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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# AUTHORS #
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# LICENCE #
# This program is free software; you can redistribute it and/or modify #
# it under the terms of the GNU General Public License version 2.0, #
# as published by the Free Software Foundation. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# ==================================================================== #
#' Frequency table
#'
#' Create a frequency table of a vector with items or a data frame. Supports quasiquotation and markdown for reports. \code{top_freq} can be used to get the top/bottom \emph{n} items of a frequency table, with counts as names.
#' @param x vector of any class or a \code{\link{data.frame}}, \code{\link{tibble}} (may contain a grouping variable) or \code{\link{table}}
#' @param ... up to nine different columns of \code{x} when \code{x} is a \code{data.frame} or \code{tibble}, to calculate frequencies from - see Examples
#' @param sort.count sort on count, i.e. frequencies. This will be \code{TRUE} at default for everything except when using grouping variables.
#' @param nmax number of row to print. The default, \code{15}, uses \code{\link{getOption}("max.print.freq")}. Use \code{nmax = 0}, \code{nmax = Inf}, \code{nmax = NULL} or \code{nmax = NA} to print all rows.
#' @param na.rm a logical value indicating whether \code{NA} values should be removed from the frequency table. The header (if set) will always print the amount of \code{NA}s.
#' @param row.names a logical value indicating whether row indices should be printed as \code{1:nrow(x)}
#' @param markdown a logical value indicating whether the frequency table should be printed in markdown format. This will print all rows and is default behaviour in non-interactive R sessions (like when knitting RMarkdown files).
#' @param digits how many significant digits are to be used for numeric values in the header (not for the items themselves, that depends on \code{\link{getOption}("digits")})
#' @param quote a logical value indicating whether or not strings should be printed with surrounding quotes
#' @param header a logical value indicating whether an informative header should be printed
#' @param title text to show above frequency table, at default to tries to coerce from the variables passed to \code{x}
#' @param na a character string to should be used to show empty (\code{NA}) values (only useful when \code{na.rm = FALSE})
#' @param sep a character string to separate the terms when selecting multiple columns
#' @param f a frequency table
#' @param n number of top \emph{n} items to return, use -n for the bottom \emph{n} items. It will include more than \code{n} rows if there are ties.
#' @details Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the `freq` function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. This package also has a vignette available to explain the use of this function further, run \code{browseVignettes("AMR")} to read it.
#'
#' For numeric values of any class, these additional values will all be calculated with \code{na.rm = TRUE} and shown into the header:
#' \itemize{
#' \item{Mean, using \code{\link[base]{mean}}}
#' \item{Standard Deviation, using \code{\link[stats]{sd}}}
#' \item{Coefficient of Variation (CV), the standard deviation divided by the mean}
#' \item{Mean Absolute Deviation (MAD), using \code{\link[stats]{mad}}}
#' \item{Tukey Five-Number Summaries (minimum, Q1, median, Q3, maximum), using \code{\link[stats]{fivenum}}}
#' \item{Interquartile Range (IQR) calculated as \code{Q3 - Q1} using the Tukey Five-Number Summaries, i.e. \strong{not} using the \code{\link[stats]{quantile}} function}
#' \item{Coefficient of Quartile Variation (CQV, sometimes called coefficient of dispersion), calculated as \code{(Q3 - Q1) / (Q3 + Q1)} using the Tukey Five-Number Summaries}
#' \item{Outliers (total count and unique count), using \code{\link[grDevices]{boxplot.stats}}}
#' }
#'
#' For dates and times of any class, these additional values will be calculated with \code{na.rm = TRUE} and shown into the header:
#' \itemize{
#' \item{Oldest, using \code{\link{min}}}
#' \item{Newest, using \code{\link{max}}, with difference between newest and oldest}
#' \item{Median, using \code{\link[stats]{median}}, with percentage since oldest}
#' }
#'
#'
#' The function \code{top_freq} uses \code{\link[dplyr]{top_n}} internally and will include more than \code{n} rows if there are ties.
#' @importFrom stats fivenum sd mad
#' @importFrom grDevices boxplot.stats
#' @importFrom dplyr %>% arrange arrange_at desc filter_at funs group_by mutate mutate_at n_distinct pull select summarise tibble ungroup vars all_vars
#' @importFrom utils browseVignettes
#' @importFrom hms is.hms
#' @importFrom crayon red green silver
#' @keywords summary summarise frequency freq
#' @rdname freq
#' @name freq
#' @return A \code{data.frame} with an additional class \code{"frequency_tbl"}
#' @export
#' @examples
#' library(dplyr)
#'
#' # this all gives the same result:
#' freq(septic_patients$hospital_id)
#' freq(septic_patients[, "hospital_id"])
#' septic_patients$hospital_id %>% freq()
#' septic_patients[, "hospital_id"] %>% freq()
#' septic_patients %>% freq("hospital_id")
#' septic_patients %>% freq(hospital_id) #<- easiest to remember (tidyverse)
#'
#' # you could also use `select` or `pull` to get your variables
#' septic_patients %>%
#' filter(hospital_id == "A") %>%
#' select(mo) %>%
#' freq()
#'
#' # multiple selected variables will be pasted together
#' septic_patients %>%
#' left_join_microorganisms %>%
#' filter(hospital_id == "A") %>%
#' freq(genus, species)
#'
#' # group a variable and analyse another
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' freq(gender)
#'
#' # get top 10 bugs of hospital A as a vector
#' septic_patients %>%
#' filter(hospital_id == "A") %>%
#' freq(mo) %>%
#' top_freq(10)
#'
#' # save frequency table to an object
#' years <- septic_patients %>%
#' mutate(year = format(date, "%Y")) %>%
#' freq(year)
#'
#' # show only the top 5
#' years %>% print(nmax = 5)
#'
#' # save to an object with formatted percentages
#' years <- format(years)
#'
#' # print a histogram of numeric values
#' septic_patients %>%
#' freq(age) %>%
#' hist()
#'
#' # or print all points to a regular plot
#' septic_patients %>%
#' freq(age) %>%
#' plot()
#'
#' # transform to a data.frame or tibble
#' septic_patients %>%
#' freq(age) %>%
#' as.data.frame()
#'
#' # or transform (back) to a vector
#' septic_patients %>%
#' freq(age) %>%
#' as.vector()
#'
#' identical(septic_patients %>%
#' freq(age) %>%
#' as.vector() %>%
#' sort(),
#' sort(septic_patients$age)) # TRUE
#'
#' # it also supports `table` objects:
#' table(septic_patients$gender,
#' septic_patients$age) %>%
#' freq(sep = " **sep** ")
#'
#' # check differences between frequency tables
#' diff(freq(septic_patients$trim),
#' freq(septic_patients$trsu))
frequency_tbl <- function(x,
...,
sort.count = TRUE,
nmax = getOption("max.print.freq"),
na.rm = TRUE,
row.names = TRUE,
markdown = !interactive(),
digits = 2,
quote = FALSE,
header = !markdown,
title = NULL,
na = "<NA>",
sep = " ") {
mult.columns <- 0
x.group = character(0)
df <- NULL
x.name <- NULL
cols <- NULL
if (any(class(x) == 'list')) {
cols <- names(x)
x <- as.data.frame(x, stringsAsFactors = FALSE)
x.name <- "a list"
} else if (any(class(x) == 'matrix')) {
x <- as.data.frame(x, stringsAsFactors = FALSE)
x.name <- "a matrix"
cols <- colnames(x)
if (all(cols %like% 'V[0-9]')) {
cols <- NULL
}
}
if (any(class(x) == 'data.frame')) {
x.group <- group_vars(x)
if (length(x.group) > 1) {
x.group <- x.group[1L]
warning("freq supports one grouping variable, only `", x.group, "` will be kept.", call. = FALSE)
}
if (is.null(x.name)) {
x.name <- deparse(substitute(x))
}
if (x.name == ".") {
x.name <- NULL
}
dots <- base::eval(base::substitute(base::alist(...)))
ndots <- length(dots)
if (ndots < 10) {
cols <- as.character(dots)
if (!all(cols %in% colnames(x))) {
stop("one or more columns not found: `", paste(cols, collapse = "`, `"), '`', call. = FALSE)
}
if (length(x.group) > 0) {
x.group_cols <- c(x.group, cols)
df <- x %>%
group_by_at(vars(x.group_cols)) %>%
summarise(count = n())
if (na.rm == TRUE) {
df <- df %>% filter_at(vars(cols), all_vars(!is.na(.)))
}
if (!missing(sort.count)) {
if (sort.count == TRUE) {
df <- df %>% arrange_at(c(x.group, "count"), desc)
}
}
df <- df %>%
mutate(cum_count = cumsum(count))
df.topleft <- df[1, 1]
df <- df %>%
ungroup() %>%
# do not repeat group labels
mutate_at(vars(x.group), funs(ifelse(lag(.) == ., "", .)))
df[1, 1] <- df.topleft
colnames(df)[1:2] <- c("group", "item")
}
if (length(cols) > 0) {
x <- x[, cols]
}
} else if (ndots >= 10) {
stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
} else {
cols <- NULL
}
} else if (any(class(x) == 'table')) {
x <- as.data.frame(x, stringsAsFactors = FALSE)
# now this DF contains 3 columns: the 2 vars and a Freq column
# paste the first 2 cols and repeat them Freq times:
x <- rep(x = do.call(paste, c(x[colnames(x)[1:2]], sep = sep)),
times = x$Freq)
x.name <- "a `table` object"
cols <- NULL
#mult.columns <- 2
} else {
x.name <- NULL
cols <- NULL
}
if (!is.null(ncol(x))) {
if (ncol(x) == 1 & any(class(x) == 'data.frame')) {
x <- x %>% pull(1)
} else if (ncol(x) < 10) {
mult.columns <- ncol(x)
x <- do.call(paste, c(x[colnames(x)], sep = sep))
} else {
stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
}
}
if (mult.columns > 1) {
NAs <- x[is.na(x) | x == trimws(strrep('NA ', mult.columns))]
} else {
NAs <- x[is.na(x)]
}
if (na.rm == TRUE) {
x_class <- class(x)
x <- x[!x %in% NAs]
class(x) <- x_class
}
# if (sort.count == FALSE & 'factor' %in% class(x)) {
# warning("Sorting a factor sorts on factor level, not necessarily alphabetically.", call. = FALSE)
# }
header_txt <- character(0)
markdown_line <- ''
if (markdown == TRUE) {
markdown_line <- '\n'
}
x_align <- 'l'
if (mult.columns > 0) {
header_txt <- header_txt %>% paste0(markdown_line, 'Columns: ', mult.columns)
} else {
header_txt <- header_txt %>% paste0(markdown_line, 'Class: ', class(x) %>% rev() %>% paste(collapse = " > "))
if (!mode(x) %in% class(x)) {
header_txt <- header_txt %>% paste0(silver(paste0(" (", mode(x), ")")))
}
}
na_txt <- paste0(NAs %>% length() %>% format(), ' = ',
(NAs %>% length() / (NAs %>% length() + x %>% length())) %>% percent(force_zero = TRUE, round = digits) %>%
sub('NaN', '0', ., fixed = TRUE))
if (!na_txt %like% "^0 =") {
na_txt <- red(na_txt)
} else {
na_txt <- green(na_txt)
}
header_txt <- header_txt %>% paste0(markdown_line, '\nLength: ', (NAs %>% length() + x %>% length()) %>% format(),
' (of which NA: ', na_txt, ')')
header_txt <- header_txt %>% paste0(markdown_line, '\nUnique: ', x %>% n_distinct() %>% format())
if (NROW(x) > 0 & any(class(x) == "character")) {
header_txt <- header_txt %>% paste0('\n')
header_txt <- header_txt %>% paste0(markdown_line, '\nShortest: ', x %>% base::nchar() %>% base::min(na.rm = TRUE))
header_txt <- header_txt %>% paste0(markdown_line, '\nLongest: ', x %>% base::nchar() %>% base::max(na.rm = TRUE))
}
if (NROW(x) > 0 & any(class(x) == "difftime")) {
header_txt <- header_txt %>% paste0('\n')
header_txt <- header_txt %>% paste(markdown_line, '\nUnits: ', attributes(x)$units)
x <- as.double(x)
# after this, the numeric header_txt continues
}
if (NROW(x) > 0 & any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
# right align number
Tukey_five <- stats::fivenum(x, na.rm = TRUE)
x_align <- 'r'
header_txt <- header_txt %>% paste0('\n')
header_txt <- header_txt %>% paste(markdown_line, '\nMean: ', x %>% base::mean(na.rm = TRUE) %>% format(digits = digits))
header_txt <- header_txt %>% paste0(markdown_line, '\nStd. dev.: ', x %>% stats::sd(na.rm = TRUE) %>% format(digits = digits),
' (CV: ', x %>% cv(na.rm = TRUE) %>% format(digits = digits),
', MAD: ', x %>% stats::mad(na.rm = TRUE) %>% format(digits = digits), ')')
header_txt <- header_txt %>% paste0(markdown_line, '\nFive-Num: ', Tukey_five %>% format(digits = digits) %>% trimws() %>% paste(collapse = ' | '),
' (IQR: ', (Tukey_five[4] - Tukey_five[2]) %>% format(digits = digits),
', CQV: ', x %>% cqv(na.rm = TRUE) %>% format(digits = digits), ')')
outlier_length <- length(boxplot.stats(x)$out)
header_txt <- header_txt %>% paste0(markdown_line, '\nOutliers: ', outlier_length)
if (outlier_length > 0) {
header_txt <- header_txt %>% paste0(' (unique count: ', boxplot.stats(x)$out %>% n_distinct(), ')')
}
}
if (NROW(x) > 0 & any(class(x) == "rsi")) {
header_txt <- header_txt %>% paste0('\n')
cnt_S <- sum(x == "S", na.rm = TRUE)
cnt_IR <- sum(x %in% c("I", "R"), na.rm = TRUE)
header_txt <- header_txt %>% paste(markdown_line, '\n%IR: ',
(cnt_IR / sum(!is.na(x), na.rm = TRUE)) %>% percent(force_zero = TRUE, round = digits),
paste0('(ratio S : IR = 1.0 : ', (cnt_IR / cnt_S) %>% format(digits = 1, nsmall = 1), ")"))
if (NROW(x) < 30) {
header_txt <- header_txt %>% paste(markdown_line, red('\nToo few isolates for reliable resistance interpretation.'))
}
}
formatdates <- "%e %B %Y" # = d mmmm yyyy
if (is.hms(x)) {
x <- x %>% as.POSIXlt()
formatdates <- "%H:%M:%S"
}
if (NROW(x) > 0 & any(class(x) %in% c('Date', 'POSIXct', 'POSIXlt'))) {
header_txt <- header_txt %>% paste0('\n')
mindate <- x %>% min(na.rm = TRUE)
maxdate <- x %>% max(na.rm = TRUE)
maxdate_days <- difftime(maxdate, mindate, units = 'auto') %>% as.double()
mediandate <- x %>% median(na.rm = TRUE)
median_days <- difftime(mediandate, mindate, units = 'auto') %>% as.double()
if (formatdates == "%H:%M:%S") {
# hms
header_txt <- header_txt %>% paste0(markdown_line, '\nEarliest: ', mindate %>% format(formatdates) %>% trimws())
header_txt <- header_txt %>% paste0(markdown_line, '\nLatest: ', maxdate %>% format(formatdates) %>% trimws(),
' (+', difftime(maxdate, mindate, units = 'mins') %>% as.double() %>% format(digits = digits), ' min.)')
} else {
# other date formats
header_txt <- header_txt %>% paste0(markdown_line, '\nOldest: ', mindate %>% format(formatdates) %>% trimws())
header_txt <- header_txt %>% paste0(markdown_line, '\nNewest: ', maxdate %>% format(formatdates) %>% trimws(),
' (+', difftime(maxdate, mindate, units = 'auto') %>% as.double() %>% format(digits = digits), ')')
}
header_txt <- header_txt %>% paste0(markdown_line, '\nMedian: ', mediandate %>% format(formatdates) %>% trimws(),
' (~', percent(median_days / maxdate_days, round = 0), ')')
}
if (any(class(x) == 'POSIXlt')) {
x <- x %>% format(formatdates)
}
nmax.set <- !missing(nmax)
if (!nmax.set & is.null(nmax) & is.null(base::getOption("max.print.freq", default = NULL))) {
# default for max print setting
nmax <- 15
} else if (is.null(nmax)) {
nmax <- length(x)
}
if (nmax %in% c(0, Inf, NA, NULL)) {
nmax <- length(x)
}
column_names <- c('Item', 'Count', 'Percent', 'Cum. Count', 'Cum. Percent')
column_names_df <- c('item', 'count', 'percent', 'cum_count', 'cum_percent')
column_align <- c(x_align, 'r', 'r', 'r', 'r')
if (is.null(df)) {
# create table with counts and percentages
df <- tibble(item = x) %>%
group_by(item) %>%
summarise(count = n())
# sort according to setting
if (sort.count == TRUE) {
df <- df %>% arrange(desc(count), item)
} else {
df <- df %>% arrange(item)
}
} else {
column_names <- c("Group", column_names)
column_names_df <-c("group", column_names_df)
column_align <- c("l", column_align)
}
if (df$item %>% paste(collapse = ',') %like% '\033') {
# remove escape char
# see https://en.wikipedia.org/wiki/Escape_character#ASCII_escape_character
df <- df %>% mutate(item = item %>% gsub('\033', ' ', ., fixed = TRUE))
}
if (quote == TRUE) {
df$item <- paste0('"', df$item, '"')
if (length(x.group) != 0) {
df$group <- paste0('"', df$group, '"')
}
}
df <- as.data.frame(df, stringsAsFactors = FALSE)
df$percent <- df$count / base::sum(df$count, na.rm = TRUE)
if (length(x.group) == 0) {
df$cum_count <- base::cumsum(df$count)
}
df$cum_percent <- df$cum_count / base::sum(df$count, na.rm = TRUE)
if (length(x.group) != 0) {
# sort columns
df <- df[, column_names_df]
}
if (markdown == TRUE) {
tbl_format <- 'markdown'
} else {
tbl_format <- 'pandoc'
}
if (!is.null(title)) {
title <- trimws(gsub("^Frequency table of", "", title[1L], ignore.case = TRUE))
}
structure(.Data = df,
class = c('frequency_tbl', class(df)),
opt = list(title = title,
data = x.name,
vars = cols,
group_var = x.group,
header = header,
header_txt = header_txt,
row_names = row.names,
column_names = column_names,
column_align = column_align,
tbl_format = tbl_format,
na = na,
nmax = nmax,
nmax.set = nmax.set))
}
#' @rdname freq
#' @export
freq <- frequency_tbl
#' @rdname freq
#' @export
#' @importFrom dplyr top_n pull
top_freq <- function(f, n) {
if (!'frequency_tbl' %in% class(f)) {
stop('top_freq can only be applied to frequency tables', call. = FALSE)
}
if (!is.numeric(n) | length(n) != 1L) {
stop('For top_freq, `nmax` must be a number of length 1', call. = FALSE)
}
top <- f %>% top_n(n, count)
vect <- top %>% pull(item)
names(vect) <- top %>% pull(count)
if (length(vect) > abs(n)) {
message("top_freq: selecting ", length(vect), " items instead of ", abs(n), ", because of ties")
}
vect
}
#' @noRd
#' @exportMethod diff.frequency_tbl
#' @importFrom dplyr %>% full_join mutate
#' @export
diff.frequency_tbl <- function(x, y, ...) {
# check classes
if (!"frequency_tbl" %in% class(x)
| !"frequency_tbl" %in% class(y)) {
stop("Both x and y must be a frequency table.")
}
cat("Differences between frequency tables")
if (identical(x, y)) {
cat("\n\nNo differences found.\n")
return(invisible())
}
x.attr <- attributes(x)$opt
# only keep item and count
x <- x[, 1:2]
y <- y[, 1:2]
x <- x %>%
full_join(y,
by = colnames(x)[1],
suffix = c(".x", ".y")) %>%
mutate(
diff = case_when(
is.na(count.y) ~ -count.x,
is.na(count.x) ~ count.y,
TRUE ~ count.y - count.x)) %>%
mutate(
diff.percent = percent(
diff / count.x,
force_zero = TRUE)) %>%
mutate(diff = ifelse(diff %like% '^-',
diff,
paste0("+", diff)),
diff.percent = ifelse(diff.percent %like% '^-',
diff.percent,
paste0("+", diff.percent)))
print(
knitr::kable(x,
format = x.attr$tbl_format,
col.names = c("Item", "Count #1", "Count #2", "Difference", "Diff. percent"),
align = paste0(x.attr$column_align[1], "rrrr"),
padding = 1)
)
}
#' @rdname freq
#' @exportMethod print.frequency_tbl
#' @importFrom knitr kable
#' @importFrom dplyr n_distinct
#' @importFrom crayon bold silver
#' @export
print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default = 15),
markdown = !interactive(), header = !markdown, ...) {
opt <- attr(x, 'opt')
if (length(opt$vars) == 0) {
opt$vars <- NULL
}
if (is.null(opt$title)) {
if (!is.null(opt$data) & !is.null(opt$vars)) {
title <- paste0("`", paste0(opt$vars, collapse = "` and `"), "` from ", opt$data)
} else if (!is.null(opt$data) & is.null(opt$vars)) {
title <- opt$data
} else if (is.null(opt$data) & !is.null(opt$vars)) {
title <- paste0("`", paste0(opt$vars, collapse = "` and `"), "`")
} else {
title <- ""
}
if (title != "" & length(opt$group_var) != 0) {
group_var <- paste0("(grouped by `", opt$group_var, "`)")
if (opt$tbl_format == "pandoc") {
group_var <- silver(group_var)
}
title <- paste(title, group_var)
}
} else {
title <- opt$title
}
if (!missing(nmax)) {
opt$nmax <- nmax
opt$nmax.set <- TRUE
}
dots <- list(...)
if ("markdown" %in% names(dots)) {
if (dots$markdown == TRUE) {
opt$tbl_format <- "markdown"
} else {
opt$tbl_format <- "pandoc"
}
}
if (!missing(markdown)) {
opt$tbl_format <- "markdown"
}
if (!missing(header)) {
opt$header <- header
}
if (trimws(title) == "") {
title <- "Frequency table"
} else {
title <- paste("Frequency table of", trimws(title))
}
# bold title
if (opt$tbl_format == "pandoc") {
title <- bold(title)
} else if (opt$tbl_format == "markdown") {
title <- paste0("\n**", title, "**")
}
if (opt$header == TRUE) {
cat(title, "\n")
if (!is.null(opt$header_txt)) {
cat(opt$header_txt)
}
} else if (opt$tbl_format == "markdown") {
# do print title as caption in markdown
cat("\n", title, sep = "")
}
if (NROW(x) == 0) {
cat('\n\nNo observations.\n')
return(invisible())
}
if (all(x$count == 1)) {
warning('All observations are unique.', call. = FALSE)
}
# save old NA setting for kable
opt.old <- options()$knitr.kable.NA
if (is.null(opt$na)) {
opt$na <- "<NA>"
}
options(knitr.kable.NA = opt$na)
if (nrow(x) > opt$nmax & opt$tbl_format != "markdown") {
x.rows <- nrow(x)
x.unprinted <- base::sum(x[(opt$nmax + 1):nrow(x), 'count'], na.rm = TRUE)
x.printed <- base::sum(x$count) - x.unprinted
if (opt$nmax.set == TRUE) {
nmax <- opt$nmax
} else {
nmax <- getOption("max.print.freq", default = 15)
}
x <- x[1:nmax,]
if (opt$nmax.set == TRUE) {
footer <- paste('[ reached `nmax = ', opt$nmax, '`', sep = '')
} else {
footer <- '[ reached getOption("max.print.freq")'
}
footer <- paste(footer,
' -- omitted ',
format(x.rows - opt$nmax),
' entries, n = ',
format(x.unprinted),
' (',
(x.unprinted / (x.unprinted + x.printed)) %>% percent(force_zero = TRUE),
') ]\n', sep = '')
if (opt$tbl_format == "pandoc") {
footer <- silver(footer) # only silver in regular printing
}
} else {
footer <- NULL
}
if (any(class(x$item) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
x$item <- format(x$item)
}
x$count <- format(x$count)
x$percent <- percent(x$percent, force_zero = TRUE)
x$cum_count <- format(x$cum_count)
x$cum_percent <- percent(x$cum_percent, force_zero = TRUE)
if (opt$tbl_format == "markdown") {
cat("\n")
}
print(
knitr::kable(x,
format = opt$tbl_format,
row.names = opt$row_names,
col.names = opt$column_names,
align = opt$column_align,
padding = 1)
)
if (!is.null(footer)) {
cat(footer)
}
if (opt$tbl_format == "markdown") {
cat("\n\n")
} else {
cat('\n')
}
# reset old kable setting
options(knitr.kable.NA = opt.old)
return(invisible())
}
#' @noRd
#' @exportMethod as.data.frame.frequency_tbl
#' @export
as.data.frame.frequency_tbl <- function(x, ...) {
attr(x, 'package') <- NULL
attr(x, 'opt') <- NULL
as.data.frame.data.frame(x, ...)
}
#' @noRd
#' @exportMethod as_tibble.frequency_tbl
#' @export
#' @importFrom dplyr as_tibble
as_tibble.frequency_tbl <- function(x, validate = TRUE, ..., rownames = NA) {
attr(x, 'package') <- NULL
attr(x, 'opt') <- NULL
as_tibble(x = as.data.frame(x), validate = validate, ..., rownames = rownames)
}
#' @noRd
#' @exportMethod hist.frequency_tbl
#' @export
#' @importFrom graphics hist
hist.frequency_tbl <- function(x, breaks = "Sturges", main = NULL, ...) {
opt <- attr(x, 'opt')
if (!class(x$item) %in% c("numeric", "double", "integer", "Date")) {
stop("'x' must be numeric or Date.", call. = FALSE)
}
if (!is.null(opt$vars)) {
title <- opt$vars
} else if (!is.null(opt$data)) {
title <- opt$data
} else {
title <- "frequency table"
}
if (class(x$item) == "Date") {
x <- as.Date(as.vector(x), origin = "1970-01-01")
} else {
x <- as.vector(x)
}
if (is.null(main)) {
main <- paste("Histogram of", title)
}
hist(x, main = main, xlab = title, ...)
}
#' @noRd
#' @exportMethod plot.frequency_tbl
#' @export
plot.frequency_tbl <- function(x, y, ...) {
opt <- attr(x, 'opt')
if (!is.null(opt$vars)) {
title <- opt$vars
} else {
title <- ""
}
plot(x = x$item, y = x$count, ylab = "Count", xlab = title, ...)
}
#' @noRd
#' @exportMethod as.vector.frequency_tbl
#' @export
as.vector.frequency_tbl <- function(x, mode = "any") {
as.vector(rep(x$item, x$count), mode = mode)
}
#' @noRd
#' @exportMethod format.frequency_tbl
#' @export
format.frequency_tbl <- function(x, digits = 1, ...) {
opt <- attr(x, 'opt')
if (opt$nmax.set == TRUE) {
nmax <- opt$nmax
} else {
nmax <- getOption("max.print.freq", default = 15)
}
x <- x[1:nmax,]
x$percent <- percent(x$percent, round = digits, force_zero = TRUE)
x$cum_percent <- percent(x$cum_percent, round = digits, force_zero = TRUE)
base::format.data.frame(x, ...)
}
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