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freq: fix na.rm in groups
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@ -173,6 +173,7 @@ importFrom(crayon,green)
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importFrom(crayon,italic)
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importFrom(crayon,red)
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importFrom(crayon,silver)
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importFrom(crayon,strip_style)
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importFrom(curl,nslookup)
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importFrom(data.table,as.data.table)
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importFrom(data.table,data.table)
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@ -190,6 +191,7 @@ importFrom(dplyr,desc)
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importFrom(dplyr,everything)
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importFrom(dplyr,filter)
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importFrom(dplyr,filter_all)
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importFrom(dplyr,filter_at)
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importFrom(dplyr,full_join)
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importFrom(dplyr,funs)
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importFrom(dplyr,group_by)
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@ -97,7 +97,7 @@
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#' @rdname EUCAST
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#' @export
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#' @importFrom dplyr %>% select pull mutate_at vars
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#' @importFrom crayon bold bgGreen bgYellow bgRed black green blue italic
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#' @importFrom crayon bold bgGreen bgYellow bgRed black green blue italic strip_style
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#' @return The input of \code{tbl}, possibly with edited values of antibiotics. Or, if \code{verbose = TRUE}, a \code{data.frame} with verbose info.
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#' @source
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#' \itemize{
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@ -376,8 +376,8 @@ EUCAST_rules <- function(tbl,
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if (verbose == TRUE) {
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for (i in 1:length(cols)) {
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# add new row for every affected column
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verbose_new <- data.frame(rule_type = rule[1],
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rule_set = rule[2],
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verbose_new <- data.frame(rule_type = strip_style(rule[1]),
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rule_set = strip_style(rule[2]),
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force_to = to,
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found = length(before),
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changed = sum(before != after, na.rm = TRUE),
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5
R/freq.R
5
R/freq.R
@ -59,7 +59,7 @@
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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 funs group_by mutate mutate_at n_distinct pull select summarise tibble ungroup vars
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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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@ -206,6 +206,9 @@ frequency_tbl <- function(x,
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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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@ -63,6 +63,8 @@ test_that("frequency table works", {
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# grouping variable
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expect_output(print(septic_patients %>% group_by(gender) %>% freq(hospital_id)))
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expect_output(print(septic_patients %>% group_by(gender) %>% freq(amox, quote = TRUE)))
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expect_output(print(septic_patients %>% group_by(gender) %>% freq(amox, markdown = TRUE)))
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# top 5
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expect_equal(
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