You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

739 lines
26 KiB

# ==================================================================== #
# 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}} 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 for factors.
#' @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 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 print table in markdown format (this forces \code{nmax = NA})
#' @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 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 %>% select pull n_distinct group_by arrange desc mutate summarise n_distinct tibble
#' @importFrom utils browseVignettes installed.packages
#' @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 when you're used to 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)
#'
#' # 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$trsu))
#'
#' \dontrun{
#' # send frequency table to clipboard (e.g. for pasting in Excel)
#' septic_patients %>%
#' freq(age) %>%
#' format() %>% # this will format the percentages
#' clipboard_export()
#' }
frequency_tbl <- function(x,
...,
sort.count = TRUE,
nmax = getOption("max.print.freq"),
na.rm = TRUE,
row.names = TRUE,
markdown = FALSE,
digits = 2,
quote = FALSE,
sep = " ") {
mult.columns <- 0
x.name <- NULL
cols <- NULL
if (any(class(x) == 'list')) {
cols <- names(x)
x <- as.data.frame(x, stringsAsFactors = FALSE)
x.name <- "a list"
} else if (any(class(x) == 'matrix')) {
x <- as.data.frame(x, stringsAsFactors = FALSE)
x.name <- "a matrix"
cols <- colnames(x)
if (all(cols %like% 'V[0-9]')) {
cols <- NULL
}
}
if (any(class(x) == 'data.frame')) {
if (is.null(x.name)) {
x.name <- deparse(substitute(x))
}
if (x.name == ".") {
x.name <- NULL
}
dots <- base::eval(base::substitute(base::alist(...)))
ndots <- length(dots)
if (ndots < 10) {
cols <- as.character(dots)
if (!all(cols %in% colnames(x))) {
stop("one or more columns not found: `", paste(cols, collapse = "`, `"), '`', call. = FALSE)
}
if (length(cols) > 0) {
x <- x[, cols]
}
} else if (ndots >= 10) {
stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
} else {
cols <- NULL
}
} else if (any(class(x) == 'table')) {
if (!"tidyr" %in% rownames(installed.packages())) {
stop('transformation from `table` to frequency table requires the tidyr package.', call. = FALSE)
}
x <- x %>%
as.data.frame(stringsAsFactors = FALSE) %>%
# paste first two columns
tidyr::unite(col = "Pasted", 1:2, sep = sep, remove = TRUE)
x <- rep(x %>% pull(Pasted), x %>% pull(Freq))
x.name <- "a `table` object"
cols <- NULL
mult.columns <- 2
} else {
x.name <- NULL
cols <- NULL
}
if (!is.null(ncol(x))) {
if (ncol(x) == 1 & any(class(x) == 'data.frame')) {
x <- x %>% pull(1)
} else if (ncol(x) < 10) {
mult.columns <- ncol(x)
colnames(x) <- LETTERS[1:ncol(x)]
if (ncol(x) == 2) {
x$total <- paste(x$A %>% as.character(),
x$B %>% as.character(),
sep = sep)
} else if (ncol(x) == 3) {
x$total <- paste(x$A %>% as.character(),
x$B %>% as.character(),
x$C %>% as.character(),
sep = sep)
} else if (ncol(x) == 4) {
x$total <- paste(x$A %>% as.character(),
x$B %>% as.character(),
x$C %>% as.character(),
x$D %>% as.character(),
sep = sep)
} else if (ncol(x) == 5) {
x$total <- paste(x$A %>% as.character(),
x$B %>% as.character(),
x$C %>% as.character(),
x$D %>% as.character(),
x$E %>% as.character(),
sep = sep)
} else if (ncol(x) == 6) {
x$total <- paste(x$A %>% as.character(),
x$B %>% as.character(),
x$C %>% as.character(),
x$D %>% as.character(),
x$E %>% as.character(),
x$F %>% as.character(),
sep = sep)
} else if (ncol(x) == 7) {
x$total <- paste(x$A %>% as.character(),
x$B %>% as.character(),
x$C %>% as.character(),
x$D %>% as.character(),
x$E %>% as.character(),
x$F %>% as.character(),
x$G %>% as.character(),
sep = sep)
} else if (ncol(x) == 8) {
x$total <- paste(x$A %>% as.character(),
x$B %>% as.character(),
x$C %>% as.character(),
x$D %>% as.character(),
x$E %>% as.character(),
x$F %>% as.character(),
x$G %>% as.character(),
x$H %>% as.character(),
sep = sep)
} else if (ncol(x) == 9) {
x$total <- paste(x$A %>% as.character(),
x$B %>% as.character(),
x$C %>% as.character(),
x$D %>% as.character(),
x$E %>% as.character(),
x$F %>% as.character(),
x$G %>% as.character(),
x$H %>% as.character(),
x$I %>% as.character(),
sep = sep)
}
x <- x$total
} 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 (missing(sort.count) & 'factor' %in% class(x)) {
# sort on factor level at default when x is a factor and sort.count is not set
sort.count <- FALSE
}
header <- character(0)
markdown_line <- ''
if (markdown == TRUE) {
markdown_line <- '\n'
}
x_align <- 'l'
if (mult.columns > 0) {
header <- header %>% paste0(markdown_line, 'Columns: ', mult.columns)
} else {
header <- header %>% paste0(markdown_line, 'Class: ', class(x) %>% rev() %>% paste(collapse = " > "))
if (!mode(x) %in% class(x)) {
header <- header %>% paste0(" (", mode(x), ")")
}
}
header <- header %>% paste0(markdown_line, '\nLength: ', (NAs %>% length() + x %>% length()) %>% format(),
' (of which NA: ', NAs %>% length() %>% format(),
' = ', (NAs %>% length() / (NAs %>% length() + x %>% length())) %>% percent(force_zero = TRUE, round = digits) %>% sub('NaN', '0', ., fixed = TRUE), ')')
header <- header %>% paste0(markdown_line, '\nUnique: ', x %>% n_distinct() %>% format())
if (NROW(x) > 0 & any(class(x) == "character")) {
header <- header %>% paste0('\n')
header <- header %>% paste0(markdown_line, '\nShortest: ', x %>% base::nchar() %>% base::min(na.rm = TRUE))
header <- header %>% paste0(markdown_line, '\nLongest: ', x %>% base::nchar() %>% base::max(na.rm = TRUE))
}
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 <- header %>% paste0('\n')
header <- header %>% paste(markdown_line, '\nMean: ', x %>% base::mean(na.rm = TRUE) %>% format(digits = digits))
header <- header %>% 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 <- header %>% 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)$out)
header <- header %>% paste0(markdown_line, '\nOutliers: ', outlier_length)
if (outlier_length > 0) {
header <- header %>% paste0(' (unique: ', boxplot.stats(x)$out %>% n_distinct(), ')')
}
}
if (NROW(x) > 0 & any(class(x) == "rsi")) {
header <- header %>% paste0('\n')
cnt_S <- sum(x == "S")
cnt_I <- sum(x == "I")
cnt_R <- sum(x == "R")
header <- header %>% paste(markdown_line, '\n%IR: ',
((cnt_I + cnt_R) / sum(!is.na(x))) %>% percent(force_zero = TRUE, round = digits))
header <- header %>% paste0(markdown_line, '\nRatio SIR: 1.0 : ',
(cnt_I / cnt_S) %>% format(digits = 1, nsmall = 1), " : ",
(cnt_R / cnt_S) %>% format(digits = 1, nsmall = 1))
}
formatdates <- "%e %B %Y" # = d mmmm yyyy
if (any(class(x) == 'hms')) {
x <- x %>% as.POSIXlt()
formatdates <- "%H:%M:%S"
}
if (NROW(x) > 0 & any(class(x) %in% c('Date', 'POSIXct', 'POSIXlt'))) {
header <- header %>% 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 <- header %>% paste0(markdown_line, '\nEarliest: ', mindate %>% format(formatdates) %>% trimws())
header <- header %>% paste0(markdown_line, '\nLatest: ', maxdate %>% format(formatdates) %>% trimws(),
' (+', difftime(maxdate, mindate, units = 'mins') %>% as.double() %>% format(digits = digits), ' min.)')
} else {
# other date formats
header <- header %>% paste0(markdown_line, '\nOldest: ', mindate %>% format(formatdates) %>% trimws())
header <- header %>% paste0(markdown_line, '\nNewest: ', maxdate %>% format(formatdates) %>% trimws(),
' (+', difftime(maxdate, mindate, units = 'auto') %>% as.double() %>% format(digits = digits), ')')
}
header <- header %>% 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)
}
# create table with counts and percentages
column_names <- c('Item', 'Count', 'Percent', 'Cum. Count', 'Cum. Percent', '(Factor Level)')
column_names_df <- c('item', 'count', 'percent', 'cum_count', 'cum_percent', 'factor_level')
if (any(class(x) == 'factor')) {
df <- tibble(item = x,
fctlvl = x %>% as.integer()) %>%
group_by(item, fctlvl)
column_align <- c('l', 'r', 'r', 'r', 'r', 'r')
} else {
df <- tibble(item = x) %>%
group_by(item)
# strip factor lvl from col names
column_names <- column_names[1:length(column_names) - 1]
column_names_df <- column_names_df[1:length(column_names_df) - 1]
column_align <- c(x_align, 'r', 'r', 'r', 'r')
}
df <- df %>% summarise(count = n())
if (df$item %>% paste(collapse = ',') %like% '\033') {
# remove escape char
# see https://en.wikipedia.org/wiki/Escape_character#ASCII_escape_character
df <- df %>% mutate(item = item %>% gsub('\033', ' ', ., fixed = TRUE))
}
# sort according to setting
if (sort.count == TRUE) {
df <- df %>% arrange(desc(count), item)
} else {
if (any(class(x) == 'factor')) {
df <- df %>% arrange(fctlvl, item)
} else {
df <- df %>% arrange(item)
}
}
if (quote == TRUE) {
df$item <- paste0('"', df$item, '"')
}
df <- as.data.frame(df, stringsAsFactors = FALSE)
df$percent <- df$count / base::sum(df$count, na.rm = TRUE)
df$cum_count <- base::cumsum(df$count)
df$cum_percent <- df$cum_count / base::sum(df$count, na.rm = TRUE)
if (any(class(x) == 'factor')) {
# put factor last
df <- df %>% select(item, count, percent, cum_count, cum_percent, fctlvl)
}
colnames(df) <- column_names_df
class(df) <- c('frequency_tbl', class(df))
attr(df, 'package') <- 'AMR'
if (markdown == TRUE) {
tbl_format <- 'markdown'
} else {
tbl_format <- 'pandoc'
}
attr(df, 'opt') <- list(data = x.name,
vars = cols,
header = header,
row_names = row.names,
column_names = column_names,
column_align = column_align,
tbl_format = tbl_format,
nmax = nmax,
nmax.set = nmax.set)
df
}
#' @rdname freq
#' @export
freq <- frequency_tbl
#' @rdname freq
#' @export
#' @importFrom dplyr top_n pull
top_freq <- function(f, n) {
if (!'frequency_tbl' %in% class(f)) {
stop('top_freq can only be applied to frequency tables', call. = FALSE)
}
if (!is.numeric(n) | length(n) != 1L) {
stop('For top_freq, `nmax` must be a number of length 1', call. = FALSE)
}
top <- f %>% top_n(n, count)
vect <- top %>% pull(item)
names(vect) <- top %>% pull(count)
if (length(vect) > abs(n)) {
message("top_freq: selecting ", length(vect), " items instead of ", abs(n), ", because of ties")
}
vect
}
#' @noRd
#' @exportMethod diff.frequency_tbl
#' @importFrom dplyr %>% full_join mutate
#' @export
diff.frequency_tbl <- function(x, y, ...) {
# check classes
if (!"frequency_tbl" %in% class(x)
| !"frequency_tbl" %in% class(y)) {
stop("Both x and y must be a frequency table.")
}
x.attr <- attributes(x)$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)))
cat("Differences between frequency tables")
print(
knitr::kable(x,
format = x.attr$tbl_format,
col.names = c("Item", "Count #1", "Count #2", "Difference", "Diff. percent"),
align = "lrrrr",
padding = 1)
)
}
#' @rdname freq
#' @exportMethod print.frequency_tbl
#' @importFrom knitr kable
#' @importFrom dplyr n_distinct
#' @export
print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default = 15), ...) {
opt <- attr(x, 'opt')
if (length(opt$vars) == 0) {
opt$vars <- NULL
}
if (!is.null(opt$data) & !is.null(opt$vars)) {
title <- paste0("of `", paste0(opt$vars, collapse = "` and `"), "` from ", opt$data)
} else if (!is.null(opt$data) & is.null(opt$vars)) {
title <- paste("of", opt$data)
} else if (is.null(opt$data) & !is.null(opt$vars)) {
title <- paste0("of `", paste0(opt$vars, collapse = "` and `"), "`")
} else {
title <- ""
}
if (!missing(nmax)) {
opt$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"
}
}
cat("Frequency table", title, "\n")
if (!is.null(opt$header)) {
cat(opt$header)
}
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
options(knitr.kable.NA = "<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$nmax),
' entries, n = ',
format(x.unprinted),
' (',
(x.unprinted / (x.unprinted + x.printed)) %>% percent(force_zero = TRUE),
') ]\n', sep = '')
} else {
footer <- NULL
}
if (any(class(x$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)
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)
}
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, ...) {
opt <- attr(x, 'opt')
if (!is.null(opt$vars)) {
title <- opt$vars
} else {
title <- ""
}
hist(as.vector(x), main = paste("Histogram of", title), 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, ...)
}