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Programming with R

Writing Data

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Overview

Teaching: 10 min
Exercises: 15 min
Questions

  • How can I save plots and data created in R?

Objectives

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

Saving plots

So making publication quality plots is great but does us little good if we cannot get them out of R and into our documents.

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.

export plots in rstudio

But what if you are working in a command line environment, or want to create multiple plots without user interaction? In ggplot2 you can use the ggsave function to save your plots quickly. This function can be used to save the last displayed plot in a format specified by the file name. You can save as several different formats, such as a PDF:

ggsave("My_most_recent_plot.pdf")

Or as a JPG:

ggsave("My_most_recent_plot.jpg")

ggsave also allows you to specify size and quality of the image. You can check out all of the options using the ?ggsave command to view the help file.

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("Life_Exp_vs_time.pdf", width=12, height=4)
ggplot(data=gapminder, aes(x=year, y=lifeExp, color=continent)) +
  geom_point()

dev.off()

The pdf command opens the pdf file, and any output between this command and the dev.off command will be added to that file. Forgetting to “close” your pdf device by using the dev.off command can lead to an incorrect file, so be sure to include it immediately after your output.

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 of the same data with one panel per continent.

Hint: Remember that we used the facet_wrap command previously to create a facet plot.

Solution to Challenge 1

You can output a second plot, by adding a second ggplot command with the facet_wrap command before dev.off command.

pdf("Life_Exp_vs_time2.pdf", width=12, height=4)
ggplot(data=gapminder, aes(x=year, y=lifeExp)) +
  geom_point()
ggplot(data=gapminder, aes(x=year, y=lifeExp)) +
  geom_point() + facet_wrap(~ continent)

# don't forget to close your pdf device!
dev.off()

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 the read.table function that we mentioned previously in Lesson 2. For more information on reading in your own data in R and the read.table function, check out the supplemental lesson Reading and Writing CSV Files.

Let’s create a data-cleaning script, for this analysis, we only want to focus on the gapminder data for Australia:

aust_subset <- gapminder[gapminder$country == "Australia",]

write.table(aust_subset,
  file="gapminder-aus.csv",
  sep=","
)

Remember in our last lesson when we discussed line breaks and the different approaches for neat and tidy code. Here, the line breaks have been placed between the different parameters of our command to make the code easier to read. One benefit to this approach is that we can then comment each line to remind ourselves what they do. Here’s our previous example with these in line comments

aust_subset <- gapminder[gapminder$country == "Australia",]

write.table(aust_subset,     # Gapminder data for countries located in Australia
  file="gapminder-aus.csv",  # Name of the output file
  sep=","                    # Comma separated
)

This approach becomes really beneficial when you start writing commands which use a lot of parameters.

We can examine our file right from within RStudio. In the lower-left pane under the Files tab, find the gapminder-aus.csv file. Click on it and select View File.

"country","year","pop","continent","lifeExp","gdpPercap"
"61","Australia",1952,8691212,"Oceania",69.12,10039.59564
"62","Australia",1957,9712569,"Oceania",70.33,10949.64959
"63","Australia",1962,10794968,"Oceania",70.93,12217.22686
"64","Australia",1967,11872264,"Oceania",71.1,14526.12465
"65","Australia",1972,13177000,"Oceania",71.93,16788.62948
"66","Australia",1977,14074100,"Oceania",73.49,18334.19751
"67","Australia",1982,15184200,"Oceania",74.74,19477.00928
"68","Australia",1987,16257249,"Oceania",76.32,21888.88903
"69","Australia",1992,17481977,"Oceania",77.56,23424.76683

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(aust_subset,     # Gapminder data for countries located in Australia
  file="gapminder-aus.csv",  # Name of the output file
  sep=",",                   # Comma separated
  quote=FALSE,               # Turn off quotation marks
  row.names=FALSE            # No row names
)

Now lets look at the file again:

head gapminder-aus.csv

country,year,pop,continent,lifeExp,gdpPercap
Australia,1952,8691212,Oceania,69.12,10039.59564
Australia,1957,9712569,Oceania,70.33,10949.64959
Australia,1962,10794968,Oceania,70.93,12217.22686
Australia,1967,11872264,Oceania,71.1,14526.12465
Australia,1972,13177000,Oceania,71.93,16788.62948
Australia,1977,14074100,Oceania,73.49,18334.19751
Australia,1982,15184200,Oceania,74.74,19477.00928
Australia,1987,16257249,Oceania,76.32,21888.88903
Australia,1992,17481977,Oceania,77.56,23424.76683

That looks better!

Challenge 2

Write a data-cleaning script file that subsets the gapminder data to include only data points collected since 1990.

Use this script to write out the new subset to a file your working directory.

Remember to use a different file name so that the new output doesn’t overwrite your old output

Solution to Challenge 2

new_data <- gapminder[gapminder$year >= 1990,]
write.table(new_data,
  file="gapminder-1990.csv",
  sep=",",
  quote=FALSE,
  row.names=FALSE
)

Key Points

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