With the function mdro(), you can determine which micro-organisms are multi-drug resistant organisms (MDRO).
The mdro() function takes a data set as input, such as a regular data.frame. It tries to automatically determine the right columns for info about your isolates, like the name of the species and all columns with results of antimicrobial agents. See the help page for more info about how to set the right settings for your data with the command ?mdro.
For WHONET data (and most other data), all settings are automatically set correctly.
The function support multiple guidelines. You can select a guideline with the guideline parameter. Currently supported guidelines are (case-insensitive):
Please suggest your own (country-specific) guidelines by letting us know: https://github.com/msberends/AMR/issues/new.
-You can also use your own custom guideline. Custom guidelines can be set with the custom_mdro_guideline() function. This is of great importance if you have custom rules to determine MDROs in your hospital, e.g., rules that are dependent on ward, state of contact isolation or other variables in your data.
If you are familiar with case_when() of the dplyr package, you will recognise the input method to set your own rules. Rules must be set using what considers to be the ‘formula notation’:
+custom <- custom_mdro_guideline(CIP == "R" & age > 60 ~ "Elderly Type A",
+ ERY == "R" & age > 60 ~ "Elderly Type B")If a row/an isolate matches the first rule, the value after the first ~ (in this case ‘Elderly Type A’) will be set as MDRO value. Otherwise, the second rule will be tried and so on. The number of rules is unlimited.
You can print the rules set in the console for an overview. Colours will help reading it if your console supports colours.
+
+custom
+# A set of custom MDRO rules:
+# 1. CIP is "R" and age is higher than 60 -> Elderly Type A
+# 2. ERY is "R" and age is higher than 60 -> Elderly Type B
+# 3. Otherwise -> Negative
+#
+# Unmatched rows will return NA.
+# Results will be of class <factor>, with ordered levels: Negative < Elderly Type A < Elderly Type BThe outcome of the function can be used for the guideline argument in the [mdro()] function:
+x <- mdro(example_isolates, guideline = custom)
+table(x)
+# x
+# Negative Elderly Type A Elderly Type B
+# 1066 43 891The rules set (the custom object in this case) could be exported to a shared file location using saveRDS() if you collaborate with multiple users. The custom rules set could then be imported using readRDS().
The mdro() function always returns an ordered factor. For example, the output of the default guideline by Magiorakos et al. returns a factor with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in that order.
The next example uses the example_isolates data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:
+ --+@@ -236,13 +236,13 @@ Source:example_isolates %>% mdro() %>% freq() # show frequency table of the result @@ -272,23 +300,23 @@ Unique: 21 Negative -1616 -92.50% -1616 -92.50% +1617 +92.56% +1617 +92.56% 2 Multi-drug-resistant (MDR) -131 -7.50% +130 +7.44% 1747 100.00% For another example, I will create a data set to determine multi-drug resistant TB:
-diff --git a/docs/news/index.html b/docs/news/index.html index d396b2d4..a81565ee 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -81,7 +81,7 @@+# random_rsi() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame(rifampicin = random_rsi(5000), @@ -299,7 +327,7 @@ Unique: 2 moxifloxacin = random_rsi(5000), kanamycin = random_rsi(5000))Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same:
-diff --git a/docs/index.html b/docs/index.html index 118fea25..e0757438 100644 --- a/docs/index.html +++ b/docs/index.html @@ -43,7 +43,7 @@+my_TB_data <- data.frame(RIF = random_rsi(5000), INH = random_rsi(5000), GAT = random_rsi(5000), @@ -308,32 +336,32 @@ Unique: 2 MFX = random_rsi(5000), KAN = random_rsi(5000))The data set now looks like this:
-++# 6 Ihead(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 S R S S I R -# 2 R I S I S I -# 3 I I R S I S -# 4 R S R I R S -# 5 I R I I I R -# 6 I R I R S R +# 1 I R I R I I +# 2 R R I S R I +# 3 I S S I S I +# 4 I I R S R I +# 5 I R I S S I +# 6 S I I I R R # kanamycin -# 1 R -# 2 I -# 3 I -# 4 I +# 1 I +# 2 R +# 3 S +# 4 R # 5 S -# 6 SWe can now add the interpretation of MDR-TB to our data set. You can use:
-diff --git a/docs/authors.html b/docs/authors.html index ac9000cc..53287bd7 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -81,7 +81,7 @@+mdro(my_TB_data, guideline = "TB")or its shortcut
-mdr_tb():++# NOTE: No column found as input for `col_mo`, assuming all records +# containMycobacterium tuberculosis.my_TB_data$mdr <- mdr_tb(my_TB_data) -# NOTE: No column found as input for `col_mo`, assuming all records contain -# Mycobacterium tuberculosis.Create a frequency table of the results:
-@@ -193,7 +193,7 @@+freq(my_TB_data$mdr)Frequency table
Class: factor > ordered (numeric)
@@ -354,40 +382,40 @@ Unique: 51 Mono-resistant -3286 -65.72% -3286 -65.72% +3228 +64.56% +3228 +64.56% 2 Negative -992 -19.84% -4278 -85.56% +1017 +20.34% +4245 +84.90% 3 Multi-drug-resistant -424 -8.48% -4702 -94.04% +421 +8.42% +4666 +93.32% 4 Poly-resistant -214 -4.28% -4916 -98.32% +231 +4.62% +4897 +97.94% diff --git a/docs/articles/SPSS.html b/docs/articles/SPSS.html index c452efdd..f9183cac 100644 --- a/docs/articles/SPSS.html +++ b/docs/articles/SPSS.html @@ -39,7 +39,7 @@ 5 Extensively drug-resistant -84 -1.68% +103 +2.06% 5000 100.00% How to import data from SPSS / SAS / Stata
Matthijs S. Berends
-22 January 2021
+24 January 2021
Source:vignettes/SPSS.Rmd@@ -228,7 +228,7 @@SPSS.RmdR 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, 383,346 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, 384,445 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/index.html b/docs/articles/index.html index 4c0fd66b..78f77492 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -81,7 +81,7 @@NEWS.md-+ freq(gender)-AMR 1.5.0.9009 Unreleased +
++AMR 1.5.0.9010 Unreleased
-++ freq(mo_genus(mo))@@ -1220,7 +1221,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/-Last updated: 22 January 2021 +Last updated: 24 January 2021
@@ -288,6 +288,7 @@
Fix for verbose output of mdro(..., verbose = TRUE)for German guideline (3MGRN and 4MGRN) and Dutch guideline (BRMO, only P. aeruginosa)+ is.rsi.eligible()now returnsFALSEimmediately if the input does not contain any of the values “R”, “S” or “I”. This drastically improves speed, also for a lot of other functions that rely on automatic determination of antibiotic columns.Functions get_episode()andis_new_episode()now support less than a day as value for argumentepisode_days(e.g., to include one patient/test per hour)@@ -614,7 +615,7 @@Making this package independent of especially the tidyverse (e.g. packages
dplyrandtidyr) 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
@@ -951,7 +952,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/ #> invalid microorganism code, NA generatedmoandabnow both also inherit classcharacter, to support any data transformation. This change invalidates code that checks for class length == 1.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. Thefreq()function still works, since it is re-exported from thecleanpackage (which will be installed automatically upon updating thisAMRpackage).Function
freq()has moved to a new package,clean(CRAN link), since creating frequency tables actually does not fit the scope of this package. Thefreq()function still works, since it is re-exported from thecleanpackage (which will be installed automatically upon updating thisAMRpackage).Renamed data set
septic_patientstoexample_isolatesThe age()function gained a new argumentexactto determine ages with decimalsRemoved deprecated functions -guess_mo(),guess_atc(),EUCAST_rules(),interpretive_reading(),rsi()Frequency tables ( freq()): +Frequency tables ( @@ -1245,7 +1246,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/freq()):
speed improvement for microbial IDs
- @@ -1230,12 +1231,12 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
fixed factor level names for R Markdown
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 areNA+Fix for freq()for when all values areNAFix for first_isolate()for when dates are missingImproved speed of -guess_ab_col()@@ -1486,7 +1487,7 @@ 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:
@@ -1496,15 +1497,15 @@ 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
headerfunction@@ -1587,7 +1588,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/ The argument
headeris now set toTRUEat default, even for markdownUsing
portion_*functions now throws a warning when total available isolate is below argumentminimumFunctions
as.mo,as.rsi,as.mic,as.atcandfreqwill not set package name as attribute anymore- Frequency tables -
+freq():Frequency tables -
freq():
Support for grouping variables, test with:
@@ -1595,14 +1596,14 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/septic_patients %>% group_by(hospital_id) %>% - freq(gender)
Support for (un)selecting columns:
Check for hms::is.hms
Removed diacritics from all authors (columns microorganisms$ref and microorganisms.old$ref) to comply with CRAN policy to only allow ASCII characters
Fix for mo_property not working properly
Fix for eucast_rules where some Streptococci would become ceftazidime R in EUCAST rule 4.5
Support for named vectors of class mo, useful for top_freq()
Support for named vectors of class mo, useful for top_freq()
ggplot_rsi and scale_y_percent have breaks argument
AI improvements for as.mo:
For lists, subsetting is possible:
my_list = list(age = septic_patients$age, gender = septic_patients$gender)
-my_list %>% freq(age)
-my_list %>% freq(gender)rsi (antimicrobial resistance) to use as inputtable to use as input: freq(table(x, y))
+table to use as input: freq(table(x, y))
hist and plot to use a frequency table as input: hist(freq(df$age))
as.vector, as.data.frame, as_tibble and format
freq(mydata, mycolumn) is the same as mydata %>% freq(mycolumn)
+freq(mydata, mycolumn) is the same as mydata %>% freq(mycolumn)
top_freq function to return the top/below n items as vectorfilter_ab_class() for the filter() equivalent.