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(v2.1.1.9054) fix examples
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Package: AMR
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Version: 2.1.1.9053
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Version: 2.1.1.9054
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Date: 2024-06-17
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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6
NEWS.md
6
NEWS.md
@ -1,6 +1,6 @@
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# AMR 2.1.1.9053
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# AMR 2.1.1.9054
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*(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!)*
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*(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).)*
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#### A New Milestone: One Health Support (= Human + Veterinary + Environmental)
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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.
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@ -15,11 +15,11 @@ This package now supports not only tools for AMR data analysis in clinical setti
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* The `antibiotics` data set contains all veterinary antibiotics, such as pradofloxacin and enrofloxacin. All WHOCC codes for veterinary use have been added as well.
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* `ab_atc()` now supports ATC codes of veterinary antibiotics (that all start with "Q")
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* `ab_url()` now supports retrieving the WHOCC url of their ATCvet pages
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* 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.
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* `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.
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* 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.
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* 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.
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* 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.
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* 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.
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## Changed
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* 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.
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105
R/sir.R
105
R/sir.R
@ -159,32 +159,88 @@
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#'
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#' # For INTERPRETING disk diffusion and MIC values -----------------------
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#'
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#' # example data sets, with combined MIC values and disk zones
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#' df_wide <- data.frame(
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#' microorganism = "Escherichia coli",
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#' AMP = as.mic(8),
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#' CIP = as.mic(0.256),
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#' GEN = as.disk(18),
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#' TOB = as.disk(16),
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#' ERY = "R"
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#' )
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#' df_long <- data.frame(
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#' bacteria = rep("Escherichia coli", 3),
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#' antibiotic = c("amoxicillin", "cipro", "tobra", "genta"),
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#' mics = as.mic(c(0.01, 1, 4, 8)),
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#' disks = as.disk(c(6, 10, 14, 18))
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#' )
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#'
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#' \donttest{
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#' ## Using dplyr -------------------------------------------------
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#' if (require("dplyr")) {
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#' # approaches that all work without additional arguments:
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#' df %>% mutate_if(is.mic, as.sir)
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#' df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
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#' df %>% mutate(across(where(is.mic), as.sir))
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#' df %>% mutate_at(vars(AMP:TOB), as.sir)
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#' df %>% mutate(across(AMP:TOB, as.sir))
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#' df_wide %>% mutate_if(is.mic, as.sir)
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#' df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
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#' df_wide %>% mutate(across(where(is.mic), as.sir))
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#' df_wide %>% mutate_at(vars(AMP:TOB), as.sir)
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#' df_wide %>% mutate(across(AMP:TOB, as.sir))
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#'
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#' # approaches that all work with additional arguments:
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#' df %>% mutate_if(is.mic, as.sir, mo = "column1", guideline = "CLSI")
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#' df %>% mutate(across(where(is.mic),
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#' function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
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#' df %>% mutate_at(vars(AMP:TOB), as.sir, mo = "column1", guideline = "CLSI")
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#' df %>% mutate(across(AMP:TOB,
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#' function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
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#' df_long %>%
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#' # given a certain data type, e.g. MIC values
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#' mutate_if(is.mic, as.sir,
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#' mo = "bacteria",
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#' ab = "antibiotic",
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#' guideline = "CLSI")
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#' df_long %>%
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#' mutate(across(where(is.mic),
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#' function(x) as.sir(x,
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#' mo = "bacteria",
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#' ab = "antibiotic",
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#' guideline = "CLSI")))
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#' df_long %>%
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#' # given certain columns, e.g. from AMP to TOB
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#' mutate_at(vars(AMP:TOB), as.sir,
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#' mo = "bacteria",
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#' ab = "antibiotic",
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#' guideline = "CLSI")
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#' df_long %>%
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#' mutate(across(AMP:TOB,
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#' function(x) as.sir(x,
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#' mo = "bacteria",
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#' ab = "antibiotic",
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#' guideline = "CLSI")))
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#'
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#' # for veterinary breakpoints, add 'host':
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#' df %>% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "species_column")
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#' df %>% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "horse")
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#' df %>% mutate(across(where(is.mic),
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#' function(x) as.sir(x, guideline = "CLSI", host = "species_column")))
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#' df %>% mutate_at(vars(AMP:TOB), as.sir, guideline = "CLSI", host = "species_column")
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#' df %>% mutate(across(AMP:TOB,
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#' function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
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#' df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
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#' df_long %>%
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#' # given a certain data type, e.g. MIC values
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#' mutate_if(is.mic, as.sir,
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#' mo = "bacteria",
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#' ab = "antibiotic",
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#' host = "animal_species",
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#' guideline = "CLSI")
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#' df_long %>%
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#' mutate(across(where(is.mic),
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#' function(x) as.sir(x,
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#' mo = "bacteria",
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#' ab = "antibiotic",
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#' host = "animal_species",
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#' guideline = "CLSI")))
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#' df_long %>%
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#' # given certain columns, e.g. from AMP to TOB
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#' mutate_at(vars(AMP:TOB), as.sir,
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#' mo = "bacteria",
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#' ab = "antibiotic",
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#' host = "animal_species",
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#' guideline = "CLSI")
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#' df_long %>%
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#' mutate(across(AMP:TOB,
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#' function(x) as.sir(x,
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#' mo = "bacteria",
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#' ab = "antibiotic",
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#' host = "animal_species",
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#' guideline = "CLSI")))
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#'
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#' # to include information about urinary tract infections (UTI)
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#' data.frame(mo = "E. coli",
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@ -197,23 +253,14 @@
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#' specimen = c("urine", "blood")) %>%
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#' as.sir() # automatically determines urine isolates
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#'
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#' df %>%
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#' df_wide %>%
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#' mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
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#' }
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#'
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#'
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#' ## Using base R ------------------------------------------------
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#'
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#' # a whole data set, even with combined MIC values and disk zones
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#' df <- data.frame(
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#' microorganism = "Escherichia coli",
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#' AMP = as.mic(8),
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#' CIP = as.mic(0.256),
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#' GEN = as.disk(18),
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#' TOB = as.disk(16),
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#' ERY = "R"
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#' )
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#' as.sir(df)
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#' as.sir(df_wide)
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#'
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#' # return a 'logbook' about the results:
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#' sir_interpretation_history()
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105
man/as.sir.Rd
105
man/as.sir.Rd
@ -256,32 +256,88 @@ summary(example_isolates) # see all SIR results at a glance
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# For INTERPRETING disk diffusion and MIC values -----------------------
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# example data sets, with combined MIC values and disk zones
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df_wide <- data.frame(
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microorganism = "Escherichia coli",
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AMP = as.mic(8),
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CIP = as.mic(0.256),
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GEN = as.disk(18),
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TOB = as.disk(16),
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ERY = "R"
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)
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df_long <- data.frame(
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bacteria = rep("Escherichia coli", 3),
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antibiotic = c("amoxicillin", "cipro", "tobra", "genta"),
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mics = as.mic(c(0.01, 1, 4, 8)),
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disks = as.disk(c(6, 10, 14, 18))
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)
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\donttest{
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## Using dplyr -------------------------------------------------
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if (require("dplyr")) {
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# approaches that all work without additional arguments:
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df \%>\% mutate_if(is.mic, as.sir)
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df \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
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df \%>\% mutate(across(where(is.mic), as.sir))
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df \%>\% mutate_at(vars(AMP:TOB), as.sir)
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df \%>\% mutate(across(AMP:TOB, as.sir))
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df_wide \%>\% mutate_if(is.mic, as.sir)
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df_wide \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
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df_wide \%>\% mutate(across(where(is.mic), as.sir))
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df_wide \%>\% mutate_at(vars(AMP:TOB), as.sir)
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df_wide \%>\% mutate(across(AMP:TOB, as.sir))
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# approaches that all work with additional arguments:
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df \%>\% mutate_if(is.mic, as.sir, mo = "column1", guideline = "CLSI")
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df \%>\% mutate(across(where(is.mic),
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function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
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df \%>\% mutate_at(vars(AMP:TOB), as.sir, mo = "column1", guideline = "CLSI")
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df \%>\% mutate(across(AMP:TOB,
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function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
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df_long \%>\%
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# given a certain data type, e.g. MIC values
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mutate_if(is.mic, as.sir,
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mo = "bacteria",
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ab = "antibiotic",
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guideline = "CLSI")
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df_long \%>\%
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mutate(across(where(is.mic),
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function(x) as.sir(x,
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mo = "bacteria",
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ab = "antibiotic",
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guideline = "CLSI")))
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df_long \%>\%
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# given certain columns, e.g. from AMP to TOB
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mutate_at(vars(AMP:TOB), as.sir,
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mo = "bacteria",
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ab = "antibiotic",
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guideline = "CLSI")
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df_long \%>\%
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mutate(across(AMP:TOB,
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function(x) as.sir(x,
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mo = "bacteria",
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ab = "antibiotic",
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guideline = "CLSI")))
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# for veterinary breakpoints, add 'host':
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df \%>\% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "species_column")
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df \%>\% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "horse")
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df \%>\% mutate(across(where(is.mic),
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function(x) as.sir(x, guideline = "CLSI", host = "species_column")))
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df \%>\% mutate_at(vars(AMP:TOB), as.sir, guideline = "CLSI", host = "species_column")
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df \%>\% mutate(across(AMP:TOB,
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function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
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df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
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df_long \%>\%
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# given a certain data type, e.g. MIC values
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mutate_if(is.mic, as.sir,
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mo = "bacteria",
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ab = "antibiotic",
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host = "animal_species",
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guideline = "CLSI")
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df_long \%>\%
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mutate(across(where(is.mic),
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function(x) as.sir(x,
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mo = "bacteria",
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ab = "antibiotic",
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host = "animal_species",
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guideline = "CLSI")))
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df_long \%>\%
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# given certain columns, e.g. from AMP to TOB
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mutate_at(vars(AMP:TOB), as.sir,
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mo = "bacteria",
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ab = "antibiotic",
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host = "animal_species",
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guideline = "CLSI")
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df_long \%>\%
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mutate(across(AMP:TOB,
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function(x) as.sir(x,
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mo = "bacteria",
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ab = "antibiotic",
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host = "animal_species",
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guideline = "CLSI")))
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# to include information about urinary tract infections (UTI)
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data.frame(mo = "E. coli",
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@ -294,23 +350,14 @@ if (require("dplyr")) {
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specimen = c("urine", "blood")) \%>\%
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as.sir() # automatically determines urine isolates
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df \%>\%
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df_wide \%>\%
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mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
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}
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## Using base R ------------------------------------------------
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# a whole data set, even with combined MIC values and disk zones
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df <- data.frame(
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microorganism = "Escherichia coli",
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AMP = as.mic(8),
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CIP = as.mic(0.256),
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GEN = as.disk(18),
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TOB = as.disk(16),
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ERY = "R"
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)
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as.sir(df)
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as.sir(df_wide)
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# return a 'logbook' about the results:
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sir_interpretation_history()
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