You’ll want to use short and easy-to-type column names in your dataframe to make coding easier (e.g., “wt”), but you should use longer and more meaningful labeling in plots and tables that others need to interpret (e.g., “Weight (kg)”).įurthermore, text labels are often aligned in a way that makes them hard to read. While this is convenient for exploratory plots, it’s often not adequate for plots for presentations and papers. For example, the default is for ggplot2 plots to use column names as labels for the x- and y-axes of a scatterplot. Graphs often default to use abbreviations for axis labels and other labeling. Guideline 2: Use clear, meaningful labels. For example, to use the “classic” theme for a scatterplot using the World Cup 2010 data, you can run: To use a theme, you can add it to a ggplot object by using a theme function like theme_bw. There are several themes that come with ggplot2, including a black-and-white theme and a minimal theme.
One quick way to increase data density in ggplot2 is to change the theme for the plot, which will quickly change several elements of the plot’s appearance. Notice how the plots on the left in Figures 4.18 and 4.19 are already cluttered and leave little room for adding extra elements, while the plots on the right of those figures have much more room for additions. Further, you leave room to add some of the other elements we’ll talk about, including elements to highlight interesting data and useful references. Each plot shows daily mortality in Chicago, IL, in July 1995 using the chicagoNMMAPS data from the dlnm package.īy increasing the data-to-ink ratio in a plot, you can help viewers see the message of the data more quickly. Howard Wainer’s article “How to display data badly” in The American Statistician is a particularly good place to start.įor the examples in this subsection, we’ll use dplyr for data cleaning and, for plotting, the packages ggplot2, gridExtra, and ggthemes, so you should load those packages if you plan to follow along with the examples.įigure 4.19: Example of plots with lower (left) and higher (right) data-to-ink ratios. If you are interested in learning more about creating effective visualizations, you should read some of the thorough and thoughtful books written by the authors listed above.
#R GGPLOT PLOT RENAME X VALUES HOW TO#
I> While we overview some guidelines for effective plots here, this is mostly to provide a framework for showing how to customize ggplot objects.
#R GGPLOT PLOT RENAME X VALUES SOFTWARE#
2.3.2 Core Functional Programming Functions.
1.10.1 How to Google Your Way Out of a Jam.1.8.1 Back of the Envelope Calculations.1.7 Text Processing and Regular Expressions.1.6.2 Pulling out date and time elements.1.6.1 Converting to a date or date-time class.1.6 Working with Dates, Times, Time Zones.1.5.4 Adding, changing, or renaming columns.1.4.2 Requesting data through a web API.1.3 Reading Tabular Data with the readr Package.
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