diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 8f840204..234b6383 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -93,7 +93,6 @@ R-release:
allow_failure: true
script:
- Rscript -e 'sessionInfo()'
- - Rscript -e 'AMR::eucast_rules(AMR::example_isolates_unclean, info = TRUE)'
# install missing and outdated packages
- Rscript -e 'source(".gitlab-ci.R"); gl_update_pkg_all(repos = "https://cran.rstudio.com", quiet = TRUE, install_pkgdown = TRUE, install_lintr = TRUE)'
# remove vignettes folder and get VignetteBuilder field out of DESCRIPTION file
@@ -102,6 +101,8 @@ R-release:
# build package
- R CMD build . --no-build-vignettes --no-manual
- PKG_FILE_NAME=$(ls -1t *.tar.gz | head -n 1)
+ - Rscript -e 'devtools::install_local("${PKG_FILE_NAME}")'
+ - Rscript -e 'AMR::eucast_rules(AMR::example_isolates_unclean, info = TRUE)'
- Rscript -e 'Sys.setenv(NOT_CRAN = "true")'
- R CMD check "${PKG_FILE_NAME}" --no-build-vignettes --no-manual --as-cran
artifacts:
@@ -121,7 +122,6 @@ R-devel:
allow_failure: true
script:
- Rscriptdevel -e 'sessionInfo()'
- - Rscriptdevel -e 'AMR::eucast_rules(AMR::example_isolates_unclean, info = TRUE)'
# install missing and outdated packages
- Rscriptdevel -e 'source(".gitlab-ci.R"); gl_update_pkg_all(repos = "https://cran.rstudio.com", quiet = TRUE)'
# remove vignettes folder and get VignetteBuilder field out of DESCRIPTION file
@@ -130,6 +130,8 @@ R-devel:
# build package
- Rdevel CMD build . --no-build-vignettes --no-manual
- PKG_FILE_NAME=$(ls -1t *.tar.gz | head -n 1)
+ - Rscript -e 'devtools::install_local("${PKG_FILE_NAME}")'
+ - Rscript -e 'AMR::eucast_rules(AMR::example_isolates_unclean, info = TRUE)'
- Rscript -e 'Sys.setenv(NOT_CRAN = "true")'
- Rdevel CMD check "${PKG_FILE_NAME}" --no-build-vignettes --no-manual --as-cran
artifacts:
diff --git a/DESCRIPTION b/DESCRIPTION
index 9ec90077..3329ab74 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: AMR
-Version: 1.1.0.9008
-Date: 2020-05-17
+Version: 1.1.0.9009
+Date: 2020-05-18
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(role = c("aut", "cre"),
diff --git a/NEWS.md b/NEWS.md
index 57a65e32..522d7c4a 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,5 @@
-# AMR 1.1.0.9008
-## Last updated: 17-May-2020
+# AMR 1.1.0.9009
+## Last updated: 18-May-2020
### Breaking
* Removed previously deprecated function `p.symbol()` - it was replaced with `p_symbol()`
diff --git a/R/atc_online.R b/R/atc_online.R
index 0b056755..00330f27 100644
--- a/R/atc_online.R
+++ b/R/atc_online.R
@@ -132,7 +132,7 @@ atc_online_property <- function(atc_code,
for (i in seq_len(length(atc_code))) {
- progress$tick()$print()
+ progress$tick()
atc_url <- sub("%s", atc_code[i], url, fixed = TRUE)
diff --git a/R/first_isolate.R b/R/first_isolate.R
index 3d21899b..1c61cc0c 100755
--- a/R/first_isolate.R
+++ b/R/first_isolate.R
@@ -46,12 +46,9 @@
#'
#' All isolates with a microbial ID of `NA` will be excluded as first isolate.
#'
-#' The functions [filter_first_isolate()] and [filter_first_weighted_isolate()] are helper functions to quickly filter on first isolates. The function [filter_first_isolate()] is essentially equal to:
+#' The functions [filter_first_isolate()] and [filter_first_weighted_isolate()] are helper functions to quickly filter on first isolates. The function [filter_first_isolate()] is essentially equal to one of:
#' ```
-#' x %>%
-#' mutate(only_firsts = first_isolate(x, ...)) %>%
-#' filter(only_firsts == TRUE) %>%
-#' select(-only_firsts)
+#' x %>% filter(first_isolate(., ...))
#' ```
#' The function [filter_first_weighted_isolate()] is essentially equal to:
#' ```
@@ -60,7 +57,7 @@
#' mutate(only_weighted_firsts = first_isolate(x,
#' col_keyantibiotics = "keyab", ...)) %>%
#' filter(only_weighted_firsts == TRUE) %>%
-#' select(-only_weighted_firsts)
+#' select(-only_weighted_firsts, -keyab)
#' ```
#' @section Key antibiotics:
#' There are two ways to determine whether isolates can be included as first *weighted* isolates which will give generally the same results:
@@ -451,7 +448,7 @@ filter_first_isolate <- function(x,
col_patient_id = NULL,
col_mo = NULL,
...) {
- filter(x, first_isolate(x = x,
+ subset(x, first_isolate(x = x,
col_date = col_date,
col_patient_id = col_patient_id,
col_mo = col_mo,
@@ -466,15 +463,23 @@ filter_first_weighted_isolate <- function(x,
col_mo = NULL,
col_keyantibiotics = NULL,
...) {
- tbl_keyab <- x %>%
- mutate(keyab = suppressMessages(key_antibiotics(.,
- col_mo = col_mo,
- ...))) %>%
- mutate(firsts = first_isolate(.,
- col_date = col_date,
- col_patient_id = col_patient_id,
- col_mo = col_mo,
- col_keyantibiotics = "keyab",
- ...))
- x[which(tbl_keyab$firsts == TRUE), ]
+ y <- x
+ if (is.null(col_keyantibiotics)) {
+ # first try to look for it
+ col_keyantibiotics <- search_type_in_df(x = x, type = "keyantibiotics")
+ # still NULL? Then create it since we are calling filter_first_WEIGHTED_isolate()
+ if (is.null(col_keyantibiotics)) {
+ y$keyab <- suppressMessages(key_antibiotics(x,
+ col_mo = col_mo,
+ ...))
+ col_keyantibiotics <- "keyab"
+ }
+ }
+
+ subset(x, first_isolate(x = y,
+ col_date = col_date,
+ col_patient_id = col_patient_id,
+ col_mo = col_mo,
+ col_keyantibiotics = col_keyantibiotics,
+ ...))
}
diff --git a/R/key_antibiotics.R b/R/key_antibiotics.R
index 9dc7c233..9bfc4770 100755
--- a/R/key_antibiotics.R
+++ b/R/key_antibiotics.R
@@ -214,11 +214,9 @@ key_antibiotics <- function(x,
warning("only using ", length(gram_negative), " different antibiotics as key antibiotics for Gram-negatives. See ?key_antibiotics.", call. = FALSE)
}
- # join to microorganisms data set
- x <- x %>% as.data.frame(stringsAsFactors = FALSE)
+ x <- as.data.frame(x, stringsAsFactors = FALSE)
x[, col_mo] <- as.mo(x[, col_mo, drop = TRUE])
x$gramstain <- mo_gramstain(x[, col_mo, drop = TRUE], language = NULL)
-
x$key_ab <- NA_character_
# Gram +
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 37c5c086..7ae14e2d 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -96,7 +96,6 @@ reference:
- "`eucast_rules`"
- "`guess_ab_col`"
- "`mo_source`"
- - "`read.4D`"
- title: "Enhancing your data"
desc: >
Functions to add new data to your existing data, such as the determination
diff --git a/docs/404.html b/docs/404.html
index 34dd5f5b..51e5383b 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -81,7 +81,7 @@
AMR (for R)
- 1.1.0.9008
+ 1.1.0.9009
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 410fa440..3e6c41d9 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -81,7 +81,7 @@
AMR (for R)
- 1.1.0.9008
+ 1.1.0.9009
diff --git a/docs/articles/AMR.html b/docs/articles/AMR.html
index 48eb7097..bbc4833d 100644
--- a/docs/articles/AMR.html
+++ b/docs/articles/AMR.html
@@ -39,7 +39,7 @@
AMR (for R)
- 1.1.0
+ 1.1.0.9009
@@ -186,7 +186,7 @@
How to conduct AMR analysis
Matthijs S. Berends
- 15 April 2020
+ 18 May 2020
Source: vignettes/AMR.Rmd
AMR.Rmd
@@ -195,15 +195,15 @@
-Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 15 April 2020.
+Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 18 May 2020.
Introduction
Conducting antimicrobial resistance analysis unfortunately requires in-depth knowledge from different scientific fields, which makes it hard to do right. At least, it requires:
-- Good questions (always start with these!)
+- Good questions (always start with those!)
- A thorough understanding of (clinical) epidemiology, to understand the clinical and epidemiological relevance and possible bias of results
-- A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment
+- A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment, as well as understanding intrinsic and acquired microbial resistance
- Experience with data analysis with microbiological tests and their results, to understand the determination and limitations of MIC values and their interpretations to RSI values
- Availability of the biological taxonomy of microorganisms and probably normalisation factors for pharmaceuticals, such as defined daily doses (DDD)
- Available (inter-)national guidelines, and profound methods to apply them
@@ -226,21 +226,21 @@
-| 2020-04-15 |
+2020-05-18 |
abcd |
Escherichia coli |
S |
S |
-| 2020-04-15 |
+2020-05-18 |
abcd |
Escherichia coli |
S |
R |
-| 2020-04-15 |
+2020-05-18 |
efgh |
Escherichia coli |
R |
@@ -258,7 +258,7 @@
library(AMR)
# (if not yet installed, install with:)
-# install.packages(c("tidyverse", "AMR"))