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AMR
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small fixes

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dr. M.S. (Matthijs) Berends 2018-07-28 09:34:03 +02:00
parent 498e88b5cf
commit feab1cad6b
7 changed files with 37 additions and 31 deletions
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2
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 0.2.0.9017
Date: 2018-07-25
Date: 2018-07-28
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(

1
NAMESPACE
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@ -111,6 +111,7 @@ importFrom(dplyr,arrange)
importFrom(dplyr,arrange_at)
importFrom(dplyr,as_tibble)
importFrom(dplyr,between)
importFrom(dplyr,case_when)
importFrom(dplyr,desc)
importFrom(dplyr,filter)
importFrom(dplyr,filter_at)

1
R/globals.R
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@ -60,6 +60,7 @@ globalVariables(c('abname',
'septic_patients',
'species',
'umcg',
'value',
'values',
'View',
'y',

36
R/resistance.R
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@ -429,7 +429,7 @@ rsi_df <- function(tbl,
#' @return \code{data.frame} with columns:
#' \itemize{
#' \item{\code{year}}
#' \item{\code{resistance}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
#' \item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
#' \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0}}
#' \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1}}
#' \item{\code{observations}, the total number of observations, i.e. S + I + R}
@ -440,7 +440,7 @@ rsi_df <- function(tbl,
#' @rdname resistance_predict
#' @export
#' @importFrom stats predict glm lm
#' @importFrom dplyr %>% pull mutate group_by_at summarise filter n_distinct arrange
#' @importFrom dplyr %>% pull mutate group_by_at summarise filter n_distinct arrange case_when
# @importFrom tidyr spread
#' @examples
#' \dontrun{
@ -493,7 +493,7 @@ rsi_df <- function(tbl,
#'
#' ggplot(data,
#' aes(x = year)) +
#' geom_col(aes(y = resistance),
#' geom_col(aes(y = value),
#' fill = "grey75") +
#' geom_errorbar(aes(ymin = se_min,
#' ymax = se_max),
@ -626,13 +626,13 @@ resistance_predict <- function(tbl,
}
# prepare the output dataframe
prediction <- data.frame(year = years_predict, resistance = prediction, stringsAsFactors = FALSE)
prediction <- data.frame(year = years_predict, value = prediction, stringsAsFactors = FALSE)
prediction$se_min <- prediction$resistance - se
prediction$se_max <- prediction$resistance + se
prediction$se_min <- prediction$value - se
prediction$se_max <- prediction$value + se
if (model == 'loglin') {
prediction$resistance <- prediction$resistance %>%
prediction$value <- prediction$value %>%
format(scientific = FALSE) %>%
as.integer()
prediction$se_min <- prediction$se_min %>% as.integer()
@ -653,12 +653,12 @@ resistance_predict <- function(tbl,
if (!'I' %in% colnames(df)) {
df$I <- 0
}
df$resistance <- df$R / rowSums(df[, c('R', 'S', 'I')])
df$value <- df$R / rowSums(df[, c('R', 'S', 'I')])
} else {
df$resistance <- df$R / rowSums(df[, c('R', 'S')])
df$value <- df$R / rowSums(df[, c('R', 'S')])
}
measurements <- data.frame(year = df$year,
resistance = df$resistance,
value = df$value,
se_min = NA,
se_max = NA,
observations = df$total,
@ -668,16 +668,18 @@ resistance_predict <- function(tbl,
total <- rbind(measurements,
prediction %>% filter(!year %in% df$year))
if (model %in% c('binomial', 'binom', 'logit')) {
total <- total %>% mutate(observed = ifelse(is.na(observations), NA, resistance),
estimated = prediction$resistance)
total <- total %>% mutate(observed = ifelse(is.na(observations), NA, value),
estimated = prediction$value)
}
}
try(
total$resistance[which(total$resistance > 1)] <- 1,
total$resistance[which(total$resistance < 0)] <- 0,
silent = TRUE
)
if ("value" %in% colnames(total)) {
total <- total %>%
mutate(value = case_when(value > 1 ~ 1,
value < 0 ~ 0,
TRUE ~ value))
}
total %>% arrange(year)
}

4
man/resistance_predict.Rd
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@ -40,7 +40,7 @@ rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
\code{data.frame} with columns:
\itemize{
\item{\code{year}}
\item{\code{resistance}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
\item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
\item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0}}
\item{\code{se_max} the upper bound of the standard error with a maximum of \code{1}}
\item{\code{observations}, the total number of observations, i.e. S + I + R}
@ -102,7 +102,7 @@ if (!require(ggplot2)) {
ggplot(data,
aes(x = year)) +
geom_col(aes(y = resistance),
geom_col(aes(y = value),
fill = "grey75") +
geom_errorbar(aes(ymin = se_min,
ymax = se_max),

22
tests/testthat/test-atc.R
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@ -1,20 +1,22 @@
context("atc.R")
test_that("atc_property works", {
expect_equal(tolower(atc_property("J01CA04", property = "Name")), "amoxicillin")
expect_equal(atc_property("J01CA04", property = "unit"), "g")
if (!is.null(curl::nslookup("www.whocc.no", error = FALSE))) {
expect_equal(tolower(atc_property("J01CA04", property = "Name")), "amoxicillin")
expect_equal(atc_property("J01CA04", property = "unit"), "g")
expect_equal(atc_property("J01CA04", property = "DDD"),
atc_ddd("J01CA04"))
expect_equal(atc_property("J01CA04", property = "DDD"),
atc_ddd("J01CA04"))
expect_identical(atc_property("J01CA04", property = "Groups"),
atc_groups("J01CA04"))
expect_identical(atc_property("J01CA04", property = "Groups"),
atc_groups("J01CA04"))
expect_warning(atc_property("ABCDEFG", property = "DDD"))
expect_warning(atc_property("ABCDEFG", property = "DDD"))
expect_error(atc_property("J01CA04", property = c(1:5)))
expect_error(atc_property("J01CA04", property = "test"))
expect_error(atc_property("J01CA04", property = "test", administration = c(1:5)))
expect_error(atc_property("J01CA04", property = c(1:5)))
expect_error(atc_property("J01CA04", property = "test"))
expect_error(atc_property("J01CA04", property = "test", administration = c(1:5)))
}
})
test_that("abname works", {

2
tests/testthat/test-resistance.R
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@ -86,7 +86,7 @@ test_that("prediction of rsi works", {
col_date = "date",
minimum = 10,
info = TRUE) %>%
pull("resistance")
pull("value")
# amox resistance will increase according to data set `septic_patients`
expect_true(amox_R[3] < amox_R[20])