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15
DESCRIPTION
15
DESCRIPTION
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@ -24,10 +24,17 @@ Description: Functions to simplify the analysis of Antimicrobial Resistance (AMR
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of microbial isolates, by using new S3 classes and applying EUCAST expert rules
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on antibiograms according to Leclercq (2013)
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<doi:10.1111/j.1469-0691.2011.03703.x>.
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Depends: R (>= 3.0)
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Imports: dplyr (>= 0.7.0), reshape2 (>= 1.4.0), xml2, rvest
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Suggests: testthat
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URL: https://cran.r-project.org/package=AMR
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Depends:
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R (>= 3.2.0)
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Imports:
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dplyr (>= 0.7.0),
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reshape2 (>= 1.4.0),
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xml2 (>= 1.0.0),
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rvest (>= 0.3.2)
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Suggests:
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testthat (>= 2.0.0),
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covr (>= 3.0.1)
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URL: https://github.com/msberends/AMR
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BugReports: https://github.com/msberends/AMR/issues
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License: GPL-2 | file LICENSE
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Encoding: UTF-8
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25
NEWS
25
NEWS
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@ -1,14 +1,19 @@
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## 0.1.2
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- NEW: Function `guess_bactid` to determine the ID of a microorganism based on genus/species
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- NEW: 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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- NEW: New algorithm to determine weighted isolates, can now be `"points"` or `"keyantibiotics"`, see `?first_isolate`
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- EDIT: Renamed dataset `ablist` to `antibiotics`
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- EDIT: Renamed dataset `bactlist` to `microorganisms`
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- EDIT: Added support for character vector in join functions
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- EDIT: Altered `%like%` to make it case insensitive
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- EDIT: Functions `first_isolate`, `EUCAST_rules` and `rsi_predict` supports tidyverse-like evaluation of parameters (no need to quote columns them anymore)
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- EDIT: For functions `first_isolate`, `EUCAST_rules` the antibiotic column names are case-insensitive
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- EDIT: Functions `as.rsi` and `as.mic` now add the package name and version as attribute
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- Added full support for Windows, Linux and macOS; this package now works everywhere :)
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- New function `guess_bactid` to determine the ID of a microorganism based on genus/species
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- New 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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- New algorithm to determine weighted isolates, can now be `"points"` or `"keyantibiotics"`, see `?first_isolate`
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- Renamed dataset `ablist` to `antibiotics`
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- Renamed dataset `bactlist` to `microorganisms`
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- Added analysis examples on help page of dataset `septic_patients`
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- Added support for character vector in join functions
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- Added warnings when applying a join results in more rows after than before the join
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- Altered `%like%` to make it case insensitive
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- For parameters of functions `first_isolate`, `EUCAST_rules` the column names are now case-insensitive
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- Functions `as.rsi` and `as.mic` now add the package name and version as attribute
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- Expanded README.md
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- Added unit testing with Travis CI (https://travis-ci.org/msberends/AMR)
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- Added code coverage checking with Codecov (https://codecov.io/gh/msberends/AMR/tree/master/R)
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## 0.1.1
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- `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
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122
R/EUCAST.R
122
R/EUCAST.R
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@ -48,7 +48,7 @@
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#' cfur = "-", # Cefuroxime
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#' stringsAsFactors = FALSE)
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#' a
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#'
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#'
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#' b <- EUCAST_rules(a)
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#' b
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EUCAST_rules <- function(tbl,
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@ -114,71 +114,11 @@ EUCAST_rules <- function(tbl,
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vanc = 'vanc') {
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EUCAST_VERSION <- "3.1"
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# support using columns as objects; the tidyverse way
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amcl <- quasiquotate(deparse(substitute(amcl)), amcl)
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amik <- quasiquotate(deparse(substitute(amik)), amik)
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amox <- quasiquotate(deparse(substitute(amox)), amox)
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ampi <- quasiquotate(deparse(substitute(ampi)), ampi)
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azit <- quasiquotate(deparse(substitute(azit)), azit)
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aztr <- quasiquotate(deparse(substitute(aztr)), aztr)
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cefa <- quasiquotate(deparse(substitute(cefa)), cefa)
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cfra <- quasiquotate(deparse(substitute(cfra)), cfra)
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cfep <- quasiquotate(deparse(substitute(cfep)), cfep)
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cfot <- quasiquotate(deparse(substitute(cfot)), cfot)
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cfox <- quasiquotate(deparse(substitute(cfox)), cfox)
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cfta <- quasiquotate(deparse(substitute(cfta)), cfta)
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cftr <- quasiquotate(deparse(substitute(cftr)), cftr)
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cfur <- quasiquotate(deparse(substitute(cfur)), cfur)
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chlo <- quasiquotate(deparse(substitute(chlo)), chlo)
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cipr <- quasiquotate(deparse(substitute(cipr)), cipr)
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clar <- quasiquotate(deparse(substitute(clar)), clar)
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clin <- quasiquotate(deparse(substitute(clin)), clin)
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clox <- quasiquotate(deparse(substitute(clox)), clox)
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coli <- quasiquotate(deparse(substitute(coli)), coli)
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czol <- quasiquotate(deparse(substitute(czol)), czol)
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dapt <- quasiquotate(deparse(substitute(dapt)), dapt)
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doxy <- quasiquotate(deparse(substitute(doxy)), doxy)
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erta <- quasiquotate(deparse(substitute(erta)), erta)
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eryt <- quasiquotate(deparse(substitute(eryt)), eryt)
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fosf <- quasiquotate(deparse(substitute(fosf)), fosf)
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fusi <- quasiquotate(deparse(substitute(fusi)), fusi)
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gent <- quasiquotate(deparse(substitute(gent)), gent)
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imip <- quasiquotate(deparse(substitute(imip)), imip)
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kana <- quasiquotate(deparse(substitute(kana)), kana)
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levo <- quasiquotate(deparse(substitute(levo)), levo)
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linc <- quasiquotate(deparse(substitute(linc)), linc)
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line <- quasiquotate(deparse(substitute(line)), line)
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mero <- quasiquotate(deparse(substitute(mero)), mero)
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mino <- quasiquotate(deparse(substitute(mino)), mino)
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moxi <- quasiquotate(deparse(substitute(moxi)), moxi)
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nali <- quasiquotate(deparse(substitute(nali)), nali)
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neom <- quasiquotate(deparse(substitute(neom)), neom)
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neti <- quasiquotate(deparse(substitute(neti)), neti)
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nitr <- quasiquotate(deparse(substitute(nitr)), nitr)
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novo <- quasiquotate(deparse(substitute(novo)), novo)
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norf <- quasiquotate(deparse(substitute(norf)), norf)
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oflo <- quasiquotate(deparse(substitute(oflo)), oflo)
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peni <- quasiquotate(deparse(substitute(peni)), peni)
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pita <- quasiquotate(deparse(substitute(pita)), pita)
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poly <- quasiquotate(deparse(substitute(poly)), poly)
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qida <- quasiquotate(deparse(substitute(qida)), qida)
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rifa <- quasiquotate(deparse(substitute(rifa)), rifa)
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roxi <- quasiquotate(deparse(substitute(roxi)), roxi)
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siso <- quasiquotate(deparse(substitute(siso)), siso)
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teic <- quasiquotate(deparse(substitute(teic)), teic)
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tetr <- quasiquotate(deparse(substitute(tetr)), tetr)
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tica <- quasiquotate(deparse(substitute(tica)), tica)
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tige <- quasiquotate(deparse(substitute(tige)), tige)
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tobr <- quasiquotate(deparse(substitute(tobr)), tobr)
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trim <- quasiquotate(deparse(substitute(trim)), trim)
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trsu <- quasiquotate(deparse(substitute(trsu)), trsu)
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vanc <- quasiquotate(deparse(substitute(vanc)), vanc)
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if (!col_bactid %in% colnames(tbl)) {
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stop('Column ', col_bactid, ' not found.')
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}
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# check columns
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col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot,
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cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli,
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@ -209,7 +149,7 @@ EUCAST_rules <- function(tbl,
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call. = FALSE)
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}
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}
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amcl <- col.list[1]
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amik <- col.list[2]
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amox <- col.list[3]
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@ -268,10 +208,10 @@ EUCAST_rules <- function(tbl,
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trim <- col.list[56]
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trsu <- col.list[57]
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vanc <- col.list[58]
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total <- 0
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total_rows <- integer(0)
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# helper function for editing the table
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edit_rsi <- function(to, rows, cols) {
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cols <- cols[!is.na(cols)]
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@ -281,12 +221,12 @@ EUCAST_rules <- function(tbl,
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total_rows <<- c(total_rows, rows)
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}
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}
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# join to microorganisms table
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joinby <- colnames(AMR::microorganisms)[1]
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names(joinby) <- col_bactid
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tbl <- tbl %>% left_join(y = AMR::microorganisms, by = joinby, suffix = c("_tempmicroorganisms", ""))
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# antibiotic classes
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aminoglycosides <- c(tobr, gent, kana, neom, neti, siso)
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tetracyclines <- c(doxy, mino, tetr) # since EUCAST v3.1 tige(cycline) is set apart
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@ -299,7 +239,7 @@ EUCAST_rules <- function(tbl,
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aminopenicillins <- c(ampi, amox)
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ureidopenicillins <- pita # should officially also be azlo and mezlo
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fluoroquinolones <- c(oflo, cipr, norf, levo, moxi)
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if (info == TRUE) {
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cat(
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paste0(
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@ -308,7 +248,7 @@ EUCAST_rules <- function(tbl,
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' rows according to "EUCAST Expert Rules Version ', EUCAST_VERSION, '"\n')
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)
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}
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# Table 1: Intrinsic resistance in Enterobacteriaceae ----
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if (info == TRUE) {
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cat('...Table 1: Intrinsic resistance in Enterobacteriaceae\n')
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@ -378,8 +318,8 @@ EUCAST_rules <- function(tbl,
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edit_rsi(to = 'R',
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rows = which(tbl$fullname %like% '^Yersinia pseudotuberculosis'),
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cols = c(poly, coli))
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# Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria ----
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if (info == TRUE) {
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cat('...Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria\n')
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@ -426,8 +366,8 @@ EUCAST_rules <- function(tbl,
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edit_rsi(to = 'R',
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rows = which(tbl$fullname %like% '^Stenotrophomonas maltophilia'),
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cols = c(aminopenicillins, amcl, tica, pita, czol, cfot, cftr, cfta, aztr, erta, imip, mero, aminoglycosides, trim, fosf, tetr))
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# Table 3: Intrinsic resistance in other Gram-negative bacteria ----
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if (info == TRUE) {
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cat('...Table 3: Intrinsic resistance in other Gram-negative bacteria\n')
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@ -458,8 +398,8 @@ EUCAST_rules <- function(tbl,
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edit_rsi(to = 'R',
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rows = which(tbl$fullname %like% '^Campylobacter (jejuni|coli)'),
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cols = c(fusi, streptogramins, trim))
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# Table 4: Intrinsic resistance in Gram-positive bacteria ----
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if (info == TRUE) {
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cat('...Table 4: Intrinsic resistance in Gram-positive bacteria\n')
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@ -513,7 +453,7 @@ EUCAST_rules <- function(tbl,
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edit_rsi(to = 'R',
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rows = which(tbl$fullname %like% '^Clostridium (ramosum|innocuum)'),
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cols = vanc)
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# Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci ----
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if (info == TRUE) {
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cat('...Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci\n')
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@ -538,7 +478,7 @@ EUCAST_rules <- function(tbl,
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& tbl[, amox] == 'R'),
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cols = c(ureidopenicillins, carbapenems))
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}
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# Table 9: Interpretive rules for B-lactam agents and Gram-negative rods ----
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if (info == TRUE) {
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cat('...Table 9: Interpretive rules for B-lactam agents and Gram-negative rods\n')
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@ -551,7 +491,7 @@ EUCAST_rules <- function(tbl,
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& tbl[, pita] == 'S'),
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cols = pita)
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}
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# Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria ----
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if (info == TRUE) {
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cat('...Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria\n')
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@ -564,7 +504,7 @@ EUCAST_rules <- function(tbl,
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# & tbl[, ampi] == 'R'),
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# cols = c(ampi, amox, amcl, pita, cfur))
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}
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# Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins ----
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if (info == TRUE) {
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cat('...Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins\n')
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@ -578,7 +518,7 @@ EUCAST_rules <- function(tbl,
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tbl[, clar] <- tbl[, eryt]
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}
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}
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# Table 12: Interpretive rules for aminoglycosides ----
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if (info == TRUE) {
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cat('...Table 12: Interpretive rules for aminoglycosides\n')
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@ -613,8 +553,8 @@ EUCAST_rules <- function(tbl,
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& tbl[, gent] == 'R'),
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cols = tobr)
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}
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# Table 13: Interpretive rules for quinolones ----
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if (info == TRUE) {
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cat('...Table 13: Interpretive rules for quinolones\n')
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@ -647,8 +587,8 @@ EUCAST_rules <- function(tbl,
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& tbl[, cipr] == 'R'),
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cols = fluoroquinolones)
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}
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# Other ----
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if (info == TRUE) {
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cat('...Non-EUCAST: trim = R where trsu = R and ampi = R where amcl = R\n')
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@ -666,21 +606,21 @@ EUCAST_rules <- function(tbl,
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if (!is.na(ampi) & !is.na(amox)) {
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tbl[, amox] <- tbl %>% pull(ampi)
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}
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# Remove added columns again
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microorganisms.ncol <- ncol(AMR::microorganisms) - 2
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tbl.ncol <- ncol(tbl)
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tbl <- tbl %>% select(-c((tbl.ncol - microorganisms.ncol):tbl.ncol))
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# and remove added suffices
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colnames(tbl) <- gsub("_tempmicroorganisms", "", colnames(tbl))
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if (info == TRUE) {
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cat('Done.\n\nEUCAST Expert rules applied to',
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total_rows %>% unique() %>% length() %>% format(big.mark = ","),
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'different rows (isolates); edited a total of',
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total %>% format(big.mark = ","), 'test results.\n\n')
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}
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tbl
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}
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@ -698,12 +638,12 @@ interpretive_reading <- function(...) {
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#' @importFrom dplyr %>% filter select
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#' @seealso \code{\link{microorganisms}}
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mo_property <- function(bactid, property = 'fullname') {
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mocode <- as.character(bactid)
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for (i in 1:length(mocode)) {
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bug <- mocode[i]
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if (!is.na(bug)) {
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result = tryCatch({
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mocode[i] <-
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@ -720,7 +660,7 @@ mo_property <- function(bactid, property = 'fullname') {
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}
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})
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}
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}
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mocode
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}
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37
R/classes.R
37
R/classes.R
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@ -29,18 +29,23 @@
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#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
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#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
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#' is.rsi(rsi_data)
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#'
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#'
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#' plot(rsi_data) # for percentages
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#' barplot(rsi_data) # for frequencies
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as.rsi <- function(x) {
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if (is.rsi(x)) {
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x
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} else {
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x <- x %>% unlist()
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x.bak <- x
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na_before <- x[is.na(x) | x == ''] %>% length()
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# remove all spaces
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x <- gsub(' {2,55}', '', x)
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# disallow more than 3 characters
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x[nchar(x) > 3] <- NA
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# remove all invalid characters
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x <- gsub('[^RSI]+', '', x %>% toupper())
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# needed for UMCG in cases of "S;S" but also "S;I"; the latter will be NA:
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x <- gsub('^S+$', 'S', x)
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@ -48,7 +53,7 @@ as.rsi <- function(x) {
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x <- gsub('^R+$', 'R', x)
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x[!x %in% c('S', 'I', 'R')] <- NA
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na_after <- x[is.na(x) | x == ''] %>% length()
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if (na_before != na_after) {
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list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
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unique() %>%
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@ -59,7 +64,7 @@ as.rsi <- function(x) {
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'%) that were invalid antimicrobial interpretations: ',
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list_missing, call. = FALSE)
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}
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x <- x %>% toupper() %>% factor(levels = c("S", "I", "R"), ordered = TRUE)
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class(x) <- c('rsi', 'ordered', 'factor')
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attr(x, 'package') <- 'AMR'
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@ -128,7 +133,7 @@ summary.rsi <- function(object, ...) {
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#' @noRd
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plot.rsi <- function(x, ...) {
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x_name <- deparse(substitute(x))
|
||||
|
||||
|
||||
data <- data.frame(x = x,
|
||||
y = 1,
|
||||
stringsAsFactors = TRUE) %>%
|
||||
|
|
@ -137,7 +142,7 @@ plot.rsi <- function(x, ...) {
|
|||
filter(!is.na(x)) %>%
|
||||
mutate(s = round((n / sum(n)) * 100, 1))
|
||||
data$x <- factor(data$x, levels = c('S', 'I', 'R'), ordered = TRUE)
|
||||
|
||||
|
||||
ymax <- if_else(max(data$s) > 95, 105, 100)
|
||||
|
||||
plot(x = data$x,
|
||||
|
|
@ -154,7 +159,7 @@ plot.rsi <- function(x, ...) {
|
|||
axis(side = 1, at = 1:n_distinct(data$x), labels = levels(data$x), lwd = 0)
|
||||
# y axis, 0-100%
|
||||
axis(side = 2, at = seq(0, 100, 5))
|
||||
|
||||
|
||||
text(x = data$x,
|
||||
y = data$s + 4,
|
||||
labels = paste0(data$s, '% (n = ', data$n, ')'))
|
||||
|
|
@ -169,7 +174,7 @@ plot.rsi <- function(x, ...) {
|
|||
barplot.rsi <- function(height, ...) {
|
||||
x <- height
|
||||
x_name <- deparse(substitute(height))
|
||||
|
||||
|
||||
data <- data.frame(rsi = x, cnt = 1) %>%
|
||||
group_by(rsi) %>%
|
||||
summarise(cnt = sum(cnt)) %>%
|
||||
|
|
@ -199,7 +204,7 @@ barplot.rsi <- function(height, ...) {
|
|||
#' @examples
|
||||
#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
|
||||
#' is.mic(mic_data)
|
||||
#'
|
||||
#'
|
||||
#' plot(mic_data)
|
||||
#' barplot(mic_data)
|
||||
as.mic <- function(x, na.rm = FALSE) {
|
||||
|
|
@ -211,7 +216,7 @@ as.mic <- function(x, na.rm = FALSE) {
|
|||
x <- x[!is.na(x)]
|
||||
}
|
||||
x.bak <- x
|
||||
|
||||
|
||||
# comma to dot
|
||||
x <- gsub(',', '.', x, fixed = TRUE)
|
||||
# starting dots must start with 0
|
||||
|
|
@ -224,7 +229,7 @@ as.mic <- function(x, na.rm = FALSE) {
|
|||
x <- gsub('[^0-9]$', '', x)
|
||||
# remove last zeroes
|
||||
x <- gsub('[.]?0+$', '', x)
|
||||
|
||||
|
||||
lvls <- c("<0.002", "<=0.002", "0.002", ">=0.002", ">0.002",
|
||||
"<0.003", "<=0.003", "0.003", ">=0.003", ">0.003",
|
||||
"<0.004", "<=0.004", "0.004", ">=0.004", ">0.004",
|
||||
|
|
@ -282,11 +287,11 @@ as.mic <- function(x, na.rm = FALSE) {
|
|||
"<512", "<=512", "512", ">=512", ">512",
|
||||
"<1024", "<=1024", "1024", ">=1024", ">1024")
|
||||
x <- x %>% as.character()
|
||||
|
||||
|
||||
na_before <- x[is.na(x) | x == ''] %>% length()
|
||||
x[!x %in% lvls] <- NA
|
||||
na_after <- x[is.na(x) | x == ''] %>% length()
|
||||
|
||||
|
||||
if (na_before != na_after) {
|
||||
list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
|
||||
unique() %>%
|
||||
|
|
@ -297,7 +302,7 @@ as.mic <- function(x, na.rm = FALSE) {
|
|||
'%) that were invalid MICs: ',
|
||||
list_missing, call. = FALSE)
|
||||
}
|
||||
|
||||
|
||||
x <- factor(x = x,
|
||||
levels = lvls,
|
||||
ordered = TRUE)
|
||||
|
|
@ -407,7 +412,7 @@ create_barplot_mic <- function(x, x_name, ...) {
|
|||
barplot(table(droplevels(x)),
|
||||
ylab = 'Frequency',
|
||||
xlab = 'MIC value',
|
||||
main = paste('MIC values of', x_name),
|
||||
main = paste('MIC values of', x_name),
|
||||
axes = FALSE,
|
||||
...)
|
||||
axis(2, seq(0, max(data$cnt)))
|
||||
|
|
|
|||
19
R/misc.R
19
R/misc.R
|
|
@ -31,22 +31,3 @@
|
|||
percent <- function(x, round = 1, ...) {
|
||||
base::paste0(base::round(x * 100, digits = round), "%")
|
||||
}
|
||||
|
||||
# No export, no Rd
|
||||
quasiquotate <- function(deparsed, parsed) {
|
||||
# when text: remove first and last "
|
||||
if (any(deparsed %like% '^".+"$' | deparsed %like% "^'.+'$")) {
|
||||
deparsed <- deparsed %>% substr(2, nchar(.) - 1)
|
||||
}
|
||||
# apply if needed
|
||||
if (any(!deparsed %like% '[[$:()]'
|
||||
& !deparsed %in% c('""', "''", "", # empty text
|
||||
".", ".data", # dplyr references
|
||||
"TRUE", "FALSE", # logicals
|
||||
"NA", "NaN", "NULL", # empty values
|
||||
ls(.GlobalEnv)))) {
|
||||
deparsed
|
||||
} else {
|
||||
parsed
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -41,7 +41,7 @@
|
|||
#' library(dplyr)
|
||||
#' # calculate current empiric therapy of Helicobacter gastritis:
|
||||
#' my_table %>%
|
||||
#' filter(first_isolate == TRUE,
|
||||
#' filter(first_isolate == TRUE,
|
||||
#' genus == "Helicobacter") %>%
|
||||
#' rsi_df(ab = c("amox", "metr"))
|
||||
#' }
|
||||
|
|
@ -55,7 +55,7 @@ rsi_df <- function(tbl,
|
|||
|
||||
# in case tbl$interpretation already exists:
|
||||
interpretations_to_check <- paste(interpretation, collapse = "")
|
||||
|
||||
|
||||
# validate:
|
||||
if (min(grepl('^[a-z]{3,4}$', ab)) == 0 &
|
||||
min(grepl('^rsi[1-2]$', ab)) == 0) {
|
||||
|
|
@ -71,7 +71,7 @@ rsi_df <- function(tbl,
|
|||
warning('Dataset contains isolates from the Intensive Care. Exclude them from proper epidemiological analysis.')
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# transform when checking for different results
|
||||
if (interpretations_to_check %in% c('SI', 'IS')) {
|
||||
for (i in 1:length(ab)) {
|
||||
|
|
@ -101,7 +101,7 @@ rsi_df <- function(tbl,
|
|||
denominator <- tbl %>%
|
||||
filter(pull(., ab[1]) %in% c("S", "I", "R")) %>%
|
||||
nrow()
|
||||
|
||||
|
||||
} else if (length(ab) == 2) {
|
||||
numerator <- tbl %>%
|
||||
filter_at(vars(ab[1], ab[2]),
|
||||
|
|
@ -109,12 +109,12 @@ rsi_df <- function(tbl,
|
|||
filter_at(vars(ab[1], ab[2]),
|
||||
all_vars(. %in% c("S", "R", "I"))) %>%
|
||||
nrow()
|
||||
|
||||
|
||||
denominator <- tbl %>%
|
||||
filter_at(vars(ab[1], ab[2]),
|
||||
all_vars(. %in% c("S", "R", "I"))) %>%
|
||||
nrow()
|
||||
|
||||
|
||||
} else if (length(ab) == 3) {
|
||||
numerator <- tbl %>%
|
||||
filter_at(vars(ab[1], ab[2], ab[3]),
|
||||
|
|
@ -122,16 +122,16 @@ rsi_df <- function(tbl,
|
|||
filter_at(vars(ab[1], ab[2], ab[3]),
|
||||
all_vars(. %in% c("S", "R", "I"))) %>%
|
||||
nrow()
|
||||
|
||||
|
||||
denominator <- tbl %>%
|
||||
filter_at(vars(ab[1], ab[2], ab[3]),
|
||||
all_vars(. %in% c("S", "R", "I"))) %>%
|
||||
nrow()
|
||||
|
||||
|
||||
} else {
|
||||
stop('Maximum of 3 drugs allowed.')
|
||||
}
|
||||
|
||||
|
||||
# build text part
|
||||
if (info == TRUE) {
|
||||
cat('n =', denominator)
|
||||
|
|
@ -147,7 +147,7 @@ rsi_df <- function(tbl,
|
|||
info.txt2 <- gsub('rsi1', 'this drug', info.txt2, fixed = TRUE)
|
||||
cat(paste0(' (of ', nrow(tbl), ' in total; ', info.txt1, ' tested on ', info.txt2, ')\n'))
|
||||
}
|
||||
|
||||
|
||||
# calculate and format
|
||||
y <- numerator / denominator
|
||||
if (percent == TRUE) {
|
||||
|
|
@ -159,7 +159,7 @@ rsi_df <- function(tbl,
|
|||
}
|
||||
y <- NA
|
||||
}
|
||||
|
||||
|
||||
# output
|
||||
y
|
||||
}
|
||||
|
|
@ -178,14 +178,14 @@ rsi_df <- function(tbl,
|
|||
#' tbl %>%
|
||||
#' group_by(hospital) %>%
|
||||
#' summarise(cipr = rsi(cipr))
|
||||
#'
|
||||
#'
|
||||
#' tbl %>%
|
||||
#' group_by(year, hospital) %>%
|
||||
#' summarise(
|
||||
#' isolates = n(),
|
||||
#' cipro = rsi(cipr %>% as.rsi(), percent = TRUE),
|
||||
#' amoxi = rsi(amox %>% as.rsi(), percent = TRUE))
|
||||
#'
|
||||
#'
|
||||
#' rsi(as.rsi(isolates$amox))
|
||||
#'
|
||||
#' rsi(as.rsi(isolates$amcl), interpretation = "S")
|
||||
|
|
@ -207,12 +207,12 @@ rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FA
|
|||
if (!ab2.name %like% '^[a-z]{3,4}$') {
|
||||
ab2.name <- 'rsi2'
|
||||
}
|
||||
|
||||
|
||||
interpretation <- paste(interpretation, collapse = "")
|
||||
|
||||
|
||||
tbl <- tibble(rsi1 = ab1, rsi2 = ab2)
|
||||
colnames(tbl) <- c(ab1.name, ab2.name)
|
||||
|
||||
|
||||
if (length(ab2) == 1) {
|
||||
return(rsi_df(tbl = tbl,
|
||||
ab = ab1.name,
|
||||
|
|
@ -260,7 +260,7 @@ rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FA
|
|||
#' # use it directly:
|
||||
#' rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
||||
#' col_ab = "amcl", col_date = "date")
|
||||
#'
|
||||
#'
|
||||
#' # or with dplyr so you can actually read it:
|
||||
#' library(dplyr)
|
||||
#' tbl %>%
|
||||
|
|
@ -274,22 +274,22 @@ rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FA
|
|||
#' library(dplyr)
|
||||
#' septic_patients %>%
|
||||
#' # get bacteria properties like genus and species
|
||||
#' left_join_microorganisms("bactid") %>%
|
||||
#' left_join_microorganisms("bactid") %>%
|
||||
#' # calculate first isolates
|
||||
#' mutate(first_isolate =
|
||||
#' mutate(first_isolate =
|
||||
#' first_isolate(.,
|
||||
#' "date",
|
||||
#' "patient_id",
|
||||
#' "bactid",
|
||||
#' col_specimen = NA,
|
||||
#' col_icu = NA)) %>%
|
||||
#' col_icu = NA)) %>%
|
||||
#' # filter on first E. coli isolates
|
||||
#' filter(genus == "Escherichia",
|
||||
#' species == "coli",
|
||||
#' filter(genus == "Escherichia",
|
||||
#' species == "coli",
|
||||
#' first_isolate == TRUE) %>%
|
||||
#' # predict resistance of cefotaxime for next years
|
||||
#' rsi_predict(col_ab = cfot,
|
||||
#' col_date = date,
|
||||
#' rsi_predict(col_ab = "cfot",
|
||||
#' col_date = "date",
|
||||
#' year_max = 2025,
|
||||
#' preserve_measurements = FALSE)
|
||||
#'
|
||||
|
|
@ -302,16 +302,15 @@ rsi_predict <- function(tbl,
|
|||
I_as_R = TRUE,
|
||||
preserve_measurements = TRUE,
|
||||
info = TRUE) {
|
||||
|
||||
|
||||
if (nrow(tbl) == 0) {
|
||||
stop('This table does not contain any observations.')
|
||||
}
|
||||
|
||||
col_ab <- quasiquotate(deparse(substitute(col_ab)), col_ab)
|
||||
|
||||
if (!col_ab %in% colnames(tbl)) {
|
||||
stop('Column ', col_ab, ' not found.')
|
||||
}
|
||||
col_date <- quasiquotate(deparse(substitute(col_date)), col_date)
|
||||
|
||||
if (!col_date %in% colnames(tbl)) {
|
||||
stop('Column ', col_date, ' not found.')
|
||||
}
|
||||
|
|
@ -327,7 +326,7 @@ rsi_predict <- function(tbl,
|
|||
if (!all(tbl %>% pull(col_ab) %>% as.rsi() %in% c(NA, 'S', 'I', 'R'))) {
|
||||
stop('Column ', col_ab, ' must contain antimicrobial interpretations (S, I, R).')
|
||||
}
|
||||
|
||||
|
||||
year <- function(x) {
|
||||
if (all(grepl('^[0-9]{4}$', x))) {
|
||||
x
|
||||
|
|
@ -335,9 +334,9 @@ rsi_predict <- function(tbl,
|
|||
as.integer(format(as.Date(x), '%Y'))
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
years_predict <- seq(from = min(year(tbl %>% pull(col_date))), to = year_max, by = year_every)
|
||||
|
||||
|
||||
df <- tbl %>%
|
||||
mutate(year = year(tbl %>% pull(col_date))) %>%
|
||||
group_by_at(c('year', col_ab)) %>%
|
||||
|
|
@ -345,7 +344,7 @@ rsi_predict <- function(tbl,
|
|||
colnames(df) <- c('year', 'antibiotic', 'count')
|
||||
df <- df %>%
|
||||
reshape2::dcast(year ~ antibiotic, value.var = 'count')
|
||||
|
||||
|
||||
if (model %in% c('binomial', 'binom', 'logit')) {
|
||||
logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
|
||||
if (info == TRUE) {
|
||||
|
|
@ -353,11 +352,11 @@ rsi_predict <- function(tbl,
|
|||
cat('\n------------------------------------------------------------\n')
|
||||
print(summary(logitmodel))
|
||||
}
|
||||
|
||||
|
||||
predictmodel <- stats::predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
|
||||
} else if (model == 'loglin') {
|
||||
loglinmodel <- with(df, glm(R ~ year, family = poisson))
|
||||
if (info == TRUE) {
|
||||
|
|
@ -365,11 +364,11 @@ rsi_predict <- function(tbl,
|
|||
cat('\n--------------------------------------------------------------\n')
|
||||
print(summary(loglinmodel))
|
||||
}
|
||||
|
||||
|
||||
predictmodel <- stats::predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
|
||||
} else if (model %in% c('lin', 'linear')) {
|
||||
linmodel <- with(df, lm((R / (R + S)) ~ year))
|
||||
if (info == TRUE) {
|
||||
|
|
@ -377,36 +376,36 @@ rsi_predict <- function(tbl,
|
|||
cat('\n-----------------------\n')
|
||||
print(summary(linmodel))
|
||||
}
|
||||
|
||||
|
||||
predictmodel <- stats::predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
|
||||
} else {
|
||||
stop('No valid model selected.')
|
||||
}
|
||||
|
||||
|
||||
# prepare the output dataframe
|
||||
prediction <- data.frame(year = years_predict, probR = prediction, stringsAsFactors = FALSE)
|
||||
|
||||
|
||||
prediction$se_min <- prediction$probR - se
|
||||
prediction$se_max <- prediction$probR + se
|
||||
|
||||
|
||||
if (model == 'loglin') {
|
||||
prediction$probR <- prediction$probR %>%
|
||||
format(scientific = FALSE) %>%
|
||||
as.integer()
|
||||
prediction$se_min <- prediction$se_min %>% as.integer()
|
||||
prediction$se_max <- prediction$se_max %>% as.integer()
|
||||
|
||||
|
||||
colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
|
||||
} else {
|
||||
prediction$se_max[which(prediction$se_max > 1)] <- 1
|
||||
}
|
||||
prediction$se_min[which(prediction$se_min < 0)] <- 0
|
||||
|
||||
|
||||
total <- prediction
|
||||
|
||||
|
||||
if (preserve_measurements == TRUE) {
|
||||
# geschatte data vervangen door gemeten data
|
||||
if (I_as_R == TRUE) {
|
||||
|
|
@ -424,10 +423,10 @@ rsi_predict <- function(tbl,
|
|||
stringsAsFactors = FALSE)
|
||||
colnames(measurements) <- colnames(prediction)
|
||||
prediction <- prediction %>% filter(!year %in% df$year)
|
||||
|
||||
|
||||
total <- rbind(measurements, prediction)
|
||||
}
|
||||
|
||||
|
||||
total
|
||||
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ This package is available on CRAN and also here on GitHub.
|
|||
- `install.packages("AMR")`
|
||||
|
||||
- <img src="https://exploratory.io/favicon.ico" alt="Exploratory favicon" height="20px"> In [Exploratory.io](https://exploratory.io):
|
||||
- (Exploratory.io costs $40/month but is free for students and teachers; if you have an `@umcg.nl` or `@rug.nl` email address, [click here to enroll](https://exploratory.io/plan?plan=Community))
|
||||
- (Exploratory.io costs $40/month, but is free for students and teachers; if you have an `@umcg.nl` or `@rug.nl` email address, [click here to enroll](https://exploratory.io/plan?plan=Community))
|
||||
- Start the software and log in
|
||||
- Click on your username at the right hand side top
|
||||
- Click on `R Packages`
|
||||
|
|
|
|||
|
|
@ -34,14 +34,14 @@ This function uses the \code{\link{rsi_df}} function internally.
|
|||
tbl \%>\%
|
||||
group_by(hospital) \%>\%
|
||||
summarise(cipr = rsi(cipr))
|
||||
|
||||
|
||||
tbl \%>\%
|
||||
group_by(year, hospital) \%>\%
|
||||
summarise(
|
||||
isolates = n(),
|
||||
cipro = rsi(cipr \%>\% as.rsi(), percent = TRUE),
|
||||
amoxi = rsi(amox \%>\% as.rsi(), percent = TRUE))
|
||||
|
||||
|
||||
rsi(as.rsi(isolates$amox))
|
||||
|
||||
rsi(as.rsi(isolates$amcl), interpretation = "S")
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ rsi_df(tbl_with_bloodcultures, c('amcl', 'gent'), interpretation = 'IR')
|
|||
library(dplyr)
|
||||
# calculate current empiric therapy of Helicobacter gastritis:
|
||||
my_table \%>\%
|
||||
filter(first_isolate == TRUE,
|
||||
filter(first_isolate == TRUE,
|
||||
genus == "Helicobacter") \%>\%
|
||||
rsi_df(ab = c("amox", "metr"))
|
||||
}
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ Create a prediction model to predict antimicrobial resistance for the next years
|
|||
# use it directly:
|
||||
rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
||||
col_ab = "amcl", col_date = "date")
|
||||
|
||||
|
||||
# or with dplyr so you can actually read it:
|
||||
library(dplyr)
|
||||
tbl \%>\%
|
||||
|
|
@ -53,22 +53,22 @@ tbl \%>\%
|
|||
library(dplyr)
|
||||
septic_patients \%>\%
|
||||
# get bacteria properties like genus and species
|
||||
left_join_microorganisms("bactid") \%>\%
|
||||
left_join_microorganisms("bactid") \%>\%
|
||||
# calculate first isolates
|
||||
mutate(first_isolate =
|
||||
mutate(first_isolate =
|
||||
first_isolate(.,
|
||||
"date",
|
||||
"patient_id",
|
||||
"bactid",
|
||||
col_specimen = NA,
|
||||
col_icu = NA)) \%>\%
|
||||
col_icu = NA)) \%>\%
|
||||
# filter on first E. coli isolates
|
||||
filter(genus == "Escherichia",
|
||||
species == "coli",
|
||||
filter(genus == "Escherichia",
|
||||
species == "coli",
|
||||
first_isolate == TRUE) \%>\%
|
||||
# predict resistance of cefotaxime for next years
|
||||
rsi_predict(col_ab = cfot,
|
||||
col_date = date,
|
||||
rsi_predict(col_ab = "cfot",
|
||||
col_date = "date",
|
||||
year_max = 2025,
|
||||
preserve_measurements = FALSE)
|
||||
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ test_that("atc_property works", {
|
|||
test_that("abname works", {
|
||||
expect_equal(abname("AMOX"), "Amoxicillin")
|
||||
expect_equal(abname(c("AMOX", "GENT")), c("Amoxicillin", "Gentamicin"))
|
||||
expect_equal(abname(c("AMOX+GENT")), "Amoxicillin + gentamicin")
|
||||
expect_equal(abname("AMOX", from = 'umcg'), "Amoxicillin")
|
||||
expect_equal(abname("amox", from = 'molis'), "Amoxicillin")
|
||||
expect_equal(abname("J01CA04", from = 'atc'), "Amoxicillin")
|
||||
|
|
|
|||
|
|
@ -5,11 +5,14 @@ test_that("rsi works", {
|
|||
expect_true(as.rsi("I") < as.rsi("R"))
|
||||
expect_true(as.rsi("R") > as.rsi("S"))
|
||||
expect_true(is.rsi(as.rsi("S")))
|
||||
|
||||
|
||||
# print plots, should not raise errors
|
||||
barplot(as.rsi(c("S", "I", "R")))
|
||||
plot(as.rsi(c("S", "I", "R")))
|
||||
print(as.rsi(c("S", "I", "R")))
|
||||
|
||||
expect_equal(suppressWarnings(as.logical(as.rsi("INVALID VALUE"))), NA)
|
||||
|
||||
expect_equal(class(barplot(as.rsi(c("S", "I", "R")))), "numeric")
|
||||
|
||||
|
||||
expect_equal(summary(as.rsi(c("S", "R"))), c("Mode" = 'rsi',
|
||||
"<NA>" = "0",
|
||||
"Sum S" = "1",
|
||||
|
|
@ -23,13 +26,16 @@ test_that("mic works", {
|
|||
expect_true(as.mic("1") > as.mic("<=0.0625"))
|
||||
expect_true(as.mic("1") < as.mic(">=32"))
|
||||
expect_true(is.mic(as.mic(8)))
|
||||
|
||||
|
||||
expect_equal(as.double(as.mic(">=32")), 32)
|
||||
expect_equal(as.integer(as.mic(">=32")), 32)
|
||||
expect_equal(suppressWarnings(as.logical(as.mic("INVALID VALUE"))), NA)
|
||||
|
||||
expect_equal(class(plot(as.mic(c(1, 2, 4, 8)))), "numeric")
|
||||
|
||||
|
||||
# print plots, should not raise errors
|
||||
barplot(as.mic(c(1, 2, 4, 8)))
|
||||
plot(as.mic(c(1, 2, 4, 8)))
|
||||
print(as.mic(c(1, 2, 4, 8)))
|
||||
|
||||
expect_equal(summary(as.mic(c(2, 8))), c("Mode" = 'mic',
|
||||
"<NA>" = "0",
|
||||
"Min." = "2",
|
||||
|
|
|
|||
|
|
@ -12,8 +12,8 @@ test_that("EUCAST rules work", {
|
|||
amox = "R", # Amoxicillin
|
||||
stringsAsFactors = FALSE)
|
||||
expect_equal(EUCAST_rules(a, info = FALSE), b)
|
||||
expect_equal(interpretive_reading(a, info = FALSE), b)
|
||||
|
||||
expect_equal(suppressWarnings(interpretive_reading(a, info = TRUE)), b)
|
||||
|
||||
a <- data.frame(bactid = c("STAAUR", # Staphylococcus aureus
|
||||
"STCGRA"), # Streptococcus pyognenes (Lancefield Group A)
|
||||
coli = "-", # Colistin
|
||||
|
|
|
|||
|
|
@ -7,7 +7,6 @@ test_that("keyantibiotics work", {
|
|||
expect_false(key_antibiotics_equal("SSS", "SIS", ignore_I = FALSE))
|
||||
})
|
||||
|
||||
|
||||
test_that("guess_bactid works", {
|
||||
expect_equal(guess_bactid("E. coli"), "ESCCOL")
|
||||
expect_equal(guess_bactid("Escherichia coli"), "ESCCOL")
|
||||
|
|
@ -15,9 +14,23 @@ test_that("guess_bactid works", {
|
|||
|
||||
test_that("first isolates work", {
|
||||
# septic_patients contains 1960 out of 2000 first isolates
|
||||
expect_equal(sum(first_isolate(septic_patients,
|
||||
"date",
|
||||
"patient_id",
|
||||
"bactid",
|
||||
info = FALSE)), 1960)
|
||||
septic_ptns <- septic_patients
|
||||
expect_equal(sum(first_isolate(tbl = septic_ptns,
|
||||
col_date = "date",
|
||||
col_patient_id = "patient_id",
|
||||
col_bactid = "bactid",
|
||||
info = FALSE)), 1960)
|
||||
|
||||
# septic_patients contains 1962 out of 2000 first weighted isolates
|
||||
septic_ptns$keyab <- suppressWarnings(key_antibiotics(septic_ptns))
|
||||
expect_equal(
|
||||
suppressWarnings(sum(
|
||||
first_isolate(tbl = septic_ptns,
|
||||
col_date = "date",
|
||||
col_patient_id = "patient_id",
|
||||
col_bactid = "bactid",
|
||||
col_keyantibiotics = "keyab",
|
||||
type = "keyantibiotics",
|
||||
info = TRUE))),
|
||||
1962)
|
||||
})
|
||||
|
|
|
|||
|
|
@ -12,9 +12,3 @@ test_that("percentages works", {
|
|||
expect_equal(percent(0.5), "50%")
|
||||
expect_equal(percent(0.1234), "12.3%")
|
||||
})
|
||||
|
||||
test_that("quasiquotation works", {
|
||||
expect_equal(quasiquotate(deparse(substitute("test")), "test"), "test")
|
||||
expect_equal(quasiquotate(deparse(substitute('test')), "'test'"), "test")
|
||||
expect_equal(quasiquotate(deparse(substitute(test)), test), "test")
|
||||
})
|
||||
|
|
|
|||
Loading…
Reference in New Issue