R ggplot plot rename x values8/15/2023 ![]() 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. I> While we overview some guidelines for effective plots here, this is mostly to provide a framework for showing how to customize ggplot objects. Highlight interesting aspects of the data.In this section, we’ll overview six guidelines for good graphics, based on the writings of these and other specialists in data display. Edward Tufte (his book The Visual Display of Quantitative Information is a classic).Some of the authors we highly recommend (and from whose work we’ve pulled and aggregated the guidelines for good graphics we’ll go over) are: 4.7.4 Example: Normal Confidence IntervalsĪ number of very thoughtful books and articles have been written about creating graphics that effectively communicate information.4.7.2 Example: An Automatic Transparency Geom.4.5.6 Find out more about grid graphics.4.3.5 Where to find more on mapping with R.4.3.4 More advanced mapping – Spatial objects.3.10.1 Web Services for Continuous Integration.3.8.5 Linking local repo to GitHub repo.3.5 Software Testing Framework for R Packages.3.4.3 Common Markdown formatting elements.2.10.2 Compose simple functions with the pipe.2.10 Gaining Your ‘tidyverse’ Citizenship.2.5.3 When to generate errors or warnings.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.This results in different appearances, as shown below. With a coordinate transform, the transformation happens after the breaks and scale range are decided. With a scale transform, the data is transformed before properties such as breaks (the tick locations) and range of the axis are decided. One is to use a scale transform, and the other is to use a coordinate transform. There are two ways of transforming an axis. It is possible to transform the axes with log, power, roots, and so on. # The scale will show only the ones that are within range (3.50-6.25 in this case)īp + scale_y_continuous ( breaks = seq ( 1, 10, 1 / 4 )) # The breaks can be spaced unevenlyīp + scale_y_continuous ( breaks = c ( 4, 4.25, 4.5, 5, 6, 8 )) # Suppress ticks and gridlinesīp + scale_y_continuous ( breaks = NULL ) # Hide tick marks and labels (on Y axis), but keep the gridlinesīp + theme ( axis.ticks = element_blank (), = element_blank ())īy default, the axes are linearly scaled. # This will show tick marks on every 0.25 from 1 to 10
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