![]() ![]() Here we rotate the labels 30°, but we could also do 45°, 90°, or whatever we want. Since we want to avoid manually recoding categories, we can do some visual tricks to make the labels readable without changing any of the lable text. Verdict: 6/10, we have more control over the labels, but too much abbreviation reduces readibility, and it’s not automatic. If a new longer category gets added in a later iteration of the data, this code won’t automatically shorten it. It also requires more manual work and a lot of extra typing. Hous.” mean affordable housing? affluent housing? affable housing?). That works great! However, it reduces readibility (does “Aff. "Homeless Shelter" = "Homeless\nShelter"))Īes(x = CATEGORY, y = total)) geom_col() scale_y_continuous(labels = comma) labs(x = NULL, y = "Total projects") "Hospital / Health Care" = "Hosp./Health", Manual pages can be displayed using the ?function_name notation in the R console.Essential_by_category_shorter % mutate(CATEGORY = recode(CATEGORY, Note that paste, toupper, substring, and abbreviate built-in functions are utilized to implement capitalize_all functionality, but a full review of these methods are out of this article’s scope. In this case, we implemented the capitalize_all function that abbreviates each label first and then converts the starting character of the string to the capital letter. Scale_x_discrete parameter labels can take a custom function object to modify each tick label accordingly. ![]() ![]() Use scale_x_discrete With Custom Function to Modify ggplot X Axis Tick Labels in R P3 <- ggplot(mpg, aes(manufacturer, cty)) geom_point(colour = "blue") P2 <- ggplot(mpg, aes(manufacturer, cty)) geom_point() P1 <- ggplot(mpg, aes(manufacturer, cty)) geom_point() Namely, several scatter plots are drawn from the mpg data set. Scale_x_discrete works similarly on different graphs, and the label manipulation technique is applicable, as shown in the following example. P3 <- p1 scale_x_discrete("Cut Type", labels = abbreviate) The next code snippet uses the abbreviate function to automatically shorten the labels and then draw graphs as two columns. P2 <- p1 scale_x_discrete("Cut Type", labels = c("Fair" = "F","Good" = "G", "Very Good" = "VG","Premium" = "P","Ideal" = "I"))Īnother useful method to modify the labels on the x axis is to pass a function object as a labels parameter. P1 <- ggplot(diamonds, aes(cut)) geom_bar(fill = "orange") scale_x_discrete("Cut") Both graphs are drawn side-by-side using the grid.arrange function, part of the gridExtra package. Alternatively, we can pass specific string values for each label by constructing a vector and assigning it to the labels parameter. x axis has the default title - cut, which can be modified by passing the string as the first argument of scale_x_discrete. The graph uses the cut column and plots the count of each type on the y axis. Notice that the first ggplot object is a bar graph based on the diamonds data set. In this case, we utilize scale_x_discrete to modify x axis tick labels for ggplot objects. Scale_x_discrete together with scale_y_discrete are used for advanced manipulation of plot scale labels and limits. Use scale_x_discrete to Modify ggplot X Axis Tick Labels in R ![]() This article will introduce how to modify ggplot x-axis tick labels in R. Use scale_x_discrete With Custom Function to Modify ggplot X Axis Tick Labels in R.Use scale_x_discrete to Modify ggplot X Axis Tick Labels in R. ![]()
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