+
+
+# $ RIF <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…
+# 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA
+# Total observations available: 7.
+# Cumulative Proportion 0.580 0.9331 0.98012 0.99449 0.99988 1.00000 1.000e+00
How to conduct principal component analysis (PCA) for AMR
@@ -210,9 +211,9 @@ TransformingFor PCA, we need to transform our AMR data first. This is what the example_isolates data set in this package looks like:
-library(AMR) -library(dplyr) -glimpse(example_isolates) +library(AMR) +library(dplyr) +glimpse(example_isolates) # Rows: 2,000 # Columns: 49 # $ date <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002… @@ -263,18 +264,17 @@ # $ CHL <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… # $ COL <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, … # $ MUP <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… -# $ RIF <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R… -
Now to transform this to a data set with only resistance percentages per taxonomic order and genus:
-resistance_data <- example_isolates %>% - group_by(order = mo_order(mo), # group on anything, like order - genus = mo_genus(mo)) %>% # and genus as we do here - summarise_if(is.rsi, resistance) %>% # then get resistance of all drugs - select(order, genus, AMC, CXM, CTX, - CAZ, GEN, TOB, TMP, SXT) # and select only relevant columns +resistance_data <- example_isolates %>% + group_by(order = mo_order(mo), # group on anything, like order + genus = mo_genus(mo)) %>% # and genus as we do here + summarise_if(is.rsi, resistance) %>% # then get resistance of all drugs + select(order, genus, AMC, CXM, CTX, + CAZ, GEN, TOB, TMP, SXT) # and select only relevant columns -head(resistance_data) +head(resistance_data) # # A tibble: 6 x 10 # # Groups: order [2] # order genus AMC CXM CTX CAZ GEN TOB TMP SXT @@ -284,46 +284,40 @@ # 3 Actinomycetales Cutibacterium NA NA NA NA NA NA NA NA # 4 Actinomycetales Dermabacter NA NA NA NA NA NA NA NA # 5 Actinomycetales Micrococcus NA NA NA NA NA NA NA NA -# 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA -
Perform principal component analysis
The new pca() function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:
-pca_result <- pca(resistance_data) +pca_result <- pca(resistance_data) # NOTE: Columns selected for PCA: AMC CXM CTX CAZ GEN TOB TMP SXT. -# Total observations available: 7. -
The result can be reviewed with the good old summary() function:
-summary(pca_result) +summary(pca_result) # Importance of components: # PC1 PC2 PC3 PC4 PC5 PC6 PC7 # Standard deviation 2.154 1.6807 0.61365 0.33902 0.20757 0.03136 1.733e-16 # Proportion of Variance 0.580 0.3531 0.04707 0.01437 0.00539 0.00012 0.000e+00 -# Cumulative Proportion 0.580 0.9331 0.98012 0.99449 0.99988 1.00000 1.000e+00 -
Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the Proportion of Variance. We can create a so-called biplot with the base R biplot() function, to see which antimicrobial resistance per drug explain the difference per microorganism.
But we can’t see the explanation of the points. Perhaps this works better with our new ggplot_pca() function, that automatically adds the right labels and even groups:
-ggplot_pca(pca_result) -
You can also print an ellipse per group, and edit the appearance:
-ggplot_pca(pca_result, ellipse = TRUE) + - ggplot2::labs(title = "An AMR/PCA biplot!") -
+
+
@@ -249,38 +250,37 @@
+ab_atc("floxapen")
+# [1] "J01CF05"
+# # … with 4,193 more rows
+library(haven)
-
+
+write_sav(data = yourdata, path = "path/to/file", compress = TRUE)
How to import data from SPSS / SAS / Stata
Matthijs S. Berends
-08 October 2020
+26 October 2020
Source:vignettes/SPSS.Rmd
SPSS.RmdTo demonstrate the first point:
# not all values are valid MIC values: -as.mic(0.125) +as.mic(0.125) # Class <mic> # [1] 0.125 -as.mic("testvalue") +as.mic("testvalue") # Class <mic> # [1] <NA> # the Gram stain is avaiable for all bacteria: -mo_gramstain("E. coli") +mo_gramstain("E. coli") # [1] "Gram-negative" # Klebsiella is intrinsic resistant to amoxicllin, according to EUCAST: -klebsiella_test <- data.frame(mo = "klebsiella", - amox = "S", - stringsAsFactors = FALSE) -klebsiella_test # (our original data) +klebsiella_test <- data.frame(mo = "klebsiella", + amox = "S", + stringsAsFactors = FALSE) +klebsiella_test # (our original data) # mo amox # 1 klebsiella S -eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules) +eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules) # mo amox # 1 klebsiella R # hundreds of trade names can be translated to a name, trade name or an ATC code: -ab_name("floxapen") +ab_name("floxapen") # [1] "Flucloxacillin" -ab_tradenames("floxapen") +ab_tradenames("floxapen") # [1] "floxacillin" "floxapen" "floxapen sodium salt" # [4] "fluclox" "flucloxacilina" "flucloxacillin" # [7] "flucloxacilline" "flucloxacillinum" "fluorochloroxacillin" -ab_atc("floxapen") -# [1] "J01CF05" -
@@ -294,10 +294,10 @@
If additional packages are needed, RStudio will ask you if they should be installed on beforehand.
In the the window that opens, you can define all options (parameters) that should be used for import and you’re ready to go:
If you want named variables to be imported as factors so it resembles SPSS more, use as_factor().
If you want named variables to be imported as factors so it resembles SPSS more, use as_factor().
The difference is this:
-SPSS_data +SPSS_data # # A tibble: 4,203 x 4 # v001 sex status statusage # <dbl> <dbl+lbl> <dbl+lbl> <dbl> @@ -313,7 +313,7 @@ # 10 10018 0 1 66.6 # # … with 4,193 more rows -as_factor(SPSS_data) +as_factor(SPSS_data) # # A tibble: 4,203 x 4 # v001 sex status statusage # <dbl> <fct> <fct> <dbl> @@ -327,8 +327,7 @@ # 8 10011 Male alive 73.1 # 9 10017 Male alive 56.7 # 10 10018 Female alive 66.6 -# # … with 4,193 more rows -
@@ -336,10 +335,9 @@
To import data from SPSS, SAS or Stata, you can use the great haven package yourself:
# download and install the latest version: -install.packages("haven") +install.packages("haven") # load the package you just installed: -library(haven) -
You can now import files as follows:
@@ -347,23 +345,21 @@
To read files from SPSS into R:
# read any SPSS file based on file extension (best way): -read_spss(file = "path/to/file") +read_spss(file = "path/to/file") # read .sav or .zsav file: -read_sav(file = "path/to/file") +read_sav(file = "path/to/file") # read .por file: -read_por(file = "path/to/file") -
Do not forget about as_factor(), as mentioned above.
Do not forget about as_factor(), as mentioned above.
To export your R objects to the SPSS file format:
# save as .sav file: -write_sav(data = yourdata, path = "path/to/file") +write_sav(data = yourdata, path = "path/to/file") # save as compressed .zsav file: -write_sav(data = yourdata, path = "path/to/file", compress = TRUE) -
@@ -371,20 +367,18 @@
To read files from SAS into R:
# read .sas7bdat + .sas7bcat files: -read_sas(data_file = "path/to/file", catalog_file = NULL) +read_sas(data_file = "path/to/file", catalog_file = NULL) # read SAS transport files (version 5 and version 8): -read_xpt(file = "path/to/file") -
To export your R objects to the SAS file format:
# save as regular SAS file: -write_sas(data = yourdata, path = "path/to/file") +write_sas(data = yourdata, path = "path/to/file") # the SAS transport format is an open format # (required for submission of the data to the FDA) -write_xpt(data = yourdata, path = "path/to/file", version = 8) -
@@ -392,17 +386,15 @@
To read files from Stata into R:
# read .dta file: -read_stata(file = "/path/to/file") +read_stata(file = "/path/to/file") # works exactly the same: -read_dta(file = "/path/to/file") -
To export your R objects to the Stata file format:
# save as .dta file, Stata version 14: # (supports Stata v8 until v15 at the time of writing) -write_dta(data = yourdata, path = "/path/to/file", version = 14) -
+
+
+library(readxl)
+data <- read_excel(path = "path/to/your/file.xlsx")
+ mutate_at(vars(AMP_ND10:CIP_EE), as.rsi)
+data %>% freq(mo_name(mo), nmax = 10)
How to work with WHONET data
@@ -206,9 +207,8 @@This tutorial assumes you already imported the WHONET data with e.g. the readxl package. In RStudio, this can be done using the menu button ‘Import Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and select your exported file. Make sure date fields are imported correctly.
An example syntax could look like this:
-library(readxl) -data <- read_excel(path = "path/to/your/file.xlsx") -
This package comes with an example data set WHONET. We will use it for this analysis.
@@ -216,11 +216,10 @@
Preparation
+library(dplyr) # part of tidyverse
+library(ggplot2) # part of tidyverse
+library(AMR) # this package
+library(cleaner) # to create frequency tables
First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’t know it yet, I suggest you read about it on their website: https://www.tidyverse.org/.
-library(dplyr) # part of tidyverse -library(ggplot2) # part of tidyverse -library(AMR) # this package -library(cleaner) # to create frequency tables -
We will have to transform some variables to simplify and automate the analysis:
- Microorganisms should be transformed to our own microorganism IDs (called an
mo) using our Catalogue of Life reference data set, which contains all ~70,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation withas.mo(). This function also recognises almost all WHONET abbreviations of microorganisms.
@@ -228,19 +227,17 @@
# transform variables -data <- WHONET %>% +data <- WHONET %>% # get microbial ID based on given organism - mutate(mo = as.mo(Organism)) %>% + mutate(mo = as.mo(Organism)) %>% # transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class - mutate_at(vars(AMP_ND10:CIP_EE), as.rsi) -
No errors or warnings, so all values are transformed succesfully.
We also created a package dedicated to data cleaning and checking, called the cleaner package. Its freq() function can be used to create frequency tables.
So let’s check our data, with a couple of frequency tables:
# our newly created `mo` variable, put in the mo_name() function -data %>% freq(mo_name(mo), nmax = 10) -
Frequency table
Class: character
Length: 500
@@ -344,15 +341,16 @@ Longest: 40
# our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example -data %>% freq(AMC_ND2) -
Frequency table
Class: factor > ordered > rsi (numeric)
Length: 500
Levels: 3: S < I < R
Available: 481 (96.2%, NA: 19 = 3.8%)
Unique: 3
%SI: 78.59%
+Drug: Amoxicillin/clavulanic acid (AMC, J01CR02)
+Drug group: Beta-lactams/penicillins
+%SI: 78.59%
| @@ -395,11 +393,10 @@ Unique: 3 A first glimpse at results |
|---|
For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:
-example_isolates %>% - filter(mo_gramstain(mo, language = NULL) == "Gram-positive") %>% - resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE, model = "linear") %>% - ggplot_rsi_predict() -# NOTE: Using column `date` as input for `col_date`. -
This seems more likely, doesn’t it?
The model itself is also available from the object, as an attribute:
-model <- attributes(predict_TZP)$model +model <- attributes(predict_TZP)$model -summary(model)$family +summary(model)$family # # Family: binomial # Link function: logit -summary(model)$coefficients +summary(model)$coefficients # Estimate Std. Error z value Pr(>|z|) # (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 -# year 0.09883005 0.02295317 4.305725 1.664395e-05 -
+
+
diff --git a/docs/articles/welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.css b/docs/articles/welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.css
new file mode 100644
index 00000000..07aee5fc
--- /dev/null
+++ b/docs/articles/welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.css
@@ -0,0 +1,4 @@
+/* Styles for section anchors */
+a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
+a.anchor-section::before {content: '#';}
+.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/articles/welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.js b/docs/articles/welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.js
new file mode 100644
index 00000000..570f99a0
--- /dev/null
+++ b/docs/articles/welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.js
@@ -0,0 +1,33 @@
+// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
+document.addEventListener('DOMContentLoaded', function() {
+ // Do nothing if AnchorJS is used
+ if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
+ return;
+ }
+
+ const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
+
+ // Do nothing if sections are already anchored
+ if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
+ return null;
+ }
+
+ // Use section id when pandoc runs with --section-divs
+ const section_id = function(x) {
+ return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
+ ? x.id : '');
+ };
+
+ // Add anchors
+ h.forEach(function(x) {
+ const id = x.id || section_id(x.parentElement);
+ if (id === '') {
+ return null;
+ }
+ let anchor = document.createElement('a');
+ anchor.href = '#' + id;
+ anchor.classList = ['anchor-section'];
+ x.classList.add('hasAnchor');
+ x.appendChild(anchor);
+ });
+});
diff --git a/docs/authors.html b/docs/authors.html
index 5f89f267..cddf97b3 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -81,7 +81,7 @@
diff --git a/docs/index.html b/docs/index.html
index 6880b98b..efafa8fa 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -43,7 +43,7 @@
diff --git a/docs/news/index.html b/docs/news/index.html
index 520cbd93..47933806 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -81,7 +81,7 @@
@@ -236,20 +236,20 @@
Source: Welcome to the AMR package
@@ -249,7 +250,7 @@NEWS.md
-
-
-AMR 1.4.0.9007 Unreleased
+
-AMR 1.4.0.9007 Unreleased
+
+
+AMR 1.4.0.9008 Unreleased
-
+
-Last updated: 21 October 2020
+Last updated: 26 October 2020
New
- Functions
is_gram_negative() and is_gram_positive() as wrappers around mo_gramstain(). They always return TRUE or FALSE, thus always return FALSE for species outside the taxonomic kingdom of Bacteria.
-- Functions
%not_like% and %like_perl% as wrappers around %like%.
+- Functions
%not_like% and %not_like_case% as wrappers around %like% and %like_case%. The RStudio addin to insert the text " %like% " as provided in this package now iterates over all like variants. So if you have defined the keyboard shortcut Ctrl/Cmd + L to this addin, it will first insert %like% and by pressing it again it will be replaced with %not_like%, etc.
@@ -260,12 +260,14 @@
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.
Better determination of disk zones and MIC values when running as.rsi() on a data.frame
Updated coagulase-negative staphylococci 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 parameter reference_df in as.mo() and mo_*() functions that contain old microbial codes (from previous package versions)
@@ -468,7 +470,7 @@
Making this package independent of especially the tidyverse (e.g. packages dplyr and tidyr) tremendously increases sustainability on the long term, since tidyverse functions change quite often. Good for users, but hard for package maintainers. Most of our functions are replaced with versions that only rely on base R, which keeps this package fully functional for many years to come, without requiring a lot of maintenance to keep up with other packages anymore. Another upside it that this package can now be used with all versions of R since R-3.0.0 (April 2013). Our package is being used in settings where the resources are very limited. Fewer dependencies on newer software is helpful for such settings.
Negative effects of this change are:
-- Function
freq() that was borrowed from the cleaner package was removed. Use cleaner::freq(), or run library("cleaner") before you use freq().
+- Function
freq() that was borrowed from the cleaner package was removed. Use cleaner::freq(), or run library("cleaner") before you use freq().
Printing values of class mo or rsi in a tibble will no longer be in colour and printing rsi in a tibble will show the class <ord>, not <rsi> anymore. This is purely a visual effect.All functions from the mo_* family (like mo_name() and mo_gramstain()) are noticeably slower when running on hundreds of thousands of rows.- For developers: classes
mo and ab now both also inherit class character, to support any data transformation. This change invalidates code that checks for class length == 1.
@@ -796,7 +798,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
#> invalid microorganism code, NA generated
This is important, because a value like "testvalue" could never be understood by e.g. mo_name(), although the class would suggest a valid microbial code.
-Function freq() has moved to a new package, clean (CRAN link), since creating frequency tables actually does not fit the scope of this package. The freq() function still works, since it is re-exported from the clean package (which will be installed automatically upon updating this AMR package).
Function freq() has moved to a new package, clean (CRAN link), since creating frequency tables actually does not fit the scope of this package. The freq() function still works, since it is re-exported from the clean package (which will be installed automatically upon updating this AMR package).
Renamed data set septic_patients to example_isolates
The age() function gained a new parameter exact to determine ages with decimals
Removed deprecated functions guess_mo(), guess_atc(), EUCAST_rules(), interpretive_reading(), rsi()
-Frequency tables (freq()):
+ Frequency tables (freq()):
speed improvement for microbial IDs
fixed factor level names for R Markdown
support for boxplots:
@@ -1083,7 +1085,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Added ceftazidim intrinsic resistance to Streptococci
Changed default settings for age_groups(), to let groups of fives and tens end with 100+ instead of 120+
-Fix for freq() for when all values are NA
+ Fix for freq() for when all values are NA
Fix for first_isolate() for when dates are missing
Improved speed of guess_ab_col()
@@ -1315,7 +1317,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
-Frequency tables (freq() function):
+ Frequency tables (freq() function):
-
Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:
@@ -1324,15 +1326,15 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
# OLD WAY
septic_patients %>%
mutate(genus = mo_genus(mo)) %>%
- freq(genus)
+ freq(genus)
# NEW WAY
septic_patients %>%
- freq(mo_genus(mo))
+ freq(mo_genus(mo))
# Even supports grouping variables:
septic_patients %>%
group_by(gender) %>%
- freq(mo_genus(mo))
+ freq(mo_genus(mo))
+AMR 1.4.0.9008 Unreleased
-
+
@@ -1059,7 +1061,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
-Last updated: 21 October 2020 +Last updated: 26 October 2020
New
- Functions
is_gram_negative()andis_gram_positive()as wrappers aroundmo_gramstain(). They always returnTRUEorFALSE, thus always returnFALSEfor species outside the taxonomic kingdom of Bacteria.
- - Functions
%not_like%and%like_perl%as wrappers around%like%.
+ - Functions
%not_like%and%not_like_case%as wrappers around%like%and%like_case%. The RStudio addin to insert the text " %like% " as provided in this package now iterates over all like variants. So if you have defined the keyboard shortcut Ctrl/Cmd + L to this addin, it will first insert%like%and by pressing it again it will be replaced with%not_like%, etc.
@@ -260,12 +260,14 @@
Deprecated function
Better determination of disk zones and MIC values when running
Updated coagulase-negative staphylococci 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 parameter
@@ -468,7 +470,7 @@
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.as.rsi() on a data.framereference_df in as.mo() and mo_*() functions that contain old microbial codes (from previous package versions)Making this package independent of especially the tidyverse (e.g. packages dplyr and tidyr) tremendously increases sustainability on the long term, since tidyverse functions change quite often. Good for users, but hard for package maintainers. Most of our functions are replaced with versions that only rely on base R, which keeps this package fully functional for many years to come, without requiring a lot of maintenance to keep up with other packages anymore. Another upside it that this package can now be used with all versions of R since R-3.0.0 (April 2013). Our package is being used in settings where the resources are very limited. Fewer dependencies on newer software is helpful for such settings.
Negative effects of this change are:
-
-
- Function
freq()that was borrowed from thecleanerpackage was removed. Usecleaner::freq(), or runlibrary("cleaner")before you usefreq().
+ - Function
freq()that was borrowed from thecleanerpackage was removed. Usecleaner::freq(), or runlibrary("cleaner")before you usefreq(). Printing values of classmoorrsiin a tibble will no longer be in colour and printingrsiin a tibble will show the class<ord>, not<rsi>anymore. This is purely a visual effect.All functions from themo_*family (likemo_name()andmo_gramstain()) are noticeably slower when running on hundreds of thousands of rows.- For developers: classes
moandabnow both also inherit classcharacter, to support any data transformation. This change invalidates code that checks for class length == 1.
@@ -796,7 +798,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
#> invalid microorganism code, NA generated
This is important, because a value like "testvalue" could never be understood by e.g. mo_name(), although the class would suggest a valid microbial code.
Function freq() has moved to a new package, clean (CRAN link), since creating frequency tables actually does not fit the scope of this package. The freq() function still works, since it is re-exported from the clean package (which will be installed automatically upon updating this AMR package).
Function freq() has moved to a new package, clean (CRAN link), since creating frequency tables actually does not fit the scope of this package. The freq() function still works, since it is re-exported from the clean package (which will be installed automatically upon updating this AMR package).
Renamed data set septic_patients to example_isolates
age() function gained a new parameter exact to determine ages with decimalsguess_mo(), guess_atc(), EUCAST_rules(), interpretive_reading(), rsi()
freq()):
+freq()):
speed improvement for microbial IDs
fixed factor level names for R Markdown
@@ -1068,12 +1070,12 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
support for boxplots:
age_groups(), to let groups of fives and tens end with 100+ instead of 120+freq() for when all values are NA
+freq() for when all values are NA
first_isolate() for when dates are missingguess_ab_col()
@@ -1315,7 +1317,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
freq() function):
+freq() function):
-
Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:
@@ -1324,15 +1326,15 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/ # OLD WAY septic_patients %>% mutate(genus = mo_genus(mo)) %>% - freq(genus) + freq(genus) # NEW WAY septic_patients %>% - freq(mo_genus(mo)) + freq(mo_genus(mo)) # Even supports grouping variables: septic_patients %>% group_by(gender) %>% - freq(mo_genus(mo))
Header info is now available as a list, with the header function
The parameter header is now set to TRUE at default, even for markdown
Using portion_* functions now throws a warning when total available isolate is below parameter minimum
Functions as.mo, as.rsi, as.mic, as.atc and freq will not set package name as attribute anymore
Frequency tables - freq():
Frequency tables - freq():
-
Support for grouping variables, test with:
+ freq(gender)septic_patients %>% group_by(hospital_id) %>% - freq(gender)
-
Support for (un)selecting columns:
- Check for
hms::is.hms
+Check for
hms::is.hmsNow prints in markdown at default in non-interactive sessions
No longer adds the factor level column and sorts factors on count again
Support for class
difftime
@@ -1445,7 +1447,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Removed diacritics from all authors (columns
microorganisms$refandmicroorganisms.old$ref) to comply with CRAN policy to only allow ASCII charactersFix for
mo_propertynot working properlyFix for
eucast_ruleswhere some Streptococci would become ceftazidime R in EUCAST rule 4.5
-Support for named vectors of class
mo, useful fortop_freq()
+Support for named vectors of class
mo, useful fortop_freq()ggplot_rsiandscale_y_percenthavebreaksparameter-
AI improvements for
@@ -1606,12 +1608,12 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/as.mo:Support for types (classes) list and matrix for
+freq(my_matrix)freqFor lists, subsetting is possible:
+my_list %>% freq(age) +my_list %>% freq(gender)my_list = list(age = septic_patients$age, gender = septic_patients$gender) -my_list %>% freq(age) -my_list %>% freq(gender)
- A vignette to explain its usage
- Support for
rsi(antimicrobial resistance) to use as input
- - Support for
tableto use as input:freq(table(x, y))+ - Support for
tableto use as input:freq(table(x, y)) - Support for existing functions
histandplotto use a frequency table as input:hist(freq(df$age)) - Support for
as.vector,as.data.frame,as_tibbleandformat
- - Support for quasiquotation:
freq(mydata, mycolumn)is the same asmydata %>% freq(mycolumn)+ - Support for quasiquotation:
freq(mydata, mycolumn)is the same asmydata %>% freq(mycolumn) - Function
top_freqfunction to return the top/below n items as vector - Header of frequency tables now also show Mean Absolute Deviaton (MAD) and Interquartile Range (IQR) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index cdcc68f9..c5b3b9a4 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,4 +1,4 @@ -pandoc: 2.9.2.1 +pandoc: 2.7.3 pkgdown: 1.6.1 pkgdown_sha: ~ articles: @@ -12,7 +12,7 @@ articles: datasets: datasets.html resistance_predict: resistance_predict.html welcome_to_AMR: welcome_to_AMR.html -last_built: 2020-10-21T13:25Z +last_built: 2020-10-26T10:52Z 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 a36f0183..75c040cf 100644 --- a/docs/reference/AMR-deprecated.html +++ b/docs/reference/AMR-deprecated.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/age.html b/docs/reference/age.html index ae1c4d73..b9481718 100644 --- a/docs/reference/age.html +++ b/docs/reference/age.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/age_groups.html b/docs/reference/age_groups.html index b8c07046..df8b7115 100644 --- a/docs/reference/age_groups.html +++ b/docs/reference/age_groups.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/antibiotic_class_selectors.html b/docs/reference/antibiotic_class_selectors.html index 74776212..9878f8be 100644 --- a/docs/reference/antibiotic_class_selectors.html +++ b/docs/reference/antibiotic_class_selectors.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/as.mo.html b/docs/reference/as.mo.html index 2929590f..64a0351b 100644 --- a/docs/reference/as.mo.html +++ b/docs/reference/as.mo.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/as.rsi.html b/docs/reference/as.rsi.html index 54b4007e..5dcbf2fc 100644 --- a/docs/reference/as.rsi.html +++ b/docs/reference/as.rsi.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/bug_drug_combinations.html b/docs/reference/bug_drug_combinations.html index 1a483cac..f7ae9ab5 100644 --- a/docs/reference/bug_drug_combinations.html +++ b/docs/reference/bug_drug_combinations.html @@ -82,7 +82,7 @@ diff --git a/docs/reference/filter_ab_class.html b/docs/reference/filter_ab_class.html index c0c462e6..b3a04a3d 100644 --- a/docs/reference/filter_ab_class.html +++ b/docs/reference/filter_ab_class.html @@ -82,7 +82,7 @@ @@ -242,33 +242,33 @@
Filter isolates on results in specific antimicrobial classes. This makes it easy to filter on isolates that were tested for e.g. any aminoglycoside, or to filter on carbapenem-resistant isolates without the need to specify the drugs.
-filter_ab_class(x, ab_class, result = NULL, scope = "any", ...) +filter_ab_class(x, ab_class, result = NULL, scope = "any", ...) -filter_aminoglycosides(x, result = NULL, scope = "any", ...) +filter_aminoglycosides(x, result = NULL, scope = "any", ...) -filter_carbapenems(x, result = NULL, scope = "any", ...) +filter_carbapenems(x, result = NULL, scope = "any", ...) -filter_cephalosporins(x, result = NULL, scope = "any", ...) +filter_cephalosporins(x, result = NULL, scope = "any", ...) -filter_1st_cephalosporins(x, result = NULL, scope = "any", ...) +filter_1st_cephalosporins(x, result = NULL, scope = "any", ...) -filter_2nd_cephalosporins(x, result = NULL, scope = "any", ...) +filter_2nd_cephalosporins(x, result = NULL, scope = "any", ...) -filter_3rd_cephalosporins(x, result = NULL, scope = "any", ...) +filter_3rd_cephalosporins(x, result = NULL, scope = "any", ...) -filter_4th_cephalosporins(x, result = NULL, scope = "any", ...) +filter_4th_cephalosporins(x, result = NULL, scope = "any", ...) -filter_5th_cephalosporins(x, result = NULL, scope = "any", ...) +filter_5th_cephalosporins(x, result = NULL, scope = "any", ...) -filter_fluoroquinolones(x, result = NULL, scope = "any", ...) +filter_fluoroquinolones(x, result = NULL, scope = "any", ...) -filter_glycopeptides(x, result = NULL, scope = "any", ...) +filter_glycopeptides(x, result = NULL, scope = "any", ...) -filter_macrolides(x, result = NULL, scope = "any", ...) +filter_macrolides(x, result = NULL, scope = "any", ...) -filter_penicillins(x, result = NULL, scope = "any", ...) +filter_penicillins(x, result = NULL, scope = "any", ...) -filter_tetracyclines(x, result = NULL, scope = "any", ...)+filter_tetracyclines(x, result = NULL, scope = "any", ...)
Arguments
|
-
+
|
- Pattern Matching |
+ Pattern matching with keyboard shortcut |
||
|
diff --git a/docs/reference/key_antibiotics.html b/docs/reference/key_antibiotics.html
index 14e314da..89c5d3be 100644
--- a/docs/reference/key_antibiotics.html
+++ b/docs/reference/key_antibiotics.html
@@ -82,7 +82,7 @@
diff --git a/docs/reference/lifecycle.html b/docs/reference/lifecycle.html
index e6a5443d..2372b3f7 100644
--- a/docs/reference/lifecycle.html
+++ b/docs/reference/lifecycle.html
@@ -84,7 +84,7 @@ This page contains a section for every lifecycle (with text borrowed from the af
@@ -295,7 +295,7 @@ The lifecycle of this function is questioning. This function mi
diff --git a/docs/reference/like.html b/docs/reference/like.html
index 9459a30f..e7d875a9 100644
--- a/docs/reference/like.html
+++ b/docs/reference/like.html
@@ -6,7 +6,7 @@
-
@@ -246,7 +246,11 @@
x %like% pattern
-x %like_case% pattern
+x %not_like% pattern
+
+x %like_case% pattern
+
+x %not_like_case% pattern
Arguments
|