2019-02-04 12:24:07 +01:00
# ==================================================================== #
2023-07-08 17:30:05 +02:00
# TITLE: #
2022-10-05 09:12:22 +02:00
# AMR: An R Package for Working with Antimicrobial Resistance Data #
2019-02-04 12:24:07 +01:00
# #
2023-07-08 17:30:05 +02:00
# SOURCE CODE: #
2020-07-08 14:48:06 +02:00
# https://github.com/msberends/AMR #
2019-02-04 12:24:07 +01:00
# #
2023-07-08 17:30:05 +02:00
# PLEASE CITE THIS SOFTWARE AS: #
2024-07-16 14:51:57 +02:00
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
2023-05-27 10:39:22 +02:00
# https://doi.org/10.18637/jss.v104.i03 #
2022-10-05 09:12:22 +02:00
# #
2022-12-27 15:16:15 +01:00
# Developed at the University of Groningen and the University Medical #
# Center Groningen in The Netherlands, in collaboration with many #
# colleagues from around the world, see our website. #
2019-02-04 12:24:07 +01:00
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
2020-01-05 17:22:09 +01:00
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
2020-10-08 11:16:03 +02:00
# #
# Visit our website for the full manual and a complete tutorial about #
2021-02-02 23:57:35 +01:00
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
2019-02-04 12:24:07 +01:00
# ==================================================================== #
2021-01-18 16:57:56 +01:00
#' Check Availability of Columns
2019-02-04 12:24:07 +01:00
#'
2020-01-05 17:22:09 +01:00
#' Easy check for data availability of all columns in a data set. This makes it easy to get an idea of which antimicrobial combinations can be used for calculation with e.g. [susceptibility()] and [resistance()].
2020-09-18 16:05:53 +02:00
#' @param tbl a [data.frame] or [list]
2023-02-22 14:38:57 +01:00
#' @param width number of characters to present the visual availability - the default is filling the width of the console
2020-09-18 16:05:53 +02:00
#' @details The function returns a [data.frame] with columns `"resistant"` and `"visual_resistance"`. The values in that columns are calculated with [resistance()].
#' @return [data.frame] with column names of `tbl` as row names
2019-02-04 12:24:07 +01:00
#' @export
#' @examples
2019-08-27 16:45:42 +02:00
#' availability(example_isolates)
2021-05-24 09:00:11 +02:00
#' \donttest{
2020-09-29 23:35:46 +02:00
#' if (require("dplyr")) {
#' example_isolates %>%
2022-08-27 20:49:37 +02:00
#' filter(mo == as.mo("Escherichia coli")) %>%
2023-01-21 23:47:20 +01:00
#' select_if(is.sir) %>%
2020-09-29 23:35:46 +02:00
#' availability()
2020-05-16 21:40:50 +02:00
#' }
2021-05-24 09:00:11 +02:00
#' }
2019-03-26 15:34:04 +01:00
availability <- function ( tbl , width = NULL ) {
2020-10-19 17:09:19 +02:00
meet_criteria ( tbl , allow_class = " data.frame" )
2021-01-24 14:48:56 +01:00
meet_criteria ( width , allow_class = c ( " numeric" , " integer" ) , has_length = 1 , allow_NULL = TRUE , is_positive = TRUE , is_finite = TRUE )
2022-08-28 10:31:50 +02:00
2022-08-27 20:49:37 +02:00
tbl <- as.data.frame ( tbl , stringsAsFactors = FALSE )
2022-08-28 10:31:50 +02:00
2020-12-28 22:24:33 +01:00
x <- vapply ( FUN.VALUE = double ( 1 ) , tbl , function ( x ) {
2022-08-28 10:31:50 +02:00
1 - sum ( is.na ( x ) ) / length ( x )
2019-10-11 17:21:02 +02:00
} )
2020-12-28 22:24:33 +01:00
n <- vapply ( FUN.VALUE = double ( 1 ) , tbl , function ( x ) length ( x [ ! is.na ( x ) ] ) )
2023-01-21 23:47:20 +01:00
R <- vapply ( FUN.VALUE = double ( 1 ) , tbl , function ( x ) ifelse ( is.sir ( x ) , resistance ( x , minimum = 0 ) , NA_real_ ) )
2019-08-25 22:53:22 +02:00
R_print <- character ( length ( R ) )
2019-09-30 16:45:36 +02:00
R_print [ ! is.na ( R ) ] <- percentage ( R [ ! is.na ( R ) ] )
2019-08-25 22:53:22 +02:00
R_print [is.na ( R ) ] <- " "
2022-08-28 10:31:50 +02:00
2019-03-26 15:34:04 +01:00
if ( is.null ( width ) ) {
2022-12-17 14:31:33 +01:00
width <- getOption ( " width" , 100 ) -
2019-03-26 15:34:04 +01:00
( max ( nchar ( colnames ( tbl ) ) ) +
2022-08-28 10:31:50 +02:00
# count col
8 +
# available % column
10 +
# resistant % column
10 +
# extra margin
5 )
2019-03-26 15:34:04 +01:00
width <- width / 2
}
2022-08-28 10:31:50 +02:00
2019-08-25 22:53:22 +02:00
if ( length ( R [is.na ( R ) ] ) == ncol ( tbl ) ) {
2019-03-26 15:34:04 +01:00
width <- width * 2 + 10
}
2022-08-28 10:31:50 +02:00
2019-08-25 22:53:22 +02:00
x_chars_R <- strrep ( " #" , round ( width * R , digits = 2 ) )
x_chars_SI <- strrep ( " -" , width - nchar ( x_chars_R ) )
vis_resistance <- paste0 ( " |" , x_chars_R , x_chars_SI , " |" )
vis_resistance [is.na ( R ) ] <- " "
2022-08-28 10:31:50 +02:00
2019-03-26 15:34:04 +01:00
x_chars <- strrep ( " #" , round ( x , digits = 2 ) / ( 1 / width ) )
x_chars_empty <- strrep ( " -" , width - nchar ( x_chars ) )
2022-08-28 10:31:50 +02:00
df <- data.frame (
count = n ,
available = percentage ( x ) ,
visual_availabilty = paste0 ( " |" , x_chars , x_chars_empty , " |" ) ,
resistant = R_print ,
visual_resistance = vis_resistance ,
stringsAsFactors = FALSE
)
2019-08-25 22:53:22 +02:00
if ( length ( R [is.na ( R ) ] ) == ncol ( tbl ) ) {
2022-08-27 20:49:37 +02:00
df [ , 1 : 3 , drop = FALSE ]
2019-03-26 15:34:04 +01:00
} else {
df
}
2019-02-04 12:24:07 +01:00
}