ggbrick: histogram like dot plot based on ggplot2

Install

devtools::install_github("abikoushi/ggbrick")

Example

library(ggplot2)library(ggbrick)
ggplot(data = iris)+
  geom_brick(aes(y = Sepal.Length, x=Species), binwidth =0.1)

The argument binwidth or bins make change bins width.

ggplot(data = iris)+
  geom_brick(aes(y = Sepal.Length, x=Species), binwidth =0.5)

fill.

ggplot(data = iris)+
  geom_brick(aes(y = Sepal.Length, x=Species), binwidth =0.5, fill ="black")

You can change the color and stack the rectangles.

ggplot(data = mpg,aes(y = cty, x=factor(year), fill=factor(cyl)))+
  geom_brick(binwidth =1)

If stackgroups = FALSE:

ggplot(data = mpg,aes(y = cty, x=factor(year), fill=factor(cyl)))+
  geom_brick(binwidth =1, stackgroups =FALSE, alpha =0.5)

If stackdir = "centerwhole":

ggplot(data = mpg,aes(y = cty, x=factor(year), fill=factor(cyl)))+
  geom_brick(binwidth =1, stackgroups =FALSE, alpha =0.5,
            stackdir ="centerwhole", position = position_dodge(0.5))

When you want to turn sideways, use coord_flip:

ggplot(data = diamonds, aes(x = color, y=carat, colour=cut))+
  geom_brick(binwidth=0.2)+
  coord_flip()

You can add stat_summary:

ggplot(data = iris,aes(y = Sepal.Length, x=Species))+
  geom_brick(binwidth =0.1, stackdir ="centerwhole")+
  stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median,
               geom ="crossbar")

You can use facet:

iris2 <- tidyr::gather(iris,key,value,-Species)
ggplot(data = iris2,aes(y = value, x=Species))+
  geom_brick(binwidth =0.3,fill="black")+
  facet_wrap(~key,scales ="free_y")

Anscombe's quartet

I'd like to plot the data set available from the following page in several ways.

The Datasaurus Dozen - Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing | Autodesk Research

geom_jitter:

It is a visualization which is faithful to the data. However, when the data points increases, it is difficult to show the frequency.

geom_boxplot:

The boxplot only shows summarized statistics. In this data set, you can not see any difference in the distributions.

geom_brick:

I think that the distribution can be understood.

geom_violin:

pretty good, but the violinplots sometimes make over smoothing.

R code is here:

library(tidyverse)library(ggbrick)
dat <- read_tsv("~/Downloads/SameStatsDataAndImages/datasets/BoxPlots.tsv") 
dat_t <- gather(dat,key,value,-X1)

ggplot(dat_t,aes(x=key,y=value))+
  geom_jitter()

ggplot(dat_t,aes(x=key,y=value))+
  geom_boxplot()

ggplot(dat_t,aes(x=key,y=value))+
  geom_brick()

ggplot(dat_t,aes(x=key,y=value))+
  geom_violin()