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# AMR Plots with `ggplot2`

Source: `R/ggplot_sir.R`
`ggplot_sir.Rd`

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", SDD = "#8FD6C4", 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,
      ...
    )

## 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 antimicrobials 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.

## Details

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

`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 antimicrobials on the x axis.

Additional functions include:

- `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 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(aesthetics = "fill") +
        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
        )
    }
    #> Warning: Ignoring unknown parameters: `size`

    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(
          aesthetics = "fill",
          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()`

    # }