Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal ggplot functions.

ggplot_rsi(data, position = NULL, x = "Antibiotic",
  fill = "Interpretation", facet = NULL, breaks = seq(0, 1, 0.1),
  limits = NULL, translate_ab = "official", fun = count_df,
  nrow = NULL, datalabels = TRUE, datalabels.size = 3,
  datalabels.colour = "grey15", ...)

geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
  fill = "Interpretation", translate_ab = "official", fun = count_df,
  ...)

facet_rsi(facet = c("Interpretation", "Antibiotic"), nrow = NULL)

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

scale_rsi_colours()

theme_rsi()

labels_rsi_count(position = NULL, x = "Antibiotic",
  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" (default when fun is count_df), "stack" (default when fun is portion_df) 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 into, using abname. Default behaviour is to translate to official names according to the WHO. Use translate_ab = FALSE to disable translation.

fun

function to transform data, either count_df (default) or portion_df

nrow

(when using facet) number of rows

datalabels

show datalabels using labels_rsi_count, will at default only be shown when fun = count_df

datalabels.size

size of the datalabels

datalabels.colour

colour of the datalabels

...

other parameters passed on to geom_rsi

Details

At default, the names of antibiotics will be shown on the plots using abname. This can be set with the option get_antibiotic_names (a logical value), so change it e.g. to FALSE with options(get_antibiotic_names = FALSE).

The functions
geom_rsi will take any variable from the data that has an rsi class (created with as.rsi) using fun (count_df at default, can also be portion_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 facet_wrap.

scale_y_percent transforms the y axis to a 0 to 100% range using scale_continuous.

scale_rsi_colours sets colours to the bars: green for S, yellow for I and red for R, using scale_brewer.

theme_rsi is a ggplot theme with minimal distraction.

labels_rsi_count print datalabels on the bars with percentage and amount of isolates using 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.

Read more on our website!


On our website https://msberends.gitlab.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.

Examples

# NOT RUN {
library(dplyr)
library(ggplot2)

# get antimicrobial results for drugs against a UTI:
ggplot(septic_patients %>% select(amox, nitr, fosf, trim, cipr)) +
  geom_rsi()

# prettify the plot using some additional functions:
df <- septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
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:
septic_patients %>%
  select(amox, nitr, fosf, trim, cipr) %>%
  ggplot_rsi()

# get only portions and no counts:
septic_patients %>%
  select(amox, nitr, fosf, trim, cipr) %>%
  ggplot_rsi(fun = portion_df)

# add other ggplot2 parameters as you like:
septic_patients %>%
  select(amox, nitr, fosf, trim, cipr) %>%
  ggplot_rsi(width = 0.5,
             colour = "black",
             size = 1,
             linetype = 2,
             alpha = 0.25)

# resistance of ciprofloxacine per age group
septic_patients %>%
  mutate(first_isolate = first_isolate(.)) %>%
  filter(first_isolate == TRUE,
         mo == as.mo("E. coli")) %>%
  # `age_group` is also a function of this package:
  group_by(age_group = age_groups(age)) %>%
  select(age_group,
         cipr) %>%
  ggplot_rsi(x = "age_group")
# }# NOT RUN {
# for colourblind mode, use divergent colours from the viridis package:
septic_patients %>%
  select(amox, nitr, fosf, trim, cipr) %>%
  ggplot_rsi() + scale_fill_viridis_d()


# it also supports groups (don't forget to use the group var on `x` or `facet`):
septic_patients %>%
  select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
  group_by(hospital_id) %>%
  ggplot_rsi(x = hospital_id,
             facet = Antibiotic,
             nrow = 1) +
  labs(title = "AMR of Anti-UTI Drugs Per Hospital",
       x = "Hospital")

# genuine analysis: check 2 most prevalent microorganisms
septic_patients %>%
  # create new bacterial ID's, with all CoNS under the same group (Becker et al.)
  mutate(mo = as.mo(mo, Becker = TRUE)) %>%
  # filter on top three bacterial ID's
  filter(mo %in% top_freq(freq(.$mo), 3)) %>%
  # determine first isolates
  mutate(first_isolate = first_isolate(.,
                                       col_date = "date",
                                       col_patient_id = "patient_id",
                                       col_mo = "mo")) %>%
  # filter on first isolates
  filter(first_isolate == TRUE) %>%
  # get short MO names (like "E. coli")
  mutate(mo = mo_shortname(mo, Becker = TRUE)) %>%
  # select this short name and some antiseptic drugs
  select(mo, cfur, gent, cipr) %>%
  # group by MO
  group_by(mo) %>%
  # plot the thing, putting MOs on the facet
  ggplot_rsi(x = Antibiotic,
             facet = mo,
             translate_ab = FALSE,
             nrow = 1) +
  labs(title = "AMR of Top Three Microorganisms In Blood Culture Isolates",
       subtitle = "Only First Isolates, CoNS grouped according to Becker et al. (2014)",
       x = "Microorganisms")
# }