Writing Data

Last updated on 2024-08-19 | Edit this page

Estimated time: 20 minutes

Overview

Questions

How can I save plots and data created in R?

Objectives

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

Instructor Note

Learners will need to have created the directory structure described in Project Management With RStudio in order for the code in this episode to work.

First, let’s load in all relevant libraries and data to be used in this lesson:

R

library(ggplot2)
library(dplyr)

gapminder <- read.csv("data/gapminder_data.csv", header = TRUE)

We also need to create a cleaned-data folder within the data folder and a figures folder within the main project folder. We can do this manually or using code:

R

dir.create("data/cleaned-data")
dir.create("figures")

Saving plots

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, 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. In this case you can use flexible approach. The ggsave function saves the latest plot by default. You can control the size and resolution using the arguments to this function.

R

ggplot(data = gapminder, mapping = aes(x = gdpPercap)) +
  geom_histogram()
ggsave("figures/Distribution-of-gdpPercap.pdf", width=12, height=4)

Open up this document and have a look.

Challenge 1

Create and save a new plot showing the side-by-side bar plot of gdp per capita in countries in the Americas in the years 1952 and 2007.

Show me the solution

R

gapminder_small <- gapminder %>% 
  filter(continent == "Americas" & year %in% c(1952, 2007))

ggplot(data = gapminder_small, 
       mapping = aes(x = country, y = gdpPercap, fill = as.factor(year))) +
  geom_col(position = "dodge") +
  coord_flip()

# Note that ggsave saves by default the latest plot.
ggsave("figures/Distribution-of-gdpPercap.pdf", width = 12, height = 4)

To produce documents in different formats, change the file extension for jpeg, png, tiff, or bmp.

Writing data

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

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

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

R

aust_subset <- gapminder %>% 
  filter(country == "Australia")

write.csv(aust_subset,
  file="data/cleaned-data/gapminder-aus.csv"
)

Let’s open the file to make sure it contains the data we expect. Navigate to your cleaned-data directory and double-click the file name. It will open using your computer’s default for opening files with a .csv extension. To open in a specific application, right click and select the application. Using a spreadsheet program (like Excel) to open this file shows us that we do have properly formatted data including only the data points from Australia. However, there are row numbers associated with the data that are not useful to us (they refer to the row numbers from the gapminder data frame).

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

R

?write.csv

By default R will write out the row and column names when writing data to a file. To over write this behavior, we can do the following:

R

write.csv(
  aust_subset,
  file="data/cleaned-data/gapminder-aus.csv",
  row.names=FALSE
)

Challenge 2

Subset the gapminder data to include only data points collected since 1990. Write out the new subset to a file in the cleaned-data/ directory.

Show me the solution

R

gapminder_after_1990 <- gapminder %>% 
  filter(year > 1990)

write.csv(gapminder_after_1990,
  file = "cleaned-data/gapminder-after-1990.csv",
  row.names = FALSE)

Key Points

Save plots using ggsave().
Use write.csv to save tabular data.

Instructor Note

Now that learners know the fundamentals of R, the rest of the workshop will apply these concepts to working with geospatial data in R.
Packages and functions specific for working with geospatial data will be the focus of the rest of the workshop.
They will have lots of challenges to practice applying and expanding these skills in the next lesson.