![]() This uses code coming from jbburant and David Robinson. Thus, a good alternative is a half violin plot showing the raw data. However, it is sometimes better to show the data points themselves. This is a good practice and shows that group C is under-represented. On the previous chart, the sample size of each group is indicated on the x-axis, below the group name. ![]() Violin plots are a powerful way to display information–they are probably under-utilized compared to boxplots. The bimodal distribution of group B becomes obvious. Here it is very clear that the groups have different distributions. ) ggtitle( "A boxplot with jitter") xlab( "") Sample_size = data %>% group_by(name) %>% summarize( num= n())ĭata %>% left_join(sample_size) %>% mutate( myaxis = paste0(name, " \n ", "n=", num)) %>% ggplot( aes( x=myaxis, y=value, fill=name)) geom_violin( width= 1.4) geom_boxplot( width= 0.1, color= "grey", alpha= 0.2) scale_fill_viridis( discrete = TRUE) theme_ipsum() theme( ![]() However, we cannot see the underlying distribution of dots in each group or their number of observations. If we consider the boxplot below, it is easy to conclude that group C has a higher value than the others. The problem is that summarizing also means losing information, and that can be a pitfall. Here is a diagram showing the boxplot anatomy:Ī boxplot can summarize the distribution of a numeric variable for several groups.
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