Use these functions to create bar plots for AMR data analysis. All functions rely on 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, minimum = 30, language = get_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_rsi( position = NULL, x = c("antibiotic", "interpretation"), fill = "interpretation", translate_ab = "name", minimum = 30, language = get_locale(), combine_SI = TRUE, combine_IR = FALSE, ... ) facet_rsi(facet = c("interpretation", "antibiotic"), nrow = NULL) scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL) scale_rsi_colours(..., aesthetics = "fill") theme_rsi() labels_rsi_count( position = NULL, x = "antibiotic", translate_ab = "name", minimum = 30, language = get_locale(), combine_SI = TRUE, combine_IR = FALSE, datalabels.size = 3, datalabels.colour = "grey15" )
Arguments
| data | a data.frame with column(s) of class |
|---|---|
| position | position adjustment of bars, either |
| x | variable to show on x axis, either |
| fill | variable to categorise using the plots legend, either |
| facet | variable to split plots by, either |
| breaks | a numeric vector of positions |
| limits | a numeric vector of length two providing limits of the scale, use |
| translate_ab | a column name of the antibiotics data set to translate the antibiotic abbreviations to, using |
| 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 argument |
| 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 This is outdated, see argument |
| minimum | the minimum allowed number of available (tested) isolates. Any isolate count lower than |
| language | language of the returned text, defaults to system language (see |
| nrow | (when using |
| colours | a named vactor with colour to be used for filling. The default colours are colour-blind friendly. |
| datalabels | show datalabels using |
| 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 |
| aesthetics | aesthetics to apply the colours to, defaults to "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_rsi() will take any variable from the data that has an rsi class (created with as.rsi()) using rsi_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 ggplot2::facet_wrap().
scale_y_percent() transforms the y axis to a 0 to 100% range using ggplot2::scale_y_continuous().
scale_rsi_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_rsi() is a [ggplot2 theme][ggplot2::theme() with minimal distraction.
labels_rsi_count() print datalabels on the bars with percentage and amount of isolates using ggplot2::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.
Stable Lifecycle
The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
Read more on Our Website!
On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
if (require("ggplot2") & require("dplyr")) { # get antimicrobial results for drugs against a UTI: ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) + geom_rsi() # prettify the plot using some additional functions: df <- example_isolates %>% 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: example_isolates %>% select(AMX, NIT, FOS, TMP, CIP) %>% ggplot_rsi() # get only proportions and no counts: example_isolates %>% select(AMX, NIT, FOS, TMP, CIP) %>% ggplot_rsi(datalabels = FALSE) # add other ggplot2 arguments as you like: example_isolates %>% select(AMX, NIT, FOS, TMP, CIP) %>% ggplot_rsi(width = 0.5, colour = "black", size = 1, linetype = 2, alpha = 0.25) # you can alter the colours with colour names: example_isolates %>% select(AMX) %>% ggplot_rsi(colours = c(SI = "yellow")) # 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_rsi_colours(Value4 = "S", Value5 = "I", Value6 = "R") } # \donttest{ # resistance of ciprofloxacine per age group example_isolates %>% mutate(first_isolate = first_isolate(.)) %>% filter(first_isolate == TRUE, mo == as.mo("E. coli")) %>% # age_groups() is also a function in this AMR package: group_by(age_group = age_groups(age)) %>% select(age_group, CIP) %>% ggplot_rsi(x = "age_group") # a shorter version which also adjusts data label colours: example_isolates %>% 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`): example_isolates %>% 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) # }