Once you've added the necessary layers, you can view your plot by simply running the code in R or RStudio. # Run the plot code to view the scatter plot plot <- ggplot ( data = mtcars, aes ( x = wt, y = mpg)) + geom_point () print ( plot) 📌. This code assigns the plot to a variable plot and then prints it. Fortunately it’s easy to create boxplots in R using the visualization library ggplot2. It’s also to create boxplots grouped by a particular variable in a dataset. For example, suppose we have the following dataset that displays the increase in efficiency for 150 basketball players on three different teams based on two different training This post describes how to build a dual Y axis chart using R and ggplot2.It uses the sec.axis attribute to add the second Y axis. Note that this kind of chart has major drawbacks. The ggplot box plots can be customized making use of the arguments of stat_boxplot and geom_boxplot. In the following examples we are changing the colors and line It all has to do with the philosophy of ggplot2, which is built around what is called the Grammar of Graphics.Basically, it's a layered approach to building data visualizations, starting with your data as the base layer and building from that your aesthetics, the summary statistics, the shapes/things to draw ("geoms"), the coordinate system, and finally, the actual plot. geom_abline () is like saying y=ax+b, and you specify the slope and intercept. geom_line () plots a line fitting data. Check the docs.ggplot2.org site as @Dennis commented. So you have two options here. If your regression line is linear, use geom_abline () and specify your slope and intercept, or, if you want to plot predicted data, use geom By default, ggplot2 uses (I believe) a color palette based on evenly-spaced hue values. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces. Same transparency for all lines. Let us first change the transparency of all the lines to the same value, for this alpha parameter is used. The maximum value it takes is 1, which makes it a solid line. There are two major functions in ggplot2 package: qplot() and ggplot() functions. qplot() stands for quick plot, which can be used to produce easily simple plots. ggplot() function is more flexible and robust than qplot for building a plot piece by piece. This document provides R course material for producing different types of plots using ggplot2. Boxplot. This is the boxplot section of the gallery. If you want to know more about this kind of chart, visit data-to-viz.com. If you're looking for a simple way to implement it in R or ggplot2, pick an example below. Note: this online course on ggplot2 covers several geometries including geom_boxplot. Anatomy of a boxplot - Explanation - Image nMt2wI.