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first isolate missing dates fix

This commit is contained in:
dr. M.S. (Matthijs) Berends 2019-05-13 14:56:23 +02:00
parent c4aa92b4a7
commit cc403169c6
15 changed files with 200 additions and 146 deletions
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7
NEWS.md
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@ -25,10 +25,11 @@
* This package now honours the new EUCAST insight (2019) that S and I are but classified as susceptible, where I is defined as 'increased exposure' and not 'intermediate' anymore. For functions like `portion_df()` and `count_df()` this means that their new parameter `combine_SI` is TRUE at default. * This package now honours the new EUCAST insight (2019) that S and I are but classified as susceptible, where I is defined as 'increased exposure' and not 'intermediate' anymore. For functions like `portion_df()` and `count_df()` this means that their new parameter `combine_SI` is TRUE at default.
* Removed deprecated functions `guess_mo()`, `guess_atc()`, `EUCAST_rules()`, `interpretive_reading()`, `rsi()` * Removed deprecated functions `guess_mo()`, `guess_atc()`, `EUCAST_rules()`, `interpretive_reading()`, `rsi()`
* Frequency tables of microbial IDs speed improvement * Frequency tables of microbial IDs speed improvement
* Removed all hardcoded EUCAST rules and replaced them with a new reference file: `./inst/eucast/eucast.tsv`. * Removed all hardcoded EUCAST rules and replaced them with a new reference file: `./inst/eucast/eucast.tsv`
* Added ceftazidim intrinsic resistance to *Streptococci* * Added ceftazidim intrinsic resistance to *Streptococci*
* Changed default settings for `age_groups()`, to let groups of fives and tens end with 100+ instead of 120+. * Changed default settings for `age_groups()`, to let groups of fives and tens end with 100+ instead of 120+
* Fix for `freq()` for when all values are `NA`. * Fix for `freq()` for when all values are `NA`
* Fix for `first_isolate()` for when dates are missing
#### Other #### Other
* Support for R 3.6.0 * Support for R 3.6.0

2
R/data.R
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@ -55,7 +55,7 @@
#' #'
#' A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using \code{\link{as.mo}}. #' A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using \code{\link{as.mo}}.
#' @inheritSection catalogue_of_life Catalogue of Life #' @inheritSection catalogue_of_life Catalogue of Life
#' @format A \code{\link{data.frame}} with 65,629 observations and 16 variables: #' @format A \code{\link{data.frame}} with 67,903 observations and 16 variables:
#' \describe{ #' \describe{
#' \item{\code{mo}}{ID of microorganism as used by this package} #' \item{\code{mo}}{ID of microorganism as used by this package}
#' \item{\code{col_id}}{Catalogue of Life ID} #' \item{\code{col_id}}{Catalogue of Life ID}

139
R/first_isolate.R
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@ -22,7 +22,7 @@
#' Determine first (weighted) isolates #' Determine first (weighted) isolates
#' #'
#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. #' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
#' @param tbl a \code{data.frame} containing isolates. #' @param x a \code{data.frame} containing isolates.
#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class #' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class
#' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive) #' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)
#' @param col_mo column name of the unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}. #' @param col_mo column name of the unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.
@ -44,16 +44,16 @@
#' #'
#' The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to: #' The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to:
#' \preformatted{ #' \preformatted{
#' tbl \%>\% #' x \%>\%
#' mutate(only_firsts = first_isolate(tbl, ...)) \%>\% #' mutate(only_firsts = first_isolate(x, ...)) \%>\%
#' filter(only_firsts == TRUE) \%>\% #' filter(only_firsts == TRUE) \%>\%
#' select(-only_firsts) #' select(-only_firsts)
#' } #' }
#' The function \code{filter_first_weighted_isolate} is essentially equal to: #' The function \code{filter_first_weighted_isolate} is essentially equal to:
#' \preformatted{ #' \preformatted{
#' tbl \%>\% #' x \%>\%
#' mutate(keyab = key_antibiotics(.)) \%>\% #' mutate(keyab = key_antibiotics(.)) \%>\%
#' mutate(only_weighted_firsts = first_isolate(tbl, #' mutate(only_weighted_firsts = first_isolate(x,
#' col_keyantibiotics = "keyab", ...)) \%>\% #' col_keyantibiotics = "keyab", ...)) \%>\%
#' filter(only_weighted_firsts == TRUE) \%>\% #' filter(only_weighted_firsts == TRUE) \%>\%
#' select(-only_weighted_firsts) #' select(-only_weighted_firsts)
@ -118,43 +118,43 @@
#' \dontrun{ #' \dontrun{
#' #'
#' # set key antibiotics to a new variable #' # set key antibiotics to a new variable
#' tbl$keyab <- key_antibiotics(tbl) #' x$keyab <- key_antibiotics(x)
#' #'
#' tbl$first_isolate <- #' x$first_isolate <-
#' first_isolate(tbl) #' first_isolate(x)
#' #'
#' tbl$first_isolate_weighed <- #' x$first_isolate_weighed <-
#' first_isolate(tbl, #' first_isolate(x,
#' col_keyantibiotics = 'keyab') #' col_keyantibiotics = 'keyab')
#' #'
#' tbl$first_blood_isolate <- #' x$first_blood_isolate <-
#' first_isolate(tbl, #' first_isolate(x,
#' specimen_group = 'Blood') #' specimen_group = 'Blood')
#' #'
#' tbl$first_blood_isolate_weighed <- #' x$first_blood_isolate_weighed <-
#' first_isolate(tbl, #' first_isolate(x,
#' specimen_group = 'Blood', #' specimen_group = 'Blood',
#' col_keyantibiotics = 'keyab') #' col_keyantibiotics = 'keyab')
#' #'
#' tbl$first_urine_isolate <- #' x$first_urine_isolate <-
#' first_isolate(tbl, #' first_isolate(x,
#' specimen_group = 'Urine') #' specimen_group = 'Urine')
#' #'
#' tbl$first_urine_isolate_weighed <- #' x$first_urine_isolate_weighed <-
#' first_isolate(tbl, #' first_isolate(x,
#' specimen_group = 'Urine', #' specimen_group = 'Urine',
#' col_keyantibiotics = 'keyab') #' col_keyantibiotics = 'keyab')
#' #'
#' tbl$first_resp_isolate <- #' x$first_resp_isolate <-
#' first_isolate(tbl, #' first_isolate(x,
#' specimen_group = 'Respiratory') #' specimen_group = 'Respiratory')
#' #'
#' tbl$first_resp_isolate_weighed <- #' x$first_resp_isolate_weighed <-
#' first_isolate(tbl, #' first_isolate(x,
#' specimen_group = 'Respiratory', #' specimen_group = 'Respiratory',
#' col_keyantibiotics = 'keyab') #' col_keyantibiotics = 'keyab')
#' } #' }
first_isolate <- function(tbl, first_isolate <- function(x,
col_date = NULL, col_date = NULL,
col_patient_id = NULL, col_patient_id = NULL,
col_mo = NULL, col_mo = NULL,
@ -172,8 +172,8 @@ first_isolate <- function(tbl,
info = TRUE, info = TRUE,
...) { ...) {
if (!is.data.frame(tbl)) { if (!is.data.frame(x)) {
stop("`tbl` must be a data.frame.", call. = FALSE) stop("`x` must be a data.frame.", call. = FALSE)
} }
dots <- unlist(list(...)) dots <- unlist(list(...))
@ -183,12 +183,15 @@ first_isolate <- function(tbl,
if ('filter_specimen' %in% dots.names) { if ('filter_specimen' %in% dots.names) {
specimen_group <- dots[which(dots.names == 'filter_specimen')] specimen_group <- dots[which(dots.names == 'filter_specimen')]
} }
if ('tbl' %in% dots.names) {
x <- dots[which(dots.names == 'tbl')]
}
} }
# try to find columns based on type # try to find columns based on type
# -- mo # -- mo
if (is.null(col_mo)) { if (is.null(col_mo)) {
col_mo <- search_type_in_df(tbl = tbl, type = "mo") col_mo <- search_type_in_df(tbl = x, type = "mo")
} }
if (is.null(col_mo)) { if (is.null(col_mo)) {
stop("`col_mo` must be set.", call. = FALSE) stop("`col_mo` must be set.", call. = FALSE)
@ -196,23 +199,25 @@ first_isolate <- function(tbl,
# -- date # -- date
if (is.null(col_date)) { if (is.null(col_date)) {
col_date <- search_type_in_df(tbl = tbl, type = "date") col_date <- search_type_in_df(tbl = x, type = "date")
} }
if (is.null(col_date)) { if (is.null(col_date)) {
stop("`col_date` must be set.", call. = FALSE) stop("`col_date` must be set.", call. = FALSE)
} }
# convert to Date (pipes/pull for supporting tibbles too) # convert to Date (pipes/pull for supporting tibbles too)
tbl[, col_date] <- tbl %>% pull(col_date) %>% as.Date() dates <- x %>% pull(col_date) %>% as.Date()
dates[is.na(dates)] <- as.Date("1970-01-01")
x[, col_date] <- dates
# -- patient id # -- patient id
if (is.null(col_patient_id)) { if (is.null(col_patient_id)) {
if (all(c("First name", "Last name", "Sex", "Identification number") %in% colnames(tbl))) { if (all(c("First name", "Last name", "Sex", "Identification number") %in% colnames(x))) {
# WHONET support # WHONET support
tbl <- tbl %>% mutate(patient_id = paste(`First name`, `Last name`, Sex)) x <- x %>% mutate(patient_id = paste(`First name`, `Last name`, Sex))
col_patient_id <- "patient_id" col_patient_id <- "patient_id"
message(blue(paste0("NOTE: Using combined columns ", bold("`First name`, `Last name` and `Sex`"), " as input for `col_patient_id`."))) message(blue(paste0("NOTE: Using combined columns ", bold("`First name`, `Last name` and `Sex`"), " as input for `col_patient_id`.")))
} else { } else {
col_patient_id <- search_type_in_df(tbl = tbl, type = "patient_id") col_patient_id <- search_type_in_df(tbl = x, type = "patient_id")
} }
} }
if (is.null(col_patient_id)) { if (is.null(col_patient_id)) {
@ -221,7 +226,7 @@ first_isolate <- function(tbl,
# -- key antibiotics # -- key antibiotics
if (is.null(col_keyantibiotics)) { if (is.null(col_keyantibiotics)) {
col_keyantibiotics <- search_type_in_df(tbl = tbl, type = "keyantibiotics") col_keyantibiotics <- search_type_in_df(tbl = x, type = "keyantibiotics")
} }
if (isFALSE(col_keyantibiotics)) { if (isFALSE(col_keyantibiotics)) {
col_keyantibiotics <- NULL col_keyantibiotics <- NULL
@ -229,14 +234,14 @@ first_isolate <- function(tbl,
# -- specimen # -- specimen
if (is.null(col_specimen)) { if (is.null(col_specimen)) {
col_specimen <- search_type_in_df(tbl = tbl, type = "specimen") col_specimen <- search_type_in_df(tbl = x, type = "specimen")
} }
if (isFALSE(col_specimen)) { if (isFALSE(col_specimen)) {
col_specimen <- NULL col_specimen <- NULL
} }
# check if columns exist # check if columns exist
check_columns_existance <- function(column, tblname = tbl) { check_columns_existance <- function(column, tblname = x) {
if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) { if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
stop('Please check tbl for existance.') stop('Please check tbl for existance.')
} }
@ -256,7 +261,7 @@ first_isolate <- function(tbl,
check_columns_existance(col_keyantibiotics) check_columns_existance(col_keyantibiotics)
# join to microorganisms data set # join to microorganisms data set
tbl <- tbl %>% x <- x %>%
mutate_at(vars(col_mo), as.mo) %>% mutate_at(vars(col_mo), as.mo) %>%
left_join_microorganisms(by = col_mo) left_join_microorganisms(by = col_mo)
col_genus <- "genus" col_genus <- "genus"
@ -273,8 +278,8 @@ first_isolate <- function(tbl,
if (is.null(col_icu)) { if (is.null(col_icu)) {
icu_exclude <- FALSE icu_exclude <- FALSE
} else { } else {
tbl <- tbl %>% x <- x %>%
mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical()) mutate(col_icu = x %>% pull(col_icu) %>% as.logical())
} }
if (is.null(col_specimen)) { if (is.null(col_specimen)) {
@ -283,13 +288,13 @@ first_isolate <- function(tbl,
# filter on specimen group and keyantibiotics when they are filled in # filter on specimen group and keyantibiotics when they are filled in
if (!is.null(specimen_group)) { if (!is.null(specimen_group)) {
check_columns_existance(col_specimen, tbl) check_columns_existance(col_specimen, x)
if (info == TRUE) { if (info == TRUE) {
cat('[Criterion] Excluded other than specimen group \'', specimen_group, '\'\n', sep = '') cat('[Criterion] Excluded other than specimen group \'', specimen_group, '\'\n', sep = '')
} }
} }
if (!is.null(col_keyantibiotics)) { if (!is.null(col_keyantibiotics)) {
tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics)) x <- x %>% mutate(key_ab = x %>% pull(col_keyantibiotics))
} }
if (is.null(testcodes_exclude)) { if (is.null(testcodes_exclude)) {
@ -297,12 +302,12 @@ first_isolate <- function(tbl,
} }
# create new dataframe with original row index and right sorting # create new dataframe with original row index and right sorting
tbl <- tbl %>% x <- x %>%
mutate(first_isolate_row_index = 1:nrow(tbl), mutate(first_isolate_row_index = 1:nrow(x),
date_lab = tbl %>% pull(col_date), date_lab = x %>% pull(col_date),
patient_id = tbl %>% pull(col_patient_id), patient_id = x %>% pull(col_patient_id),
species = tbl %>% pull(col_species), species = x %>% pull(col_species),
genus = tbl %>% pull(col_genus)) %>% genus = x %>% pull(col_genus)) %>%
mutate(species = if_else(is.na(species) | species == "(no MO)", "", species), mutate(species = if_else(is.na(species) | species == "(no MO)", "", species),
genus = if_else(is.na(genus) | genus == "(no MO)", "", genus)) genus = if_else(is.na(genus) | genus == "(no MO)", "", genus))
@ -312,18 +317,18 @@ first_isolate <- function(tbl,
if (info == TRUE & !is.null(col_icu)) { if (info == TRUE & !is.null(col_icu)) {
cat('[Criterion] Included isolates from ICU.\n') cat('[Criterion] Included isolates from ICU.\n')
} }
tbl <- tbl %>% x <- x %>%
arrange_at(c(col_patient_id, arrange_at(c(col_patient_id,
col_genus, col_genus,
col_species, col_species,
col_date)) col_date))
row.start <- 1 row.start <- 1
row.end <- nrow(tbl) row.end <- nrow(x)
} else { } else {
if (info == TRUE) { if (info == TRUE) {
cat('[Criterion] Excluded isolates from ICU.\n') cat('[Criterion] Excluded isolates from ICU.\n')
} }
tbl <- tbl %>% x <- x %>%
arrange_at(c(col_icu, arrange_at(c(col_icu,
col_patient_id, col_patient_id,
col_genus, col_genus,
@ -331,10 +336,10 @@ first_isolate <- function(tbl,
col_date)) col_date))
suppressWarnings( suppressWarnings(
row.start <- which(tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE) row.start <- which(x %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
) )
suppressWarnings( suppressWarnings(
row.end <- which(tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE) row.end <- which(x %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
) )
} }
@ -344,23 +349,23 @@ first_isolate <- function(tbl,
if (info == TRUE & !is.null(col_icu)) { if (info == TRUE & !is.null(col_icu)) {
cat('[Criterion] Included isolates from ICU.\n') cat('[Criterion] Included isolates from ICU.\n')
} }
tbl <- tbl %>% x <- x %>%
arrange_at(c(col_specimen, arrange_at(c(col_specimen,
col_patient_id, col_patient_id,
col_genus, col_genus,
col_species, col_species,
col_date)) col_date))
suppressWarnings( suppressWarnings(
row.start <- which(tbl %>% pull(col_specimen) == specimen_group) %>% min(na.rm = TRUE) row.start <- which(x %>% pull(col_specimen) == specimen_group) %>% min(na.rm = TRUE)
) )
suppressWarnings( suppressWarnings(
row.end <- which(tbl %>% pull(col_specimen) == specimen_group) %>% max(na.rm = TRUE) row.end <- which(x %>% pull(col_specimen) == specimen_group) %>% max(na.rm = TRUE)
) )
} else { } else {
if (info == TRUE) { if (info == TRUE) {
cat('[Criterion] Excluded isolates from ICU.\n') cat('[Criterion] Excluded isolates from ICU.\n')
} }
tbl <- tbl %>% x <- x %>%
arrange_at(c(col_icu, arrange_at(c(col_icu,
col_specimen, col_specimen,
col_patient_id, col_patient_id,
@ -368,12 +373,12 @@ first_isolate <- function(tbl,
col_species, col_species,
col_date)) col_date))
suppressWarnings( suppressWarnings(
row.start <- which(tbl %>% pull(col_specimen) == specimen_group row.start <- which(x %>% pull(col_specimen) == specimen_group
& tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE) & x %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
) )
suppressWarnings( suppressWarnings(
row.end <- which(tbl %>% pull(col_specimen) == specimen_group row.end <- which(x %>% pull(col_specimen) == specimen_group
& tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE) & x %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
) )
} }
@ -384,7 +389,7 @@ first_isolate <- function(tbl,
message(paste("=> Found", bold("no isolates"))) message(paste("=> Found", bold("no isolates")))
} }
# NAs where genus is unavailable # NAs where genus is unavailable
return(tbl %>% return(x %>%
mutate(real_first_isolate = if_else(genus == '', NA, FALSE)) %>% mutate(real_first_isolate = if_else(genus == '', NA, FALSE)) %>%
pull(real_first_isolate) pull(real_first_isolate)
) )
@ -392,7 +397,7 @@ first_isolate <- function(tbl,
# suppress warnings because dplyr wants us to use library(dplyr) when using filter(row_number()) # suppress warnings because dplyr wants us to use library(dplyr) when using filter(row_number())
suppressWarnings( suppressWarnings(
scope.size <- tbl %>% scope.size <- x %>%
filter( filter(
row_number() %>% between(row.start, row_number() %>% between(row.start,
row.end), row.end),
@ -424,7 +429,7 @@ first_isolate <- function(tbl,
} }
# Analysis of first isolate ---- # Analysis of first isolate ----
all_first <- tbl %>% all_first <- x %>%
mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id) mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
& genus == lag(genus) & genus == lag(genus)
& species == lag(species), & species == lag(species),
@ -513,7 +518,7 @@ first_isolate <- function(tbl,
decimal.mark <- getOption("OutDec") decimal.mark <- getOption("OutDec")
big.mark <- ifelse(decimal.mark != ",", ",", ".") big.mark <- ifelse(decimal.mark != ",", ",", ".")
n_found <- base::sum(all_first, na.rm = TRUE) n_found <- base::sum(all_first, na.rm = TRUE)
p_found_total <- percent(n_found / nrow(tbl), force_zero = TRUE) p_found_total <- percent(n_found / nrow(x), force_zero = TRUE)
p_found_scope <- percent(n_found / scope.size, force_zero = TRUE) p_found_scope <- percent(n_found / scope.size, force_zero = TRUE)
# mark up number of found # mark up number of found
n_found <- base::format(n_found, big.mark = big.mark, decimal.mark = decimal.mark) n_found <- base::format(n_found, big.mark = big.mark, decimal.mark = decimal.mark)
@ -536,12 +541,12 @@ first_isolate <- function(tbl,
#' @rdname first_isolate #' @rdname first_isolate
#' @importFrom dplyr filter #' @importFrom dplyr filter
#' @export #' @export
filter_first_isolate <- function(tbl, filter_first_isolate <- function(x,
col_date = NULL, col_date = NULL,
col_patient_id = NULL, col_patient_id = NULL,
col_mo = NULL, col_mo = NULL,
...) { ...) {
filter(tbl, first_isolate(tbl = tbl, filter(x, first_isolate(x = x,
col_date = col_date, col_date = col_date,
col_patient_id = col_patient_id, col_patient_id = col_patient_id,
col_mo = col_mo, col_mo = col_mo,
@ -551,13 +556,13 @@ filter_first_isolate <- function(tbl,
#' @rdname first_isolate #' @rdname first_isolate
#' @importFrom dplyr %>% mutate filter #' @importFrom dplyr %>% mutate filter
#' @export #' @export
filter_first_weighted_isolate <- function(tbl, filter_first_weighted_isolate <- function(x,
col_date = NULL, col_date = NULL,
col_patient_id = NULL, col_patient_id = NULL,
col_mo = NULL, col_mo = NULL,
col_keyantibiotics = NULL, col_keyantibiotics = NULL,
...) { ...) {
tbl_keyab <- tbl %>% tbl_keyab <- x %>%
mutate(keyab = suppressMessages(key_antibiotics(., mutate(keyab = suppressMessages(key_antibiotics(.,
col_mo = col_mo, col_mo = col_mo,
...))) %>% ...))) %>%
@ -567,5 +572,5 @@ filter_first_weighted_isolate <- function(tbl,
col_mo = col_mo, col_mo = col_mo,
col_keyantibiotics = "keyab", col_keyantibiotics = "keyab",
...)) ...))
tbl[which(tbl_keyab$firsts == TRUE),] x[which(tbl_keyab$firsts == TRUE),]
} }

2
codecov.yml
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@ -7,7 +7,7 @@ codecov:
comment: no comment: no
coverage: coverage:
precision: 5 precision: 1
round: up round: up
range: "0...100" range: "0...100"
status: status:

9
docs/news/index.html
View File

@ -276,11 +276,14 @@ Please create an issue in one of our repositories if you want additions in this
<li>Removed deprecated functions <code>guess_mo()</code>, <code>guess_atc()</code>, <code>EUCAST_rules()</code>, <code>interpretive_reading()</code>, <code>rsi()</code> <li>Removed deprecated functions <code>guess_mo()</code>, <code>guess_atc()</code>, <code>EUCAST_rules()</code>, <code>interpretive_reading()</code>, <code>rsi()</code>
</li> </li>
<li>Frequency tables of microbial IDs speed improvement</li> <li>Frequency tables of microbial IDs speed improvement</li>
<li>Removed all hardcoded EUCAST rules and replaced them with a new reference file: <code>./inst/eucast/eucast.tsv</code>.</li> <li>Removed all hardcoded EUCAST rules and replaced them with a new reference file: <code>./inst/eucast/eucast.tsv</code>
</li>
<li>Added ceftazidim intrinsic resistance to <em>Streptococci</em> <li>Added ceftazidim intrinsic resistance to <em>Streptococci</em>
</li> </li>
<li>Changed default settings for <code><a href="../reference/age_groups.html">age_groups()</a></code>, to let groups of fives and tens end with 100+ instead of 120+.</li> <li>Changed default settings for <code><a href="../reference/age_groups.html">age_groups()</a></code>, to let groups of fives and tens end with 100+ instead of 120+</li>
<li>Fix for <code><a href="../reference/freq.html">freq()</a></code> for when all values are <code>NA</code>.</li> <li>Fix for <code><a href="../reference/freq.html">freq()</a></code> for when all values are <code>NA</code>
</li>
<li>Fix for <code><a href="../reference/first_isolate.html">first_isolate()</a></code> for when dates are missing</li>
</ul> </ul>
</div> </div>
<div id="other" class="section level4"> <div id="other" class="section level4">

50
docs/reference/first_isolate.html
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@ -241,17 +241,17 @@
</div> </div>
<pre class="usage"><span class='fu'>first_isolate</span>(<span class='no'>tbl</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_patient_id</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <pre class="usage"><span class='fu'>first_isolate</span>(<span class='no'>x</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_patient_id</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>col_mo</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_testcode</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_specimen</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_mo</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_testcode</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_specimen</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>col_icu</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>episode_days</span> <span class='kw'>=</span> <span class='fl'>365</span>, <span class='kw'>col_icu</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>episode_days</span> <span class='kw'>=</span> <span class='fl'>365</span>,
<span class='kw'>testcodes_exclude</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>icu_exclude</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>testcodes_exclude</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>icu_exclude</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
<span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"keyantibiotics"</span>, <span class='kw'>ignore_I</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"keyantibiotics"</span>, <span class='kw'>ignore_I</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>points_threshold</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='no'>...</span>) <span class='kw'>points_threshold</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='no'>...</span>)
<span class='fu'>filter_first_isolate</span>(<span class='no'>tbl</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_patient_id</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='fu'>filter_first_isolate</span>(<span class='no'>x</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_patient_id</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>col_mo</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='no'>...</span>) <span class='kw'>col_mo</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='no'>...</span>)
<span class='fu'>filter_first_weighted_isolate</span>(<span class='no'>tbl</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='fu'>filter_first_weighted_isolate</span>(<span class='no'>x</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>col_patient_id</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_mo</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_patient_id</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_mo</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='no'>...</span>)</pre> <span class='no'>...</span>)</pre>
@ -259,7 +259,7 @@
<table class="ref-arguments"> <table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup> <colgroup><col class="name" /><col class="desc" /></colgroup>
<tr> <tr>
<th>tbl</th> <th>x</th>
<td><p>a <code>data.frame</code> containing isolates.</p></td> <td><p>a <code>data.frame</code> containing isolates.</p></td>
</tr> </tr>
<tr> <tr>
@ -341,14 +341,14 @@
<p><strong>WHY THIS IS SO IMPORTANT</strong> <br /> <p><strong>WHY THIS IS SO IMPORTANT</strong> <br />
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode <a href='https://www.ncbi.nlm.nih.gov/pubmed/17304462'>[1]</a>. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all <em>S. aureus</em> isolates would be overestimated, because you included this MRSA more than once. It would be <a href='https://en.wikipedia.org/wiki/Selection_bias'>selection bias</a>.</p> To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode <a href='https://www.ncbi.nlm.nih.gov/pubmed/17304462'>[1]</a>. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all <em>S. aureus</em> isolates would be overestimated, because you included this MRSA more than once. It would be <a href='https://en.wikipedia.org/wiki/Selection_bias'>selection bias</a>.</p>
<p>The functions <code>filter_first_isolate</code> and <code>filter_first_weighted_isolate</code> are helper functions to quickly filter on first isolates. The function <code>filter_first_isolate</code> is essentially equal to:</p><pre> <p>The functions <code>filter_first_isolate</code> and <code>filter_first_weighted_isolate</code> are helper functions to quickly filter on first isolates. The function <code>filter_first_isolate</code> is essentially equal to:</p><pre>
tbl %&gt;% x %&gt;%
mutate(only_firsts = first_isolate(tbl, ...)) %&gt;% mutate(only_firsts = first_isolate(x, ...)) %&gt;%
filter(only_firsts == TRUE) %&gt;% filter(only_firsts == TRUE) %&gt;%
select(-only_firsts) select(-only_firsts)
</pre><p>The function <code>filter_first_weighted_isolate</code> is essentially equal to:</p><pre> </pre><p>The function <code>filter_first_weighted_isolate</code> is essentially equal to:</p><pre>
tbl %&gt;% x %&gt;%
mutate(keyab = key_antibiotics(.)) %&gt;% mutate(keyab = key_antibiotics(.)) %&gt;%
mutate(only_weighted_firsts = first_isolate(tbl, mutate(only_weighted_firsts = first_isolate(x,
col_keyantibiotics = "keyab", ...)) %&gt;% col_keyantibiotics = "keyab", ...)) %&gt;%
filter(only_weighted_firsts == TRUE) %&gt;% filter(only_weighted_firsts == TRUE) %&gt;%
select(-only_weighted_firsts) select(-only_weighted_firsts)
@ -416,39 +416,39 @@ To conduct an analysis of antimicrobial resistance, you should only include the
<span class='co'># }</span><span class='co'># NOT RUN {</span> <span class='co'># }</span><span class='co'># NOT RUN {</span>
<span class='co'># set key antibiotics to a new variable</span> <span class='co'># set key antibiotics to a new variable</span>
<span class='no'>tbl</span>$<span class='no'>keyab</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='key_antibiotics.html'>key_antibiotics</a></span>(<span class='no'>tbl</span>) <span class='no'>x</span>$<span class='no'>keyab</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='key_antibiotics.html'>key_antibiotics</a></span>(<span class='no'>x</span>)
<span class='no'>tbl</span>$<span class='no'>first_isolate</span> <span class='kw'>&lt;-</span> <span class='no'>x</span>$<span class='no'>first_isolate</span> <span class='kw'>&lt;-</span>
<span class='fu'>first_isolate</span>(<span class='no'>tbl</span>) <span class='fu'>first_isolate</span>(<span class='no'>x</span>)
<span class='no'>tbl</span>$<span class='no'>first_isolate_weighed</span> <span class='kw'>&lt;-</span> <span class='no'>x</span>$<span class='no'>first_isolate_weighed</span> <span class='kw'>&lt;-</span>
<span class='fu'>first_isolate</span>(<span class='no'>tbl</span>, <span class='fu'>first_isolate</span>(<span class='no'>x</span>,
<span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='st'>'keyab'</span>) <span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='st'>'keyab'</span>)
<span class='no'>tbl</span>$<span class='no'>first_blood_isolate</span> <span class='kw'>&lt;-</span> <span class='no'>x</span>$<span class='no'>first_blood_isolate</span> <span class='kw'>&lt;-</span>
<span class='fu'>first_isolate</span>(<span class='no'>tbl</span>, <span class='fu'>first_isolate</span>(<span class='no'>x</span>,
<span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Blood'</span>) <span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Blood'</span>)
<span class='no'>tbl</span>$<span class='no'>first_blood_isolate_weighed</span> <span class='kw'>&lt;-</span> <span class='no'>x</span>$<span class='no'>first_blood_isolate_weighed</span> <span class='kw'>&lt;-</span>
<span class='fu'>first_isolate</span>(<span class='no'>tbl</span>, <span class='fu'>first_isolate</span>(<span class='no'>x</span>,
<span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Blood'</span>, <span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Blood'</span>,
<span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='st'>'keyab'</span>) <span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='st'>'keyab'</span>)
<span class='no'>tbl</span>$<span class='no'>first_urine_isolate</span> <span class='kw'>&lt;-</span> <span class='no'>x</span>$<span class='no'>first_urine_isolate</span> <span class='kw'>&lt;-</span>
<span class='fu'>first_isolate</span>(<span class='no'>tbl</span>, <span class='fu'>first_isolate</span>(<span class='no'>x</span>,
<span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Urine'</span>) <span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Urine'</span>)
<span class='no'>tbl</span>$<span class='no'>first_urine_isolate_weighed</span> <span class='kw'>&lt;-</span> <span class='no'>x</span>$<span class='no'>first_urine_isolate_weighed</span> <span class='kw'>&lt;-</span>
<span class='fu'>first_isolate</span>(<span class='no'>tbl</span>, <span class='fu'>first_isolate</span>(<span class='no'>x</span>,
<span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Urine'</span>, <span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Urine'</span>,
<span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='st'>'keyab'</span>) <span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='st'>'keyab'</span>)
<span class='no'>tbl</span>$<span class='no'>first_resp_isolate</span> <span class='kw'>&lt;-</span> <span class='no'>x</span>$<span class='no'>first_resp_isolate</span> <span class='kw'>&lt;-</span>
<span class='fu'>first_isolate</span>(<span class='no'>tbl</span>, <span class='fu'>first_isolate</span>(<span class='no'>x</span>,
<span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Respiratory'</span>) <span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Respiratory'</span>)
<span class='no'>tbl</span>$<span class='no'>first_resp_isolate_weighed</span> <span class='kw'>&lt;-</span> <span class='no'>x</span>$<span class='no'>first_resp_isolate_weighed</span> <span class='kw'>&lt;-</span>
<span class='fu'>first_isolate</span>(<span class='no'>tbl</span>, <span class='fu'>first_isolate</span>(<span class='no'>x</span>,
<span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Respiratory'</span>, <span class='kw'>specimen_group</span> <span class='kw'>=</span> <span class='st'>'Respiratory'</span>,
<span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='st'>'keyab'</span>) <span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='st'>'keyab'</span>)
<span class='co'># }</span></pre> <span class='co'># }</span></pre>

2
docs/reference/microorganisms.html
View File

@ -245,7 +245,7 @@
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>A <code><a href='https://www.rdocumentation.org/packages/base/topics/data.frame'>data.frame</a></code> with 65,629 observations and 16 variables:</p><dl class='dl-horizontal'> <p>A <code><a href='https://www.rdocumentation.org/packages/base/topics/data.frame'>data.frame</a></code> with 67,903 observations and 16 variables:</p><dl class='dl-horizontal'>
<dt><code>mo</code></dt><dd><p>ID of microorganism as used by this package</p></dd> <dt><code>mo</code></dt><dd><p>ID of microorganism as used by this package</p></dd>
<dt><code>col_id</code></dt><dd><p>Catalogue of Life ID</p></dd> <dt><code>col_id</code></dt><dd><p>Catalogue of Life ID</p></dd>
<dt><code>fullname</code></dt><dd><p>Full name, like <code>"Echerichia coli"</code></p></dd> <dt><code>fullname</code></dt><dd><p>Full name, like <code>"Echerichia coli"</code></p></dd>

8
docs/reference/resistance_predict.html
View File

@ -261,10 +261,6 @@
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments"> <table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup> <colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>tbl</th>
<td><p>a <code>data.frame</code> containing isolates.</p></td>
</tr>
<tr> <tr>
<th>col_ab</th> <th>col_ab</th>
<td><p>column name of <code>tbl</code> with antimicrobial interpretations (<code>R</code>, <code>I</code> and <code>S</code>)</p></td> <td><p>column name of <code>tbl</code> with antimicrobial interpretations (<code>R</code>, <code>I</code> and <code>S</code>)</p></td>
@ -307,9 +303,7 @@
</tr> </tr>
<tr> <tr>
<th>x</th> <th>x</th>
<td><p>the coordinates of points in the plot. Alternatively, a <td><p>a <code>data.frame</code> containing isolates.</p></td>
single plotting structure, function or <em>any <span style="R">R</span> object with a
<code>plot</code> method</em> can be provided.</p></td>
</tr> </tr>
<tr> <tr>
<th>main</th> <th>main</th>

50
man/first_isolate.Rd
View File

@ -9,22 +9,22 @@
Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
} }
\usage{ \usage{
first_isolate(tbl, col_date = NULL, col_patient_id = NULL, first_isolate(x, col_date = NULL, col_patient_id = NULL,
col_mo = NULL, col_testcode = NULL, col_specimen = NULL, col_mo = NULL, col_testcode = NULL, col_specimen = NULL,
col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365, col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365,
testcodes_exclude = NULL, icu_exclude = FALSE, testcodes_exclude = NULL, icu_exclude = FALSE,
specimen_group = NULL, type = "keyantibiotics", ignore_I = TRUE, specimen_group = NULL, type = "keyantibiotics", ignore_I = TRUE,
points_threshold = 2, info = TRUE, ...) points_threshold = 2, info = TRUE, ...)
filter_first_isolate(tbl, col_date = NULL, col_patient_id = NULL, filter_first_isolate(x, col_date = NULL, col_patient_id = NULL,
col_mo = NULL, ...) col_mo = NULL, ...)
filter_first_weighted_isolate(tbl, col_date = NULL, filter_first_weighted_isolate(x, col_date = NULL,
col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL, col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL,
...) ...)
} }
\arguments{ \arguments{
\item{tbl}{a \code{data.frame} containing isolates.} \item{x}{a \code{data.frame} containing isolates.}
\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class} \item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class}
@ -70,16 +70,16 @@ To conduct an analysis of antimicrobial resistance, you should only include the
The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to: The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to:
\preformatted{ \preformatted{
tbl \%>\% x \%>\%
mutate(only_firsts = first_isolate(tbl, ...)) \%>\% mutate(only_firsts = first_isolate(x, ...)) \%>\%
filter(only_firsts == TRUE) \%>\% filter(only_firsts == TRUE) \%>\%
select(-only_firsts) select(-only_firsts)
} }
The function \code{filter_first_weighted_isolate} is essentially equal to: The function \code{filter_first_weighted_isolate} is essentially equal to:
\preformatted{ \preformatted{
tbl \%>\% x \%>\%
mutate(keyab = key_antibiotics(.)) \%>\% mutate(keyab = key_antibiotics(.)) \%>\%
mutate(only_weighted_firsts = first_isolate(tbl, mutate(only_weighted_firsts = first_isolate(x,
col_keyantibiotics = "keyab", ...)) \%>\% col_keyantibiotics = "keyab", ...)) \%>\%
filter(only_weighted_firsts == TRUE) \%>\% filter(only_weighted_firsts == TRUE) \%>\%
select(-only_weighted_firsts) select(-only_weighted_firsts)
@ -144,39 +144,39 @@ B <- septic_patients \%>\%
\dontrun{ \dontrun{
# set key antibiotics to a new variable # set key antibiotics to a new variable
tbl$keyab <- key_antibiotics(tbl) x$keyab <- key_antibiotics(x)
tbl$first_isolate <- x$first_isolate <-
first_isolate(tbl) first_isolate(x)
tbl$first_isolate_weighed <- x$first_isolate_weighed <-
first_isolate(tbl, first_isolate(x,
col_keyantibiotics = 'keyab') col_keyantibiotics = 'keyab')
tbl$first_blood_isolate <- x$first_blood_isolate <-
first_isolate(tbl, first_isolate(x,
specimen_group = 'Blood') specimen_group = 'Blood')
tbl$first_blood_isolate_weighed <- x$first_blood_isolate_weighed <-
first_isolate(tbl, first_isolate(x,
specimen_group = 'Blood', specimen_group = 'Blood',
col_keyantibiotics = 'keyab') col_keyantibiotics = 'keyab')
tbl$first_urine_isolate <- x$first_urine_isolate <-
first_isolate(tbl, first_isolate(x,
specimen_group = 'Urine') specimen_group = 'Urine')
tbl$first_urine_isolate_weighed <- x$first_urine_isolate_weighed <-
first_isolate(tbl, first_isolate(x,
specimen_group = 'Urine', specimen_group = 'Urine',
col_keyantibiotics = 'keyab') col_keyantibiotics = 'keyab')
tbl$first_resp_isolate <- x$first_resp_isolate <-
first_isolate(tbl, first_isolate(x,
specimen_group = 'Respiratory') specimen_group = 'Respiratory')
tbl$first_resp_isolate_weighed <- x$first_resp_isolate_weighed <-
first_isolate(tbl, first_isolate(x,
specimen_group = 'Respiratory', specimen_group = 'Respiratory',
col_keyantibiotics = 'keyab') col_keyantibiotics = 'keyab')
} }

2
man/microorganisms.Rd
View File

@ -4,7 +4,7 @@
\name{microorganisms} \name{microorganisms}
\alias{microorganisms} \alias{microorganisms}
\title{Data set with ~65,000 microorganisms} \title{Data set with ~65,000 microorganisms}
\format{A \code{\link{data.frame}} with 65,629 observations and 16 variables: \format{A \code{\link{data.frame}} with 67,903 observations and 16 variables:
\describe{ \describe{
\item{\code{mo}}{ID of microorganism as used by this package} \item{\code{mo}}{ID of microorganism as used by this package}
\item{\code{col_id}}{Catalogue of Life ID} \item{\code{col_id}}{Catalogue of Life ID}

6
man/resistance_predict.Rd
View File

@ -24,8 +24,6 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
attributes(x)$ab), ribbon = TRUE, ...) attributes(x)$ab), ribbon = TRUE, ...)
} }
\arguments{ \arguments{
\item{tbl}{a \code{data.frame} containing isolates.}
\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})} \item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}
\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class} \item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class}
@ -46,9 +44,7 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
\item{info}{a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.} \item{info}{a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.}
\item{x}{the coordinates of points in the plot. Alternatively, a \item{x}{a \code{data.frame} containing isolates.}
single plotting structure, function or \emph{any \R object with a
\code{plot} method} can be provided.}
\item{main}{title of the plot} \item{main}{title of the plot}

6
tests/testthat/test-ab.R
View File

@ -42,9 +42,12 @@ test_that("as.ab works", {
expect_warning(as.ab("Z00ZZ00")) # not yet available in data set expect_warning(as.ab("Z00ZZ00")) # not yet available in data set
expect_warning(as.ab("UNKNOWN")) expect_warning(as.ab("UNKNOWN"))
expect_warning(as.ab(""))
expect_output(print(as.ab("amox"))) expect_output(print(as.ab("amox")))
expect_identical(class(pull(antibiotics, ab)), "ab")
# first 5 chars of official name # first 5 chars of official name
expect_equal(as.character(as.atc(c("nitro", "cipro"))), expect_equal(as.character(as.atc(c("nitro", "cipro"))),
c("J01XE01", "J01MA02")) c("J01XE01", "J01MA02"))
@ -53,4 +56,7 @@ test_that("as.ab works", {
expect_equal(as.character(as.atc("AMX")), expect_equal(as.character(as.atc("AMX")),
"J01CA04") "J01CA04")
expect_equal(as.character(as.ab("Phloxapen")),
"FLC")
}) })

2
tests/testthat/test-data.R
View File

@ -48,4 +48,6 @@ test_that("creation of data sets is valid", {
test_that("CoL version info works", { test_that("CoL version info works", {
expect_identical(class(catalogue_of_life_version()), expect_identical(class(catalogue_of_life_version()),
c("catalogue_of_life_version", "list")) c("catalogue_of_life_version", "list"))
expect_output(print(catalogue_of_life_version()))
}) })

35
tests/testthat/test-disk.R Executable file
View File

@ -0,0 +1,35 @@
# ==================================================================== #
# TITLE #
# Antidiskrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://gitlab.com/msberends/AMR #
# #
# LICENCE #
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# 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. #
# #
# This R package was created for academic research and was publicly #
# released in the hope that it will be useful, but it comes WITHOUT #
# ANY WARRANTY OR LIABILITY. #
# Visit our website for more info: https://msberends.gitlab.io/AMR. #
# ==================================================================== #
context("disk.R")
test_that("disk works", {
expect_true(as.disk(8) == as.disk("8"))
expect_true(is.disk(as.disk(8)))
expect_equal(suppressWarnings(as.logical(as.disk("INVALID VALUE"))), NA)
# all levels should be valid disks
expect_silent(as.disk(levels(as.disk(15))))
expect_warning(as.disk("INVALID VALUE"))
})

26
tests/testthat/test-first_isolate.R
View File

@ -25,7 +25,7 @@ test_that("first isolates work", {
# first isolates # first isolates
expect_equal( expect_equal(
sum( sum(
first_isolate(tbl = septic_patients, first_isolate(x = septic_patients,
col_date = "date", col_date = "date",
col_patient_id = "patient_id", col_patient_id = "patient_id",
col_mo = "mo", col_mo = "mo",
@ -37,7 +37,7 @@ test_that("first isolates work", {
expect_equal( expect_equal(
suppressWarnings( suppressWarnings(
sum( sum(
first_isolate(tbl = septic_patients %>% mutate(keyab = key_antibiotics(.)), first_isolate(x = septic_patients %>% mutate(keyab = key_antibiotics(.)),
# let syntax determine these automatically: # let syntax determine these automatically:
# col_date = "date", # col_date = "date",
# col_patient_id = "patient_id", # col_patient_id = "patient_id",
@ -51,7 +51,7 @@ test_that("first isolates work", {
expect_equal( expect_equal(
suppressWarnings( suppressWarnings(
sum( sum(
first_isolate(tbl = septic_patients %>% dplyr::as_tibble() %>% mutate(keyab = key_antibiotics(.)), first_isolate(x = septic_patients %>% dplyr::as_tibble() %>% mutate(keyab = key_antibiotics(.)),
# let syntax determine these automatically: # let syntax determine these automatically:
# col_date = "date", # col_date = "date",
# col_patient_id = "patient_id", # col_patient_id = "patient_id",
@ -65,7 +65,7 @@ test_that("first isolates work", {
expect_equal( expect_equal(
suppressWarnings( suppressWarnings(
sum( sum(
first_isolate(tbl = septic_patients %>% mutate(keyab = key_antibiotics(.)), first_isolate(x = septic_patients %>% mutate(keyab = key_antibiotics(.)),
col_date = "date", col_date = "date",
col_patient_id = "patient_id", col_patient_id = "patient_id",
col_mo = "mo", col_mo = "mo",
@ -79,7 +79,7 @@ test_that("first isolates work", {
expect_equal( expect_equal(
suppressWarnings( suppressWarnings(
sum( sum(
first_isolate(tbl = septic_patients %>% mutate(keyab = key_antibiotics(.)), first_isolate(x = septic_patients %>% mutate(keyab = key_antibiotics(.)),
col_date = "date", col_date = "date",
col_patient_id = "patient_id", col_patient_id = "patient_id",
col_mo = "mo", col_mo = "mo",
@ -106,7 +106,7 @@ test_that("first isolates work", {
random_rows <- sample(x = 1:2000, size = 1500, replace = FALSE) random_rows <- sample(x = 1:2000, size = 1500, replace = FALSE)
expect_lt( expect_lt(
sum( sum(
first_isolate(tbl = mutate(septic_patients, first_isolate(x = mutate(septic_patients,
specimen = if_else(row_number() %in% random_rows, specimen = if_else(row_number() %in% random_rows,
"Urine", "Urine",
"Other")), "Other")),
@ -121,7 +121,7 @@ test_that("first isolates work", {
# same, but now exclude ICU # same, but now exclude ICU
expect_lt( expect_lt(
sum( sum(
first_isolate(tbl = mutate(septic_patients, first_isolate(x = mutate(septic_patients,
specimen = if_else(row_number() %in% random_rows, specimen = if_else(row_number() %in% random_rows,
"Urine", "Urine",
"Other")), "Other")),
@ -175,4 +175,16 @@ test_that("first isolates work", {
col_mo = "mo", col_mo = "mo",
col_patient_id = "patient_id")) col_patient_id = "patient_id"))
df <- septic_patients
df[1:100, "date"] <- NA
expect_equal(
sum(
first_isolate(x = df,
col_date = "date",
col_patient_id = "patient_id",
col_mo = "mo",
info = TRUE),
na.rm = TRUE),
1279)
}) })