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(v2.1.1.9054) fix examples

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dr. M.S. (Matthijs) Berends 2024-06-17 13:52:02 +02:00
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2
DESCRIPTION
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Package: AMR Package: AMR
Version: 2.1.1.9053 Version: 2.1.1.9054
Date: 2024-06-17 Date: 2024-06-17
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)

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NEWS.md
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# AMR 2.1.1.9053 # AMR 2.1.1.9054
*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support!)* *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
#### A New Milestone: One Health Support (= Human + Veterinary + Environmental) #### A New Milestone: One Health Support (= Human + Veterinary + Environmental)
This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the [University of Prince Edward Island](https://www.upei.ca/avc), Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change. This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the [University of Prince Edward Island](https://www.upei.ca/avc), Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.
@ -15,11 +15,11 @@ This package now supports not only tools for AMR data analysis in clinical setti
* The `antibiotics` data set contains all veterinary antibiotics, such as pradofloxacin and enrofloxacin. All WHOCC codes for veterinary use have been added as well. * The `antibiotics` data set contains all veterinary antibiotics, such as pradofloxacin and enrofloxacin. All WHOCC codes for veterinary use have been added as well.
* `ab_atc()` now supports ATC codes of veterinary antibiotics (that all start with "Q") * `ab_atc()` now supports ATC codes of veterinary antibiotics (that all start with "Q")
* `ab_url()` now supports retrieving the WHOCC url of their ATCvet pages * `ab_url()` now supports retrieving the WHOCC url of their ATCvet pages
* EUCAST 2024 and CLSI 2024 are now supported, by adding all of their over 4,000 clinical breakpoints to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2024 (v14.0) is now the new default guideline for all MIC and disks diffusion interpretations.
* `as.sir()` now brings additional factor levels: "NI" for non-interpretable and "SDD" for susceptible dose-dependent. Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI. Also, to get quantitative values, `as.double()` or a `sir` object will return 1 for S, 2 for SDD/I, and 3 for R (NI will become `NA`). Other functions using `sir` classes (e.g., `summary()`) are updated to reflect the change to contain NI and SDD. * `as.sir()` now brings additional factor levels: "NI" for non-interpretable and "SDD" for susceptible dose-dependent. Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI. Also, to get quantitative values, `as.double()` or a `sir` object will return 1 for S, 2 for SDD/I, and 3 for R (NI will become `NA`). Other functions using `sir` classes (e.g., `summary()`) are updated to reflect the change to contain NI and SDD.
* The function group `scale_*_mic()`, namely: `scale_x_mic()`, `scale_y_mic()`, `scale_colour_mic()` and `scale_fill_mic()`. They are advanced ggplot2 extensions to allow easy plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels. * The function group `scale_*_mic()`, namely: `scale_x_mic()`, `scale_y_mic()`, `scale_colour_mic()` and `scale_fill_mic()`. They are advanced ggplot2 extensions to allow easy plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels.
* Function `rescale_mic()`, which allows to rescale MIC values to a manually set range. This is the powerhouse behind the `scale_*_mic()` functions, but it can be used by users directly to e.g. compare equality in MIC distributions by rescaling them to the same range first. * Function `rescale_mic()`, which allows to rescale MIC values to a manually set range. This is the powerhouse behind the `scale_*_mic()` functions, but it can be used by users directly to e.g. compare equality in MIC distributions by rescaling them to the same range first.
* Function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that are in that group. * Function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that are in that group.
* Clinical breakpoints and intrinsic resistance of EUCAST 2024 and CLSI 2024 have been added to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2024 (v14.0) is now the new default guideline for all MIC and disks diffusion interpretations.
## Changed ## Changed
* For SIR interpretation, it is now possible to use column names for argument `ab`, `mo`, and `uti`: `as.sir(..., ab = "column1", mo = "column2", uti = "column3")`. This greatly improves the flexibility for users. * For SIR interpretation, it is now possible to use column names for argument `ab`, `mo`, and `uti`: `as.sir(..., ab = "column1", mo = "column2", uti = "column3")`. This greatly improves the flexibility for users.

105
R/sir.R
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@ -158,33 +158,89 @@
#' summary(example_isolates) # see all SIR results at a glance #' summary(example_isolates) # see all SIR results at a glance
#' #'
#' # For INTERPRETING disk diffusion and MIC values ----------------------- #' # For INTERPRETING disk diffusion and MIC values -----------------------
#'
#' # example data sets, with combined MIC values and disk zones
#' df_wide <- data.frame(
#' microorganism = "Escherichia coli",
#' AMP = as.mic(8),
#' CIP = as.mic(0.256),
#' GEN = as.disk(18),
#' TOB = as.disk(16),
#' ERY = "R"
#' )
#' df_long <- data.frame(
#' bacteria = rep("Escherichia coli", 3),
#' antibiotic = c("amoxicillin", "cipro", "tobra", "genta"),
#' mics = as.mic(c(0.01, 1, 4, 8)),
#' disks = as.disk(c(6, 10, 14, 18))
#' )
#' #'
#' \donttest{ #' \donttest{
#' ## Using dplyr ------------------------------------------------- #' ## Using dplyr -------------------------------------------------
#' if (require("dplyr")) { #' if (require("dplyr")) {
#' # approaches that all work without additional arguments: #' # approaches that all work without additional arguments:
#' df %>% mutate_if(is.mic, as.sir) #' df_wide %>% mutate_if(is.mic, as.sir)
#' df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir) #' df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
#' df %>% mutate(across(where(is.mic), as.sir)) #' df_wide %>% mutate(across(where(is.mic), as.sir))
#' df %>% mutate_at(vars(AMP:TOB), as.sir) #' df_wide %>% mutate_at(vars(AMP:TOB), as.sir)
#' df %>% mutate(across(AMP:TOB, as.sir)) #' df_wide %>% mutate(across(AMP:TOB, as.sir))
#' #'
#' # approaches that all work with additional arguments: #' # approaches that all work with additional arguments:
#' df %>% mutate_if(is.mic, as.sir, mo = "column1", guideline = "CLSI") #' df_long %>%
#' df %>% mutate(across(where(is.mic), #' # given a certain data type, e.g. MIC values
#' function(x) as.sir(x, mo = "column1", guideline = "CLSI"))) #' mutate_if(is.mic, as.sir,
#' df %>% mutate_at(vars(AMP:TOB), as.sir, mo = "column1", guideline = "CLSI") #' mo = "bacteria",
#' df %>% mutate(across(AMP:TOB, #' ab = "antibiotic",
#' function(x) as.sir(x, mo = "column1", guideline = "CLSI"))) #' guideline = "CLSI")
#' df_long %>%
#' mutate(across(where(is.mic),
#' function(x) as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' guideline = "CLSI")))
#' df_long %>%
#' # given certain columns, e.g. from AMP to TOB
#' mutate_at(vars(AMP:TOB), as.sir,
#' mo = "bacteria",
#' ab = "antibiotic",
#' guideline = "CLSI")
#' df_long %>%
#' mutate(across(AMP:TOB,
#' function(x) as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' guideline = "CLSI")))
#' #'
#' # for veterinary breakpoints, add 'host': #' # for veterinary breakpoints, add 'host':
#' df %>% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "species_column") #' df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
#' df %>% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "horse") #' df_long %>%
#' df %>% mutate(across(where(is.mic), #' # given a certain data type, e.g. MIC values
#' function(x) as.sir(x, guideline = "CLSI", host = "species_column"))) #' mutate_if(is.mic, as.sir,
#' df %>% mutate_at(vars(AMP:TOB), as.sir, guideline = "CLSI", host = "species_column") #' mo = "bacteria",
#' df %>% mutate(across(AMP:TOB, #' ab = "antibiotic",
#' function(x) as.sir(x, mo = "column1", guideline = "CLSI"))) #' host = "animal_species",
#' guideline = "CLSI")
#' df_long %>%
#' mutate(across(where(is.mic),
#' function(x) as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI")))
#' df_long %>%
#' # given certain columns, e.g. from AMP to TOB
#' mutate_at(vars(AMP:TOB), as.sir,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI")
#' df_long %>%
#' mutate(across(AMP:TOB,
#' function(x) as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI")))
#' #'
#' # to include information about urinary tract infections (UTI) #' # to include information about urinary tract infections (UTI)
#' data.frame(mo = "E. coli", #' data.frame(mo = "E. coli",
@ -197,23 +253,14 @@
#' specimen = c("urine", "blood")) %>% #' specimen = c("urine", "blood")) %>%
#' as.sir() # automatically determines urine isolates #' as.sir() # automatically determines urine isolates
#' #'
#' df %>% #' df_wide %>%
#' mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE) #' mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
#' } #' }
#' #'
#' #'
#' ## Using base R ------------------------------------------------ #' ## Using base R ------------------------------------------------
#' #'
#' # a whole data set, even with combined MIC values and disk zones #' as.sir(df_wide)
#' df <- data.frame(
#' microorganism = "Escherichia coli",
#' AMP = as.mic(8),
#' CIP = as.mic(0.256),
#' GEN = as.disk(18),
#' TOB = as.disk(16),
#' ERY = "R"
#' )
#' as.sir(df)
#' #'
#' # return a 'logbook' about the results: #' # return a 'logbook' about the results:
#' sir_interpretation_history() #' sir_interpretation_history()

105
man/as.sir.Rd
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@ -256,32 +256,88 @@ summary(example_isolates) # see all SIR results at a glance
# For INTERPRETING disk diffusion and MIC values ----------------------- # For INTERPRETING disk diffusion and MIC values -----------------------
# example data sets, with combined MIC values and disk zones
df_wide <- data.frame(
microorganism = "Escherichia coli",
AMP = as.mic(8),
CIP = as.mic(0.256),
GEN = as.disk(18),
TOB = as.disk(16),
ERY = "R"
)
df_long <- data.frame(
bacteria = rep("Escherichia coli", 3),
antibiotic = c("amoxicillin", "cipro", "tobra", "genta"),
mics = as.mic(c(0.01, 1, 4, 8)),
disks = as.disk(c(6, 10, 14, 18))
)
\donttest{ \donttest{
## Using dplyr ------------------------------------------------- ## Using dplyr -------------------------------------------------
if (require("dplyr")) { if (require("dplyr")) {
# approaches that all work without additional arguments: # approaches that all work without additional arguments:
df \%>\% mutate_if(is.mic, as.sir) df_wide \%>\% mutate_if(is.mic, as.sir)
df \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir) df_wide \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
df \%>\% mutate(across(where(is.mic), as.sir)) df_wide \%>\% mutate(across(where(is.mic), as.sir))
df \%>\% mutate_at(vars(AMP:TOB), as.sir) df_wide \%>\% mutate_at(vars(AMP:TOB), as.sir)
df \%>\% mutate(across(AMP:TOB, as.sir)) df_wide \%>\% mutate(across(AMP:TOB, as.sir))
# approaches that all work with additional arguments: # approaches that all work with additional arguments:
df \%>\% mutate_if(is.mic, as.sir, mo = "column1", guideline = "CLSI") df_long \%>\%
df \%>\% mutate(across(where(is.mic), # given a certain data type, e.g. MIC values
function(x) as.sir(x, mo = "column1", guideline = "CLSI"))) mutate_if(is.mic, as.sir,
df \%>\% mutate_at(vars(AMP:TOB), as.sir, mo = "column1", guideline = "CLSI") mo = "bacteria",
df \%>\% mutate(across(AMP:TOB, ab = "antibiotic",
function(x) as.sir(x, mo = "column1", guideline = "CLSI"))) guideline = "CLSI")
df_long \%>\%
mutate(across(where(is.mic),
function(x) as.sir(x,
mo = "bacteria",
ab = "antibiotic",
guideline = "CLSI")))
df_long \%>\%
# given certain columns, e.g. from AMP to TOB
mutate_at(vars(AMP:TOB), as.sir,
mo = "bacteria",
ab = "antibiotic",
guideline = "CLSI")
df_long \%>\%
mutate(across(AMP:TOB,
function(x) as.sir(x,
mo = "bacteria",
ab = "antibiotic",
guideline = "CLSI")))
# for veterinary breakpoints, add 'host': # for veterinary breakpoints, add 'host':
df \%>\% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "species_column") df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
df \%>\% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "horse") df_long \%>\%
df \%>\% mutate(across(where(is.mic), # given a certain data type, e.g. MIC values
function(x) as.sir(x, guideline = "CLSI", host = "species_column"))) mutate_if(is.mic, as.sir,
df \%>\% mutate_at(vars(AMP:TOB), as.sir, guideline = "CLSI", host = "species_column") mo = "bacteria",
df \%>\% mutate(across(AMP:TOB, ab = "antibiotic",
function(x) as.sir(x, mo = "column1", guideline = "CLSI"))) host = "animal_species",
guideline = "CLSI")
df_long \%>\%
mutate(across(where(is.mic),
function(x) as.sir(x,
mo = "bacteria",
ab = "antibiotic",
host = "animal_species",
guideline = "CLSI")))
df_long \%>\%
# given certain columns, e.g. from AMP to TOB
mutate_at(vars(AMP:TOB), as.sir,
mo = "bacteria",
ab = "antibiotic",
host = "animal_species",
guideline = "CLSI")
df_long \%>\%
mutate(across(AMP:TOB,
function(x) as.sir(x,
mo = "bacteria",
ab = "antibiotic",
host = "animal_species",
guideline = "CLSI")))
# to include information about urinary tract infections (UTI) # to include information about urinary tract infections (UTI)
data.frame(mo = "E. coli", data.frame(mo = "E. coli",
@ -294,23 +350,14 @@ if (require("dplyr")) {
specimen = c("urine", "blood")) \%>\% specimen = c("urine", "blood")) \%>\%
as.sir() # automatically determines urine isolates as.sir() # automatically determines urine isolates
df \%>\% df_wide \%>\%
mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE) mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
} }
## Using base R ------------------------------------------------ ## Using base R ------------------------------------------------
# a whole data set, even with combined MIC values and disk zones as.sir(df_wide)
df <- data.frame(
microorganism = "Escherichia coli",
AMP = as.mic(8),
CIP = as.mic(0.256),
GEN = as.disk(18),
TOB = as.disk(16),
ERY = "R"
)
as.sir(df)
# return a 'logbook' about the results: # return a 'logbook' about the results:
sir_interpretation_history() sir_interpretation_history()