You’ll learn a whole bunch of them throughout this chapter.Įach geom function in ggplot2 takes a mapping argument. ggplot2 comes with many geom functions that each add a different type of layer to a plot. The function geom_point() adds a layer of points to your plot, which creates a scatterplot. You complete your graph by adding one or more layers to ggplot(). So ggplot(data = mpg) creates an empty graph, but it’s not very interesting so I’m not going to show it here. The first argument of ggplot() is the dataset to use in the graph. ggplot() creates a coordinate system that you can add layers to. ![]() With ggplot2, you begin a plot with the function ggplot(). Does this confirm or refute your hypothesis about fuel efficiency and engine size? In other words, cars with big engines use more fuel. It might be useful to treat these values as equal categories when making a graph.The plot shows a negative relationship between engine size ( displ) and fuel efficiency ( hwy). In this data set, the dose is a numeric variable with values 0.5, 1.0, and 2.0. ![]() When the variable on the x-axis is numeric, it is sometimes useful to treat it as continuous, and sometimes useful to treat it as categorical. In the line graph, the reason that the legend title, “Sex of payer”, must be specified three times is so that there is only one legend. Theme_bw () + theme ( legend.position = c (. Scale_linetype_discrete ( name = "Sex of payer" ) + xlab ( "Time of day" ) + ylab ( "Total bill" ) + # Set axis labels Scale_shape_manual ( name = "Sex of payer", values = c ( 22, 21 )) + # Use points with a fill color L = 30 ) + # Use darker colors (lightness=30) Scale_colour_hue ( name = "Sex of payer", # Set legend title Geom_point ( size = 3, fill = "white" ) + # Use larger points, fill with whiteĮxpand_limits ( y = 0 ) + # Set y range to include 0 Ggplot ( data = dat1, aes ( x = time, y = total_bill, group = sex, shape = sex, colour = sex )) + geom_line ( aes ( linetype = sex ), size = 1 ) + # Set linetype by sex Ggtitle ( "Average bill for 2 people" ) + # Set title Xlab ( "Time of day" ) + ylab ( "Total bill" ) + # Set axis labels Scale_fill_hue ( name = "Sex of payer" ) + # Set legend title Ggplot ( data = dat1, aes ( x = time, y = total_bill, fill = sex )) + geom_bar ( colour = "black", stat = "identity", position = position_dodge (), size =. Here is some sample data (derived from the tips dataset in the reshape2 package): ![]() In ggplot2, the default is to use stat_bin, so that the bar height represents the count of cases. This is done with stat_identity, which leaves the y values unchanged. The value of a column in the data set. ![]() This is done with stat_bin, which calculates the number of cases in each group (if x is discrete, then each x value is a group if x is continuous, then all the data is automatically in one group, unless you specifiy grouping with group=xx). The count of cases for each group – typically, each x value represents one group.With bar graphs, there are two different things that the heights of bars commonly represent: If your data needs to be restructured, see this page for more information. To make graphs with ggplot2, the data must be in a data frame, and in “long” (as opposed to wide) format. You want to do make basic bar or line graphs.
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