AMR Plots with ggplot2

Use these functions to create bar plots for AMR data analysis. All functions rely on ggplot2 functions.

ggplot_rsi(
  data,
  position = NULL,
  x = "antibiotic",
  fill = "interpretation",
  facet = NULL,
  breaks = seq(0, 1, 0.1),
  limits = NULL,
  translate_ab = "name",
  combine_SI = TRUE,
  combine_IR = FALSE,
  minimum = 30,
  language = get_locale(),
  nrow = NULL,
  colours = c(S = "#3CAEA3", SI = "#3CAEA3", I = "#F6D55C", IR = "#ED553B", R =
    "#ED553B"),
  datalabels = TRUE,
  datalabels.size = 2.5,
  datalabels.colour = "grey15",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  x.title = "Antimicrobial",
  y.title = "Proportion",
  ...
)

geom_rsi(
  position = NULL,
  x = c("antibiotic", "interpretation"),
  fill = "interpretation",
  translate_ab = "name",
  minimum = 30,
  language = get_locale(),
  combine_SI = TRUE,
  combine_IR = FALSE,
  ...
)

facet_rsi(facet = c("interpretation", "antibiotic"), nrow = NULL)

scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)

scale_rsi_colours(..., aesthetics = "fill")

theme_rsi()

labels_rsi_count(
  position = NULL,
  x = "antibiotic",
  translate_ab = "name",
  minimum = 30,
  language = get_locale(),
  combine_SI = TRUE,
  combine_IR = FALSE,
  datalabels.size = 3,
  datalabels.colour = "grey15"
)

Arguments

data	a data.frame with column(s) of class rsi (see as.rsi())
position	position adjustment of bars, either "fill", "stack" or "dodge"
x	variable to show on x axis, either "antibiotic" (default) or "interpretation" or a grouping variable
fill	variable to categorise using the plots legend, either "antibiotic" (default) or "interpretation" or a grouping variable
facet	variable to split plots by, either "interpretation" (default) or "antibiotic" or a grouping variable
breaks	numeric vector of positions
limits	numeric vector of length two providing limits of the scale, use NA to refer to the existing minimum or maximum
translate_ab	a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property()
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 argument combine_IR, but this now follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is TRUE.
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 argument combine_SI.
minimum	the minimum allowed number of available (tested) isolates. Any isolate count lower than minimum will return NA with a warning. The default number of 30 isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.
language	language of the returned text, defaults to system language (see get_locale()) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
nrow	(when using facet) number of rows
colours	a named vactor with colour to be used for filling. The default colours are colour-blind friendly.
datalabels	show datalabels using labels_rsi_count()
datalabels.size	size of the datalabels
datalabels.colour	colour of the datalabels
title	text to show as title of the plot
subtitle	text to show as subtitle of the plot
caption	text to show as caption of the plot
x.title	text to show as x axis description
y.title	text to show as y axis description
...	other arguments passed on to geom_rsi() or, in case of scale_rsi_colours(), named values to set colours. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red. See Examples.
aesthetics	aesthetics to apply the colours to, defaults to "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"

Details

At default, the names of antibiotics will be shown on the plots using ab_name(). This can be set with the translate_ab argument. See count_df().

The Functions

geom_rsi() will take any variable from the data that has an rsi class (created with as.rsi()) using rsi_df() and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.

facet_rsi() creates 2d plots (at default based on S/I/R) using ggplot2::facet_wrap().

scale_y_percent() transforms the y axis to a 0 to 100% range using ggplot2::scale_y_continuous().

scale_rsi_colours() sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.

theme_rsi() is a [ggplot2 theme][ggplot2::theme() with minimal distraction.

labels_rsi_count() print datalabels on the bars with percentage and amount of isolates using ggplot2::geom_text().

ggplot_rsi() is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (%>%). See Examples.

Maturing Lifecycle

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').

Read more on Our Website!

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. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!

Examples

if (require("ggplot2") & require("dplyr")) {
 
  # get antimicrobial results for drugs against a UTI:
  ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +
    geom_rsi()
 
  # prettify the plot using some additional functions:
  df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)
  ggplot(df) +
    geom_rsi() +
    scale_y_percent() +
    scale_rsi_colours() +
    labels_rsi_count() +
    theme_rsi()
 
  # or better yet, simplify this using the wrapper function - a single command:
  example_isolates %>%
    select(AMX, NIT, FOS, TMP, CIP) %>%
    ggplot_rsi()
 
  # get only proportions and no counts:
  example_isolates %>%
    select(AMX, NIT, FOS, TMP, CIP) %>%
    ggplot_rsi(datalabels = FALSE)
 
  # add other ggplot2 arguments as you like:
  example_isolates %>%
    select(AMX, NIT, FOS, TMP, CIP) %>%
    ggplot_rsi(width = 0.5,
               colour = "black",
               size = 1,
               linetype = 2,
               alpha = 0.25)

  # you can alter the colours with colour names:
  example_isolates %>%
    select(AMX) %>%
    ggplot_rsi(colours = c(SI = "yellow"))

  # but you can also use the built-in colour-blind friendly colours for
  # your plots, where "S" is green, "I" is yellow and "R" is red:
  data.frame(x = c("Value1", "Value2", "Value3"),
             y = c(1, 2, 3),
             z = c("Value4", "Value5", "Value6")) %>%
    ggplot() +
    geom_col(aes(x = x, y = y, fill = z)) +
    scale_rsi_colours(Value4 = "S", Value5 = "I", Value6 = "R")
}
  
# \donttest{
# resistance of ciprofloxacine per age group
example_isolates %>%
  mutate(first_isolate = first_isolate(.)) %>%
  filter(first_isolate == TRUE,
         mo == as.mo("E. coli")) %>%
  # age_groups() is also a function in this AMR package:
  group_by(age_group = age_groups(age)) %>%
  select(age_group,
         CIP) %>%
  ggplot_rsi(x = "age_group")
  
# a shorter version which also adjusts data label colours:
example_isolates %>%
  select(AMX, NIT, FOS, TMP, CIP) %>%
  ggplot_rsi(colours = FALSE)


# it also supports groups (don't forget to use the group var on `x` or `facet`):
example_isolates %>%
  select(hospital_id, AMX, NIT, FOS, TMP, CIP) %>%
  group_by(hospital_id) %>%
  ggplot_rsi(x = "hospital_id",
             facet = "antibiotic",
             nrow = 1,
             title = "AMR of Anti-UTI Drugs Per Hospital",
             x.title = "Hospital",
             datalabels = FALSE)
# }