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@ -138,13 +138,11 @@ At default, the names of antibiotics will be shown on the plots using \code{\lin
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\examples{
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\donttest{
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if (require("ggplot2") && require("dplyr")) {
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# get antimicrobial results for drugs against a UTI:
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ggplot(example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
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geom_sir()
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}
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if (require("ggplot2") && require("dplyr")) {
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# prettify the plot using some additional functions:
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df <- example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)
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ggplot(df) +
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@ -155,21 +153,18 @@ if (require("ggplot2") && require("dplyr")) {
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theme_sir()
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}
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if (require("ggplot2") && require("dplyr")) {
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# or better yet, simplify this using the wrapper function - a single command:
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example_isolates \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_sir()
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}
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if (require("ggplot2") && require("dplyr")) {
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# get only proportions and no counts:
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example_isolates \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_sir(datalabels = FALSE)
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}
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if (require("ggplot2") && require("dplyr")) {
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# add other ggplot2 arguments as you like:
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example_isolates \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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@ -182,14 +177,12 @@ if (require("ggplot2") && require("dplyr")) {
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)
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}
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if (require("ggplot2") && require("dplyr")) {
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# you can alter the colours with colour names:
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example_isolates \%>\%
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select(AMX) \%>\%
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ggplot_sir(colours = c(SI = "yellow"))
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}
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if (require("ggplot2") && require("dplyr")) {
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# but you can also use the built-in colour-blind friendly colours for
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# your plots, where "S" is green, "I" is yellow and "R" is red:
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data.frame(
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@ -202,7 +195,6 @@ if (require("ggplot2") && require("dplyr")) {
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scale_sir_colours(Value4 = "S", Value5 = "I", Value6 = "R")
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}
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if (require("ggplot2") && require("dplyr")) {
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# resistance of ciprofloxacine per age group
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example_isolates \%>\%
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mutate(first_isolate = first_isolate()) \%>\%
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@ -216,14 +208,12 @@ if (require("ggplot2") && require("dplyr")) {
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ggplot_sir(x = "age_group")
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}
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if (require("ggplot2") && require("dplyr")) {
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# a shorter version which also adjusts data label colours:
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example_isolates \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_sir(colours = FALSE)
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}
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if (require("ggplot2") && require("dplyr")) {
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# it also supports groups (don't forget to use the group var on `x` or `facet`):
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example_isolates \%>\%
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filter(mo_is_gram_negative(), ward != "Outpatient") \%>\%
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