diff --git a/.github/workflows/check.yaml b/.github/workflows/check.yaml
index c2df5cf4c..aa0128f0b 100644
--- a/.github/workflows/check.yaml
+++ b/.github/workflows/check.yaml
@@ -171,5 +171,5 @@ jobs:
if: always()
uses: actions/upload-artifact@v2
with:
- name: artifacts-r-${{ matrix.config.r }}-${{ matrix.config.os }}
+ name: ${{ matrix.config.os }}-r-${{ matrix.config.r }}-artifacts
path: AMR.Rcheck
diff --git a/DESCRIPTION b/DESCRIPTION
index f9a9748ee..63807d16b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: AMR
-Version: 1.7.1.9029
-Date: 2021-08-21
+Version: 1.7.1.9030
+Date: 2021-08-29
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by
diff --git a/NEWS.md b/NEWS.md
index aa7a6f4a4..f1d62d840 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,5 @@
-# `AMR` 1.7.1.9029
-## Last updated: 21 August 2021
+# `AMR` 1.7.1.9030
+## Last updated: 29 August 2021
### Breaking changes
* Removed `p_symbol()` and all `filter_*()` functions (except for `filter_first_isolate()`), which were all deprecated in a previous package version
@@ -12,11 +12,12 @@
### Changed
* The `antibiotics` data set now contains **all ATC codes** that are available through the [WHOCC website](https://www.whocc.no), regardless of drugs being present in more than one ATC group. This means that:
* Some drugs now contain multiple ATC codes (e.g., metronidazole contains 5)
- * `antibiotics$atc` is now a `list` instead of a `character`, and this `atc` column was moved to the 5th position of the `antibiotics` data set
- * `ab_atc()` does not always return a character vector with length 1, and returns a `list` if the input is larger than length 1
+ * `antibiotics$atc` is now a `list` containing `character` vectors, and this `atc` column was moved to the 5th position of the `antibiotics` data set
+ * `ab_atc()` does not always return a character vector of length 1, and returns a `list` if the input is larger than length 1
+ * `ab_info()` has a slightly different output
* Some DDDs (daily defined doses) were added or updated according to newly included ATC codes
* Antibiotic selectors
- * They now also work in R-3.0 and R-3.1, supporting every version of R since 2013
+ * They now also work in R-3.0 and R-3.1, supporting every version of R since 2013 like the rest of the package
* Added more selectors for antibiotic classes: `aminopenicillins()`, `antifungals()`, `antimycobacterials()`, `lincosamides()`, `lipoglycopeptides()`, `polymyxins()`, `quinolones()`, `streptogramins()`, `trimethoprims()` and `ureidopenicillins()`
* Added specific selectors for certain types for treatment: `administrable_per_os()` and `administrable_iv()`, which are based on available Defined Daily Doses (DDDs), as defined by the WHOCC. These are ideal for e.g. analysing pathogens in primary care where IV treatment is not an option. They can be combined with other AB selectors, e.g. to select penicillins that are only administrable per os (i.e., orally):
```r
@@ -30,8 +31,7 @@
```
* Fix for using selectors multiple times in one call (e.g., using them in `dplyr::filter()` and immediately after in `dplyr::select()`)
* Added argument `only_treatable`, which defaults to `TRUE` and will exclude drugs that are only for laboratory tests and not for treating patients (such as imipenem/EDTA and gentamicin-high)
-* Fixed the Gram stain (`mo_gramstain()`) determination of the class Negativicutes within the phylum of Firmicutes - they were considered Gram-positives because of their phylum but are actually Gram-negative. This impacts 137 taxonomic species, genera and families, such as *Negativicoccus* and *Veillonella*.
-* Fix for duplicate ATC codes in the `antibiotics` data set
+* Fixed the Gram stain (`mo_gramstain()`) determination of the taxonomic class Negativicutes within the phylum of Firmicutes - they were considered Gram-positives because of their phylum but are actually Gram-negative. This impacts 137 taxonomic species, genera and families, such as *Negativicoccus* and *Veillonella*.
* Fix to prevent introducing `NA`s for old MO codes when running `as.mo()` on them
* Added more informative error messages when any of the `proportion_*()` and `count_*()` functions fail
* When printing a tibble with any old MO code, a warning will be thrown that old codes should be updated using `as.mo()`
diff --git a/R/ab_property.R b/R/ab_property.R
index 93c308ba0..d7939610e 100644
--- a/R/ab_property.R
+++ b/R/ab_property.R
@@ -288,7 +288,7 @@ ab_ddd <- function(x, administration = "oral", ...) {
units <- list(...)$units
if (!is.null(units) && isTRUE(units)) {
if (message_not_thrown_before("ab_ddd", entire_session = TRUE)) {
- warning_("Using `ab_ddd(..., units = TRUE)` is deprecated, use `ab_ddd_units()` instead. ",
+ warning_("Using `ab_ddd(..., units = TRUE)` is deprecated, use `ab_ddd_units()` to retrieve units instead. ",
"This warning will be shown once per session.", call = FALSE)
}
ddd_prop <- paste0(ddd_prop, "_units")
@@ -297,7 +297,7 @@ ab_ddd <- function(x, administration = "oral", ...) {
}
out <- ab_validate(x = x, property = ddd_prop)
- if (any(ab_name(x, language = NULL) %like% "/" & is.na(out)) ) {
+ if (any(ab_name(x, language = NULL) %like% "/" & is.na(out))) {
warning_("DDDs of some combined products are available for different dose combinations and not (yet) part of the AMR package. ",
"Please refer to the WHOCC website:\n",
"www.whocc.no/ddd/list_of_ddds_combined_products/", call = FALSE)
@@ -330,17 +330,18 @@ ab_info <- function(x, language = get_locale(), ...) {
x <- as.ab(x, ...)
list(ab = as.character(x),
- atc = ab_atc(x),
- cid = ab_cid(x),
- name = ab_name(x, language = language),
- group = ab_group(x, language = language),
- atc_group1 = ab_atc_group1(x, language = language),
- atc_group2 = ab_atc_group2(x, language = language),
- tradenames = ab_tradenames(x),
- ddd = list(oral = list(amount = ab_ddd(x, administration = "oral"),
- units = ab_ddd_units(x, administration = "oral")),
- iv = list(amount = ab_ddd(x, administration = "iv"),
- units = ab_ddd_units(x, administration = "iv"))))
+ cid = ab_cid(x),
+ name = ab_name(x, language = language),
+ group = ab_group(x, language = language),
+ atc = ab_atc(x),
+ atc_group1 = ab_atc_group1(x, language = language),
+ atc_group2 = ab_atc_group2(x, language = language),
+ tradenames = ab_tradenames(x),
+ loinc = ab_loinc(x),
+ ddd = list(oral = list(amount = ab_ddd(x, administration = "oral"),
+ units = ab_ddd_units(x, administration = "oral")),
+ iv = list(amount = ab_ddd(x, administration = "iv"),
+ units = ab_ddd_units(x, administration = "iv"))))
}
diff --git a/data-raw/AMR_latest.tar.gz b/data-raw/AMR_latest.tar.gz
index 1028ad199..d1a048b80 100644
Binary files a/data-raw/AMR_latest.tar.gz and b/data-raw/AMR_latest.tar.gz differ
diff --git a/docs/404.html b/docs/404.html
index 4eb001865..e6bfdad70 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -43,7 +43,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -173,13 +173,6 @@
Source Code
-
-
-
-
-
- Survey
-
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 1140365c6..e7f642ac1 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -92,7 +92,7 @@
AMR (for R)
- 1.7.1.9025
+ 1.7.1.9030
diff --git a/docs/articles/AMR.html b/docs/articles/AMR.html
index bded04148..46fcbb4c8 100644
--- a/docs/articles/AMR.html
+++ b/docs/articles/AMR.html
@@ -44,7 +44,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -174,13 +174,6 @@
Source Code
-
-
-
-
-
- Survey
-
@@ -198,7 +191,7 @@
How to conduct AMR data analysis
Matthijs S. Berends
- 17 August 2021
+ 29 August 2021
Source: vignettes/AMR.Rmd
AMR.Rmd
@@ -207,7 +200,7 @@
-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 17 August 2021.
+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 29 August 2021.
Introduction
@@ -238,21 +231,21 @@
-| 2021-08-17 |
+2021-08-29 |
abcd |
Escherichia coli |
S |
S |
-| 2021-08-17 |
+2021-08-29 |
abcd |
Escherichia coli |
S |
R |
-| 2021-08-17 |
+2021-08-29 |
efgh |
Escherichia coli |
R |
@@ -349,68 +342,68 @@
-| 2012-12-11 |
-B5 |
-Hospital D |
-Streptococcus pneumoniae |
-R |
-S |
-S |
-R |
-M |
-
-
-| 2012-01-03 |
-D5 |
-Hospital B |
-Escherichia coli |
-S |
-S |
-S |
-S |
-M |
-
-
-| 2013-03-20 |
-E3 |
+2012-06-29 |
+E6 |
Hospital A |
Escherichia coli |
-I |
-I |
-S |
-S |
-M |
-
-
-| 2017-02-14 |
-L5 |
-Hospital B |
-Klebsiella pneumoniae |
R |
S |
S |
S |
M |
-
-| 2010-04-27 |
-Y6 |
+
+| 2014-08-25 |
+D8 |
Hospital D |
-Escherichia coli |
+Klebsiella pneumoniae |
+R |
+I |
S |
S |
+M |
+
+
+| 2017-01-14 |
+V1 |
+Hospital C |
+Streptococcus pneumoniae |
+I |
+S |
S |
S |
F |
-| 2011-08-04 |
-O2 |
+2010-10-15 |
+X2 |
Hospital B |
Staphylococcus aureus |
+S |
+S |
+S |
+R |
+F |
+
+
+| 2011-04-05 |
+H6 |
+Hospital A |
+Streptococcus pneumoniae |
+S |
+I |
+S |
+S |
+M |
+
+
+| 2012-01-10 |
+Y7 |
+Hospital D |
+Streptococcus pneumoniae |
R |
S |
-S |
+R |
S |
F |
@@ -446,16 +439,16 @@ Longest: 1
| 1 |
M |
-10,443 |
-52.22% |
-10,443 |
-52.22% |
+10,326 |
+51.63% |
+10,326 |
+51.63% |
| 2 |
F |
-9,557 |
-47.79% |
+9,674 |
+48.37% |
20,000 |
100.00% |
@@ -510,9 +503,9 @@ Longest: 1
# ℹ Using column 'patient_id' as input for `col_patient_id`.
# Basing inclusion on all antimicrobial results, using a points threshold of
# 2
-# => Found 10,606 first weighted isolates (phenotype-based, 53.0% of total
+# => Found 10,571 first weighted isolates (phenotype-based, 52.9% of total
# where a microbial ID was available)
-So only 53.0% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
+So only 52.9% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
data_1st <- data %>%
filter(first == TRUE)
So we end up with 10,606 isolates for analysis. Now our data looks like:
+So we end up with 10,571 isolates for analysis. Now our data looks like:
@@ -559,27 +552,11 @@ Longest: 1
| 1 |
-2012-12-11 |
-B5 |
-Hospital D |
-B_STRPT_PNMN |
-R |
-R |
-S |
-R |
-M |
-Gram-positive |
-Streptococcus |
-pneumoniae |
-TRUE |
-
-
-| 2 |
-2012-01-03 |
-D5 |
-Hospital B |
+2012-06-29 |
+E6 |
+Hospital A |
B_ESCHR_COLI |
-S |
+R |
S |
S |
S |
@@ -589,14 +566,14 @@ Longest: 1
coli |
TRUE |
-
-| 4 |
-2017-02-14 |
-L5 |
-Hospital B |
+
+| 2 |
+2014-08-25 |
+D8 |
+Hospital D |
B_KLBSL_PNMN |
R |
-S |
+I |
S |
S |
M |
@@ -605,52 +582,68 @@ Longest: 1
pneumoniae |
TRUE |
-
-| 7 |
-2012-03-07 |
-I8 |
-Hospital B |
-B_ESCHR_COLI |
-S |
-S |
-S |
-S |
-M |
-Gram-negative |
-Escherichia |
-coli |
-TRUE |
-
-| 9 |
-2010-12-20 |
-H10 |
+4 |
+2010-10-15 |
+X2 |
Hospital B |
B_STPHY_AURS |
-R |
+S |
+S |
S |
R |
-S |
-M |
+F |
Gram-positive |
Staphylococcus |
aureus |
TRUE |
-| 11 |
-2014-01-24 |
-M2 |
-Hospital B |
-B_ESCHR_COLI |
+6 |
+2012-01-10 |
+Y7 |
+Hospital D |
+B_STRPT_PNMN |
R |
R |
R |
+R |
+F |
+Gram-positive |
+Streptococcus |
+pneumoniae |
+TRUE |
+
+
+| 8 |
+2016-02-07 |
+V2 |
+Hospital A |
+B_KLBSL_PNMN |
+R |
S |
-M |
+S |
+S |
+F |
Gram-negative |
-Escherichia |
-coli |
+Klebsiella |
+pneumoniae |
+TRUE |
+
+
+| 10 |
+2016-11-08 |
+D4 |
+Hospital A |
+B_STPHY_AURS |
+R |
+S |
+S |
+S |
+M |
+Gram-positive |
+Staphylococcus |
+aureus |
TRUE |
@@ -674,8 +667,8 @@ Longest: 1
data_1st %>% freq(genus, species)
Frequency table
Class: character
-Length: 10,606
-Available: 10,606 (100.0%, NA: 0 = 0.0%)
+Length: 10,571
+Available: 10,571 (100.0%, NA: 0 = 0.0%)
Unique: 4
Shortest: 16
Longest: 24
@@ -692,33 +685,33 @@ Longest: 24
| 1 |
Escherichia coli |
-4,494 |
-42.37% |
-4,494 |
-42.37% |
+4,507 |
+42.64% |
+4,507 |
+42.64% |
| 2 |
Staphylococcus aureus |
-2,785 |
-26.26% |
-7,279 |
-68.63% |
+2,799 |
+26.48% |
+7,306 |
+69.11% |
| 3 |
Streptococcus pneumoniae |
-2,106 |
-19.86% |
-9,385 |
-88.49% |
+2,087 |
+19.74% |
+9,393 |
+88.86% |
| 4 |
Klebsiella pneumoniae |
-1,221 |
-11.51% |
-10,606 |
+1,178 |
+11.14% |
+10,571 |
100.00% |
@@ -765,8 +758,83 @@ Longest: 24
-| 2012-12-11 |
-B5 |
+2010-10-15 |
+X2 |
+Hospital B |
+B_STPHY_AURS |
+S |
+S |
+S |
+R |
+F |
+Gram-positive |
+Staphylococcus |
+aureus |
+TRUE |
+
+
+| 2012-01-10 |
+Y7 |
+Hospital D |
+B_STRPT_PNMN |
+R |
+R |
+R |
+R |
+F |
+Gram-positive |
+Streptococcus |
+pneumoniae |
+TRUE |
+
+
+| 2014-02-02 |
+K7 |
+Hospital A |
+B_STPHY_AURS |
+S |
+S |
+S |
+R |
+M |
+Gram-positive |
+Staphylococcus |
+aureus |
+TRUE |
+
+
+| 2011-08-05 |
+V3 |
+Hospital B |
+B_STRPT_PNMN |
+S |
+S |
+S |
+R |
+F |
+Gram-positive |
+Streptococcus |
+pneumoniae |
+TRUE |
+
+
+| 2014-06-16 |
+Z8 |
+Hospital B |
+B_STRPT_PNMN |
+S |
+S |
+S |
+R |
+F |
+Gram-positive |
+Streptococcus |
+pneumoniae |
+TRUE |
+
+
+| 2017-08-23 |
+J10 |
Hospital D |
B_STRPT_PNMN |
R |
@@ -779,81 +847,6 @@ Longest: 24
pneumoniae |
TRUE |
-
-| 2011-06-11 |
-L7 |
-Hospital C |
-B_STRPT_PNMN |
-R |
-R |
-S |
-R |
-M |
-Gram-positive |
-Streptococcus |
-pneumoniae |
-TRUE |
-
-
-| 2012-12-19 |
-A10 |
-Hospital B |
-B_STRPT_PNMN |
-S |
-S |
-R |
-R |
-M |
-Gram-positive |
-Streptococcus |
-pneumoniae |
-TRUE |
-
-
-| 2014-12-05 |
-M6 |
-Hospital B |
-B_ESCHR_COLI |
-R |
-S |
-S |
-R |
-M |
-Gram-negative |
-Escherichia |
-coli |
-TRUE |
-
-
-| 2017-05-14 |
-L8 |
-Hospital C |
-B_ESCHR_COLI |
-S |
-S |
-S |
-R |
-M |
-Gram-negative |
-Escherichia |
-coli |
-TRUE |
-
-
-| 2010-12-14 |
-Q1 |
-Hospital C |
-B_STPHY_AURS |
-S |
-S |
-S |
-R |
-F |
-Gram-positive |
-Staphylococcus |
-aureus |
-TRUE |
-
If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the bug_drug_combinations() function:
@@ -875,50 +868,50 @@ Longest: 24
| E. coli |
AMX |
-2137 |
-139 |
-2218 |
-4494 |
+2196 |
+119 |
+2192 |
+4507 |
| E. coli |
AMC |
-3298 |
-157 |
-1039 |
-4494 |
+3378 |
+151 |
+978 |
+4507 |
| E. coli |
CIP |
-3230 |
+3282 |
0 |
-1264 |
-4494 |
+1225 |
+4507 |
| E. coli |
GEN |
-3946 |
+3929 |
0 |
-548 |
-4494 |
+578 |
+4507 |
| K. pneumoniae |
AMX |
0 |
0 |
-1221 |
-1221 |
+1178 |
+1178 |
| K. pneumoniae |
AMC |
-967 |
-44 |
-210 |
-1221 |
+930 |
+45 |
+203 |
+1178 |
@@ -941,34 +934,34 @@ Longest: 24
| E. coli |
GEN |
-3946 |
+3929 |
0 |
-548 |
-4494 |
+578 |
+4507 |
| K. pneumoniae |
GEN |
-1088 |
+1052 |
0 |
-133 |
-1221 |
+126 |
+1178 |
| S. aureus |
GEN |
-2490 |
+2473 |
0 |
-295 |
-2785 |
+326 |
+2799 |
| S. pneumoniae |
GEN |
0 |
0 |
-2106 |
-2106 |
+2087 |
+2087 |
@@ -982,7 +975,7 @@ Longest: 24
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
+
# [1] 0.5403462
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
data_1st %>%
@@ -996,19 +989,19 @@ Longest: 24
| Hospital A |
-0.5356582 |
+0.5487147 |
| Hospital B |
-0.5408108 |
+0.5436519 |
| Hospital C |
-0.5371153 |
+0.5257549 |
| Hospital D |
-0.5614714 |
+0.5338095 |
@@ -1027,23 +1020,23 @@ Longest: 24
| Hospital A |
-0.5356582 |
-3183 |
+0.5487147 |
+3151 |
| Hospital B |
-0.5408108 |
-3700 |
+0.5436519 |
+3631 |
| Hospital C |
-0.5371153 |
-1657 |
+0.5257549 |
+1689 |
| Hospital D |
-0.5614714 |
-2066 |
+0.5338095 |
+2100 |
@@ -1064,27 +1057,27 @@ Longest: 24
| Escherichia |
-0.7688028 |
-0.8780596 |
-0.9815309 |
+0.7830042 |
+0.8717550 |
+0.9764810 |
| Klebsiella |
-0.8280098 |
-0.8910729 |
-0.9754300 |
+0.8276740 |
+0.8930390 |
+0.9787776 |
| Staphylococcus |
-0.7921005 |
-0.8940754 |
-0.9795332 |
+0.7874241 |
+0.8835298 |
+0.9828510 |
| Streptococcus |
-0.5351377 |
+0.5318639 |
0.0000000 |
-0.5351377 |
+0.5318639 |
@@ -1109,23 +1102,23 @@ Longest: 24
| Hospital A |
-53.6% |
-26.4% |
+54.9% |
+25.8% |
| Hospital B |
-54.1% |
-24.6% |
+54.4% |
+27.8% |
| Hospital C |
-53.7% |
-27.5% |
+52.6% |
+25.0% |
| Hospital D |
-56.1% |
-29.0% |
+53.4% |
+24.3% |
@@ -1209,16 +1202,16 @@ Longest: 24
mic_values <- random_mic(size = 100)
mic_values
# Class <mic>
-# [1] 0.25 16 0.0625 32 0.025 1 256 64 0.002 0.005
-# [11] 0.01 0.005 8 0.01 0.025 128 0.01 8 0.005 256
-# [21] 0.005 8 0.0625 0.125 0.005 16 8 2 0.5 4
-# [31] 2 32 0.25 0.25 8 0.025 0.25 8 64 1
-# [41] 0.005 0.01 0.005 256 0.025 0.5 2 0.025 0.5 0.005
-# [51] 0.0625 128 4 1 0.0625 0.002 16 0.002 8 0.005
-# [61] 1 1 8 32 0.002 32 0.25 8 4 0.001
-# [71] 2 16 16 0.025 0.005 0.0625 2 128 0.001 2
-# [81] 64 0.025 16 64 2 128 8 0.5 4 256
-# [91] 0.5 32 0.001 0.01 2 64 4 32 4 0.0625
# base R:
plot(mic_values)
# base R:
plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data)
# rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
-# 1 I I I S R R
-# 2 I R R R I R
-# 3 S I R S I I
-# 4 S S S S I R
-# 5 R S R I I S
-# 6 I R S R R I
+# 1 S I I R I I
+# 2 S S I I R R
+# 3 I S S I I R
+# 4 S R S R S R
+# 5 R S R S R R
+# 6 I S S R I S
# kanamycin
# 1 I
# 2 R
# 3 I
# 4 R
# 5 R
-# 6 S
We can now add the interpretation of MDR-TB to our data set. You can use:
mdro(my_TB_data, guideline = "TB")
| 1 |
Mono-resistant |
-3227 |
-64.54% |
-3227 |
-64.54% |
+3144 |
+62.88% |
+3144 |
+62.88% |
| 2 |
Negative |
-990 |
-19.80% |
-4217 |
-84.34% |
+1027 |
+20.54% |
+4171 |
+83.42% |
| 3 |
Multi-drug-resistant |
464 |
9.28% |
-4681 |
-93.62% |
+4635 |
+92.70% |
| 4 |
Poly-resistant |
-223 |
-4.46% |
-4904 |
-98.08% |
+257 |
+5.14% |
+4892 |
+97.84% |
| 5 |
Extensively drug-resistant |
-96 |
-1.92% |
+108 |
+2.16% |
5000 |
100.00% |
diff --git a/docs/articles/PCA.html b/docs/articles/PCA.html
index 72a9a3a72..a8fcbf51b 100644
--- a/docs/articles/PCA.html
+++ b/docs/articles/PCA.html
@@ -44,7 +44,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -174,13 +174,6 @@
Source Code
-
-
-
-
-
- Survey
-
diff --git a/docs/articles/SPSS.html b/docs/articles/SPSS.html
index 47e5fe01d..aff0abd19 100644
--- a/docs/articles/SPSS.html
+++ b/docs/articles/SPSS.html
@@ -44,7 +44,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -174,13 +174,6 @@
Source Code
-
-
-
-
-
- Survey
-
@@ -198,7 +191,7 @@
How to import data from SPSS / SAS / Stata
Matthijs S. Berends
- 17 August 2021
+ 29 August 2021
Source: vignettes/SPSS.Rmd
SPSS.Rmd
@@ -233,7 +226,7 @@
R has a huge community.
-Many R users just ask questions on websites like StackOverflow.com, the largest online community for programmers. At the time of writing, 414,789 R-related questions have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
+Many R users just ask questions on websites like StackOverflow.com, the largest online community for programmers. At the time of writing, 415,751 R-related questions have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
R understands any data type, including SPSS/SAS/Stata.
diff --git a/docs/articles/WHONET.html b/docs/articles/WHONET.html
index fcbbc3149..d3e2b66a7 100644
--- a/docs/articles/WHONET.html
+++ b/docs/articles/WHONET.html
@@ -44,7 +44,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -174,13 +174,6 @@
Source Code
-
-
-
-
-
- Survey
-
diff --git a/docs/articles/benchmarks.html b/docs/articles/benchmarks.html
index b88299999..6d8fa7967 100644
--- a/docs/articles/benchmarks.html
+++ b/docs/articles/benchmarks.html
@@ -44,7 +44,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -174,13 +174,6 @@
Source Code
-
-
-
-
-
- Survey
-
@@ -231,32 +224,19 @@
times = 25)
print(S.aureus, unit = "ms", signif = 2)
# Unit: milliseconds
-# expr min lq mean median uq max
-# as.mo("sau") 12.0 13.0 14 13.0 13.0 45.0
-# as.mo("stau") 55.0 58.0 72 60.0 92.0 100.0
-# as.mo("STAU") 54.0 59.0 85 91.0 94.0 200.0
-# as.mo("staaur") 11.0 12.0 19 13.0 15.0 58.0
-# as.mo("STAAUR") 12.0 13.0 21 14.0 16.0 63.0
-# as.mo("S. aureus") 24.0 28.0 41 30.0 62.0 73.0
-# as.mo("S aureus") 26.0 28.0 37 30.0 33.0 75.0
-# as.mo("Staphylococcus aureus") 3.1 3.8 4 3.9 4.2 4.7
-# as.mo("Staphylococcus aureus (MRSA)") 250.0 260.0 270 260.0 290.0 300.0
-# as.mo("Sthafilokkockus aaureuz") 160.0 200.0 200 200.0 200.0 240.0
-# as.mo("MRSA") 11.0 13.0 20 13.0 15.0 55.0
-# as.mo("VISA") 21.0 22.0 31 23.0 27.0 69.0
-# neval
-# 25
-# 25
-# 25
-# 25
-# 25
-# 25
-# 25
-# 25
-# 25
-# 25
-# 25
-# 25
+# expr min lq mean median uq max neval
+# as.mo("sau") 12.0 13.0 15.0 14.0 15.0 48 25
+# as.mo("stau") 54.0 57.0 73.0 62.0 92.0 99 25
+# as.mo("STAU") 54.0 58.0 73.0 63.0 91.0 98 25
+# as.mo("staaur") 13.0 13.0 20.0 14.0 15.0 52 25
+# as.mo("STAAUR") 11.0 13.0 18.0 14.0 16.0 47 25
+# as.mo("S. aureus") 27.0 30.0 38.0 31.0 36.0 63 25
+# as.mo("S aureus") 27.0 29.0 42.0 33.0 60.0 66 25
+# as.mo("Staphylococcus aureus") 3.8 4.1 6.8 4.2 4.6 36 25
+# as.mo("Staphylococcus aureus (MRSA)") 240.0 250.0 280.0 270.0 300.0 320 25
+# as.mo("Sthafilokkockus aaureuz") 160.0 190.0 200.0 200.0 210.0 340 25
+# as.mo("MRSA") 13.0 13.0 17.0 14.0 15.0 46 25
+# as.mo("VISA") 20.0 23.0 34.0 25.0 52.0 59 25
In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 200 milliseconds, this is only 5 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations are up to 200 times slower to determine.
To improve performance, we implemented two important algorithms to save unnecessary calculations: repetitive results and already precalculated results.
@@ -278,7 +258,7 @@
# what do these values look like? They are of class <mo>:
head(x)
# Class <mo>
-# [1] B_STPHY_AURS B_STPHY_CONS B_ESCHR_COLI B_STRPT_PYGN B_STPHY_CONS
+# [1] B_STRPT_PNMN B_KLBSL_PNMN B_ESCHR_COLI B_SERRT_MRCS B_STPHY_CONS
# [6] B_ESCHR_COLI
# as the example_isolates data set has 2,000 rows, we should have 2 million items
@@ -295,7 +275,7 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# mo_name(x) 163 204 267 233 346 398 10
+# mo_name(x) 188 202 260 233 335 408 10
So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.233 seconds. That is 116 nanoseconds on average. You only lose time on your unique input values.
@@ -310,10 +290,10 @@
print(run_it, unit
= "ms", signif
= 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-
# A 7.93 8.08 14.50 8.35 8.46 70.00 10
-
# B 22.40 22.80 24.30 24.30 24.60 27.20 10
-
# C 1.89 2.00 2.19 2.15 2.30 2.76 10So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0021 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:
+# A 8.09 8.18 8.94 8.69 9.47 10.50 10
+# B 23.40 24.90 31.90 27.90 28.20 80.70 10
+# C 2.03 2.20 2.35 2.35 2.48 2.66 10
+So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0023 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:
run_it <- microbenchmark(A = mo_species("aureus"),
B = mo_genus("Staphylococcus"),
@@ -327,14 +307,14 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# A 1.73 1.78 1.83 1.81 1.90 1.90 10
-# B 1.73 1.83 1.90 1.89 1.98 2.04 10
-# C 1.72 1.79 1.88 1.85 1.95 2.08 10
-# D 1.75 1.75 1.85 1.88 1.91 1.93 10
-# E 1.70 1.78 1.82 1.80 1.83 2.02 10
-# F 1.68 1.78 1.79 1.79 1.81 1.91 10
-# G 1.69 1.75 1.79 1.79 1.81 1.89 10
-# H 1.68 1.71 1.85 1.76 1.85 2.65 10
Of course, when running mo_phylum("Firmicutes") the function has zero knowledge about the actual microorganism, namely S. aureus. But since the result would be "Firmicutes" anyway, there is no point in calculating the result. And because this package contains all phyla of all known bacteria, it can just return the initial value immediately.
@@ -362,13 +342,13 @@
print(run_it, unit
= "ms", signif
= 4)
# Unit: milliseconds
# expr min lq mean median uq max neval
-
# en 19.47 20.23 24.54 20.72 21.85 89.57 100
-
# de 30.58 31.52 41.31 32.78 34.76 86.03 100
-
# nl 34.81 35.58 45.89 36.76 39.41 94.01 100
-
# es 34.41 35.12 40.08 36.22 37.33 94.71 100
-
# it 23.93 24.65 27.31 25.15 25.84 81.01 100
-
# fr 23.59 24.40 31.40 25.01 25.62 200.30 100
-
# pt 23.69 24.73 30.23 25.57 26.32 82.19 100Currently supported non-English languages are German, Dutch, Spanish, Italian, French and Portuguese.
diff --git a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png
index 2d6b4b660..cccc31b21 100644
Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png differ
diff --git a/docs/articles/datasets.html b/docs/articles/datasets.html
index d6ef5fdd3..306ea9268 100644
--- a/docs/articles/datasets.html
+++ b/docs/articles/datasets.html
@@ -44,7 +44,7 @@
AMR (for R)
- 1.7.1.9029
+ 1.7.1.9030
@@ -190,7 +190,7 @@
@@ -222,13 +222,6 @@
Source Code
-
-
-
-
-
- Survey
-
diff --git a/docs/articles/resistance_predict.html b/docs/articles/resistance_predict.html
index 0b9ce929d..9f4ad3b9d 100644
--- a/docs/articles/resistance_predict.html
+++ b/docs/articles/resistance_predict.html
@@ -44,7 +44,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -174,13 +174,6 @@
Source Code
-
-
-
-
-
- Survey
-
diff --git a/docs/articles/welcome_to_AMR.html b/docs/articles/welcome_to_AMR.html
index 5c3f65681..030095db9 100644
--- a/docs/articles/welcome_to_AMR.html
+++ b/docs/articles/welcome_to_AMR.html
@@ -44,7 +44,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -174,13 +174,6 @@
Source Code
-
-
-
-
-
- Survey
-
diff --git a/docs/authors.html b/docs/authors.html
index 871892e96..efbc0dd5e 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -92,7 +92,7 @@
AMR (for R)
- 1.7.1.9025
+ 1.7.1.9030
diff --git a/docs/extra.js b/docs/extra.js
index 9d0b9d6c6..b5d968d58 100644
--- a/docs/extra.js
+++ b/docs/extra.js
@@ -98,6 +98,7 @@ $(document).ready(function() {
x = x.replace(/Author, maintainer/g, "Main developer");
x = x.replace(/Author, contributor/g, "Main contributor");
x = x.replace(/Author, thesis advisor/g, "Doctoral advisor");
+ x = x.replace("Matthijs", "Dr. Matthijs");
x = x.replace("Alex", "Prof. Dr. Alex");
x = x.replace("Bhanu", "Prof. Dr. Bhanu");
x = x.replace("Casper", "Prof. Dr. Casper");
diff --git a/docs/index.html b/docs/index.html
index e6c741ea4..3c05d5d36 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -47,7 +47,7 @@
AMR (for R)
- 1.7.1.9025
+ 1.7.1.9030
@@ -194,10 +194,12 @@
+
+This package formed the basis of two PhD theses, of which the first was published and defended on 25 August 2021. Click here to read it: DOI 10.33612/diss.177417131.
+
What is AMR (for R)?
-
(To find out how to conduct AMR data analysis, please continue reading here to get started.)
AMR is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice Foundation and University Medical Center Groningen. This R package is actively maintained and is free software (see Copyright).
diff --git a/docs/news/index.html b/docs/news/index.html
index f0645f5c2..7df74c4ae 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -92,7 +92,7 @@
AMR (for R)
- 1.7.1.9029
+ 1.7.1.9030
@@ -240,17 +240,17 @@
Source: NEWS.md
-
-
-
+
+
+
-Last updated: 21 August 2021
+Last updated: 29 August 2021
Breaking changes
-
- Removed
p_symbol() and all filter_*() functions (except for filter_first_isolate()), which were all deprecated in a previous package version
+- Removed
p_symbol() and all filter_*() functions (except for filter_first_isolate()), which were all deprecated in a previous package version
- Removed the
key_antibiotics() and key_antibiotics_equal() functions, which were deprecated and superseded by key_antimicrobials() and antimicrobials_equal()
- Removed all previously implemented
ggplot2::ggplot() generics for classes <mic>, <disk>, <rsi> and <resistance_predict> as they did not follow the ggplot2 logic. They were replaced with ggplot2::autoplot() generics.
@@ -266,13 +266,15 @@
- The
antibiotics data set now contains all ATC codes that are available through the WHOCC website, regardless of drugs being present in more than one ATC group. This means that:
- Some drugs now contain multiple ATC codes (e.g., metronidazole contains 5)
-
-
antibiotics$atc is now a list instead of a character, and this atc column was moved to the 5th position of the antibiotics data set
+antibiotics$atc is now a list containing character vectors, and this atc column was moved to the 5th position of the antibiotics data set
-
-
ab_atc() does not always return a character vector with length 1, and returns a list if the input is larger than length 1
+ab_atc() does not always return a character vector of length 1, and returns a list if the input is larger than length 1
+-
+
ab_info() has a slightly different output
- Some DDDs (daily defined doses) were added or updated according to newly included ATC codes
- Antibiotic selectors
-
-- Fixed the Gram stain (
mo_gramstain()) determination of the class Negativicutes within the phylum of Firmicutes - they were considered Gram-positives because of their phylum but are actually Gram-negative. This impacts 137 taxonomic species, genera and families, such as Negativicoccus and Veillonella.
-- Fix for duplicate ATC codes in the
antibiotics data set
+- Fixed the Gram stain (
mo_gramstain()) determination of the taxonomic class Negativicutes within the phylum of Firmicutes - they were considered Gram-positives because of their phylum but are actually Gram-negative. This impacts 137 taxonomic species, genera and families, such as Negativicoccus and Veillonella.
- Fix to prevent introducing
NAs for old MO codes when running as.mo() on them
- Added more informative error messages when any of the
proportion_*() and count_*() functions fail
- When printing a tibble with any old MO code, a warning will be thrown that old codes should be updated using
as.mo()
@@ -608,7 +609,7 @@
For all function arguments in the code, it is now defined what the exact type of user input should be (inspired by the typed package). If the user input for a certain function does not meet the requirements for a specific argument (such as the class or length), an informative error will be thrown. This makes the package more robust and the use of it more reproducible and reliable. In total, more than 420 arguments were defined.
Fix for set_mo_source(), that previously would not remember the file location of the original file
Deprecated function p_symbol() that not really fits the scope of this package. It will be removed in a future version. See here for the source code to preserve it.
Deprecated function p_symbol() that not really fits the scope of this package. It will be removed in a future version. See here for the source code to preserve it.
Updated coagulase-negative staphylococci determination with Becker et al. 2020 (PMID 32056452), meaning that the species S. argensis, S. caeli, S. debuckii, S. edaphicus and S. pseudoxylosus are now all considered CoNS
Fix for using argument reference_df in as.mo() and mo_*() functions that contain old microbial codes (from previous package versions)
Fixed a bug where mo_uncertainties() would not return the results based on the MO matching score
Other
-
- Removed previously deprecated function
p.symbol() - it was replaced with p_symbol()
+- Removed previously deprecated function
p.symbol() - it was replaced with p_symbol()
- Removed function
read.4d(), that was only useful for reading data from an old test database.
@@ -1195,7 +1196,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
- Improved
filter_ab_class() to be more reliable and to support 5th generation cephalosporins
- Function
availability() now uses portion_R() instead of portion_IR(), to comply with EUCAST insights
- Functions
age() and age_groups() now have a na.rm argument to remove empty values
-- Renamed function
p.symbol() to p_symbol() (the former is now deprecated and will be removed in a future version)
+- Renamed function
p.symbol() to p_symbol() (the former is now deprecated and will be removed in a future version)
- Using negative values for
x in age_groups() will now introduce NAs and not return an error anymore
- Fix for determining the system’s language
- Fix for
key_antibiotics() on foreign systems
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 0ac221678..b1eefcfc9 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -12,7 +12,7 @@ articles:
datasets: datasets.html
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
-last_built: 2021-08-17T12:31Z
+last_built: 2021-08-29T21:41Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles
diff --git a/docs/reference/AMR-deprecated.html b/docs/reference/AMR-deprecated.html
index 550ae707e..19cbe3eaf 100644
--- a/docs/reference/AMR-deprecated.html
+++ b/docs/reference/AMR-deprecated.html
@@ -93,7 +93,7 @@
AMR (for R)
- 1.7.1.9025
+ 1.7.1.9030
diff --git a/docs/reference/AMR.html b/docs/reference/AMR.html
index f004e02ac..b0f67eaf3 100644
--- a/docs/reference/AMR.html
+++ b/docs/reference/AMR.html
@@ -93,7 +93,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -223,13 +223,6 @@
Source Code
-
-
-
-
-
- Survey
-
diff --git a/docs/reference/WHOCC.html b/docs/reference/WHOCC.html
index 79e51ba7d..3a1aca428 100644
--- a/docs/reference/WHOCC.html
+++ b/docs/reference/WHOCC.html
@@ -93,7 +93,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -223,13 +223,6 @@
Source Code
-
-
-
-
-
- Survey
-
diff --git a/docs/reference/WHONET.html b/docs/reference/WHONET.html
index d6f5aec4a..3724ee6bc 100644
--- a/docs/reference/WHONET.html
+++ b/docs/reference/WHONET.html
@@ -93,7 +93,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
@@ -223,13 +223,6 @@
Source Code
-
-
-
-
-
- Survey
-
diff --git a/docs/reference/ab_from_text.html b/docs/reference/ab_from_text.html
index 80e00b158..ce9ba0b9e 100644
--- a/docs/reference/ab_from_text.html
+++ b/docs/reference/ab_from_text.html
@@ -93,7 +93,7 @@
AMR (for R)
- 1.7.1.9024
+ 1.7.1.9030
-### What is `AMR` (for R)?
+> This package formed the basis of two PhD theses, of which the first was published and defended on 25 August 2021.
+> Click here to read it: [DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131).
-*(To find out how to conduct AMR data analysis, please [continue reading here to get started](./articles/AMR.html).)*
+### What is `AMR` (for R)?
`AMR` is a free, open-source and independent [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
diff --git a/inst/tinytest/test-first_isolate.R b/inst/tinytest/test-first_isolate.R
index f6e48fcd2..e7ddc8572 100755
--- a/inst/tinytest/test-first_isolate.R
+++ b/inst/tinytest/test-first_isolate.R
@@ -133,7 +133,9 @@ if (AMR:::pkg_is_available("dplyr")) {
first_isolate(info = TRUE))
# groups
+ message("get to first isolates L136")
x <- example_isolates %>% group_by(ward_icu) %>% mutate(first = first_isolate())
+ message("get to first isolates L138")
y <- example_isolates %>% group_by(ward_icu) %>% mutate(first = first_isolate(.))
expect_identical(x, y)
diff --git a/pkgdown/extra.js b/pkgdown/extra.js
index 9d0b9d6c6..b5d968d58 100644
--- a/pkgdown/extra.js
+++ b/pkgdown/extra.js
@@ -98,6 +98,7 @@ $(document).ready(function() {
x = x.replace(/Author, maintainer/g, "Main developer");
x = x.replace(/Author, contributor/g, "Main contributor");
x = x.replace(/Author, thesis advisor/g, "Doctoral advisor");
+ x = x.replace("Matthijs", "Dr. Matthijs");
x = x.replace("Alex", "Prof. Dr. Alex");
x = x.replace("Bhanu", "Prof. Dr. Bhanu");
x = x.replace("Casper", "Prof. Dr. Casper");