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

# AMR bar plots with `ggplot`

`ggplot_rsi.Rd`

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 omprehensive tutorial about how to conduct AMR analysis and find the complete documentation of all functions, which reads a lot easier than in R.

## 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")
    # }