R for reproducible scientific analysis

Writing data

Learning Objectives

  • To be able to write out plots and data from R

Saving plots

You have already seen how to save the most recent plot you create in ggplot2, using the command ggsave. As a refresher:

ggsave("My_most_recent_plot.pdf")

You can save a plot from within RStudio using the ‘Export’ button in the ‘Plot’ window. This will give you the option of saving as a .pdf or as .png, .jpg or other image formats.

Sometimes you will want to save plots without creating them in the ‘Plot’ window first. Perhaps you want to make a pdf document with multiple pages: each one a different plot, for example. Or perhaps you’re looping through multiple subsets of a file, plotting data from each subset, and you want to save each plot, but obviously can’t stop the loop to click ‘Export’ for each one.

In this case you can use a more flexible approach. The function pdf creates a new pdf device. You can control the size and resolution using the arguments to this function.

pdf("Health_vs_SelfRatedHealth.pdf", width=12, height=4)
ggplot(data=healthData, aes(x=selfRatedHealth, y=health, colour=HIGroup)) +
  geom_point()

# You then have to make sure to turn off the pdf device!

dev.off()

Open up this document and have a look.

Challenge 1

Rewrite your ‘pdf’ command to print a second page in the pdf, showing a facet plot (hint: use facet_grid) of the same data with one panel per education year.

The commands jpeg, png etc. are used similarly to produce documents in different formats.

Writing data

At some point, you’ll also want to write out data from R.

We can use the write.table function for this, which is very similar to read.table from before.

Let’s create a data-cleaning script. For this analysis, we only want to focus on the health data for males from sample group 1:

healthData_subset <- healthData[healthData$sex == "Male" & 
                                  healthData$HIGroup == "Group 1",]

write.table(healthData_subset,
  file="cleaned-data/healthData_subset.csv",
  sep=","
)

Now let’s have a look at the data. Provided data files aren’t very large, this can be achieved easily in R by simply opening the file from the file explorer.

Hmm, that’s not quite what we wanted. Where did all these quotation marks come from? Also the row numbers are meaningless.

Let’s look at the help file to work out how to change this behaviour.

?write.table

By default R will wrap character vectors with quotation marks when writing out to file. It will also write out the row and column names.

Let’s fix this:

write.table(
  healthData_subset,
  file="cleaned-data/healthData_subset.csv",
  sep=",", quote=FALSE, row.names=FALSE
)

Now lets look at the data again.

That looks better!

Challenge 2

Write a data-cleaning script file that subsets the health data to include only data points collected collected for students in year 8.

Use this script to write out the new subset to a file in the cleaned-data/ directory.