Multiplots

library(mrgmisc) # for binning and other functions
library(ggplot2) #plotting
library(purrr) # dataset to handle lists
suppressMessages(suppressWarnings(library(dplyr)))

dat <- sd_oral_richpk %>% filter(ID <= 20)

Messy plot can’t see individuals well

ggplot(dat, aes(x = Time, y = Conc, group= ID)) + 
  geom_line() +
  facet_wrap(~ID, scales= "free")

What we’d like to be able to do is split up into ‘bins’ of specified numbers of individuals to then plot out multiple plots

# this will automatically create a column of bins such that the
# specified number of individuals is in each bin, in this case
# 9 ids per bin
# split the original dataset into subdatasets corresponding to each bin (list of dataframes)
split_dat <- dat %>% 
  mutate(PLOTS = ids_per_plot(ID, 4)) %>% # default is 9 per subplot
  split(.[["PLOTS"]])

To handle plotting each subdataframe, you need to wrap your normal ggplot into a function. You will then apply this function with map to each subdataframe. So in this case it is just like a normal plot, but wrapped up in a function, which takes 1 argument (the dataframe) and outputs the plot

p_conc_time <- function(df) {
ggplot(df, aes(x = Time, y = Conc, group= ID)) + 
  geom_line() +
  facet_wrap(~ID, scales= "free")
}

To apply the above function we use map from purrr

split_dat %>% map(p_conc_time)

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Devin Pastoor

2026-08-28