AMR Plots with ggplot2 — ggplot

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

Usage

ggplot_sir(
  data,
  position = NULL,
  x = "antibiotic",
  fill = "interpretation",
  facet = NULL,
  breaks = seq(0, 1, 0.1),
  limits = NULL,
  translate_ab = "name",
  combine_SI = TRUE,
  minimum = 30,
  language = get_AMR_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_sir(
  position = NULL,
  x = c("antibiotic", "interpretation"),
  fill = "interpretation",
  translate_ab = "name",
  minimum = 30,
  language = get_AMR_locale(),
  combine_SI = TRUE,
  ...
)

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

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

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

theme_sir()

labels_sir_count(
  position = NULL,
  x = "antibiotic",
  translate_ab = "name",
  minimum = 30,
  language = get_AMR_locale(),
  combine_SI = TRUE,
  datalabels.size = 3,
  datalabels.colour = "grey15"
)

Arguments

data: a data.frame with column(s) of class sir (see as.sir())
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: a numeric vector of positions
limits: a 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, SDD, and I must be merged into one, so the output only consists of S+SDD+I vs. R (susceptible vs. resistant) - the default is TRUE
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 - the default is the current system language (see get_AMR_locale()) and can also be set with the package option 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_sir_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_sir() or, in case of scale_sir_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 - the default is "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_sir() will take any variable from the data that has an sir class (created with as.sir()) using sir_df() and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.

facet_sir() 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_sir_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_sir() is a [ggplot2 theme][ggplot2::theme() with minimal distraction.

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

ggplot_sir() 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.

Examples

# \donttest{
if (require("ggplot2") && require("dplyr")) {
  # get antimicrobial results for drugs against a UTI:
  ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +
    geom_sir()
}

if (require("ggplot2") && require("dplyr")) {
  # prettify the plot using some additional functions:
  df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)
  ggplot(df) +
    geom_sir() +
    scale_y_percent() +
    scale_sir_colours() +
    labels_sir_count() +
    theme_sir()
}

if (require("ggplot2") && require("dplyr")) {
  # or better yet, simplify this using the wrapper function - a single command:
  example_isolates %>%
    select(AMX, NIT, FOS, TMP, CIP) %>%
    ggplot_sir()
}

if (require("ggplot2") && require("dplyr")) {
  # get only proportions and no counts:
  example_isolates %>%
    select(AMX, NIT, FOS, TMP, CIP) %>%
    ggplot_sir(datalabels = FALSE)
}

if (require("ggplot2") && require("dplyr")) {
  # add other ggplot2 arguments as you like:
  example_isolates %>%
    select(AMX, NIT, FOS, TMP, CIP) %>%
    ggplot_sir(
      width = 0.5,
      colour = "black",
      size = 1,
      linetype = 2,
      alpha = 0.25
    )
}

if (require("ggplot2") && require("dplyr")) {
  # you can alter the colours with colour names:
  example_isolates %>%
    select(AMX) %>%
    ggplot_sir(colours = c(SI = "yellow"))
}

if (require("ggplot2") && require("dplyr")) {
  # 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_sir_colours(Value4 = "S", Value5 = "I", Value6 = "R")
}

if (require("ggplot2") && require("dplyr")) {
  # resistance of ciprofloxacine per age group
  example_isolates %>%
    mutate(first_isolate = first_isolate()) %>%
    filter(
      first_isolate == TRUE,
      mo == as.mo("Escherichia coli")
    ) %>%
    # age_groups() is also a function in this AMR package:
    group_by(age_group = age_groups(age)) %>%
    select(age_group, CIP) %>%
    ggplot_sir(x = "age_group")
}
#> Warning: Removed 6 rows containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 6 rows containing missing values or values outside the scale range
#> (`geom_text()`).

if (require("ggplot2") && require("dplyr")) {
  # a shorter version which also adjusts data label colours:
  example_isolates %>%
    select(AMX, NIT, FOS, TMP, CIP) %>%
    ggplot_sir(colours = FALSE)
}

if (require("ggplot2") && require("dplyr")) {
  # it also supports groups (don't forget to use the group var on `x` or `facet`):
  example_isolates %>%
    filter(mo_is_gram_negative(), ward != "Outpatient") %>%
    # select only UTI-specific drugs
    select(ward, AMX, NIT, FOS, TMP, CIP) %>%
    group_by(ward) %>%
    ggplot_sir(
      x = "ward",
      facet = "antibiotic",
      nrow = 1,
      title = "AMR of Anti-UTI Drugs Per Ward",
      x.title = "Ward",
      datalabels = FALSE
    )
}
#> ℹ Using column 'mo' as input for mo_is_gram_negative()

# }