ggplot2ggplot_rsi.RdUse 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(), 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 = "Antimicrobial", y.title = "Proportion", ...) geom_rsi(position = NULL, x = c("antibiotic", "interpretation"), fill = "interpretation", translate_ab = "name", 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(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")
| data | a |
|---|---|
| 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 | numeric vector of positions |
| limits | numeric vector of length two providing limits of the scale, use |
| translate_ab | a column name of the |
| 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 | 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 This is outdated, see parameter |
| language | language of the returned text, defaults to system language (see |
| nrow | (when using |
| colours | a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be |
| 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 parameters passed on to |
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 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 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.
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.
# 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(datalabels = FALSE) # 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)") # }