# AMR plots with <code>ggplot2</code> — ggplot_rsi • AMR (for R)
Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal ggplot2 functions.

# AMR plots with `ggplot2`

`ggplot_rsi.Rd`

Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal `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, language = get_locale(), fun = count_df,
      nrow = NULL, colours = c(S = "#61a8ff", SI = "#61a8ff", I =
      "#61f7ff", IR = "#ff6961", R = "#ff6961"), datalabels = TRUE,
      datalabels.size = 2.5, datalabels.colour = "gray15", title = NULL,
      subtitle = NULL, caption = NULL, x.title = NULL, y.title = NULL,
      ...)

    geom_rsi(position = NULL, x = c("antibiotic", "interpretation"),
      fill = "interpretation", translate_ab = "name",
      language = get_locale(), combine_SI = TRUE, combine_IR = FALSE,
      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(colours = c(S = "#61a8ff", SI = "#61a8ff", I =
      "#61f7ff", IR = "#ff6961", R = "#ff6961"))

    theme_rsi()

    labels_rsi_count(position = NULL, x = "antibiotic",
      translate_ab = "name", combine_SI = TRUE, combine_IR = FALSE,
      datalabels.size = 3, datalabels.colour = "gray15")

## 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 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 parameter `combine_IR`, but this now follows the redefinition by EUCAST about the interpretion 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 parameter `combine_SI`.
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.
fun | function to transform `data`, either `count_df` (default) or `portion_df`
nrow | (when using `facet`) number of rows
colours | a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be `FALSE` to use default `ggplot2` colours.
datalabels | show datalabels using `labels_rsi_count`, will only be shown when `fun = count_df`
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 parameters passed on to `geom_rsi`

## Details

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

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: pastel blue for S, pastel turquoise for I and pastel 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 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(AMX, NIT, FOS, TMP, CIP)) +
      geom_rsi()

    # prettify the plot using some additional functions:
    df <- septic_patients %>% 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:
    septic_patients %>%
      select(AMX, NIT, FOS, TMP, CIP) %>%
      ggplot_rsi()

    # get only portions and no counts:
    septic_patients %>%
      select(AMX, NIT, FOS, TMP, CIP) %>%
      ggplot_rsi(fun = portion_df)

    # add other ggplot2 parameters as you like:
    septic_patients %>%
      select(AMX, NIT, FOS, TMP, CIP) %>%
      ggplot_rsi(width = 0.5,
                 colour = "black",
                 size = 1,
                 linetype = 2,
                 alpha = 0.25)

    septic_patients %>%
      select(AMX) %>%
      ggplot_rsi(colours = c(SI = "yellow"))

    # 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,
             CIP) %>%
      ggplot_rsi(x = "age_group")
    # }# NOT RUN {
    # for colourblind mode, use divergent colours from the viridis package:
    septic_patients %>%
      select(AMX, NIT, FOS, TMP, CIP) %>%
      ggplot_rsi() + scale_fill_viridis_d()
    # a shorter version which also adjusts data label colours:
    septic_patients %>%
      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`):
    septic_patients %>%
      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)

    # genuine analysis: check 3 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)) %>%
      # filter on first isolates
      filter_first_isolate() %>%
      # get short MO names (like "E. coli")
      mutate(bug = mo_shortname(mo, Becker = TRUE)) %>%
      # select this short name and some antiseptic drugs
      select(bug, CXM, GEN, CIP) %>%
      # group by MO
      group_by(bug) %>%
      # plot the thing, putting MOs on the facet
      ggplot_rsi(x = "antibiotic",
                 facet = "bug",
                 translate_ab = FALSE,
                 nrow = 1,
                 title = "AMR of Top Three Microorganisms In Blood Culture Isolates",
                 subtitle = expression(paste("Only First Isolates, CoNS grouped according to Becker ",
                                             italic("et al."), " (2014)")),
                 x.title = "Antibiotic (EARS-Net code)")
    # }