Functions to plot classes rsi
, mic
and disk
, with support for base R and ggplot2
.
# S3 method for mic plot( x, mo = NULL, ab = NULL, guideline = "EUCAST", main = paste("MIC values of", deparse(substitute(x))), ylab = "Frequency", xlab = "Minimum Inhibitory Concentration (mg/L)", colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"), language = get_locale(), expand = TRUE, ... ) # S3 method for mic autoplot( object, mo = NULL, ab = NULL, guideline = "EUCAST", title = paste("MIC values of", deparse(substitute(object))), ylab = "Frequency", xlab = "Minimum Inhibitory Concentration (mg/L)", colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"), language = get_locale(), expand = TRUE, ... ) # S3 method for mic fortify(object, ...) # S3 method for disk plot( x, main = paste("Disk zones of", deparse(substitute(x))), ylab = "Frequency", xlab = "Disk diffusion diameter (mm)", mo = NULL, ab = NULL, guideline = "EUCAST", colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"), language = get_locale(), expand = TRUE, ... ) # S3 method for disk autoplot( object, mo = NULL, ab = NULL, title = paste("Disk zones of", deparse(substitute(object))), ylab = "Frequency", xlab = "Disk diffusion diameter (mm)", guideline = "EUCAST", colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"), language = get_locale(), expand = TRUE, ... ) # S3 method for disk fortify(object, ...) # S3 method for rsi plot( x, ylab = "Percentage", xlab = "Antimicrobial Interpretation", main = paste("Resistance Overview of", deparse(substitute(x))), ... ) # S3 method for rsi autoplot( object, title = paste("Resistance Overview of", deparse(substitute(object))), xlab = "Antimicrobial Interpretation", ylab = "Frequency", colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"), language = get_locale(), ... ) # S3 method for rsi fortify(object, ...)
x, object | values created with |
---|---|
mo | any (vector of) text that can be coerced to a valid microorganism code with |
ab | any (vector of) text that can be coerced to a valid antimicrobial code with |
guideline | interpretation guideline to use, defaults to the latest included EUCAST guideline, see Details |
main, title | title of the plot |
xlab, ylab | axis title |
colours_RSI | colours to use for filling in the bars, must be a vector of three values (in the order R, S and I). The default colours are colour-blind friendly. |
language | language to be used to translate 'Susceptible', 'Increased exposure'/'Intermediate' and 'Resistant', defaults to system language (see |
expand | a logical to indicate whether the range on the x axis should be expanded between the lowest and highest value. For MIC values, intermediate values will be factors of 2 starting from the highest MIC value. For disk diameters, the whole diameter range will be filled. |
... | arguments passed on to methods |
The autoplot()
functions return a ggplot
model that is extendible with any ggplot2
function.
The fortify()
functions return a data.frame as an extension for usage in the ggplot2::ggplot()
function.
The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the guideline
argument are: "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012", "CLSI 2011" and "CLSI 2010".
Simply using "CLSI"
or "EUCAST"
as input will automatically select the latest version of that guideline.
The lifecycle of this function is maturing. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome to suggest changes at our repository or write us an email (see section 'Contact Us').
On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data.
some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro") some_rsi_values <- random_rsi(50, prob_RSI = c(0.30, 0.55, 0.05)) plot(some_mic_values) plot(some_disk_values) plot(some_rsi_values) # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = "S. aureus", ab = "ampicillin") plot(some_disk_values, mo = "Escherichia coli", ab = "cipro") # \donttest{ if (require("ggplot2")) { autoplot(some_mic_values) autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro") autoplot(some_rsi_values) } # }