AMR.RmdNote: 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 09 November 2019.
+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 10 November 2019.
So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values M and F. From a researchers perspective: there are slightly more men. Nothing we didn’t already know.
The data is already quite clean, but we still need to transform some variables. The bacteria column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The mutate() function of the dplyr package makes this really easy:
data <- data %>%
@@ -437,14 +437,14 @@
# Pasteurella multocida (no changes)
# Staphylococcus (no changes)
# Streptococcus groups A, B, C, G (no changes)
-# Streptococcus pneumoniae (1,452 values changed)
+# Streptococcus pneumoniae (1,477 values changed)
# Viridans group streptococci (no changes)
#
# EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-# Table 01: Intrinsic resistance in Enterobacteriaceae (1,294 values changed)
+# Table 01: Intrinsic resistance in Enterobacteriaceae (1,306 values changed)
# Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)
# Table 03: Intrinsic resistance in other Gram-negative bacteria (no changes)
-# Table 04: Intrinsic resistance in Gram-positive bacteria (2,721 values changed)
+# Table 04: Intrinsic resistance in Gram-positive bacteria (2,791 values changed)
# Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)
# Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no changes)
# Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no changes)
@@ -452,24 +452,24 @@
# Table 13: Interpretive rules for quinolones (no changes)
#
# Other rules
-# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,287 values changed)
-# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (107 values changed)
+# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,203 values changed)
+# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (104 values changed)
# Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no changes)
# Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)
# Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no changes)
# Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)
#
# --------------------------------------------------------------------------
-# EUCAST rules affected 6,525 out of 20,000 rows, making a total of 7,861 edits
+# EUCAST rules affected 6,529 out of 20,000 rows, making a total of 7,881 edits
# => added 0 test results
#
-# => changed 7,861 test results
-# - 100 test results changed from S to I
-# - 4,694 test results changed from S to R
-# - 1,140 test results changed from I to S
-# - 302 test results changed from I to R
-# - 1,594 test results changed from R to S
-# - 31 test results changed from R to I
+# => changed 7,881 test results
+# - 102 test results changed from S to I
+# - 4,800 test results changed from S to R
+# - 1,035 test results changed from I to S
+# - 291 test results changed from I to R
+# - 1,625 test results changed from R to S
+# - 28 test results changed from R to I
# --------------------------------------------------------------------------
#
# Use eucast_rules(..., verbose = TRUE) (on your original data) to get a data.frame with all specified edits instead.So only 28.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient Y8, sorted on date:
+We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient S8, sorted on date:
| isolate | @@ -524,10 +524,10 @@|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-04-05 | -Y8 | +2010-01-27 | +S8 | B_ESCHR_COLI | -R | +S | S | S | S | @@ -535,10 +535,10 @@|||
| 2 | -2010-04-09 | -Y8 | +2010-06-01 | +S8 | B_ESCHR_COLI | -R | +S | S | S | S | @@ -546,32 +546,32 @@|||
| 3 | -2010-04-14 | -Y8 | +2010-09-13 | +S8 | B_ESCHR_COLI | -S | -S | +I | +I | S | S | FALSE | |
| 4 | -2010-05-23 | -Y8 | +2010-09-27 | +S8 | B_ESCHR_COLI | S | S | -S | +R | S | FALSE | ||
| 5 | -2010-07-05 | -Y8 | +2010-10-15 | +S8 | B_ESCHR_COLI | -R | +S | S | S | S | @@ -579,8 +579,8 @@|||
| 6 | -2010-07-05 | -Y8 | +2010-12-25 | +S8 | B_ESCHR_COLI | R | S | @@ -590,32 +590,32 @@||||||
| 7 | -2010-07-10 | -Y8 | +2011-02-06 | +S8 | B_ESCHR_COLI | -S | -S | R | S | -FALSE | +S | +S | +TRUE |
| 8 | -2010-08-07 | -Y8 | +2011-03-27 | +S8 | B_ESCHR_COLI | R | -R | +S | S | S | FALSE | ||
| 9 | -2010-09-24 | -Y8 | +2011-05-14 | +S8 | B_ESCHR_COLI | -R | +S | S | S | S | @@ -623,18 +623,18 @@|||
| 10 | -2011-02-13 | -Y8 | +2011-09-12 | +S8 | B_ESCHR_COLI | S | S | -R | +S | S | FALSE |
Only 1 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.
Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.
If a column exists with a name like ‘key(…)ab’ the first_isolate() function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:
data <- data %>%
mutate(keyab = key_antibiotics(.)) %>%
@@ -645,7 +645,7 @@
# NOTE: Using column `patient_id` as input for `col_patient_id`.
# NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.
# [Criterion] Inclusion based on key antibiotics, ignoring I
-# => Found 15,079 first weighted isolates (75.4% of total)| isolate | @@ -662,10 +662,10 @@||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-04-05 | -Y8 | +2010-01-27 | +S8 | B_ESCHR_COLI | -R | +S | S | S | S | @@ -674,10 +674,10 @@||||
| 2 | -2010-04-09 | -Y8 | +2010-06-01 | +S8 | B_ESCHR_COLI | -R | +S | S | S | S | @@ -686,34 +686,34 @@||||
| 3 | -2010-04-14 | -Y8 | +2010-09-13 | +S8 | +B_ESCHR_COLI | +I | +I | +S | +S | +FALSE | +FALSE | +|||
| 4 | +2010-09-27 | +S8 | B_ESCHR_COLI | S | S | -S | +R | S | FALSE | TRUE | ||||
| 4 | -2010-05-23 | -Y8 | -B_ESCHR_COLI | -S | -S | -S | -S | -FALSE | -FALSE | -|||||
| 5 | -2010-07-05 | -Y8 | +2010-10-15 | +S8 | B_ESCHR_COLI | -R | +S | S | S | S | @@ -722,46 +722,46 @@||||
| 6 | -2010-07-05 | -Y8 | +2010-12-25 | +S8 | B_ESCHR_COLI | R | S | S | S | FALSE | -FALSE | +TRUE | ||
| 7 | -2010-07-10 | -Y8 | +2011-02-06 | +S8 | B_ESCHR_COLI | -S | -S | R | S | -FALSE | +S | +S | +TRUE | TRUE |
| 8 | -2010-08-07 | -Y8 | +2011-03-27 | +S8 | B_ESCHR_COLI | R | -R | +S | S | S | FALSE | -TRUE | +FALSE | |
| 9 | -2010-09-24 | -Y8 | +2011-05-14 | +S8 | B_ESCHR_COLI | -R | +S | S | S | S | @@ -770,23 +770,23 @@||||
| 10 | -2011-02-13 | -Y8 | +2011-09-12 | +S8 | B_ESCHR_COLI | S | S | -R | +S | S | FALSE | -TRUE | +FALSE |
Instead of 1, now 7 isolates are flagged. In total, 75.4% of all isolates are marked ‘first weighted’ - 47.0% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
+Instead of 2, now 6 isolates are flagged. In total, 76.3% of all isolates are marked ‘first weighted’ - 47.9% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
As with filter_first_isolate(), there’s a shortcut for this new algorithm too:
So we end up with 15,079 isolates for analysis.
+So we end up with 15,253 isolates for analysis.
We can remove unneeded columns:
@@ -811,29 +811,29 @@Frequency table
Class: character
-Length: 15,079 (of which NA: 0 = 0%)
+Length: 15,253 (of which NA: 0 = 0%)
Unique: 4
Shortest: 16
Longest: 24
The functions portion_S(), portion_SI(), portion_I(), portion_IR() and portion_R() can be used to determine the portion of a specific antimicrobial outcome. As per the EUCAST guideline of 2019, we calculate resistance as the portion of R (portion_R()) and susceptibility as the portion of S and I (portion_SI()). These functions can be used on their own:
The functions resistance() and susceptibility() can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() and proportion_R() can be used to determine the proportion of a specific antimicrobial outcome.
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:
Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:
| hospital | @@ -992,26 +993,26 @@ Longest: 24||
|---|---|---|
| Hospital A | -0.4627198 | +0.4697103 |
| Hospital B | -0.4745283 | +0.4727821 |
| Hospital C | -0.4721269 | +0.4739112 |
| Hospital D | -0.4549763 | +0.4705116 |
Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the n_rsi() can be used, which works exactly like n_distinct() from the dplyr package. It counts all isolates available for every group (i.e. values S, I or R):
data_1st %>%
group_by(hospital) %>%
- summarise(amoxicillin = portion_R(AMX),
+ summarise(amoxicillin = resistance(AMX),
available = n_rsi(AMX))| Hospital A | -0.4627198 | -4493 | +0.4697103 | +4556 |
| Hospital B | -0.4745283 | -5300 | +0.4727821 | +5309 |
| Hospital C | -0.4721269 | -2332 | +0.4739112 | +2319 |
| Hospital D | -0.4549763 | -2954 | +0.4705116 | +3069 |
These functions can also be used to get the portion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
+These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
data_1st %>%
group_by(genus) %>%
- summarise(amoxiclav = portion_SI(AMC),
- gentamicin = portion_SI(GEN),
- amoxiclav_genta = portion_SI(AMC, GEN))| genus | @@ -1058,36 +1059,36 @@ Longest: 24||||||
|---|---|---|---|---|---|---|
| Escherichia | -0.9250202 | -0.8972051 | -0.9965063 | +0.9246912 | +0.8918847 | +0.9929606 |
| Klebsiella | -0.8160556 | -0.8982301 | -0.9841972 | +0.8309509 | +0.9030454 | +0.9869484 |
| Staphylococcus | -0.9217578 | -0.9161308 | -0.9930332 | +0.9301461 | +0.9189907 | +0.9933599 |
| Streptococcus | -0.6147223 | +0.6251064 | 0.0000000 | -0.6147223 | +0.6251064 |
To make a transition to the next part, let’s see how this difference could be plotted:
data_1st %>%
group_by(genus) %>%
- summarise("1. Amoxi/clav" = portion_SI(AMC),
- "2. Gentamicin" = portion_SI(GEN),
- "3. Amoxi/clav + genta" = portion_SI(AMC, GEN)) %>%
+ summarise("1. Amoxi/clav" = susceptibility(AMC),
+ "2. Gentamicin" = susceptibility(GEN),
+ "3. Amoxi/clav + genta" = susceptibility(AMC, GEN)) %>%
tidyr::gather("antibiotic", "S", -genus) %>%
ggplot(aes(x = genus,
y = S,
@@ -1111,7 +1112,7 @@ Longest: 24
# or as short as:
ggplot(a_data_set) +
geom_bar(aes(year))The AMR package contains functions to extend this ggplot2 package, for example geom_rsi(). It automatically transforms data with count_df() or portion_df() and show results in stacked bars. Its simplest and shortest example:
The AMR package contains functions to extend this ggplot2 package, for example geom_rsi(). It automatically transforms data with count_df() or proportion_df() and show results in stacked bars. Its simplest and shortest example:
It analyses the data with convenient functions that use well-known methods.
portion_R(), portion_IR(), portion_I(), portion_SI() and portion_S() functions. Similarly, the number of isolates can be determined with the count_R(), count_IR(), count_I(), count_SI() and count_S() functions. All these functions can be used with the dplyr package (e.g. in conjunction with summarise())susceptibility() and resistance() functions, or be even more specific with the proportion_R(), proportion_IR(), proportion_I(), proportion_SI() and proportion_S() functions. Similarly, the number of isolates can be determined with the count_resistant(), count_susceptible() and count_all() functions. All these functions can be used with the dplyr package (e.g. in conjunction with summarise())geom_rsi(), a function made for the ggplot2 packageresistance_predict() functionLast updated: 10-Nov-2019
susceptibility() and resistance() as aliases of proportion_SI() and proportion_R(), respectively. These functions were added to make it more clear that I should be considered susceptible and not resistant.mdro() functionas.rsi() - this function was replaced by ab_atc()
+portion_* functions to proportion_*. All portion_* functions are still available as deprecated functions, and will return a warning when used.as.rsi() over a data set, it will now print the guideline that will be used if it is not specified by the usereucast_rules(): Stenotrophomonas maltophilia not interpreted “R” to ceftazidime anymore (following EUCAST v3.1)microorganisms data set, which means that the new order Enterobacterales now consists of a part of the existing family Enterobacteriaceae, but that this family has been split into other families as well (like Morganellaceae and Yersiniaceae). Although published in 2016, this information is not yet in the Catalogue of Life version of 2019. All MDRO determinations with mdro() will now use the Enterobacterales order for all guidelines before 2016.as.rsi() where the input is NA
mdro() and eucast_rules()
antibiotics data setalso_single_tested w
AMR:::get_column_abx())atc - using as.atc() is now deprecated in favour of ab_atc() and this will return a character, not the atc class anymoreatc - using as.atc() is now deprecated in favour of ab_atc() and this will return a character, not the atc class anymoreabname(), ab_official(), atc_name(), atc_official(), atc_property(), atc_tradenames(), atc_trivial_nl()
mo_shortname()
@@ -403,7 +407,7 @@ Since this is a major change, usage of the old also_single_tested w
antibiotics data set is now sorted by name and all cephalosporins now have their generation between bracketsguess_ab_col() which is now 30 times faster for antibiotic abbreviationsfilter_ab_class() to be more reliable and to support 5th generation cephalosporinsavailability() now uses portion_R() instead of portion_IR(), to comply with EUCAST insightsavailability() now uses portion_R() instead of portion_IR(), to comply with EUCAST insightsage() and age_groups() now have a na.rm parameter to remove empty valuesp.symbol() to p_symbol() (the former is now deprecated and will be removed in a future version)x in age_groups() will now introduce NAs and not return an error anymorealso_single_tested w
New
Function rsi_df() to transform a data.frame to a data set containing only the microbial interpretation (S, I, R), the antibiotic, the percentage of S/I/R and the number of available isolates. This is a convenient combination of the existing functions count_df() and portion_df() to immediately show resistance percentages and number of available isolates:
Function rsi_df() to transform a data.frame to a data set containing only the microbial interpretation (S, I, R), the antibiotic, the percentage of S/I/R and the number of available isolates. This is a convenient combination of the existing functions count_df() and portion_df() to immediately show resistance percentages and number of available isolates:
septic_patients %>%
select(AMX, CIP) %>%
- rsi_df()
+ rsi_df()
# antibiotic interpretation value isolates
# 1 Amoxicillin SI 0.4442636 546
# 2 Amoxicillin R 0.5557364 683
@@ -474,7 +478,7 @@ Since this is a major change, usage of the old also_single_tested w
Changed
-- Column names of output
count_df() and portion_df() are now lowercase
+- Column names of output
count_df() and portion_df() are now lowercase
- Fixed bug in translation of microorganism names
- Fixed bug in determining taxonomic kingdoms
- Algorithm improvements for
as.ab() and as.mo() to understand even more severely misspelled input
@@ -552,7 +556,7 @@ Please guess_ab_col()
microorganisms.old data set, which leads to better results finding when using the as.mo() functionportion_df() and count_df() this means that their new parameter combine_SI is TRUE at default. Our plotting function ggplot_rsi() also reflects this change since it uses count_df() internally.portion_df() and count_df() this means that their new parameter combine_SI is TRUE at default. Our plotting function ggplot_rsi() also reflects this change since it uses count_df() internally.age() function gained a new parameter exact to determine ages with decimalsguess_mo(), guess_atc(), EUCAST_rules(), interpretive_reading(), rsi()
as.atc() internally. The old atc_property has been renamed atc_online_property(). This is done for two reasons: firstly, not all ATC codes are of antibiotics (ab) but can also be of antivirals or antifungals. Secondly, the input must have class atc or must be coerable to this class. Properties of these classes should start with the same class name, analogous to as.mo() and e.g. mo_genus.as.atc() internally. The old atc_property has been renamed atc_online_property(). This is done for two reasons: firstly, not all ATC codes are of antibiotics (ab) but can also be of antivirals or antifungals. Secondly, the input must have class atc or must be coerable to this class. Properties of these classes should start with the same class name, analogous to as.mo() and e.g. mo_genus.
set_mo_source() and get_mo_source() to use your own predefined MO codes as input for as.mo() and consequently all mo_* functionsdplyr version 0.8.0guess_ab_col() to find an antibiotic column in a tableas.atc()Removed columns atc_group1_nl and atc_group2_nl from the antibiotics data set
- Functions
atc_ddd() and atc_groups() have been renamed atc_online_ddd() and atc_online_groups(). The old functions are deprecated and will be removed in a future version.
- Function
guess_mo() is now deprecated in favour of as.mo() and will be removed in future versions
-- Function
guess_atc() is now deprecated in favour of as.atc() and will be removed in future versions
+- Function
guess_atc() is now deprecated in favour of as.atc() and will be removed in future versions
- Improvements for
as.mo():
-
@@ -900,7 +904,7 @@ Using
as.mo(..., allow_uncertain = 3)
- Added parameter
combine_IR (TRUE/FALSE) to functions portion_df and count_df, to indicate that all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)
- Fix for
portion_*(..., as_percent = TRUE) when minimal number of isolates would not be met
-- Added parameter
also_single_tested for portion_* and count_* functions to also include cases where not all antibiotics were tested but at least one of the tested antibiotics includes the target antimicribial interpretation, see ?portion
+ - Added parameter
also_single_tested for portion_* and count_* functions to also include cases where not all antibiotics were tested but at least one of the tested antibiotics includes the target antimicribial interpretation, see ?portion
- Using
portion_* functions now throws a warning when total available isolate is below parameter minimum
@@ -1078,9 +1082,9 @@ Using as.mo(..., allow_uncertain = 3)# which is the same as:
septic_patients %>% count_IR(amox, cipr)
-septic_patients %>% portion_S(amcl)
-septic_patients %>% portion_S(amcl, gent)
-septic_patients %>% portion_S(amcl, gent, pita)
ggplot_rsi and geom_rsi so they can cope with count_df. The new fun parameter has value portion_df at default, but can be set to count_df.ggplot_rsi when the ggplot2 package was not loadedas.mo(..., allow_uncertain = 3)
Contents
These functions are so-called 'Deprecated'. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
-as.atc(x) +p.symbol(...) -p.symbol(...)+portion_R(...) + +portion_IR(...) + +portion_I(...) + +portion_SI(...) + +portion_S(...)
AMR is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any table format, including WHONET/EARS-Net data.
We created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
This package can be used for:
Reference for microorganisms, since it contains all microbial (sub)species from the Catalogue of Life
Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life
Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines
Determining first isolates to be used for AMR analysis
Calculating antimicrobial resistance
Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO)
Calculating empirical susceptibility of both mono therapy and combination therapy
Calculating (empirical) susceptibility of both mono therapy and combination therapies
Predicting future antimicrobial resistance using regression models
Getting properties for any microorganism (like Gram stain, species, genus or family)
Getting properties for any antibiotic (like name, ATC code, defined daily dose or trade name)
Getting properties for any antibiotic (like name, EARS-Net code, ATC code, PubChem code, defined daily dose or trade name)
Plotting antimicrobial resistance
Determining first isolates to be used for AMR analysis
Applying EUCAST expert rules
Descriptive statistics: frequency tables, kurtosis and skewness
Matthijs S. Berends[1,2] Christian F. Luz[1], Erwin E.A. Hassing[2], Corinna Glasner[1], Alex W. Friedrich[1], Bhanu N.M. Sinha[1]
[1] Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands - https://www.rug.nl https://www.umcg.nl
-[2] Certe Medical Diagnostics & Advice, Groningen, the Netherlands - https://www.certe.nl
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
-This AMR package honours this new insight. Use portion_SI to determine antimicrobial susceptibility and count_SI to count susceptible isolates.
This AMR package honours this new insight. Use susceptibility (equal to proportion_SI) to determine antimicrobial susceptibility and count_susceptible (equal to count_SI) to count susceptible isolates.
Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. portion_R.
Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. resistance.
availability(tbl, width = NULL)@@ -257,7 +257,7 @@
data.frame with column names of tbl as row names
The function returns a data.frame with columns "resistant" and "visual_resistance". The values in that columns are calculated with portion_R.
The function returns a data.frame with columns "resistant" and "visual_resistance". The values in that columns are calculated with resistance.
These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples.
count_R and count_IR can be used to count resistant isolates, count_S and count_SI can be used to count susceptible isolates.
count_resistant should be used to count resistant isolates, count_susceptible should be used to count susceptible isolates.
count_R(..., only_all_tested = FALSE) +count_resistant(..., only_all_tested = FALSE) + +count_susceptible(..., only_all_tested = FALSE) + +count_R(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) @@ -289,18 +293,15 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_ -Source
- -Wickham H. Tidy Data. The Journal of Statistical Software, vol. 59, 2014. http://vita.had.co.nz/papers/tidy-data.html
Value
Integer
Details
-These functions are meant to count isolates. Use the
-portion_*functions to calculate microbial resistance.The function
-n_rsiis an alias ofcount_all. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal ton_distinct. Their function is equal tocount_S(...) + count_IR(...).The function
-count_dftakes any variable fromdatathat has an"rsi"class (created withas.rsi) and counts the amounts of S, I and R. The resulting tidy data (see Source)data.framewill have three rows (S/I/R) and a column for each variable with class"rsi".The function
+rsi_dfworks exactly likecount_df, but adds the percentage of S, I and R.These functions are meant to count isolates. Use the
+resistance/susceptibilityfunctions to calculate microbial resistance/susceptibility.The function
+count_resistantis equal to the functioncount_R. The functioncount_susceptibleis equal to the functioncount_SI.The function
+n_rsiis an alias ofcount_all. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal ton_distinct. Their function is equal tocount_susceptible(...) + count_resistant(...).The function
count_dftakes any variable fromdatathat has an"rsi"class (created withas.rsi) and counts the number of S's, I's and R's. The functionrsi_dfworks exactly likecount_df, but adds the percentage of S, I and R.Interpretation of S, I and R
@@ -313,12 +314,12 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
-This AMR package honours this new insight. Use
+portion_SIto determine antimicrobial susceptibility andcount_SIto count susceptible isolates.This AMR package honours this new insight. Use
susceptibility(equal toproportion_SI) to determine antimicrobial susceptibility andcount_susceptible(equal tocount_SI) to count susceptible isolates.Combination therapy
-When using more than one variable for
+...(= combination therapy)), useonly_all_testedto only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about howportion_SIworks to calculate the %SI:When using more than one variable for
...(= combination therapy)), useonly_all_testedto only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about howsusceptibilityworks to calculate the %SI:-------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE @@ -339,11 +340,11 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_Please note that, in combination therapies, for
only_all_tested = TRUEapplies that:- count_S() + count_I() + count_R() == count_all() - portion_S() + portion_I() + portion_R() == 1 + count_S() + count_I() + count_R() = count_all() + proportion_S() + proportion_I() + proportion_R() = 1and that, in combination therapies, for
only_all_tested = FALSEapplies that:- count_S() + count_I() + count_R() >= count_all() - portion_S() + portion_I() + portion_R() >= 1 + count_S() + count_I() + count_R() >= count_all() + proportion_S() + proportion_I() + proportion_R() >= 1Using
@@ -354,29 +355,33 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_only_all_testedhas no impact when only using one antibiotic as input.On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
See also
-+
portion_*to calculate microbial resistance and susceptibility.
proportion_*to calculate microbial resistance and susceptibility.Examples
# example_isolates is a data set available in the AMR package. ?example_isolates -# Count resistant isolates -count_R(example_isolates$AMX) -count_IR(example_isolates$AMX) +count_resistant(example_isolates$AMX) # counts "R" +count_susceptible(example_isolates$AMX) # counts "S" and "I" +count_all(example_isolates$AMX) # counts "S", "I" and "R" -# Or susceptible isolates +# be more specific count_S(example_isolates$AMX) count_SI(example_isolates$AMX) +count_I(example_isolates$AMX) +count_IR(example_isolates$AMX) +count_R(example_isolates$AMX) # Count all available isolates count_all(example_isolates$AMX) n_rsi(example_isolates$AMX) -# Since n_rsi counts available isolates, you can -# calculate back to count e.g. non-susceptible isolates. -# This results in the same: -count_SI(example_isolates$AMX) -portion_SI(example_isolates$AMX) * n_rsi(example_isolates$AMX) +# n_rsi() is an alias of count_all(). +# Since it counts all available isolates, you can +# calculate back to count e.g. susceptible isolates. +# These results are the same: +count_susceptible(example_isolates$AMX) +susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX) library(dplyr) example_isolates %>% @@ -390,19 +395,16 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_ # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. -# Please mind that `portion_SI` calculates percentages right away instead. -count_SI(example_isolates$AMC) # 1433 -count_all(example_isolates$AMC) # 1879 +# Please mind that `susceptibility()` calculates percentages right away instead. +example_isolates %>% count_susceptible(AMC) # 1433 +example_isolates %>% count_all(AMC) # 1879 -count_SI(example_isolates$GEN) # 1399 -count_all(example_isolates$GEN) # 1855 +example_isolates %>% count_susceptible(GEN) # 1399 +example_isolates %>% count_all(GEN) # 1855 -with(example_isolates, - count_SI(AMC, GEN)) # 1764 -with(example_isolates, - n_rsi(AMC, GEN)) # 1936 - -# Get portions S/I/R immediately of all rsi columns +example_isolates %>% count_susceptible(AMC, GEN) # 1764 +example_isolates %>% count_all(AMC, GEN) # 1936 +# Get number of S+I vs. R immediately of selected columns example_isolates %>% select(AMX, CIP) %>% count_df(translate = FALSE) @@ -417,7 +419,6 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_Contents
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
-This AMR package honours this new insight. Use portion_SI to determine antimicrobial susceptibility and count_SI to count susceptible isolates.
This AMR package honours this new insight. Use susceptibility (equal to proportion_SI) to determine antimicrobial susceptibility and count_susceptible (equal to count_SI) to count susceptible isolates.
proportion.RdThese functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples.
resistance should be used to calculate resistance, susceptibility should be used to calculate susceptibility.
resistance(..., minimum = 30, as_percent = FALSE, + only_all_tested = FALSE) + +susceptibility(..., minimum = 30, as_percent = FALSE, + only_all_tested = FALSE) + +proportion_R(..., minimum = 30, as_percent = FALSE, + only_all_tested = FALSE) + +proportion_IR(..., minimum = 30, as_percent = FALSE, + only_all_tested = FALSE) + +proportion_I(..., minimum = 30, as_percent = FALSE, + only_all_tested = FALSE) + +proportion_SI(..., minimum = 30, as_percent = FALSE, + only_all_tested = FALSE) + +proportion_S(..., minimum = 30, as_percent = FALSE, + only_all_tested = FALSE) + +proportion_df(data, translate_ab = "name", language = get_locale(), + minimum = 30, as_percent = FALSE, combine_SI = TRUE, + combine_IR = FALSE) + +rsi_df(data, translate_ab = "name", language = get_locale(), + minimum = 30, as_percent = FALSE, combine_SI = TRUE, + combine_IR = FALSE)+ +
| ... | +one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with |
+
|---|---|
| minimum | +the minimum allowed number of available (tested) isolates. Any isolate count lower than |
+
| as_percent | +a logical to indicate whether the output must be returned as a hundred fold with % sign (a character). A value of |
+
| only_all_tested | +(for combination therapies, i.e. using more than one variable for |
+
| data | +a |
+
| translate_ab | +a column name of the |
+
| language | +language of the returned text, defaults to system language (see |
+
| combine_SI | +a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter |
+
| combine_IR | +a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter |
+
M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
+Double or, when as_percent = TRUE, a character.
The function resistance is equal to the function proportion_R. The function susceptibility is equal to the function proportion_SI.
Remember that you should filter your table to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate to determine them in your data set.
These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the count functions to count isolates. The function susceptibility() is essentially equal to count_susceptible() / count_all(). Low counts can infuence the outcome - the proportion functions may camouflage this, since they only return the proportion (albeit being dependent on the minimum parameter).
The function proportion_df takes any variable from data that has an "rsi" class (created with as.rsi) and calculates the proportions R, I and S. The function rsi_df works exactly like proportion_df, but adds the number of isolates.
When using more than one variable for ... (= combination therapy)), use only_all_tested to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how susceptibility works to calculate the %SI:
+-------------------------------------------------------------------- + only_all_tested = FALSE only_all_tested = TRUE + ----------------------- ----------------------- + Drug A Drug B include as include as include as include as + numerator denominator numerator denominator +-------- -------- ---------- ----------- ---------- ----------- + S or I S or I X X X X + R S or I X X X X + <NA> S or I X X - - + S or I R X X X X + R R - X - X + <NA> R - - - - + S or I <NA> X X - - + R <NA> - - - - + <NA> <NA> - - - - +-------------------------------------------------------------------- ++ +
Please note that, in combination therapies, for only_all_tested = TRUE applies that:
+ count_S() + count_I() + count_R() = count_all() + proportion_S() + proportion_I() + proportion_R() = 1 +
and that, in combination therapies, for only_all_tested = FALSE applies that:
+ count_S() + count_I() + count_R() >= count_all() + proportion_S() + proportion_I() + proportion_R() >= 1 ++ +
Using only_all_tested has no impact when only using one antibiotic as input.
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
+S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
+This AMR package honours this new insight. Use susceptibility (equal to proportion_SI) to determine antimicrobial susceptibility and count_susceptible (equal to count_SI) to count susceptible isolates.
On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
+count_* to count resistant and susceptible isolates.
# example_isolates is a data set available in the AMR package. +?example_isolates + +resistance(example_isolates$AMX) # determines %R +susceptibility(example_isolates$AMX) # determines %S+I + +# be more specific +proportion_S(example_isolates$AMX) +proportion_SI(example_isolates$AMX) +proportion_I(example_isolates$AMX) +proportion_IR(example_isolates$AMX) +proportion_R(example_isolates$AMX) + +library(dplyr) +example_isolates %>% + group_by(hospital_id) %>% + summarise(r = resistance(CIP), + n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi + +example_isolates %>% + group_by(hospital_id) %>% + summarise(R = resistance(CIP, as_percent = TRUE), + SI = susceptibility(CIP, as_percent = TRUE), + n1 = count_all(CIP), # the actual total; sum of all three + n2 = n_rsi(CIP), # same - analogous to n_distinct + total = n()) # NOT the number of tested isolates! + +# Calculate co-resistance between amoxicillin/clav acid and gentamicin, +# so we can see that combination therapy does a lot more than mono therapy: +example_isolates %>% susceptibility(AMC) # %SI = 76.3% +example_isolates %>% count_all(AMC) # n = 1879 + +example_isolates %>% susceptibility(GEN) # %SI = 75.4% +example_isolates %>% count_all(GEN) # n = 1855 + +example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1% +example_isolates %>% count_all(AMC, GEN) # n = 1939 + + +# See Details on how `only_all_tested` works. Example: +example_isolates %>% + summarise(numerator = count_susceptible(AMC, GEN), + denominator = count_all(AMC, GEN), + proportion = susceptibility(AMC, GEN)) +example_isolates %>% + summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE), + denominator = count_all(AMC, GEN, only_all_tested = TRUE), + proportion = susceptibility(AMC, GEN, only_all_tested = TRUE)) + + +example_isolates %>% + group_by(hospital_id) %>% + summarise(cipro_p = susceptibility(CIP, as_percent = TRUE), + cipro_n = count_all(CIP), + genta_p = susceptibility(GEN, as_percent = TRUE), + genta_n = count_all(GEN), + combination_p = susceptibility(CIP, GEN, as_percent = TRUE), + combination_n = count_all(CIP, GEN)) + +# Get proportions S/I/R immediately of all rsi columns +example_isolates %>% + select(AMX, CIP) %>% + proportion_df(translate = FALSE) + +# It also supports grouping variables +example_isolates %>% + select(hospital_id, AMX, CIP) %>% + group_by(hospital_id) %>% + proportion_df(translate = FALSE) + + +if (FALSE) { + +# calculate current empiric combination therapy of Helicobacter gastritis: +my_table %>% + filter(first_isolate == TRUE, + genus == "Helicobacter") %>% + summarise(p = susceptibility(AMX, MTR), # amoxicillin with metronidazole + n = count_all(AMX, MTR)) +}+
On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
x <- resistance_predict(example_isolates, col_ab = "AMX", year_min = 2010, model = "binomial") diff --git a/docs/reference/rsi_translation.html b/docs/reference/rsi_translation.html index e2e763e1..d061e360 100644 --- a/docs/reference/rsi_translation.html +++ b/docs/reference/rsi_translation.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/skewness.html b/docs/reference/skewness.html index f56501f7..09fbc614 100644 --- a/docs/reference/skewness.html +++ b/docs/reference/skewness.html @@ -86,7 +86,7 @@ When negative: the left tail is longer; the mass of the distribution is concentr diff --git a/docs/reference/translate.html b/docs/reference/translate.html index a7dc44dc..e1c442e9 100644 --- a/docs/reference/translate.html +++ b/docs/reference/translate.html @@ -85,7 +85,7 @@ diff --git a/docs/sitemap.xml b/docs/sitemap.xml index fb23b122..092414d3 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -118,7 +118,7 @@https://msberends.gitlab.io/AMR/reference/p_symbol.html - https://msberends.gitlab.io/AMR/reference/portion.html +https://msberends.gitlab.io/AMR/reference/proportion.html https://msberends.gitlab.io/AMR/reference/read.4D.html diff --git a/index.md b/index.md index 6c5af0ac..3965e0e7 100644 --- a/index.md +++ b/index.md @@ -143,7 +143,7 @@ The `AMR` package basically does four important things: 3. It **analyses the data** with convenient functions that use well-known methods. - * Calculate the resistance (and even co-resistance) of microbial isolates with the `portion_R()`, `portion_IR()`, `portion_I()`, `portion_SI()` and `portion_S()` functions. Similarly, the *number* of isolates can be determined with the `count_R()`, `count_IR()`, `count_I()`, `count_SI()` and `count_S()` functions. All these functions can be used with the `dplyr` package (e.g. in conjunction with `summarise()`) + * Calculate the microbial susceptibility or resistance (and even co-resistance) with the `susceptibility()` and `resistance()` functions, or be even more specific with the `proportion_R()`, `proportion_IR()`, `proportion_I()`, `proportion_SI()` and `proportion_S()` functions. Similarly, the *number* of isolates can be determined with the `count_resistant()`, `count_susceptible()` and `count_all()` functions. All these functions can be used with the `dplyr` package (e.g. in conjunction with `summarise()`) * Plot AMR results with `geom_rsi()`, a function made for the `ggplot2` package * Predict antimicrobial resistance for the nextcoming years using logistic regression models with the `resistance_predict()` function diff --git a/man/AMR-deprecated.Rd b/man/AMR-deprecated.Rd index 47350aa6..1210eac9 100644 --- a/man/AMR-deprecated.Rd +++ b/man/AMR-deprecated.Rd @@ -2,13 +2,25 @@ % Please edit documentation in R/deprecated.R \name{AMR-deprecated} \alias{AMR-deprecated} -\alias{as.atc} \alias{p.symbol} +\alias{portion_R} +\alias{portion_IR} +\alias{portion_I} +\alias{portion_SI} +\alias{portion_S} \title{Deprecated functions} \usage{ -as.atc(x) - p.symbol(...) + +portion_R(...) + +portion_IR(...) + +portion_I(...) + +portion_SI(...) + +portion_S(...) } \description{ These functions are so-called '\link{Deprecated}'. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by (if there is one). diff --git a/man/AMR.Rd b/man/AMR.Rd index 8c718125..c310063e 100644 --- a/man/AMR.Rd +++ b/man/AMR.Rd @@ -13,28 +13,19 @@ We created this package for both academic research and routine analysis at the F This package can be used for: \itemize{ - \item{Reference for microorganisms, since it contains all microbial (sub)species from the Catalogue of Life} + \item{Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life} \item{Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines} + \item{Determining first isolates to be used for AMR analysis} \item{Calculating antimicrobial resistance} \item{Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO)} - \item{Calculating empirical susceptibility of both mono therapy and combination therapy} + \item{Calculating (empirical) susceptibility of both mono therapy and combination therapies} \item{Predicting future antimicrobial resistance using regression models} \item{Getting properties for any microorganism (like Gram stain, species, genus or family)} - \item{Getting properties for any antibiotic (like name, ATC code, defined daily dose or trade name)} + \item{Getting properties for any antibiotic (like name, EARS-Net code, ATC code, PubChem code, defined daily dose or trade name)} \item{Plotting antimicrobial resistance} - \item{Determining first isolates to be used for AMR analysis} \item{Applying EUCAST expert rules} - \item{Descriptive statistics: frequency tables, kurtosis and skewness} } } -\section{Authors}{ - -Matthijs S. Berends[1,2] Christian F. Luz[1], Erwin E.A. Hassing[2], Corinna Glasner[1], Alex W. Friedrich[1], Bhanu N.M. Sinha[1] \cr - -[1] Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands - \url{https://www.rug.nl} \url{https://www.umcg.nl} \cr -[2] Certe Medical Diagnostics & Advice, Groningen, the Netherlands - \url{https://www.certe.nl} -} - \section{Read more on our website!}{ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}. diff --git a/man/as.rsi.Rd b/man/as.rsi.Rd index ab14187f..8bb4fa52 100755 --- a/man/as.rsi.Rd +++ b/man/as.rsi.Rd @@ -63,7 +63,7 @@ In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection. -This AMR package honours this new insight. Use \code{\link{portion_SI}} to determine antimicrobial susceptibility and \code{\link{count_SI}} to count susceptible isolates. +This AMR package honours this new insight. Use \code{\link{susceptibility}} (equal to \code{\link{proportion_SI}}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}} (equal to \code{\link{count_SI}}) to count susceptible isolates. } \section{Read more on our website!}{ diff --git a/man/availability.Rd b/man/availability.Rd index e499d8dc..38202d10 100644 --- a/man/availability.Rd +++ b/man/availability.Rd @@ -15,10 +15,10 @@ availability(tbl, width = NULL) \code{data.frame} with column names of \code{tbl} as row names } \description{ -Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. \code{\link{portion_R}}. +Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. \code{\link{resistance}}. } \details{ -The function returns a \code{data.frame} with columns \code{"resistant"} and \code{"visual_resistance"}. The values in that columns are calculated with \code{\link{portion_R}}. +The function returns a \code{data.frame} with columns \code{"resistant"} and \code{"visual_resistance"}. The values in that columns are calculated with \code{\link{resistance}}. } \section{Read more on our website!}{ diff --git a/man/count.Rd b/man/count.Rd index 34d5fa4d..fc6ce759 100644 --- a/man/count.Rd +++ b/man/count.Rd @@ -2,6 +2,8 @@ % Please edit documentation in R/count.R \name{count} \alias{count} +\alias{count_resistant} +\alias{count_susceptible} \alias{count_R} \alias{count_IR} \alias{count_I} @@ -11,10 +13,11 @@ \alias{n_rsi} \alias{count_df} \title{Count isolates} -\source{ -Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html} -} \usage{ +count_resistant(..., only_all_tested = FALSE) + +count_susceptible(..., only_all_tested = FALSE) + count_R(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) @@ -53,16 +56,16 @@ Integer \description{ These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. -\code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr +\code{count_resistant} should be used to count resistant isolates, \code{count_susceptible} should be used to count susceptible isolates.\cr } \details{ -These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance. +These functions are meant to count isolates. Use the \code{\link{resistance}}/\code{\link{susceptibility}} functions to calculate microbial resistance/susceptibility. -The function \code{n_rsi} is an alias of \code{count_all}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}}. Their function is equal to \code{count_S(...) + count_IR(...)}. +The function \code{count_resistant} is equal to the function \code{count_R}. The function \code{count_susceptible} is equal to the function \code{count_SI}. -The function \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of S, I and R. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}. +The function \code{n_rsi} is an alias of \code{count_all}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}}. Their function is equal to \code{count_susceptible(...) + count_resistant(...)}. -The function \code{rsi_df} works exactly like \code{count_df}, but adds the percentage of S, I and R. +The function \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the number of S's, I's and R's. The function \code{rsi_df} works exactly like \code{count_df}, but adds the percentage of S, I and R. } \section{Interpretation of S, I and R}{ @@ -76,12 +79,12 @@ In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection. -This AMR package honours this new insight. Use \code{\link{portion_SI}} to determine antimicrobial susceptibility and \code{\link{count_SI}} to count susceptible isolates. +This AMR package honours this new insight. Use \code{\link{susceptibility}} (equal to \code{\link{proportion_SI}}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}} (equal to \code{\link{count_SI}}) to count susceptible isolates. } \section{Combination therapy}{ -When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{portion_SI} works to calculate the \%SI: +When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{susceptibility} works to calculate the \%SI: \preformatted{ -------------------------------------------------------------------- @@ -104,13 +107,13 @@ When using more than one variable for \code{...} (= combination therapy)), use \ Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that: \preformatted{ - count_S() + count_I() + count_R() == count_all() - portion_S() + portion_I() + portion_R() == 1 + count_S() + count_I() + count_R() = count_all() + proportion_S() + proportion_I() + proportion_R() = 1 } and that, in combination therapies, for \code{only_all_tested = FALSE} applies that: \preformatted{ - count_S() + count_I() + count_R() >= count_all() - portion_S() + portion_I() + portion_R() >= 1 + count_S() + count_I() + count_R() >= count_all() + proportion_S() + proportion_I() + proportion_R() >= 1 } Using \code{only_all_tested} has no impact when only using one antibiotic as input. @@ -125,23 +128,27 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https:// # example_isolates is a data set available in the AMR package. ?example_isolates -# Count resistant isolates -count_R(example_isolates$AMX) -count_IR(example_isolates$AMX) +count_resistant(example_isolates$AMX) # counts "R" +count_susceptible(example_isolates$AMX) # counts "S" and "I" +count_all(example_isolates$AMX) # counts "S", "I" and "R" -# Or susceptible isolates +# be more specific count_S(example_isolates$AMX) count_SI(example_isolates$AMX) +count_I(example_isolates$AMX) +count_IR(example_isolates$AMX) +count_R(example_isolates$AMX) # Count all available isolates count_all(example_isolates$AMX) n_rsi(example_isolates$AMX) -# Since n_rsi counts available isolates, you can -# calculate back to count e.g. non-susceptible isolates. -# This results in the same: -count_SI(example_isolates$AMX) -portion_SI(example_isolates$AMX) * n_rsi(example_isolates$AMX) +# n_rsi() is an alias of count_all(). +# Since it counts all available isolates, you can +# calculate back to count e.g. susceptible isolates. +# These results are the same: +count_susceptible(example_isolates$AMX) +susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX) library(dplyr) example_isolates \%>\% @@ -155,19 +162,16 @@ example_isolates \%>\% # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. -# Please mind that `portion_SI` calculates percentages right away instead. -count_SI(example_isolates$AMC) # 1433 -count_all(example_isolates$AMC) # 1879 +# Please mind that `susceptibility()` calculates percentages right away instead. +example_isolates \%>\% count_susceptible(AMC) # 1433 +example_isolates \%>\% count_all(AMC) # 1879 -count_SI(example_isolates$GEN) # 1399 -count_all(example_isolates$GEN) # 1855 +example_isolates \%>\% count_susceptible(GEN) # 1399 +example_isolates \%>\% count_all(GEN) # 1855 -with(example_isolates, - count_SI(AMC, GEN)) # 1764 -with(example_isolates, - n_rsi(AMC, GEN)) # 1936 - -# Get portions S/I/R immediately of all rsi columns +example_isolates \%>\% count_susceptible(AMC, GEN) # 1764 +example_isolates \%>\% count_all(AMC, GEN) # 1936 +# Get number of S+I vs. R immediately of selected columns example_isolates \%>\% select(AMX, CIP) \%>\% count_df(translate = FALSE) @@ -180,5 +184,5 @@ example_isolates \%>\% } \seealso{ -\code{\link{portion}_*} to calculate microbial resistance and susceptibility. +\code{\link{proportion}_*} to calculate microbial resistance and susceptibility. } diff --git a/man/ggplot_rsi.Rd b/man/ggplot_rsi.Rd index bcc021b5..09e6f687 100644 --- a/man/ggplot_rsi.Rd +++ b/man/ggplot_rsi.Rd @@ -41,7 +41,7 @@ labels_rsi_count(position = NULL, x = "antibiotic", \arguments{ \item{data}{a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})} -\item{position}{position adjustment of bars, either \code{"fill"} (default when \code{fun} is \code{\link{count_df}}), \code{"stack"} (default when \code{fun} is \code{\link{portion_df}}) or \code{"dodge"}} +\item{position}{position adjustment of bars, either \code{"fill"}, \code{"stack"} or \code{"dodge"}} \item{x}{variable to show on x axis, either \code{"antibiotic"} (default) or \code{"interpretation"} or a grouping variable} @@ -131,7 +131,7 @@ example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP) \%>\% ggplot_rsi() -# get only portions and no counts: +# get only proportions and no counts: example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP) \%>\% ggplot_rsi(datalabels = FALSE) diff --git a/man/mdro.Rd b/man/mdro.Rd index 3ec5afd0..fb39ef0e 100644 --- a/man/mdro.Rd +++ b/man/mdro.Rd @@ -175,7 +175,7 @@ In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection. -This AMR package honours this new insight. Use \code{\link{portion_SI}} to determine antimicrobial susceptibility and \code{\link{count_SI}} to count susceptible isolates. +This AMR package honours this new insight. Use \code{\link{susceptibility}} (equal to \code{\link{proportion_SI}}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}} (equal to \code{\link{count_SI}}) to count susceptible isolates. } \section{Read more on our website!}{ diff --git a/man/portion.Rd b/man/proportion.Rd similarity index 67% rename from man/portion.Rd rename to man/proportion.Rd index 722e1280..b33b7c2e 100644 --- a/man/portion.Rd +++ b/man/proportion.Rd @@ -1,37 +1,44 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/portion.R, R/rsi_df.R -\name{portion} +% Please edit documentation in R/proportion.R, R/rsi_df.R +\name{proportion} +\alias{proportion} +\alias{resistance} \alias{portion} -\alias{portion_R} -\alias{portion_IR} -\alias{portion_I} -\alias{portion_SI} -\alias{portion_S} -\alias{portion_df} +\alias{susceptibility} +\alias{proportion_R} +\alias{proportion_IR} +\alias{proportion_I} +\alias{proportion_SI} +\alias{proportion_S} +\alias{proportion_df} \alias{rsi_df} -\title{Calculate resistance of isolates} +\title{Calculate microbial resistance} \source{ \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. - -Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html} } \usage{ -portion_R(..., minimum = 30, as_percent = FALSE, +resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) -portion_IR(..., minimum = 30, as_percent = FALSE, +susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) -portion_I(..., minimum = 30, as_percent = FALSE, +proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) -portion_SI(..., minimum = 30, as_percent = FALSE, +proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) -portion_S(..., minimum = 30, as_percent = FALSE, +proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) -portion_df(data, translate_ab = "name", language = get_locale(), +proportion_SI(..., minimum = 30, as_percent = FALSE, + only_all_tested = FALSE) + +proportion_S(..., minimum = 30, as_percent = FALSE, + only_all_tested = FALSE) + +proportion_df(data, translate_ab = "name", language = get_locale(), minimum = 30, as_percent = FALSE, combine_SI = TRUE, combine_IR = FALSE) @@ -62,22 +69,22 @@ rsi_df(data, translate_ab = "name", language = get_locale(), Double or, when \code{as_percent = TRUE}, a character. } \description{ -These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. +These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. -\code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr +\code{resistance} should be used to calculate resistance, \code{susceptibility} should be used to calculate susceptibility.\cr } \details{ +The function \code{resistance} is equal to the function \code{proportion_R}. The function \code{susceptibility} is equal to the function \code{proportion_SI}. + \strong{Remember that you should filter your table to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link{first_isolate}} to determine them in your data set. -These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. The function \code{portion_SI()} is essentially equal to \code{count_SI() / count_all()}. \emph{Low counts can infuence the outcome - the \code{portion} functions may camouflage this, since they only return the portion (albeit being dependent on the \code{minimum} parameter).} +These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. The function \code{susceptibility()} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can infuence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} parameter).} -The function \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each group and each variable with class \code{"rsi"}. - -The function \code{rsi_df} works exactly like \code{portion_df}, but adds the number of isolates. +The function \code{proportion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the proportions R, I and S. The function \code{rsi_df} works exactly like \code{proportion_df}, but adds the number of isolates. } \section{Combination therapy}{ -When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{portion_SI} works to calculate the \%SI: +When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{susceptibility} works to calculate the \%SI: \preformatted{ -------------------------------------------------------------------- @@ -100,13 +107,13 @@ When using more than one variable for \code{...} (= combination therapy)), use \ Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that: \preformatted{ - count_S() + count_I() + count_R() == count_all() - portion_S() + portion_I() + portion_R() == 1 + count_S() + count_I() + count_R() = count_all() + proportion_S() + proportion_I() + proportion_R() = 1 } and that, in combination therapies, for \code{only_all_tested = FALSE} applies that: \preformatted{ - count_S() + count_I() + count_R() >= count_all() - portion_S() + portion_I() + portion_R() >= 1 + count_S() + count_I() + count_R() >= count_all() + proportion_S() + proportion_I() + proportion_R() >= 1 } Using \code{only_all_tested} has no impact when only using one antibiotic as input. @@ -124,7 +131,7 @@ In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection. -This AMR package honours this new insight. Use \code{\link{portion_SI}} to determine antimicrobial susceptibility and \code{\link{count_SI}} to count susceptible isolates. +This AMR package honours this new insight. Use \code{\link{susceptibility}} (equal to \code{\link{proportion_SI}}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}} (equal to \code{\link{count_SI}}) to count susceptible isolates. } \section{Read more on our website!}{ @@ -136,81 +143,72 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https:// # example_isolates is a data set available in the AMR package. ?example_isolates -# Calculate resistance -portion_R(example_isolates$AMX) -portion_IR(example_isolates$AMX) +resistance(example_isolates$AMX) # determines \%R +susceptibility(example_isolates$AMX) # determines \%S+I -# Or susceptibility -portion_S(example_isolates$AMX) -portion_SI(example_isolates$AMX) +# be more specific +proportion_S(example_isolates$AMX) +proportion_SI(example_isolates$AMX) +proportion_I(example_isolates$AMX) +proportion_IR(example_isolates$AMX) +proportion_R(example_isolates$AMX) -# Do the above with pipes: library(dplyr) -example_isolates \%>\% portion_R(AMX) -example_isolates \%>\% portion_IR(AMX) -example_isolates \%>\% portion_S(AMX) -example_isolates \%>\% portion_SI(AMX) +example_isolates \%>\% + group_by(hospital_id) \%>\% + summarise(r = resistance(CIP), + n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi example_isolates \%>\% group_by(hospital_id) \%>\% - summarise(p = portion_SI(CIP), - n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr - -example_isolates \%>\% - group_by(hospital_id) \%>\% - summarise(R = portion_R(CIP, as_percent = TRUE), - I = portion_I(CIP, as_percent = TRUE), - S = portion_S(CIP, as_percent = TRUE), + summarise(R = resistance(CIP, as_percent = TRUE), + SI = susceptibility(CIP, as_percent = TRUE), n1 = count_all(CIP), # the actual total; sum of all three n2 = n_rsi(CIP), # same - analogous to n_distinct total = n()) # NOT the number of tested isolates! # Calculate co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy: -example_isolates \%>\% portion_SI(AMC) # \%SI = 76.3\% +example_isolates \%>\% susceptibility(AMC) # \%SI = 76.3\% example_isolates \%>\% count_all(AMC) # n = 1879 -example_isolates \%>\% portion_SI(GEN) # \%SI = 75.4\% +example_isolates \%>\% susceptibility(GEN) # \%SI = 75.4\% example_isolates \%>\% count_all(GEN) # n = 1855 -example_isolates \%>\% portion_SI(AMC, GEN) # \%SI = 94.1\% -example_isolates \%>\% count_all(AMC, GEN) # n = 1939 +example_isolates \%>\% susceptibility(AMC, GEN) # \%SI = 94.1\% +example_isolates \%>\% count_all(AMC, GEN) # n = 1939 # See Details on how `only_all_tested` works. Example: example_isolates \%>\% - summarise(numerator = count_SI(AMC, GEN), + summarise(numerator = count_susceptible(AMC, GEN), denominator = count_all(AMC, GEN), - portion = portion_SI(AMC, GEN)) -# numerator denominator portion -# 1764 1936 0.9408 + proportion = susceptibility(AMC, GEN)) example_isolates \%>\% - summarise(numerator = count_SI(AMC, GEN, only_all_tested = TRUE), + summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE), denominator = count_all(AMC, GEN, only_all_tested = TRUE), - portion = portion_SI(AMC, GEN, only_all_tested = TRUE)) -# numerator denominator portion -# 1687 1798 0.9383 + proportion = susceptibility(AMC, GEN, only_all_tested = TRUE)) example_isolates \%>\% group_by(hospital_id) \%>\% - summarise(cipro_p = portion_SI(CIP, as_percent = TRUE), + summarise(cipro_p = susceptibility(CIP, as_percent = TRUE), cipro_n = count_all(CIP), - genta_p = portion_SI(GEN, as_percent = TRUE), + genta_p = susceptibility(GEN, as_percent = TRUE), genta_n = count_all(GEN), - combination_p = portion_SI(CIP, GEN, as_percent = TRUE), + combination_p = susceptibility(CIP, GEN, as_percent = TRUE), combination_n = count_all(CIP, GEN)) -# Get portions S/I/R immediately of all rsi columns +# Get proportions S/I/R immediately of all rsi columns example_isolates \%>\% select(AMX, CIP) \%>\% - portion_df(translate = FALSE) + proportion_df(translate = FALSE) # It also supports grouping variables example_isolates \%>\% select(hospital_id, AMX, CIP) \%>\% group_by(hospital_id) \%>\% - portion_df(translate = FALSE) + proportion_df(translate = FALSE) \dontrun{ @@ -219,7 +217,7 @@ example_isolates \%>\% my_table \%>\% filter(first_isolate == TRUE, genus == "Helicobacter") \%>\% - summarise(p = portion_S(AMX, MTR), # amoxicillin with metronidazole + summarise(p = susceptibility(AMX, MTR), # amoxicillin with metronidazole n = count_all(AMX, MTR)) } } diff --git a/tests/appveyor/appveyor_tool.ps1 b/tests/appveyor/appveyor_tool.ps1 index bc3fd64f..18f932f5 100644 --- a/tests/appveyor/appveyor_tool.ps1 +++ b/tests/appveyor/appveyor_tool.ps1 @@ -174,7 +174,6 @@ Function Bootstrap { Progress "Downloading and installing travis_tool.sh" cp "tests\appveyor\travis_tool.sh" "..\travis_tool.sh" - # Invoke-WebRequest https://gitlab.com/msberends/AMR/raw/master/tests/appveyor/travis_tool.sh -OutFile "..\travis_tool.sh" echo '@bash.exe ../travis_tool.sh %*' | Out-File -Encoding ASCII .\travis_tool.sh.cmd cat .\travis_tool.sh.cmd bash -c "( echo; echo '^travis_tool\.sh\.cmd$' ) >> .Rbuildignore" diff --git a/tests/testthat/test-count.R b/tests/testthat/test-count.R index a85d88a1..7dde67d1 100644 --- a/tests/testthat/test-count.R +++ b/tests/testthat/test-count.R @@ -22,67 +22,67 @@ context("count.R") test_that("counts work", { + + expect_equal(count_resistant(example_isolates$AMX), count_R(example_isolates$AMX)) + expect_equal(count_susceptible(example_isolates$AMX), count_SI(example_isolates$AMX)) + expect_equal(count_all(example_isolates$AMX), n_rsi(example_isolates$AMX)) + # AMX resistance in `example_isolates` - expect_equal(count_R(example_isolates$AMX), 683) - expect_equal(count_I(example_isolates$AMX), 3) - expect_equal(count_S(example_isolates$AMX), 543) - expect_equal(count_R(example_isolates$AMX) + count_I(example_isolates$AMX), - count_IR(example_isolates$AMX)) - expect_equal(count_S(example_isolates$AMX) + count_I(example_isolates$AMX), - count_SI(example_isolates$AMX)) - + expect_equal(count_R(example_isolates$AMX), 683) + expect_equal(count_I(example_isolates$AMX), 3) + expect_equal(suppressWarnings(count_S(example_isolates$AMX)), 543) + expect_equal(count_R(example_isolates$AMX) + count_I(example_isolates$AMX), + suppressWarnings(count_IR(example_isolates$AMX))) + expect_equal(suppressWarnings(count_S(example_isolates$AMX)) + count_I(example_isolates$AMX), + count_SI(example_isolates$AMX)) + library(dplyr) - expect_equal(example_isolates %>% count_S(AMC), 1342) - expect_equal(example_isolates %>% count_S(AMC, GEN, only_all_tested = TRUE), 1660) - expect_equal(example_isolates %>% count_S(AMC, GEN, only_all_tested = FALSE), 1728) + expect_equal(example_isolates %>% count_susceptible(AMC), 1433) + expect_equal(example_isolates %>% count_susceptible(AMC, GEN, only_all_tested = TRUE), 1687) + expect_equal(example_isolates %>% count_susceptible(AMC, GEN, only_all_tested = FALSE), 1764) expect_equal(example_isolates %>% count_all(AMC, GEN, only_all_tested = TRUE), 1798) expect_equal(example_isolates %>% count_all(AMC, GEN, only_all_tested = FALSE), 1936) expect_identical(example_isolates %>% count_all(AMC, GEN, only_all_tested = TRUE), - example_isolates %>% count_S(AMC, GEN, only_all_tested = TRUE) + - example_isolates %>% count_IR(AMC, GEN, only_all_tested = TRUE)) + example_isolates %>% count_susceptible(AMC, GEN, only_all_tested = TRUE) + + example_isolates %>% count_resistant(AMC, GEN, only_all_tested = TRUE)) # count of cases expect_equal(example_isolates %>% group_by(hospital_id) %>% - summarise(cipro = count_SI(CIP), - genta = count_SI(GEN), - combination = count_SI(CIP, GEN)) %>% + summarise(cipro = count_susceptible(CIP), + genta = count_susceptible(GEN), + combination = count_susceptible(CIP, GEN)) %>% pull(combination), c(253, 465, 192, 558)) # count_df expect_equal( example_isolates %>% select(AMX) %>% count_df() %>% pull(value), - c(example_isolates$AMX %>% count_SI(), - example_isolates$AMX %>% count_R()) + c(example_isolates$AMX %>% count_susceptible(), + example_isolates$AMX %>% count_resistant()) ) expect_equal( example_isolates %>% select(AMX) %>% count_df(combine_IR = TRUE) %>% pull(value), - c(example_isolates$AMX %>% count_S(), - example_isolates$AMX %>% count_IR()) + c(suppressWarnings(example_isolates$AMX %>% count_S()), + suppressWarnings(example_isolates$AMX %>% count_IR())) ) expect_equal( example_isolates %>% select(AMX) %>% count_df(combine_SI = FALSE) %>% pull(value), - c(example_isolates$AMX %>% count_S(), + c(suppressWarnings(example_isolates$AMX %>% count_S()), example_isolates$AMX %>% count_I(), example_isolates$AMX %>% count_R()) ) - + # warning for speed loss - expect_warning(count_R(as.character(example_isolates$AMC))) - expect_warning(count_I(as.character(example_isolates$AMC))) - expect_warning(count_S(as.character(example_isolates$AMC, - example_isolates$GEN))) - expect_warning(count_S(example_isolates$AMC, - as.character(example_isolates$GEN))) - + expect_warning(count_resistant(as.character(example_isolates$AMC))) + expect_warning(count_resistant(example_isolates$AMC, + as.character(example_isolates$GEN))) + # check for errors - expect_error(count_IR("test", minimum = "test")) - expect_error(count_IR("test", as_percent = "test")) - expect_error(count_I("test", minimum = "test")) - expect_error(count_I("test", as_percent = "test")) - expect_error(count_S("test", minimum = "test")) - expect_error(count_S("test", as_percent = "test")) + expect_error(count_resistant("test", minimum = "test")) + expect_error(count_resistant("test", as_percent = "test")) + expect_error(count_susceptible("test", minimum = "test")) + expect_error(count_susceptible("test", as_percent = "test")) expect_error(count_df(c("A", "B", "C"))) expect_error(count_df(example_isolates[, "date"])) diff --git a/tests/testthat/test-deprecated.R b/tests/testthat/test-deprecated.R index 12baabce..583d6256 100644 --- a/tests/testthat/test-deprecated.R +++ b/tests/testthat/test-deprecated.R @@ -22,20 +22,12 @@ context("deprecated.R") test_that("deprecated functions work", { - - # first 5 chars of official name - expect_equal(suppressWarnings(as.character(as.atc(c("nitro", "cipro")))), - c("J01XE01", "J01MA02")) - - # EARS-Net - expect_equal(suppressWarnings(as.character(as.atc("AMX"))), - "J01CA04") - - expect_equal(suppressWarnings(guess_ab_col(data.frame(AMP_ND10 = "R", - AMC_ED20 = "S"), - as.atc("augmentin"))), - "AMC_ED20") - expect_identical(suppressWarnings(p.symbol(seq(0, 1, 0.001))), p_symbol(seq(0, 1, 0.001))) + + expect_equal(suppressWarnings(portion_S(example_isolates$AMX)), proportion_S(example_isolates$AMX)) + expect_equal(suppressWarnings(portion_SI(example_isolates$AMX)), proportion_SI(example_isolates$AMX)) + expect_equal(suppressWarnings(portion_I(example_isolates$AMX)), proportion_I(example_isolates$AMX)) + expect_equal(suppressWarnings(portion_IR(example_isolates$AMX)), proportion_IR(example_isolates$AMX)) + expect_equal(suppressWarnings(portion_R(example_isolates$AMX)), proportion_R(example_isolates$AMX)) }) diff --git a/tests/testthat/test-ggplot_rsi.R b/tests/testthat/test-ggplot_rsi.R index d9ce89ad..479ec537 100644 --- a/tests/testthat/test-ggplot_rsi.R +++ b/tests/testthat/test-ggplot_rsi.R @@ -30,26 +30,26 @@ test_that("ggplot_rsi works", { # data should be equal expect_equal( - (example_isolates %>% select(AMC, CIP) %>% ggplot_rsi())$data %>% summarise_all(portion_IR) %>% as.double(), - example_isolates %>% select(AMC, CIP) %>% summarise_all(portion_IR) %>% as.double() + (example_isolates %>% select(AMC, CIP) %>% ggplot_rsi())$data %>% summarise_all(resistance) %>% as.double(), + example_isolates %>% select(AMC, CIP) %>% summarise_all(resistance) %>% as.double() ) print(example_isolates %>% select(AMC, CIP) %>% ggplot_rsi(x = "interpretation", facet = "antibiotic")) print(example_isolates %>% select(AMC, CIP) %>% ggplot_rsi(x = "antibiotic", facet = "interpretation")) expect_equal( - (example_isolates %>% select(AMC, CIP) %>% ggplot_rsi(x = "interpretation", facet = "antibiotic"))$data %>% summarise_all(portion_IR) %>% as.double(), - example_isolates %>% select(AMC, CIP) %>% summarise_all(portion_IR) %>% as.double() + (example_isolates %>% select(AMC, CIP) %>% ggplot_rsi(x = "interpretation", facet = "antibiotic"))$data %>% summarise_all(resistance) %>% as.double(), + example_isolates %>% select(AMC, CIP) %>% summarise_all(resistance) %>% as.double() ) expect_equal( - (example_isolates %>% select(AMC, CIP) %>% ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>% summarise_all(portion_IR) %>% as.double(), - example_isolates %>% select(AMC, CIP) %>% summarise_all(portion_IR) %>% as.double() + (example_isolates %>% select(AMC, CIP) %>% ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>% summarise_all(resistance) %>% as.double(), + example_isolates %>% select(AMC, CIP) %>% summarise_all(resistance) %>% as.double() ) expect_equal( - (example_isolates %>% select(AMC, CIP) %>% ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>% summarise_all(count_IR) %>% as.double(), - example_isolates %>% select(AMC, CIP) %>% summarise_all(count_IR) %>% as.double() + (example_isolates %>% select(AMC, CIP) %>% ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>% summarise_all(count_resistant) %>% as.double(), + example_isolates %>% select(AMC, CIP) %>% summarise_all(count_resistant) %>% as.double() ) # support for scale_type ab and mo diff --git a/tests/testthat/test-portion.R b/tests/testthat/test-portion.R deleted file mode 100755 index 2c9ab72d..00000000 --- a/tests/testthat/test-portion.R +++ /dev/null @@ -1,121 +0,0 @@ -# ==================================================================== # -# TITLE # -# Antimicrobial Resistance (AMR) Analysis # -# # -# SOURCE # -# https://gitlab.com/msberends/AMR # -# # -# LICENCE # -# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) # -# # -# This R package is free software; you can freely use and distribute # -# it for both personal and commercial purposes under the terms of the # -# GNU General Public License version 2.0 (GNU GPL-2), as published by # -# the Free Software Foundation. # -# # -# This R package was created for academic research and was publicly # -# released in the hope that it will be useful, but it comes WITHOUT # -# ANY WARRANTY OR LIABILITY. # -# Visit our website for more info: https://msberends.gitlab.io/AMR. # -# ==================================================================== # - -context("portion.R") - -test_that("portions works", { - # AMX resistance in `example_isolates` - expect_equal(portion_R(example_isolates$AMX), 0.5557364, tolerance = 0.0001) - expect_equal(portion_I(example_isolates$AMX), 0.002441009, tolerance = 0.0001) - expect_equal(1 - portion_R(example_isolates$AMX) - portion_I(example_isolates$AMX), - portion_S(example_isolates$AMX)) - expect_equal(portion_R(example_isolates$AMX) + portion_I(example_isolates$AMX), - portion_IR(example_isolates$AMX)) - expect_equal(portion_S(example_isolates$AMX) + portion_I(example_isolates$AMX), - portion_SI(example_isolates$AMX)) - - expect_equal(example_isolates %>% portion_SI(AMC), - 0.7626397, - tolerance = 0.0001) - expect_equal(example_isolates %>% portion_SI(AMC, GEN), - 0.9408, - tolerance = 0.0001) - expect_equal(example_isolates %>% portion_SI(AMC, GEN, only_all_tested = TRUE), - 0.9382647, - tolerance = 0.0001) - - # percentages - expect_equal(example_isolates %>% - group_by(hospital_id) %>% - summarise(R = portion_R(CIP, as_percent = TRUE), - I = portion_I(CIP, as_percent = TRUE), - S = portion_S(CIP, as_percent = TRUE), - n = n_rsi(CIP), - total = n()) %>% - pull(n) %>% - sum(), - 1409) - - # count of cases - expect_equal(example_isolates %>% - group_by(hospital_id) %>% - summarise(cipro_p = portion_SI(CIP, as_percent = TRUE), - cipro_n = n_rsi(CIP), - genta_p = portion_SI(GEN, as_percent = TRUE), - genta_n = n_rsi(GEN), - combination_p = portion_SI(CIP, GEN, as_percent = TRUE), - combination_n = n_rsi(CIP, GEN)) %>% - pull(combination_n), - c(305, 617, 241, 711)) - - expect_warning(portion_R(as.character(example_isolates$AMC))) - expect_warning(portion_S(as.character(example_isolates$AMC))) - expect_warning(portion_S(as.character(example_isolates$AMC, - example_isolates$GEN))) - expect_warning(n_rsi(as.character(example_isolates$AMC, - example_isolates$GEN))) - expect_equal(suppressWarnings(n_rsi(as.character(example_isolates$AMC, - example_isolates$GEN))), - 1879) - - # check for errors - expect_error(portion_IR("test", minimum = "test")) - expect_error(portion_IR("test", as_percent = "test")) - expect_error(portion_I("test", minimum = "test")) - expect_error(portion_I("test", as_percent = "test")) - expect_error(portion_S("test", minimum = "test")) - expect_error(portion_S("test", as_percent = "test")) - expect_error(portion_S("test", also_single_tested = TRUE)) - - # check too low amount of isolates - expect_identical(suppressWarnings(portion_R(example_isolates$AMX, minimum = nrow(example_isolates) + 1)), - NA) - expect_identical(suppressWarnings(portion_I(example_isolates$AMX, minimum = nrow(example_isolates) + 1)), - NA) - expect_identical(suppressWarnings(portion_S(example_isolates$AMX, minimum = nrow(example_isolates) + 1)), - NA) - - # warning for speed loss - expect_warning(portion_R(as.character(example_isolates$GEN))) - expect_warning(portion_I(as.character(example_isolates$GEN))) - expect_warning(portion_S(example_isolates$AMC, as.character(example_isolates$GEN))) - - # portion_df - expect_equal( - example_isolates %>% select(AMX) %>% portion_df() %>% pull(value), - c(example_isolates$AMX %>% portion_SI(), - example_isolates$AMX %>% portion_R()) - ) - expect_equal( - example_isolates %>% select(AMX) %>% portion_df(combine_IR = TRUE) %>% pull(value), - c(example_isolates$AMX %>% portion_S(), - example_isolates$AMX %>% portion_IR()) - ) - expect_equal( - example_isolates %>% select(AMX) %>% portion_df(combine_SI = FALSE) %>% pull(value), - c(example_isolates$AMX %>% portion_S(), - example_isolates$AMX %>% portion_I(), - example_isolates$AMX %>% portion_R()) - ) - - expect_error(portion_df(c("A", "B", "C"))) - expect_error(portion_df(example_isolates[, "date"])) -}) diff --git a/tests/testthat/test-proportion.R b/tests/testthat/test-proportion.R new file mode 100755 index 00000000..5efe895b --- /dev/null +++ b/tests/testthat/test-proportion.R @@ -0,0 +1,124 @@ +# ==================================================================== # +# TITLE # +# Antimicrobial Resistance (AMR) Analysis # +# # +# SOURCE # +# https://gitlab.com/msberends/AMR # +# # +# LICENCE # +# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) # +# # +# This R package is free software; you can freely use and distribute # +# it for both personal and commercial purposes under the terms of the # +# GNU General Public License version 2.0 (GNU GPL-2), as published by # +# the Free Software Foundation. # +# # +# This R package was created for academic research and was publicly # +# released in the hope that it will be useful, but it comes WITHOUT # +# ANY WARRANTY OR LIABILITY. # +# Visit our website for more info: https://msberends.gitlab.io/AMR. # +# ==================================================================== # + +context("proportion.R") + +test_that("proportions works", { + expect_equal(proportion_R(example_isolates$AMX), resistance(example_isolates$AMX)) + expect_equal(proportion_SI(example_isolates$AMX), susceptibility(example_isolates$AMX)) + + # AMX resistance in `example_isolates` + expect_equal(proportion_R(example_isolates$AMX), 0.5557364, tolerance = 0.0001) + expect_equal(proportion_I(example_isolates$AMX), 0.002441009, tolerance = 0.0001) + expect_equal(1 - proportion_R(example_isolates$AMX) - proportion_I(example_isolates$AMX), + proportion_S(example_isolates$AMX)) + expect_equal(proportion_R(example_isolates$AMX) + proportion_I(example_isolates$AMX), + proportion_IR(example_isolates$AMX)) + expect_equal(proportion_S(example_isolates$AMX) + proportion_I(example_isolates$AMX), + proportion_SI(example_isolates$AMX)) + + expect_equal(example_isolates %>% proportion_SI(AMC), + 0.7626397, + tolerance = 0.0001) + expect_equal(example_isolates %>% proportion_SI(AMC, GEN), + 0.9408, + tolerance = 0.0001) + expect_equal(example_isolates %>% proportion_SI(AMC, GEN, only_all_tested = TRUE), + 0.9382647, + tolerance = 0.0001) + + # percentages + expect_equal(example_isolates %>% + group_by(hospital_id) %>% + summarise(R = proportion_R(CIP, as_percent = TRUE), + I = proportion_I(CIP, as_percent = TRUE), + S = proportion_S(CIP, as_percent = TRUE), + n = n_rsi(CIP), + total = n()) %>% + pull(n) %>% + sum(), + 1409) + + # count of cases + expect_equal(example_isolates %>% + group_by(hospital_id) %>% + summarise(cipro_p = proportion_SI(CIP, as_percent = TRUE), + cipro_n = n_rsi(CIP), + genta_p = proportion_SI(GEN, as_percent = TRUE), + genta_n = n_rsi(GEN), + combination_p = proportion_SI(CIP, GEN, as_percent = TRUE), + combination_n = n_rsi(CIP, GEN)) %>% + pull(combination_n), + c(305, 617, 241, 711)) + + expect_warning(proportion_R(as.character(example_isolates$AMC))) + expect_warning(proportion_S(as.character(example_isolates$AMC))) + expect_warning(proportion_S(as.character(example_isolates$AMC, + example_isolates$GEN))) + expect_warning(n_rsi(as.character(example_isolates$AMC, + example_isolates$GEN))) + expect_equal(suppressWarnings(n_rsi(as.character(example_isolates$AMC, + example_isolates$GEN))), + 1879) + + # check for errors + expect_error(proportion_IR("test", minimum = "test")) + expect_error(proportion_IR("test", as_percent = "test")) + expect_error(proportion_I("test", minimum = "test")) + expect_error(proportion_I("test", as_percent = "test")) + expect_error(proportion_S("test", minimum = "test")) + expect_error(proportion_S("test", as_percent = "test")) + expect_error(proportion_S("test", also_single_tested = TRUE)) + + # check too low amount of isolates + expect_identical(suppressWarnings(proportion_R(example_isolates$AMX, minimum = nrow(example_isolates) + 1)), + NA) + expect_identical(suppressWarnings(proportion_I(example_isolates$AMX, minimum = nrow(example_isolates) + 1)), + NA) + expect_identical(suppressWarnings(proportion_S(example_isolates$AMX, minimum = nrow(example_isolates) + 1)), + NA) + + # warning for speed loss + expect_warning(proportion_R(as.character(example_isolates$GEN))) + expect_warning(proportion_I(as.character(example_isolates$GEN))) + expect_warning(proportion_S(example_isolates$AMC, as.character(example_isolates$GEN))) + + # proportion_df + expect_equal( + example_isolates %>% select(AMX) %>% proportion_df() %>% pull(value), + c(example_isolates$AMX %>% proportion_SI(), + example_isolates$AMX %>% proportion_R()) + ) + expect_equal( + example_isolates %>% select(AMX) %>% proportion_df(combine_IR = TRUE) %>% pull(value), + c(example_isolates$AMX %>% proportion_S(), + example_isolates$AMX %>% proportion_IR()) + ) + expect_equal( + example_isolates %>% select(AMX) %>% proportion_df(combine_SI = FALSE) %>% pull(value), + c(example_isolates$AMX %>% proportion_S(), + example_isolates$AMX %>% proportion_I(), + example_isolates$AMX %>% proportion_R()) + ) + + expect_error(proportion_df(c("A", "B", "C"))) + expect_error(proportion_df(example_isolates[, "date"])) +}) diff --git a/vignettes/AMR.Rmd b/vignettes/AMR.Rmd index 45553ba8..1ff4af18 100755 --- a/vignettes/AMR.Rmd +++ b/vignettes/AMR.Rmd @@ -321,10 +321,12 @@ data_1st %>% ## Resistance percentages -The functions `portion_S()`, `portion_SI()`, `portion_I()`, `portion_IR()` and `portion_R()` can be used to determine the portion of a specific antimicrobial outcome. As per the EUCAST guideline of 2019, we calculate resistance as the portion of R (`portion_R()`) and susceptibility as the portion of S and I (`portion_SI()`). These functions can be used on their own: +The functions `resistance()` and `susceptibility()` can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions `proportion_S()`, `proportion_SI()`, `proportion_I()`, `proportion_IR()` and `proportion_R()` can be used to determine the proportion of a specific antimicrobial outcome. + +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: ```{r} -data_1st %>% portion_R(AMX) +data_1st %>% resistance(AMX) ``` Or can be used in conjuction with `group_by()` and `summarise()`, both from the `dplyr` package: @@ -332,12 +334,12 @@ Or can be used in conjuction with `group_by()` and `summarise()`, both from the ```{r, eval = FALSE} data_1st %>% group_by(hospital) %>% - summarise(amoxicillin = portion_R(AMX)) + summarise(amoxicillin = resistance(AMX)) ``` ```{r, echo = FALSE} data_1st %>% group_by(hospital) %>% - summarise(amoxicillin = portion_R(AMX)) %>% + summarise(amoxicillin = resistance(AMX)) %>% knitr::kable(align = "c", big.mark = ",") ``` @@ -346,32 +348,32 @@ Of course it would be very convenient to know the number of isolates responsible ```{r, eval = FALSE} data_1st %>% group_by(hospital) %>% - summarise(amoxicillin = portion_R(AMX), + summarise(amoxicillin = resistance(AMX), available = n_rsi(AMX)) ``` ```{r, echo = FALSE} data_1st %>% group_by(hospital) %>% - summarise(amoxicillin = portion_R(AMX), + summarise(amoxicillin = resistance(AMX), available = n_rsi(AMX)) %>% knitr::kable(align = "c", big.mark = ",") ``` -These functions can also be used to get the portion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily: +These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily: ```{r, eval = FALSE} data_1st %>% group_by(genus) %>% - summarise(amoxiclav = portion_SI(AMC), - gentamicin = portion_SI(GEN), - amoxiclav_genta = portion_SI(AMC, GEN)) + summarise(amoxiclav = susceptibility(AMC), + gentamicin = susceptibility(GEN), + amoxiclav_genta = susceptibility(AMC, GEN)) ``` ```{r, echo = FALSE} data_1st %>% group_by(genus) %>% - summarise(amoxiclav = portion_SI(AMC), - gentamicin = portion_SI(GEN), - amoxiclav_genta = portion_SI(AMC, GEN)) %>% + summarise(amoxiclav = susceptibility(AMC), + gentamicin = susceptibility(GEN), + amoxiclav_genta = susceptibility(AMC, GEN)) %>% knitr::kable(align = "c", big.mark = ",") ``` @@ -380,9 +382,9 @@ To make a transition to the next part, let's see how this difference could be pl ```{r plot 1} data_1st %>% group_by(genus) %>% - summarise("1. Amoxi/clav" = portion_SI(AMC), - "2. Gentamicin" = portion_SI(GEN), - "3. Amoxi/clav + genta" = portion_SI(AMC, GEN)) %>% + summarise("1. Amoxi/clav" = susceptibility(AMC), + "2. Gentamicin" = susceptibility(GEN), + "3. Amoxi/clav + genta" = susceptibility(AMC, GEN)) %>% tidyr::gather("antibiotic", "S", -genus) %>% ggplot(aes(x = genus, y = S, @@ -408,7 +410,7 @@ ggplot(a_data_set) + geom_bar(aes(year)) ``` -The `AMR` package contains functions to extend this `ggplot2` package, for example `geom_rsi()`. It automatically transforms data with `count_df()` or `portion_df()` and show results in stacked bars. Its simplest and shortest example: +The `AMR` package contains functions to extend this `ggplot2` package, for example `geom_rsi()`. It automatically transforms data with `count_df()` or `proportion_df()` and show results in stacked bars. Its simplest and shortest example: ```{r plot 3} ggplot(data_1st) +