7 VIsUaLiZe Distribution with ggplot2

box plot xkcd 1798

We will be using build-in data set iris.

iris %>% head(10) %>% knitr::kable()

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa
4.6	3.4	1.4	0.3	setosa
5.0	3.4	1.5	0.2	setosa
4.4	2.9	1.4	0.2	setosa
4.9	3.1	1.5	0.1	setosa

Box plots and its variants requires independent variable(x-axis) to be categorical data. Similar to most datasets, iris requires dataframe transformation to a “long form” such that we have two columns(key and value) for making the plots. we will be using dplyr::gather to perfom the task.

iris_long <- iris %>% gather(key = "flower_att", value = "measurement",
                Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)

iris_long %>% head(10) %>% knitr::kable()

Species	flower_att	measurement
setosa	Sepal.Length	5.1
setosa	Sepal.Length	4.9
setosa	Sepal.Length	4.7
setosa	Sepal.Length	4.6
setosa	Sepal.Length	5.0
setosa	Sepal.Length	5.4
setosa	Sepal.Length	4.6
setosa	Sepal.Length	5.0
setosa	Sepal.Length	4.4
setosa	Sepal.Length	4.9

7.0.1 Loading ggplot2

library(ggplot2)

7.1 Boxplot

ggplot(iris_long, aes(x=flower_att,y=measurement,fill=Species)) + 
         geom_boxplot()+
         theme_classic()

you can also do it in single line

iris %>% gather(key = "flower_att", value = "measurement",
                Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) %>% 
         ggplot(., aes(x=flower_att,y=measurement,fill=Species)) + 
         geom_boxplot()+
         theme_classic()

7.2 Violin plot

To align both Violin plot and box plot, it sometimes requires some fine-tuning to align both vionlin plot and boxplot

# position_dodge allows overlapping objects side-to-side
dodge <- position_dodge(width = 1)
ggplot(iris_long, aes(x=flower_att,y=measurement,fill=Species)) + 
         geom_violin(width=2.5,position =dodge)+
         geom_boxplot(width=0.1, alpha=0.2,outlier.colour=NA,position =dodge)+
         theme_classic()

## Warning: `position_dodge()` requires non-overlapping x intervals

7.2.1 Split Violin Plot

Split violin plots are a particularly useful in providing a direct comparison across categories to observe any overall trends. The function below can be used with ggplot to easily graph a split violin plot.

GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, 
                           draw_group = function(self, data, ..., draw_quantiles = NULL) {
                             data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x))
                             grp <- data[1, "group"]
                             newdata <- plyr::arrange(transform(data, x = if (grp %% 2 == 1) xminv else xmaxv), if (grp %% 2 == 1) y else -y)
                             newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1, ])
                             newdata[c(1, nrow(newdata) - 1, nrow(newdata)), "x"] <- round(newdata[1, "x"])
                             
                             if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) {
                               stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <=
                                                                         1))
                               quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles)
                               aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")), drop = FALSE]
                               aesthetics$alpha <- rep(1, nrow(quantiles))
                               both <- cbind(quantiles, aesthetics)
                               quantile_grob <- GeomPath$draw_panel(both, ...)
                               ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata, ...), quantile_grob))
                             }
                             else {
                               ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...))
                             }
                           })

geom_split_violin <- function(mapping = NULL, data = NULL, stat = "ydensity", position = "identity", ..., 
                              draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE, 
                              show.legend = NA, inherit.aes = TRUE) {
  layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin, 
        position = position, show.legend = show.legend, inherit.aes = inherit.aes, 
        params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...))
}

An example -

ggplot(iris_long, aes(x = flower_att, y= measurement ,fill=Species)) +
  geom_split_violin()

7.3 Dotplot

Sometimes, when the dataset is not normally distributed or having extreme outliners, the density function of violin plot produces strange shapes. In this case, a dotplot is a better alternative

ggplot(iris_long, aes(x=flower_att,y=measurement,fill=Species)) + 
         geom_dotplot(binaxis = "y", stackdir = "center",binwidth=1/10)+
         theme_classic()

To have an non-overlap dotplot

dodge <- position_dodge(width = 1)
ggplot(iris_long, aes(x=flower_att,y=measurement,fill=Species)) + 
         geom_dotplot(binaxis = "y", stackdir = "center",binwidth=1/10, position =dodge)+
         theme_classic()