The popularity of dplyr has led to the use of >%> and an increase in the awareness of functional programming in R. This is great, as side-effectful statements has long been one of my greatest causes of error. Errors are scary, and it’s halloween, so here’s a quick example of >%> for dplyr and ggplot, using M&M data from the University of Puget Sound’s Data Hoard. Although the dev version includes some new operators, this post covers only the standard %>% operator, which can be summarized as ` x %>% f == f(x)`
First, we load the data and produce a quick summary table by type of m&m. The >%> operator isn’t necessary here but helps keep the code structured and readable.
library(reshape2) # http://bit.ly/1x2OEv5
library(magrittr) # ceci n'est pas un pipe
suppressPackageStartupMessages(library(dplyr))
library(ggplot2)
mms <- read.csv('http://stat.pugetsound.edu/hoard/datasets/mms.csv')
mms %>% summary
## type color diameter mass
## peanut :153 blue :142 Min. :11.2 Min. :0.72
## peanut butter:201 brown :151 1st Qu.:13.2 1st Qu.:0.86
## plain :462 green :153 Median :13.6 Median :0.92
## orange:128 Mean :14.2 Mean :1.42
## red : 99 3rd Qu.:15.3 3rd Qu.:1.93
## yellow:143 Max. :17.9 Max. :3.62
mms %>%
group_by(type) %>%
summarise(m_diam = mean(diameter), m_mass = mean(mass))
## Source: local data frame [3 x 3]
##
## type m_diam m_mass
## 1 peanut 14.77 2.5977
## 2 peanut butter 15.77 1.7981
## 3 plain 13.28 0.8648
mms %>% melt %>% head # equivalent to head(melt(mms))
## Using type, color as id variables
## type color variable value
## 1 peanut butter blue diameter 16.20
## 2 peanut butter brown diameter 16.50
## 3 peanut butter orange diameter 15.48
## 4 peanut butter brown diameter 16.32
## 5 peanut butter yellow diameter 15.59
## 6 peanut butter brown diameter 17.43
We also might want to investigate size variation by both type and color:
mms %>%
group_by(type,color) %>%
summarise(d=mean(diameter),m=mean(mass),count = n())
## Source: local data frame [18 x 5]
## Groups: type
##
## type color d m count
## 1 peanut blue 14.78 2.5759 27
## 2 peanut brown 14.74 2.5713 23
## 3 peanut green 15.05 2.6807 27
## 4 peanut orange 14.59 2.5703 29
## 5 peanut red 15.02 2.6265 20
## 6 peanut yellow 14.53 2.5670 27
## 7 peanut butter blue 15.87 1.8525 28
## 8 peanut butter brown 15.68 1.8031 42
## 9 peanut butter green 16.04 1.9203 34
## 10 peanut butter orange 15.66 1.7300 24
## 11 peanut butter red 15.77 1.7405 21
## 12 peanut butter yellow 15.66 1.7396 52
## 13 plain blue 13.22 0.8602 87
## 14 plain brown 13.30 0.8706 86
## 15 plain green 13.26 0.8699 92
## 16 plain orange 13.28 0.8648 75
## 17 plain red 13.28 0.8545 58
## 18 plain yellow 13.37 0.8655 64
There seems to be more variation in the peanut m&ms. We make a spooky graph to investigate.
Note that >%> works fine inside other functions, and helps keep the code uncluttered from parenthesis.
ggplot(mms,
aes(x=diameter,
y=mass,
size=type,
colour=color)) +
scale_color_manual(values =
mms$color %>%
unique %>%
as.character %>% sort) +
scale_size_manual(values = c(6,4,2)) +
geom_point(alpha=2/3) +
stat_smooth(method=loess,fill="orange",alpha=.5,colour="orange",size=1,aes(group=type)) +
ggtitle("Mass vs. Diameter of M&Ms") +
theme(legend.key = element_rect(colour = "black")) +
theme(panel.background = element_rect(fill = "black"))
References: