Ever since I read Fundamentals of Data Visualization by Claus Wilke, I am fond of this color palette as I find the colors nice and apparently it is also color-blind safe. The plot titles are aligned to the left, have some spacing around them and are colored using a color from the Okabe Ito color palette.The use of the Fira Sans font with help from the showtext package.More or less, this chunk makes sure that all plots are using theme_minimal() plus a small number of tweaks. Honestly, if you have no idea what happens in the following code chunk, just skip it. Figure 8.9: Box plot with order reversed on the x-axis. Preliminariesįor completeness’ sake, let me mention the basic settings I will use for all visualizations. pgplot <- ggplot(PlantGrowth, aes(x group, y weight)) + geomboxplot() pgplot +. This post will show you how you can do that too. ggplotly (df > ggplot (aes (x date, y vIncrease, fill state)) + geomarea () +. Naturally, I began experimenting and created a few example plots using fewer colors. Luckily, I found a neat datawrapper blogpost by Lisa Charlotte Muth that shows us how to reduce the use of colors.īut as I was reading the blog post, I found myself wondering how some of the mentioned principles could be implemented in ggplot. Usually, this makes a data visualization way more effective. Thus, I have to remind myself often to keep things simple. Step 1 Set up a plot to modify: iris > filter (Sepal.Width 3.1) > ggplot () + geomboxplot (aes (xSpecies, ySepal.Length)) + thememinimal () Step 2 create a labeller function to define new labels for each factor. When creating a plot I frequently catch myself using way too many colors.
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