Creating a loop in r for multiple box plots

Question

I have designed an experiment to see how serum markers change with time since eating a meal. I have a data frame consisting of 72 observations and 23o variables this is called BreakfastM.

There are 229 variables which are serum markers and 1 which is timepoint. The observations are different samples

Iam looking for trends in the data of how the serum markers (ie cholestrol) change with the timepoint. I have created a boxplot which shows nicely the trends in a particular serum marker in relation to timepoint

This is the code I used

boxplot((BreakfastM$Variable~BreakfastM$Timepoint))

Is there a quick way to test all the variables in the dataframe against the timepoint by writing a loop code in R?

Answer 1

If you are just looking to plot, converting to long form with tidyr (and dplyr ) and then plotting with ggplot2 is probably the best starting point.

If you have only a small number of variables, you could just use facet_wrap to split the boxplots by measure. Because you didn't provide reproducible data, I am using the mtcars data, substituting "gear" for your time point, and limiting to just the numeric values to compare. select is picking the columns I want to use, then gather converts them to long format before passing to ggplot

mtcars %>%
  select(gear, mpg, disp:qsec) %>%
  gather(Measure, Value, -gear) %>%
  ggplot(aes(x = factor(gear)
             , y = Value)) +
  geom_boxplot() +
  facet_wrap(~Measure
             , scales = "free_y")

Now, with 229 variables, that is not going to be a readable plot. Instead, you may want to look at facet_multiple from ggplus which spreads facets over multiple pages. Here, I am using it to put one per "page" which you can either view in the viewer, or save, depending on your needs.

First, save the base plot (with no facetting):

basePlot <-
  mtcars %>%
  select(gear, mpg, disp:qsec) %>%
  gather(Measure, Value, -gear) %>%
  ggplot(aes(x = factor(gear)
             , y = Value)) +
  geom_boxplot()

Then, use it as an argument to facet_multiple :

facet_multiple(basePlot, "Measure"
               , nrow = 1
               , ncol = 1
               , scales = "free_y")

Will generate the same panels as above, but with one per page (changing nrow and ncol can increase the number of facets shown per page).

Answer 2

You can also use a loop to write many plots to image files in your working directory. Let's make a 10 column matrix representing 10 measured variables, each split by 3 factor levels:

data <- matrix(rnorm(150), nrow=15)
grps <- factor(c(rep("group1", 5), rep("group2", 5), rep("group3", 5)))

The loop writes each boxplot to files called var_1.png , var_2.png , etc. This will put 10 png s in your working directory.

for (i in 1:ncol(data)) {
  png(file = paste("var_", i, ".png", sep=""))
  boxplot(data[, i] ~ grps)
  dev.off()
}

The files are very small and you can flick through them quickly with a simple image viewer.