mirror of https://github.com/msberends/AMR.git
include IQR and MAD in freq
This commit is contained in:
parent
3d0d21f322
commit
10fce8382c
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@ -1,5 +1,6 @@
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# Generated by roxygen2: do not edit by hand
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S3method(as.data.frame,frequency_tbl)
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S3method(as.double,mic)
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S3method(as.integer,mic)
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S3method(as.numeric,mic)
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@ -53,6 +54,7 @@ export(rsi_df)
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export(rsi_predict)
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export(semi_join_microorganisms)
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export(top_freq)
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exportMethods(as.data.frame.frequency_tbl)
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exportMethods(as.double.mic)
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exportMethods(as.integer.mic)
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exportMethods(as.numeric.mic)
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@ -115,8 +117,8 @@ importFrom(rvest,html_node)
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importFrom(rvest,html_nodes)
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importFrom(rvest,html_table)
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importFrom(stats,fivenum)
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importFrom(stats,mad)
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importFrom(stats,pchisq)
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importFrom(stats,quantile)
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importFrom(stats,sd)
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importFrom(tibble,tibble)
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importFrom(utils,browseVignettes)
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1
NEWS.md
1
NEWS.md
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@ -2,6 +2,7 @@
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#### New
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* Function `top_freq` function to get the top/below *n* items of frequency tables
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* Vignette about frequency tables
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* Header of frequency tables now also show MAD and IQR
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* Possibility to globally set the default for the amount of items to print in frequency tables (`freq` function), with `options(max.print.freq = n)`
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* Functions `clipboard_import` and `clipboard_export` as helper functions to quickly copy and paste from/to software like Excel and SPSS
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* Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test)
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110
R/freq.R
110
R/freq.R
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@ -19,40 +19,43 @@
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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 with items, or \code{data.frame}
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#' @param x vector with items, or a \code{data.frame}
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#' @param ... up to nine different columns of \code{x} to calculate frequencies from, see Examples
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#' @param sort.count sort on count, i.e. frequencies. Use \code{FALSE} to sort alphabetically on item.
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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 = NULL} or \code{nmax = NA} to print all rows.
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#' @param na.rm a logical value indicating whether NA values should be removed from the frequency table. The header will always print the amount of \code{NA}s.
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#' @param sort.count sort on count, i.e. frequencies. This will be \code{TRUE} at default for everything except for factors.
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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 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 print table in markdown format (this forces \code{nmax = NA})
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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 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 This package also has a vignette available about this function, run: \code{browseVignettes("AMR")} to read it.
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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 be calculated and shown into the header:
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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{Five numbers of Tukey (min, Q1, median, Q3, max), using \code{\link[stats]{fivenum}}}
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#' \item{Outliers (total count and unique count), using \code{\link{boxplot.stats}}}
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#' \item{Coefficient of variation (CV), the standard deviation divided by the mean}
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#' \item{Coefficient of quartile variation (CQV, sometimes called coefficient of dispersion), calculated as \code{(Q3 - Q1) / (Q3 + Q1)} using \code{\link{quantile}} with \code{type = 6} as quantile algorithm to comply with SPSS standards}
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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 and shown into the header:
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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[base]{min}}}
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#' \item{Newest, using \code{\link[base]{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 quantile
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#' @importFrom stats fivenum sd mad
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#' @importFrom grDevices boxplot.stats
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#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise
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#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise n_distinct
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#' @importFrom utils browseVignettes
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#' @importFrom tibble tibble
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#' @keywords summary summarise frequency freq
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@ -88,17 +91,24 @@
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#' filter(hospital_id == "A") %>%
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#' freq(genus, species)
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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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#' years %>% pull(item)
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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(bactid) %>%
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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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#' # print only top 5
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#' years %>% print(nmax = 5)
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#'
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#' # transform to plain data.frame
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#' septic_patients %>%
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#' freq(age) %>%
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#' as.data.frame()
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frequency_tbl <- function(x,
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...,
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sort.count = TRUE,
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@ -135,11 +145,6 @@ frequency_tbl <- function(x,
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mult.columns <- 0
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if (NROW(x) == 0) {
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cat('\nNo observations.\n')
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return(invisible())
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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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@ -226,8 +231,8 @@ frequency_tbl <- function(x,
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x <- x[!x %in% NAs]
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}
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if (missing(sort.count) & any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single', 'factor'))) {
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# sort on item/level at default when x is numeric or a factor and sort.count is not set
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if (missing(sort.count) & 'factor' %in% class(x)) {
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# sort on factor level at default when x is a factor and sort.count is not set
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sort.count <- FALSE
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}
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@ -246,28 +251,30 @@ frequency_tbl <- function(x,
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}
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if (is.list(x) | is.matrix(x) | is.environment(x) | is.function(x)) {
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cat(header, "\n")
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stop('`freq()` does not support lists, matrices, environments or functions.', call. = FALSE)
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stop('frequency tables do not support lists, matrices, environments and functions.', call. = FALSE)
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}
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header <- header %>% paste0(markdown_line, '\nLength: ', (NAs %>% length() + x %>% length()) %>% format(),
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' (of which NA: ', NAs %>% length() %>% format(),
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' = ', (NAs %>% length() / (NAs %>% length() + x %>% length())) %>% percent(force_zero = TRUE), ')')
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' = ', (NAs %>% length() / (NAs %>% length() + x %>% length())) %>% percent(force_zero = TRUE) %>% sub('NaN', '0', ., fixed = TRUE), ')')
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header <- header %>% paste0(markdown_line, '\nUnique: ', x %>% n_distinct() %>% format())
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if (any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
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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 <- header %>% paste0('\n')
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header <- header %>% paste(markdown_line, '\nMean: ', x %>% base::mean(na.rm = TRUE) %>% format(digits = digits))
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header <- header %>% 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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header <- header %>% paste0(markdown_line, '\nFive-Num: ', x %>% stats::fivenum(na.rm = TRUE) %>% format(digits = digits) %>% trimws() %>% paste(collapse = ' | '),
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' (CQV: ', x %>% cqv(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 <- header %>% 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)$out)
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header <- header %>% paste0(markdown_line, '\nOutliers: ', outlier_length)
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if (outlier_length > 0) {
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header <- header %>% paste0(' (unique: ', boxplot.stats(x)$out %>% unique() %>% length(), ')')
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header <- header %>% paste0(' (unique: ', boxplot.stats(x)$out %>% n_distinct(), ')')
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}
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}
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@ -276,7 +283,7 @@ frequency_tbl <- function(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 (any(class(x) %in% c('Date', 'POSIXct', 'POSIXlt'))) {
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if (NROW(x) > 0 & any(class(x) %in% c('Date', 'POSIXct', 'POSIXlt'))) {
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header <- header %>% 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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@ -302,10 +309,9 @@ frequency_tbl <- function(x,
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nmax <- length(x)
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}
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if (nmax == 0 | is.na(nmax) | is.null(nmax)) {
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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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nmax.1 <- min(length(x), nmax + 1)
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# create table with counts and percentages
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column_names <- c('Item', 'Count', 'Percent', 'Cum. Count', 'Cum. Percent', '(Factor Level)')
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@ -404,12 +410,12 @@ top_freq <- function(f, n) {
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vect
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}
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#' @rdname print
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#' @rdname freq
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#' @exportMethod print.frequency_tbl
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#' @importFrom knitr kable
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#' @importFrom dplyr n_distinct
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#' @export
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print.frequency_tbl <- function(x, ...) {
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print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default = 15), ...) {
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opt <- attr(x, 'opt')
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@ -423,7 +429,12 @@ print.frequency_tbl <- function(x, ...) {
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title <- ""
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}
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cat("Frequency table", title, "\n\n")
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if (!missing(nmax)) {
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opt$nmax <- nmax
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opt$nmax.set <- TRUE
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}
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cat("Frequency table", title, "\n")
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if (!is.null(opt$header)) {
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cat(opt$header)
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@ -448,7 +459,13 @@ print.frequency_tbl <- function(x, ...) {
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x.unprinted <- base::sum(x[(opt$nmax + 1):nrow(x), 'count'], na.rm = TRUE)
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x.printed <- base::sum(x$count) - x.unprinted
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x <- x[1:opt$nmax,]
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if (opt$nmax.set == TRUE) {
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nmax <- opt$nmax
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} else {
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nmax <- getOption("max.print.freq", default = 15)
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}
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x <- x[1:nmax,]
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if (opt$nmax.set == TRUE) {
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footer <- paste('[ reached `nmax = ', opt$nmax, '`', sep = '')
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@ -496,3 +513,12 @@ print.frequency_tbl <- function(x, ...) {
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}
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#' @noRd
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#' @exportMethod as.data.frame.frequency_tbl
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#' @export
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as.data.frame.frequency_tbl <- function(x, ...) {
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attr(x, 'package') <- NULL
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attr(x, 'package.version') <- NULL
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attr(x, 'opt') <- NULL
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as.data.frame.data.frame(x, ...)
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}
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6
R/misc.R
6
R/misc.R
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@ -91,10 +91,8 @@ cv <- function(x, na.rm = TRUE) {
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# Coefficient of dispersion, or coefficient of quartile variation (CQV).
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# (Bonett et al., 2006: Confidence interval for a coefficient of quartile variation).
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cqv <- function(x, na.rm = TRUE) {
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cqv.x <-
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(stats::quantile(x, 0.75, na.rm = na.rm, type = 6) - stats::quantile(x, 0.25, na.rm = na.rm, type = 6)) /
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(stats::quantile(x, 0.75, na.rm = na.rm, type = 6) + stats::quantile(x, 0.25, na.rm = na.rm, type = 6))
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unname(cqv.x)
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fives <- stats::fivenum(x, na.rm = na.rm)
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(fives[4] - fives[2]) / (fives[4] + fives[2])
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}
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# show bytes as kB/MB/GB
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@ -124,8 +124,6 @@ prettyprint_df <- function(x,
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if ('tbl_df' %in% class(x)) {
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type <- 'tibble'
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} else if ('frequency_tbl' %in% class(x)) {
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type <- 'frequency table'
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} else if ('data.table' %in% class(x)) {
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type <- 'data.table'
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} else {
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50
man/freq.Rd
50
man/freq.Rd
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@ -4,6 +4,7 @@
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\alias{freq}
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\alias{frequency_tbl}
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\alias{top_freq}
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\alias{print.frequency_tbl}
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\title{Frequency table}
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\usage{
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frequency_tbl(x, ..., sort.count = TRUE, nmax = getOption("max.print.freq"),
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@ -15,17 +16,20 @@ freq(x, ..., sort.count = TRUE, nmax = getOption("max.print.freq"),
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sep = " ")
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top_freq(f, n)
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\method{print}{frequency_tbl}(x, nmax = getOption("max.print.freq", default =
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15), ...)
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}
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\arguments{
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\item{x}{vector with items, or \code{data.frame}}
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\item{x}{vector with items, or a \code{data.frame}}
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\item{...}{up to nine different columns of \code{x} to calculate frequencies from, see Examples}
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\item{sort.count}{sort on count, i.e. frequencies. Use \code{FALSE} to sort alphabetically on item.}
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\item{sort.count}{sort on count, i.e. frequencies. This will be \code{TRUE} at default for everything except for factors.}
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\item{nmax}{number of row to print. The default, \code{15}, uses \code{\link{getOption}("max.print.freq")}. Use \code{nmax = 0}, \code{nmax = NULL} or \code{nmax = NA} to print all rows.}
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\item{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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\item{na.rm}{a logical value indicating whether NA values should be removed from the frequency table. The header will always print the amount of \code{NA}s.}
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\item{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.}
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\item{row.names}{a logical value indicating whether row indices should be printed as \code{1:nrow(x)}}
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@ -46,25 +50,28 @@ A \code{data.frame} with an additional class \code{"frequency_tbl"}
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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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}
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\details{
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This package also has a vignette available about this function, run: \code{browseVignettes("AMR")} to read it.
|
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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 be calculated and shown into the header:
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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}}}
|
||||
\item{Five numbers of Tukey (min, Q1, median, Q3, max), using \code{\link[stats]{fivenum}}}
|
||||
\item{Outliers (total count and unique count), using \code{\link{boxplot.stats}}}
|
||||
\item{Coefficient of variation (CV), the standard deviation divided by the mean}
|
||||
\item{Coefficient of quartile variation (CQV, sometimes called coefficient of dispersion), calculated as \code{(Q3 - Q1) / (Q3 + Q1)} using \code{\link{quantile}} with \code{type = 6} as quantile algorithm to comply with SPSS standards}
|
||||
\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 and shown into the header:
|
||||
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[base]{min}}}
|
||||
\item{Newest, using \code{\link[base]{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.
|
||||
}
|
||||
\examples{
|
||||
|
|
@ -95,17 +102,24 @@ septic_patients \%>\%
|
|||
filter(hospital_id == "A") \%>\%
|
||||
freq(genus, species)
|
||||
|
||||
# save frequency table to an object
|
||||
years <- septic_patients \%>\%
|
||||
mutate(year = format(date, "\%Y")) \%>\%
|
||||
freq(year)
|
||||
years \%>\% pull(item)
|
||||
|
||||
# get top 10 bugs of hospital A as a vector
|
||||
septic_patients \%>\%
|
||||
filter(hospital_id == "A") \%>\%
|
||||
freq(bactid) \%>\%
|
||||
top_freq(10)
|
||||
|
||||
# save frequency table to an object
|
||||
years <- septic_patients \%>\%
|
||||
mutate(year = format(date, "\%Y")) \%>\%
|
||||
freq(year)
|
||||
|
||||
# print only top 5
|
||||
years \%>\% print(nmax = 5)
|
||||
|
||||
# transform to plain data.frame
|
||||
septic_patients \%>\%
|
||||
freq(age) \%>\%
|
||||
as.data.frame()
|
||||
}
|
||||
\keyword{freq}
|
||||
\keyword{frequency}
|
||||
|
|
|
|||
11
man/print.Rd
11
man/print.Rd
|
|
@ -1,15 +1,12 @@
|
|||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/freq.R, R/print.R
|
||||
\name{print.frequency_tbl}
|
||||
\alias{print.frequency_tbl}
|
||||
% Please edit documentation in R/print.R
|
||||
\name{print}
|
||||
\alias{print}
|
||||
\alias{print.tbl_df}
|
||||
\alias{print.tbl}
|
||||
\alias{print.data.table}
|
||||
\title{Printing Data Tables and Tibbles}
|
||||
\usage{
|
||||
\method{print}{frequency_tbl}(x, ...)
|
||||
|
||||
\method{print}{tbl_df}(x, nmax = 10, header = TRUE, row.names = TRUE,
|
||||
right = FALSE, width = 1, na = "<NA>", ...)
|
||||
|
||||
|
|
@ -20,8 +17,6 @@
|
|||
\arguments{
|
||||
\item{x}{object of class \code{data.frame}.}
|
||||
|
||||
\item{...}{optional arguments to \code{print} or \code{plot} methods.}
|
||||
|
||||
\item{nmax}{amount of rows to print in total. When the total amount of rows exceeds this limit, the first and last \code{nmax / 2} rows will be printed. Use \code{nmax = NA} to print all rows.}
|
||||
|
||||
\item{header}{print header with information about data size and tibble grouping}
|
||||
|
|
@ -36,6 +31,8 @@
|
|||
|
||||
\item{na}{value to print instead of NA}
|
||||
|
||||
\item{...}{optional arguments to \code{print} or \code{plot} methods.}
|
||||
|
||||
\item{print.keys}{print keys for \code{data.table}}
|
||||
}
|
||||
\description{
|
||||
|
|
|
|||
|
|
@ -1,89 +0,0 @@
|
|||
## ----setup, include = FALSE, results = 'markup'--------------------------
|
||||
knitr::opts_chunk$set(
|
||||
collapse = TRUE,
|
||||
comment = "#"
|
||||
)
|
||||
library(dplyr)
|
||||
library(AMR)
|
||||
|
||||
## ---- echo = TRUE, results = 'hide'--------------------------------------
|
||||
# just using base R
|
||||
freq(septic_patients$sex)
|
||||
|
||||
# using base R to select the variable and pass it on with a pipe from the dplyr package
|
||||
septic_patients$sex %>% freq()
|
||||
|
||||
# do it all with pipes, using the `select` function from the dplyr package
|
||||
septic_patients %>%
|
||||
select(sex) %>%
|
||||
freq()
|
||||
|
||||
# or the preferred way: using a pipe to pass the variable on to the freq function
|
||||
septic_patients %>% freq(sex) # this also shows 'age' in the title
|
||||
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
freq(septic_patients$sex)
|
||||
|
||||
## ---- echo = TRUE, results = 'hide'--------------------------------------
|
||||
my_patients <- septic_patients %>% left_join_microorganisms()
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
colnames(microorganisms)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
dim(septic_patients)
|
||||
dim(my_patients)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
my_patients %>% freq(genus, species)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
# # get age distribution of unique patients
|
||||
septic_patients %>%
|
||||
distinct(patient_id, .keep_all = TRUE) %>%
|
||||
freq(age, nmax = 5)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
septic_patients %>%
|
||||
freq(hospital_id)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
septic_patients %>%
|
||||
freq(hospital_id, sort.count = TRUE)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
septic_patients %>%
|
||||
select(amox) %>%
|
||||
freq()
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
septic_patients %>%
|
||||
select(date) %>%
|
||||
freq(nmax = 5)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
my_df <- septic_patients %>% freq(age)
|
||||
class(my_df)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
dim(my_df)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
septic_patients %>%
|
||||
freq(amox, na.rm = FALSE)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
septic_patients %>%
|
||||
freq(hospital_id, row.names = FALSE)
|
||||
|
||||
## ---- echo = TRUE--------------------------------------------------------
|
||||
septic_patients %>%
|
||||
freq(hospital_id, markdown = TRUE)
|
||||
|
||||
## ---- echo = FALSE-------------------------------------------------------
|
||||
# this will print "2018" in 2018, and "2018-yyyy" after 2018.
|
||||
yrs <- c(2018:format(Sys.Date(), "%Y"))
|
||||
yrs <- c(min(yrs), max(yrs))
|
||||
yrs <- paste(unique(yrs), collapse = "-")
|
||||
|
||||
Loading…
Reference in New Issue