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