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Gaia Shaw
aesthetic mappings are created by the aes() function, which defines connections between graphical elements of the plot and the variables from our data frame. The elements that can be mapped include the x and y axes, color, fill, shape or line type. The mapping can be created globally, provided to the ggplot() function and locally, by passing aes() to specific geoms.
geoms are constituent elements of ggplot objects. They are different graphical structures, such as histograms, boxplots or scatter plots that can be added to an initialized ggplot object using the addition operator +. Each geom uses the aesthetic mapping specified globally, unless specified otherwise locally.
The code we have listed here for graphics barely scratches the surface of what ggplot, and R as a whole, are capable of. Not only are there far more choices about the kinds of plots available, but there are many, many options for customizing the look and feel of each graph. You can choose the font, the font size, the colors, the style of the axes labels, etc., and you can customize the legends and axes legends nearly as much as you want.
In this data set we have a numeric variable, stress_psychisch and a groupingvariable gender. Our question was whether the variable stress_psychisch isrelated to the grouping variable gender. We could graphically illustrate thisrelationship in various ways: with dots, with a box plot or with a violin plot.These three methods are geoms in the language of ggplot2 and can be used asfollows: geom_point (), geom_boxplot() or geom_violin(). In addition,there is a function geom_jitter() that spatially jitters the data points (asan alternative to displaying data points with the same value on top of eachother).
If the variable that we want to represent graphically is not categorical butcontinuous a histogram is the appropriate option; we generate this with thefunction geom_histogram(). As an example we consider the psychological stresssymptoms.
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