per gallon	5	0	21.60	1.14	22.0	1.48	21	22	1.00	20	23
4, suv	highway miles per gallon	8	0	23.75	2.60	24.5	2.22	20	25	3.00	20	27
6, suv	highway miles per gallon	16	0	18.50	1.55	19.0	2.22	17	19	2.25	17	22
8, suv	highway miles per gallon	38	0	16.79	1.91	17.0	1.48	15	18	2.75	12	20
Overall	highway miles per gallon	234	0	23.44	5.95	24.0	7.41	18	27	9.00	12	44

Boxplot of highway miles per gallon by number of cylinders by type of car.

Date of Purchase of Cylinders by Comments

dpByCylByCommentsSummary <- data_summary(x = "dp", by = c("cyl", "comments"), difftime_units = "weeks", data = mpg[which(mpg$dp != "1000-05-02" | is.na(mpg$dp)), ])

make_complete_output(dpByCylByCommentsSummary)

Summary statistics of date of purchase (Date class) by number of cylinders by some random comments.

number of cylinders by some random comments	Label	N	P NA	Mean	S Dev	Med	MAD	25th P	75th P	IQR	Min	Max
4, .	date of purchase (Date class)	5	0.00	2003-01-27	252.39 weeks	1999-10-26	54.64 weeks	1999-02-10	2008-02-08	469.285714 weeks	1999-02-10	2008-08-12
6, .	date of purchase (Date class)	9	0.00	2004-08-21	241.15 weeks	2008-04-02	44.27 weeks	1999-08-28	2008-06-18	459.571429 weeks	1999-07-14	2008-10-28
8, .	date of purchase (Date class)	10	9.09	2004-11-28	246.03 weeks	2008-02-07	61.53 weeks	1999-10-05	2008-09-06	465.571429 weeks	1999-01-13	2008-11-27
4, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	9	0.00	2002-08-10	241.71 weeks	1999-09-12	38.34 weeks	1999-03-15	2008-05-25	479.857143 weeks	1999-03-08	2008-12-23
6, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	7	12.50	2004-08-16	254.96 weeks	2008-05-13	31.13 weeks	1999-06-07	2008-08-02	477.714286 weeks	1999-03-19	2008-10-07
8, Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	date of purchase (Date class)	4	0.00	2004-01-07	274.68 weeks	2004-02-23	342.16 weeks	1999-02-03	2008-06-12	488.142857 weeks	1999-02-03	2008-09-09
4, Does it also fly?	date of purchase (Date class)	5	0.00	2002-12-14	265.47 weeks	1999-08-06	43.21 weeks	1999-01-14	2008-02-14	474.000000 weeks	1999-01-14	2008-11-26
6, Does it also fly?	date of purchase (Date class)	4	42.86	2001-09-28	225.84 weeks	1999-08-23	12.71 weeks	1999-07-03	NA	NA weeks	1999-06-15	2008-03-25
8, Does it also fly?	date of purchase (Date class)	4	0.00	2004-03-25	272.68 weeks	2004-04-06	347.46 weeks	1999-07-16	2008-08-25	475.428571 weeks	1999-07-16	2008-11-10
4, Does it come in green?	date of purchase (Date class)	15	0.00	2003-07-23	243.88 weeks	1999-08-26	47.02 weeks	1999-03-24	2008-02-26	465.857143 weeks	1999-01-16	2008-10-13
6, Does it come in green?	date of purchase (Date class)	3	0.00	2002-06-23	268.42 weeks	1999-09-09	27.75 weeks	1999-05-01	1999-09-09	18.714286 weeks	1999-05-01	2008-05-31
8, Does it come in green?	date of purchase (Date class)	5	0.00	2006-07-09	217.62 weeks	2008-02-09	24.15 weeks	1999-02-01	2008-06-02	487.000000 weeks	1999-02-01	2008-12-08
4, I like this car!	date of purchase (Date class)	8	11.11	2005-04-06	247.72 weeks	2008-07-29	20.33 weeks	1999-06-23	2008-09-23	482.857143 weeks	1999-06-08	2008-12-12
6, I like this car!	date of purchase (Date class)	7	22.22	2002-01-30	232.78 weeks	1999-08-23	25.84 weeks	1999-04-23	2008-09-05	489.000000 weeks	1999-03-13	2008-09-05
8, I like this car!	date of purchase (Date class)	4	0.00	2001-11-20	246.39 weeks	1999-09-27	30.61 weeks	1999-02-12	1999-11-28	41.285714 weeks	1999-02-12	2008-12-14
4, Meh.	date of purchase (Date class)	6	0.00	1999-05-01	9.86 weeks	1999-04-25	8.47 weeks	1999-03-26	1999-05-16	7.285714 weeks	1999-01-25	1999-08-11
6, Meh.	date of purchase (Date class)	6	0.00	2005-06-05	248.14 weeks	2008-04-27	26.58 weeks	1999-07-31	2008-05-08	457.714286 weeks	1999-01-05	2008-09-26
8, Meh.	date of purchase (Date class)	5	16.67	2008-04-14	9.80 weeks	2008-04-15	11.44 weeks	2008-02-21	2008-05-27	13.714286 weeks	2008-01-27	2008-07-13
4, Missing	date of purchase (Date class)	3	50.00	2002-08-26	280.92 weeks	1999-10-09	34.74 weeks	1999-10-09	NA	NA weeks	1999-04-28	2008-11-11
6, Missing	date of purchase (Date class)	5	0.00	2004-12-23	255.73 weeks	2008-05-24	21.18 weeks	1999-07-30	2008-08-09	471.142857 weeks	1999-07-30	2008-09-01
8, Missing	date of purchase (Date class)	14	0.00	2005-11-13	226.66 weeks	2008-04-02	38.55 weeks	1999-10-04	2008-09-08	466.000000 weeks	1999-01-04	2008-11-15
4, This is the worst car ever!	date of purchase (Date class)	7	0.00	2003-05-28	247.48 weeks	1999-11-24	50.62 weeks	1999-06-03	2008-02-10	453.428571 weeks	1999-03-30	2008-09-29
6, This is the worst car ever!	date of purchase (Date class)	9	10.00	2002-08-03	237.70 weeks	1999-10-18	38.34 weeks	1999-04-20	2008-09-13	490.571429 weeks	1999-03-22	2008-10-26
8, This is the worst car ever!	date of purchase (Date class)	5	0.00	2006-09-25	213.60 weeks	2008-07-04	16.10 weeks	1999-06-02	2008-09-18	485.142857 weeks	1999-06-02	2008-09-19
4, want cheese flavoured cars.	date of purchase (Date class)	10	9.09	2003-02-13	238.91 weeks	1999-12-10	57.50 weeks	1999-06-09	2008-05-15	466.142857 weeks	1999-02-17	2008-10-31
6, want cheese flavoured cars.	date of purchase (Date class)	13	0.00	2002-04-09	231.98 weeks	1999-08-31	36.85 weeks	1999-03-10	2008-06-24	484.857143 weeks	1999-01-26	2008-12-09
8, want cheese flavoured cars.	date of purchase (Date class)	8	11.11	2005-01-02	240.53 weeks	2008-02-06	44.16 weeks	1999-05-21	2008-07-18	478.000000 weeks	1999-04-01	2008-10-18
Overall	date of purchase (Date class)	213	8.58	2003-12-21	236.59 weeks	1999-12-24	74.98 weeks	1999-07-14	2008-09-01	476.714286 weeks	1999-01-04	2008-12-23
R NA Value	date of purchase (Date class)	20	16.67	2003-12-06	236.95 weeks	2003-12-24	337.72 weeks	1999-08-14	2008-06-25	462.571429 weeks	1999-01-07	2008-12-03

Boxplot of date of purchase (Date class) by number of cylinders by type of car.

Comments by Number of Cylinders by Class

commentsByCylByClassSummary <- data_summary(x = "comments", by = c("cyl", "class"), data = mpg)

make_complete_output(commentsByCylByClassSummary)

Summary statistics of some random comments by number of cylinders by type of car.

some random comments	4, 2seater	5, 2seater	6, 2seater	8, 2seater	4, compact	5, compact	6, compact	8, compact	4, midsize	5, midsize	6, midsize	8, midsize	4, minivan	5, minivan	6, minivan	8, minivan	4, pickup	5, pickup	6, pickup	8, pickup	4, subcompact	5, subcompact	6, subcompact	8, subcompact	4, suv	5, suv	6, suv	8, suv	Overall
.	0 (NaN%)	0 (NaN%)	0 (NaN%)	1 (20%)	2 (6.25%)	0 (0%)	1 (7.69%)	0 (NaN%)	1 (6.25%)	0 (NaN%)	4 (17.39%)	0 (0%)	0 (0%)	0 (NaN%)	1 (10%)	0 (NaN%)	0 (0%)	0 (NaN%)	1 (10%)	4 (20%)	1 (4.76%)	1 (50%)	1 (14.29%)	1 (20%)	1 (12.5%)	0 (NaN%)	1 (6.25%)	5 (13.16%)	26 (11.11%)
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	0 (NaN%)	0 (NaN%)	0 (NaN%)	0 (0%)	3 (9.38%)	2 (100%)	0 (0%)	0 (NaN%)	3 (18.75%)	0 (NaN%)	3 (13.04%)	0 (0%)	0 (0%)	0 (NaN%)	0 (0%)	0 (NaN%)	1 (33.33%)	0 (NaN%)	2 (20%)	1 (5%)	0 (0%)	0 (0%)	2 (28.57%)	1 (20%)	2 (25%)	0 (NaN%)	1 (6.25%)	2 (5.26%)	23 (9.83%)
Does it also fly?	0 (NaN%)	0 (NaN%)	0 (NaN%)	0 (0%)	1 (3.12%)	0 (0%)	0 (0%)	0 (NaN%)	1 (6.25%)	0 (NaN%)	4 (17.39%)	1 (50%)	0 (0%)	0 (NaN%)	1 (10%)	0 (NaN%)	0 (0%)	0 (NaN%)	0 (0%)	0 (0%)	3 (14.29%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (NaN%)	2 (12.5%)	3 (7.89%)	16 (6.84%)
Does it come in green?	0 (NaN%)	0 (NaN%)	0 (NaN%)	0 (0%)	7 (21.88%)	0 (0%)	1 (7.69%)	0 (NaN%)	3 (18.75%)	0 (NaN%)	0 (0%)	0 (0%)	0 (0%)	0 (NaN%)	0 (0%)	0 (NaN%)	0 (0%)	0 (NaN%)	0 (0%)	0 (0%)	3 (14.29%)	0 (0%)	0 (0%)	0 (0%)	2 (25%)	0 (NaN%)	2 (12.5%)	5 (13.16%)	23 (9.83%)
I like this car!	0 (NaN%)	0 (NaN%)	0 (NaN%)	0 (0%)	4 (12.5%)	0 (0%)	3 (23.08%)	0 (NaN%)	3 (18.75%)	0 (NaN%)	1 (4.35%)	0 (0%)	0 (0%)	0 (NaN%)	1 (10%)	0 (NaN%)	1 (33.33%)	0 (NaN%)	2 (20%)	2 (10%)	2 (9.52%)	1 (50%)	0 (0%)	0 (0%)	0 (0%)	0 (NaN%)	2 (12.5%)	2 (5.26%)	24 (10.26%)
Meh.	0 (NaN%)	0 (NaN%)	0 (NaN%)	1 (20%)	1 (3.12%)	0 (0%)	0 (0%)	0 (NaN%)	1 (6.25%)	0 (NaN%)	1 (4.35%)	0 (0%)	0 (0%)	0 (NaN%)	1 (10%)	0 (NaN%)	0 (0%)	0 (NaN%)	1 (10%)	1 (5%)	4 (19.05%)	0 (0%)	1 (14.29%)	0 (0%)	0 (0%)	0 (NaN%)	2 (12.5%)	4 (10.53%)	18 (7.69%)
Missing	0 (NaN%)	0 (NaN%)	0 (NaN%)	1 (20%)	4 (12.5%)	0 (0%)	0 (0%)	0 (NaN%)	0 (0%)	0 (NaN%)	2 (8.7%)	1 (50%)	0 (0%)	0 (NaN%)	1 (10%)	0 (NaN%)	0 (0%)	0 (NaN%)	0 (0%)	5 (25%)	2 (9.52%)	0 (0%)	0 (0%)	1 (20%)	0 (0%)	0 (NaN%)	2 (12.5%)	6 (15.79%)	25 (10.68%)
This is the worst car ever!	0 (NaN%)	0 (NaN%)	0 (NaN%)	0 (0%)	5 (15.62%)	0 (0%)	2 (15.38%)	0 (NaN%)	1 (6.25%)	0 (NaN%)	3 (13.04%)	0 (0%)	1 (100%)	0 (NaN%)	2 (20%)	0 (NaN%)	0 (0%)	0 (NaN%)	1 (10%)	2 (10%)	0 (0%)	0 (0%)	0 (0%)	1 (20%)	0 (0%)	0 (NaN%)	2 (12.5%)	2 (5.26%)	22 (9.4%)
want cheese flavoured cars.	0 (NaN%)	0 (NaN%)	0 (NaN%)	1 (20%)	3 (9.38%)	0 (0%)	3 (23.08%)	0 (NaN%)	2 (12.5%)	0 (NaN%)	4 (17.39%)	0 (0%)	0 (0%)	0 (NaN%)	2 (20%)	0 (NaN%)	1 (33.33%)	0 (NaN%)	2 (20%)	4 (20%)	4 (19.05%)	0 (0%)	1 (14.29%)	0 (0%)	1 (12.5%)	0 (NaN%)	1 (6.25%)	4 (10.53%)	33 (14.1%)
R NA Value	0 (NaN%)	0 (NaN%)	0 (NaN%)	1 (20%)	2 (6.25%)	0 (0%)	3 (23.08%)	0 (NaN%)	1 (6.25%)	0 (NaN%)	1 (4.35%)	0 (0%)	0 (0%)	0 (NaN%)	1 (10%)	0 (NaN%)	0 (0%)	0 (NaN%)	1 (10%)	1 (5%)	2 (9.52%)	0 (0%)	2 (28.57%)	1 (20%)	2 (25%)	0 (NaN%)	1 (6.25%)	5 (13.16%)	24 (10.26%)

Stacked barplot of some random comments by number of cylinders by type of car.

Summary Tables

Univariate Data Summaries

invisible(setGeneric(name = "univariate_data_summary", def = function(object) standardGeneric("univariate_data_summary")))
setMethod(
    f = "univariate_data_summary",
    signature = "dataSummaries", 
    definition = function(object) 
    {
        if (all(c("Mean", "S Dev") %in% colnames(object@table))) {
            xlab <- paste("<b>", object@xLab, ", Mean (SD)</b>", sep = "")
            
            if (length(object@difftime_units) > 0) {
                res <- paste(object@table[, "Mean"], " (", object@table[, "S Dev"], " ", object@difftime_units, ")", sep = "")
            } else {
                res <- paste(object@table[, "Mean"], " (", object@table[, "S Dev"], ")", sep = "")
            }
            
            res <- data.frame(res)
            res$rname <- xlab
            res <- res[, c(2, 1)]
            colnames(res) <- c("", "")
            rownames(res) <- NULL
        } else {
            xlab <- paste("<b>", object@xLab, ", n (%)", "</b>", sep = "")
            res <- c("", object@table[, -1])
            res <- data.frame(res, stringsAsFactors = FALSE)
            res$rnames <- c(xlab, paste("&nbsp;&nbsp;", as.character(object@table[, 1]), sep = ""))
            res <- res[, c(2, 1)]
            colnames(res) <- c("", "")
            rownames(res) <- NULL
        }
        
        return(res)
    }
)

univariateDataSummaryList <- list(
    manuSummary, 
    modelSummary, 
    displSummary, 
    yearSummary, 
    dpSummary, 
    cylSummary, 
    transSummary, 
    drvSummary, 
    ctySummary, 
    hwySummary, 
    flSummary, 
    classSummary, 
    rnSummary, 
    rdifftimeSummary,
    logicalSummary,
    partySummary, 
    commentsSummary,
    missSummary
)

cars_univariate_data_summary <- do.call("rbind", lapply(univariateDataSummaryList, univariate_data_summary))

kable(cars_univariate_data_summary, caption = "Data summaries", booktabs = TRUE, escape = FALSE, format = "html", table.attr = "data-quarto-disable-processing=true") %>% 
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = TRUE, font_size = 14)

Data summaries

manufacturer, n (%)
audi	18 (7.69%)
chevrolet	19 (8.12%)
dodge	37 (15.81%)
ford	25 (10.68%)
honda	9 (3.85%)
hyundai	14 (5.98%)
jeep	8 (3.42%)
land rover	4 (1.71%)
lincoln	3 (1.28%)
mercury	4 (1.71%)
nissan	13 (5.56%)
pontiac	5 (2.14%)
subaru	14 (5.98%)
toyota	34 (14.53%)
volkswagen	27 (11.54%)
model name, n (%)
4runner 4wd	6 (2.56%)
a4	7 (2.99%)
a4 quattro	8 (3.42%)
a6 quattro	3 (1.28%)
altima	6 (2.56%)
c1500 suburban 2wd	5 (2.14%)
camry	7 (2.99%)
camry solara	7 (2.99%)
caravan 2wd	11 (4.7%)
civic	9 (3.85%)
corolla	5 (2.14%)
corvette	5 (2.14%)
dakota pickup 4wd	9 (3.85%)
durango 4wd	7 (2.99%)
expedition 2wd	3 (1.28%)
explorer 4wd	6 (2.56%)
f150 pickup 4wd	7 (2.99%)
forester awd	6 (2.56%)
grand cherokee 4wd	8 (3.42%)
grand prix	5 (2.14%)
gti	5 (2.14%)
impreza awd	8 (3.42%)
jetta	9 (3.85%)
k1500 tahoe 4wd	4 (1.71%)
land cruiser wagon 4wd	2 (0.85%)
malibu	5 (2.14%)
maxima	3 (1.28%)
mountaineer 4wd	4 (1.71%)
mustang	9 (3.85%)
navigator 2wd	3 (1.28%)
new beetle	6 (2.56%)
passat	7 (2.99%)
pathfinder 4wd	4 (1.71%)
ram 1500 pickup 4wd	10 (4.27%)
range rover	4 (1.71%)
sonata	7 (2.99%)
tiburon	7 (2.99%)
toyota tacoma 4wd	7 (2.99%)
engine displacement, in litres, Mean (SD)	3.47 (1.29)
year of manufacture, n (%)
1999	117 (50%)
2008	117 (50%)
date of purchase (Date class), Mean (SD)	2003-12-21 (236.59 weeks)
number of cylinders, n (%)
4	81 (34.62%)
5	4 (1.71%)
6	79 (33.76%)
8	70 (29.91%)
type of transmission, n (%)
auto(av)	5 (2.14%)
auto(l3)	2 (0.85%)
auto(l4)	83 (35.47%)
auto(l5)	39 (16.67%)
auto(l6)	6 (2.56%)
auto(s4)	3 (1.28%)
auto(s5)	3 (1.28%)
auto(s6)	16 (6.84%)
manual(m5)	58 (24.79%)
manual(m6)	19 (8.12%)
drive type, n (%)
front-wheel drive	106 (45.3%)
rear wheel drive	25 (10.68%)
4wd	103 (44.02%)
city miles per gallon, Mean (SD)	16.86 (4.26)
highway miles per gallon, Mean (SD)	23.44 (5.95)
fuel type, n (%)
c	1 (0.43%)
d	5 (2.14%)
e	8 (3.42%)
p	52 (22.22%)
r	168 (71.79%)
type of car, n (%)
2seater	5 (2.14%)
compact	47 (20.09%)
midsize	41 (17.52%)
minivan	11 (4.7%)
pickup	33 (14.1%)
subcompact	35 (14.96%)
suv	62 (26.5%)
some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, Mean (SD)	10.53 (5.09)
some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, Mean (SD)	9.76 (5.07 weeks)
some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, and then set to TRUE if the difference is greater than 10, n (%)
FALSE	96 (41.03%)
TRUE	88 (37.61%)
R NA Value	50 (21.37%)
some random political parties, n (%)
republican	56 (23.93%)
democrat	61 (26.07%)
independent	62 (26.5%)
R NA Value	55 (23.5%)
some random comments, n (%)
.	26 (11.11%)
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	23 (9.83%)
Does it also fly?	16 (6.84%)
Does it come in green?	23 (9.83%)
I like this car!	24 (10.26%)
Meh.	18 (7.69%)
Missing	25 (10.68%)
This is the worst car ever!	22 (9.4%)
want cheese flavoured cars.	33 (14.1%)
R NA Value	24 (10.26%)
an all missing variable, n (%)
R NA Value	234 (100%)

By Data Summaries

invisible(setGeneric(name = "by_data_summary", def = function(object) standardGeneric("by_data_summary")))
setMethod(
    f = "by_data_summary",
    signature = "dataSummaries", 
    definition = function(object) 
    {
        if (all(c("Mean", "S Dev") %in% colnames(object@table))) {
            res <- t(object@table[, c("Mean", "S Dev")])
            
            if (length(object@difftime_units) > 0) {
                res <- paste(object@table[, "Mean"], " (", object@table[, "S Dev"], " ", object@difftime_units, ")", sep = "")
            } else {
                res <- paste(object@table[, "Mean"], " (", object@table[, "S Dev"], ")", sep = "")
            }
            
            res <- data.frame(t(res))
            res$label <- paste("<b>", object@xLab, ", Mean (SD)</b>", sep = "")
            rownames(res) <- NULL
            res <- res[, c(which(colnames(res) == "label"), which(!(1:dim(res)[2] %in% which(colnames(res) == "label"))))]
            colnames(res) <- c("", as.character(object@table[, 1]))
        } else {
            res <- object@table
            res[, 1] <- paste("&nbsp;&nbsp;", res[, 1], sep = "")
            res <- rbind("", res)
            res[1,1] <- paste("<b>", object@xLab, ", N (%)</b>", sep = "")
            colnames(res)[1] <- ""
        }
        
        return(res)
    }
)

byDataSummaryList <- list(
    ctyByDrvSummary,
    hwyByDrvSummary,
    cylByDrvSummary,
    dpByDrvSummary,
    rnByDrvSummary,
    rdifftimeByDrvSummary,
    logicalByDrvSummary,
    commentsByDrvSummary,
    missByDrvSummary
)

cars_by_data_summary <- do.call("rbind", lapply(byDataSummaryList, by_data_summary))

kable(cars_by_data_summary, caption = "Data summaries", booktabs = TRUE, escape = FALSE, format = "html", table.attr = "data-quarto-disable-processing=true") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = TRUE, font_size = 14)

Data summaries

	front-wheel drive	rear wheel drive	4wd	Overall
city miles per gallon, Mean (SD)	19.97 (3.63)	14.08 (2.22)	14.33 (2.87)	16.86 (4.26)
highway miles per gallon, Mean (SD)	28.16 (4.21)	21 (3.66)	19.17 (4.08)	23.44 (5.95)
number of cylinders, N (%)
4	58 (54.72%)	0 (0%)	23 (22.33%)	81 (34.62%)
5	4 (3.77%)	0 (0%)	0 (0%)	4 (1.71%)
6	43 (40.57%)	4 (16%)	32 (31.07%)	79 (33.76%)
8	1 (0.94%)	21 (84%)	48 (46.6%)	70 (29.91%)
date of purchase (Date class), Mean (SD)	2003-06-08 (235.2 weeks)	2004-09-28 (235.47 weeks)	2004-04-21 (237.55 weeks)	2003-12-21 (236.59 weeks)
some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, Mean (SD)	10.5 (5.32)	11.59 (4.49)	10.33 (4.99)	10.53 (5.09)
some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, Mean (SD)	10.57 (5.24 weeks)	8.43 (5.17 weeks)	9.19 (4.77 weeks)	9.76 (5.07 weeks)
some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, and then set to TRUE if the difference is greater than 10, N (%)
FALSE	40 (37.74%)	11 (44%)	45 (43.69%)	96 (41.03%)
TRUE	46 (43.4%)	8 (32%)	34 (33.01%)	88 (37.61%)
R NA Value	20 (18.87%)	6 (24%)	24 (23.3%)	50 (21.37%)
some random comments, N (%)
.	9 (8.49%)	5 (20%)	12 (11.65%)	26 (11.11%)
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	12 (11.32%)	3 (12%)	8 (7.77%)	23 (9.83%)
Does it also fly?	11 (10.38%)	1 (4%)	4 (3.88%)	16 (6.84%)
Does it come in green?	12 (11.32%)	1 (4%)	10 (9.71%)	23 (9.83%)
I like this car!	13 (12.26%)	1 (4%)	10 (9.71%)	24 (10.26%)
Meh.	6 (5.66%)	2 (8%)	10 (9.71%)	18 (7.69%)
Missing	8 (7.55%)	3 (12%)	14 (13.59%)	25 (10.68%)
This is the worst car ever!	14 (13.21%)	2 (8%)	6 (5.83%)	22 (9.4%)
want cheese flavoured cars.	13 (12.26%)	2 (8%)	18 (17.48%)	33 (14.1%)
R NA Value	8 (7.55%)	5 (20%)	11 (10.68%)	24 (10.26%)
an all missing variable, N (%)
R NA Value	106 (100%)	25 (100%)	103 (100%)	234 (100%)

Bivariate Data Summaries

invisible(setGeneric(name = "bivariate_data_summary", def = function(object1, object2) standardGeneric("bivariate_data_summary")))
setMethod(
    f = "bivariate_data_summary",
    signature = "dataSummaries", 
    definition = function(object1, object2) 
    {
        rnames <- c(paste("<b>", object1@byLab, "</b>", sep = ""), paste("&nbsp;&nbsp;", object1@table[, 1], sep = ""))
        
        if (length(object1@difftime_units) > 0) {
            object1Res <- data.frame(c("", paste(object1@table[, "Mean"], " (", object1@table[, "S Dev"], " ", object1@difftime_units, ")", sep = "")), stringsAsFactors = FALSE)
        } else {
            object1Res <- data.frame(c("", paste(object1@table[, "Mean"], " (", object1@table[, "S Dev"], ")", sep = "")), stringsAsFactors = FALSE)
        }       
        
        if (length(object2@difftime_units) > 0) {
            object2Res <- data.frame(c("", paste(object2@table[, "Mean"], " (", object1@table[, "S Dev"], " ", object2@difftime_units, ")", sep = "")), stringsAsFactors = FALSE)
        } else {
            object2Res <- data.frame(c("", paste(object2@table[, "Mean"], " (", object2@table[, "S Dev"], ")", sep = "")), stringsAsFactors = FALSE)
        }           
        
        res <- cbind(object1Res, object2Res)
        res <- cbind(rnames, res)
        
        colnames(res) <- c("", paste(object1@xLab, ", Mean (SD)", sep = ""), paste(object2@xLab, ", Mean (SD)", sep = ""))
        
        return(res)
    }
)

cars_bivariate_data_summary <- rbind(
    bivariate_data_summary(ctyBymanuSummary, hwyBymanuSummary),
    bivariate_data_summary(ctyBymodelSummary, hwyBymodelSummary),
    bivariate_data_summary(ctyByYearSummary, hwyByYearSummary),
    bivariate_data_summary(ctyByCylSummary, hwyByCylSummary),
    bivariate_data_summary(ctyBytransSummary, hwyBytransSummary),
    bivariate_data_summary(ctyByDrvSummary, hwyByDrvSummary),
    bivariate_data_summary(ctyByflSummary, hwyByflSummary),
    bivariate_data_summary(ctyByclassSummary, hwyByclassSummary),
    bivariate_data_summary(ctyByPartySummary, hwyByPartySummary),
    bivariate_data_summary(ctyByCommentsSummary, hwyByCommentsSummary),
    bivariate_data_summary(ctyByMissSummary, hwyByMissSummary)
)

kable(cars_bivariate_data_summary, caption = "By data summaries of miles per gallons for city and highway.", booktabs = TRUE, escape = FALSE, format = "html", table.attr = "data-quarto-disable-processing=true") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = TRUE, font_size = 14)

By data summaries of miles per gallons for city and highway.

	city miles per gallon, Mean (SD)	highway miles per gallon, Mean (SD)
manufacturer
audi	17.61 (1.97)	26.44 (2.18)
chevrolet	15 (2.92)	21.89 (5.11)
dodge	13.14 (2.49)	17.95 (3.57)
ford	14 (1.91)	19.36 (3.33)
honda	24.44 (1.94)	32.56 (2.55)
hyundai	18.64 (1.5)	26.86 (2.18)
jeep	13.5 (2.51)	17.62 (3.25)
land rover	11.5 (0.58)	16.5 (1.73)
lincoln	11.33 (0.58)	17 (1)
mercury	13.25 (0.5)	18 (1.15)
nissan	18.08 (3.43)	24.62 (5.09)
pontiac	17 (1)	26.4 (1.14)
subaru	19.29 (0.91)	25.57 (1.16)
toyota	18.53 (4.05)	24.91 (6.17)
volkswagen	20.93 (4.56)	29.22 (5.32)
Overall	16.86 (4.26)	23.44 (5.95)
model name
4runner 4wd	15.17 (0.75)	18.83 (1.47)
a4	18.86 (1.86)	28.29 (1.98)
a4 quattro	17.12 (1.81)	25.75 (1.16)
a6 quattro	16 (1)	24 (1)
altima	20.67 (1.97)	28.67 (2.42)
c1500 suburban 2wd	12.8 (1.3)	17.8 (2.17)
camry	19.86 (1.46)	28.29 (2.14)
camry solara	19.86 (1.77)	28.14 (2.19)
caravan 2wd	15.82 (1.83)	22.36 (2.06)
civic	24.44 (1.94)	32.56 (2.55)
corolla	25.6 (1.67)	34 (2.65)
corvette	15.4 (0.55)	24.8 (1.3)
dakota pickup 4wd	12.78 (1.99)	17 (2.29)
durango 4wd	11.86 (1.57)	16 (2)
expedition 2wd	11.33 (0.58)	17.33 (0.58)
explorer 4wd	13.67 (0.82)	18 (1.1)
f150 pickup 4wd	13 (1)	16.43 (0.79)
forester awd	18.83 (0.98)	25 (1.41)
grand cherokee 4wd	13.5 (2.51)	17.62 (3.25)
grand prix	17 (1)	26.4 (1.14)
gti	20 (2)	27.4 (2.3)
impreza awd	19.62 (0.74)	26 (0.76)
jetta	21.22 (4.87)	29.11 (6.07)
k1500 tahoe 4wd	12.5 (1.73)	16.25 (2.22)
land cruiser wagon 4wd	12 (1.41)	16.5 (2.12)
malibu	18.8 (1.92)	27.6 (1.82)
maxima	18.67 (0.58)	25.33 (0.58)
mountaineer 4wd	13.25 (0.5)	18 (1.15)
mustang	15.89 (1.45)	23.22 (2.17)
navigator 2wd	11.33 (0.58)	17 (1)
new beetle	24 (6.51)	32.83 (7.63)
passat	18.57 (1.9)	27.57 (1.51)
pathfinder 4wd	13.75 (1.26)	18 (1.41)
ram 1500 pickup 4wd	11.4 (1.51)	15.3 (1.89)
range rover	11.5 (0.58)	16.5 (1.73)
sonata	19 (1.41)	27.71 (2.06)
tiburon	18.29 (1.6)	26 (2.08)
toyota tacoma 4wd	15.57 (0.79)	19.43 (1.62)
Overall	16.86 (4.26)	23.44 (5.95)
year of manufacture
1999	17.02 (4.46)	23.43 (6.08)
2008	16.7 (4.06)	23.45 (5.85)
Overall	16.86 (4.26)	23.44 (5.95)
number of cylinders
4	21.01 (3.5)	28.8 (4.52)
5	20.5 (0.58)	28.75 (0.5)
6	16.22 (1.77)	22.82 (3.69)
8	12.57 (1.81)	17.63 (3.26)
Overall	16.86 (4.26)	23.44 (5.95)
type of transmission
auto(av)	20 (2)	27.8 (2.59)
auto(l3)	21 (4.24)	27 (4.24)
auto(l4)	15.94 (3.98)	21.96 (5.64)
auto(l5)	14.72 (3.49)	20.72 (6.04)
auto(l6)	13.67 (1.86)	20 (2.37)
auto(s4)	18.67 (2.31)	25.67 (1.15)
auto(s5)	17.33 (5.03)	25.33 (6.66)
auto(s6)	17.38 (3.22)	25.19 (3.99)
manual(m5)	19.26 (4.56)	26.29 (5.99)
manual(m6)	16.89 (3.83)	24.21 (5.75)
Overall	16.86 (4.26)	23.44 (5.95)
drive type
front-wheel drive	19.97 (3.63)	28.16 (4.21)
rear wheel drive	14.08 (2.22)	21 (3.66)
4wd	14.33 (2.87)	19.17 (4.08)
Overall	16.86 (4.26)	23.44 (5.95)
fuel type
c	24 (NA)	36 (NA)
d	25.6 (9.53)	33.6 (13.05)
e	9.75 (1.04)	13.25 (1.91)
p	17.37 (3.04)	25.23 (3.93)
r	16.74 (3.89)	22.99 (5.51)
Overall	16.86 (4.26)	23.44 (5.95)
type of car
2seater	15.4 (0.55)	24.8 (1.3)
compact	20.13 (3.39)	28.3 (3.78)
midsize	18.76 (1.95)	27.29 (2.14)
minivan	15.82 (1.83)	22.36 (2.06)
pickup	13 (2.05)	16.88 (2.27)
subcompact	20.37 (4.6)	28.14 (5.38)
suv	13.5 (2.42)	18.13 (2.98)
Overall	16.86 (4.26)	23.44 (5.95)
some random political parties
republican	17.29 (4.52)	23.57 (6.42)
democrat	16.26 (4.59)	22.38 (6.25)
independent	16.84 (3.39)	23.68 (4.83)
Overall	16.86 (4.26)	23.44 (5.95)
R NA Value	17.11 (4.5)	24.22 (6.26)
some random comments
.	15.42 (3.94)	22.08 (5.6)
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	17.04 (3.23)	24.09 (4.95)
Does it also fly?	17.94 (5.52)	24.75 (7.33)
Does it come in green?	18.39 (4.31)	25.26 (5.5)
I like this car!	17.92 (5.16)	24.75 (7.25)
Meh.	16.33 (3.4)	22.61 (4.58)
Missing	15.84 (5.16)	21.96 (7.33)
This is the worst car ever!	17.09 (4)	23.82 (5.78)
want cheese flavoured cars.	16.91 (3.95)	23.33 (5.85)
Overall	16.86 (4.26)	23.44 (5.95)
R NA Value	16.17 (3.34)	22.33 (4.72)
an all missing variable
Overall	16.86 (4.26)	23.44 (5.95)
R NA Value	16.86 (4.26)	23.44 (5.95)

cars_bivariate_time_data_summary <- rbind(
    bivariate_data_summary(ctyByPartySummary, rdifftimeByPartySummary),
    bivariate_data_summary(ctyByCommentsSummary, rdifftimeByCommentsSummary)
)

kable(cars_bivariate_time_data_summary, caption = "By data summaries of miles per gallons for city and random difference in time.", booktabs = TRUE, escape = FALSE, format = "html", table.attr = "data-quarto-disable-processing=true") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = TRUE, font_size = 14)

By data summaries of miles per gallons for city and random difference in time.

	city miles per gallon, Mean (SD)	some random numbers that are generated from a normal distrubtion with mean = 10 and sd = 5, and then converted to weeks, Mean (SD)
some random political parties
republican	17.29 (4.52)	9.5 (4.52 weeks)
democrat	16.26 (4.59)	9.31 (4.59 weeks)
independent	16.84 (3.39)	10.22 (3.39 weeks)
Overall	16.86 (4.26)	9.76 (4.26 weeks)
R NA Value	17.11 (4.5)	10.08 (4.5 weeks)
some random comments
.	15.42 (3.94)	8.21 (3.94 weeks)
Blah, Blah, Blah, Blah, Blah, Blah, Blah, Blah	17.04 (3.23)	8.32 (3.23 weeks)
Does it also fly?	17.94 (5.52)	9.49 (5.52 weeks)
Does it come in green?	18.39 (4.31)	8.51 (4.31 weeks)
I like this car!	17.92 (5.16)	10.44 (5.16 weeks)
Meh.	16.33 (3.4)	10.21 (3.4 weeks)
Missing	15.84 (5.16)	11.44 (5.16 weeks)
This is the worst car ever!	17.09 (4)	9.28 (4 weeks)
want cheese flavoured cars.	16.91 (3.95)	10.88 (3.95 weeks)
Overall	16.86 (4.26)	9.76 (4.26 weeks)
R NA Value	16.17 (3.34)	10.69 (3.34 weeks)

R Session Information

sessionInfo()

R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin24.4.0
Running under: macOS Tahoe 26.0.1

Matrix products: default
BLAS:   /opt/homebrew/Cellar/openblas/0.3.30/lib/libopenblasp-r0.3.30.dylib 
LAPACK: /opt/homebrew/Cellar/r/4.5.1/lib/R/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] dplyr_1.1.4      reshape2_1.4.4   Hmisc_5.2-4      ggplot2_4.0.0   
[5] kableExtra_1.4.0 bookdown_0.45    knitr_1.50      

loaded via a namespace (and not attached):
 [1] generics_0.1.4     renv_1.0.10        xml2_1.4.0         stringi_1.8.7     
 [5] digest_0.6.37      magrittr_2.0.4     evaluate_1.0.5     grid_4.5.1        
 [9] RColorBrewer_1.1-3 fastmap_1.2.0      plyr_1.8.9         jsonlite_2.0.0    
[13] nnet_7.3-20        backports_1.5.0    Formula_1.2-5      gridExtra_2.3     
[17] viridisLite_0.4.2  scales_1.4.0       textshaping_1.0.4  cli_3.6.5         
[21] rlang_1.1.6        base64enc_0.1-3    withr_3.0.2        yaml_2.3.10       
[25] tools_4.5.1        checkmate_2.3.3    htmlTable_2.4.3    colorspace_2.1-2  
[29] vctrs_0.6.5        R6_2.6.1           rpart_4.1.24       lifecycle_1.0.4   
[33] stringr_1.5.2      htmlwidgets_1.6.4  foreign_0.8-90     cluster_2.1.8.1   
[37] pkgconfig_2.0.3    pillar_1.11.1      gtable_0.3.6       Rcpp_1.1.0        
[41] glue_1.8.0         data.table_1.17.8  systemfonts_1.3.1  xfun_0.53         
[45] tibble_3.3.0       tidyselect_1.2.1   rstudioapi_0.17.1  farver_2.1.2      
[49] htmltools_0.5.8.1  labeling_0.4.3     rmarkdown_2.30     svglite_2.2.1     
[53] compiler_4.5.1     S7_0.2.0          

References

Harrell Jr, Frank E, with contributions from Charles Dupont, and many others. 2019. Hmisc: Harrell Miscellaneous. https://CRAN.R-project.org/package=Hmisc.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wickham, Hadley. 2007. “Reshaping Data with the reshape Package.” Journal of Statistical Software 21 (12): 1–20. http://www.jstatsoft.org/v21/i12/.

———. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2019. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.

Xie, Yihui. 2018. Bookdown: Authoring Books and Technical Documents with r Markdown. https://github.com/rstudio/bookdown.

———. 2019. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.name/knitr/.

Zhu, Hao. 2019. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.