Overview of the Pipe Operator %>%

While you aren't required to ever use the pipe operator with dplyr or tidyr, it can be very useful when trying to perform multiple operations/functions on a data set. The pipe operator will allow you to avoid either a long nested operation or doing a bunch of assignmets.

In its most basic form, this is what an example of using %>% with dplyr looks like:

The Data

df <- mtcars
library(dplyr)

Using Nesting

library(dplyr)
arrange(sample_n(filter(df,mpg>20),size=5),desc(mpg))

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
1	27.3	4	79	66	4.08	1.935	18.9	1	1	4	1
2	26	4	120.3	91	4.43	2.14	16.7	0	1	5	2
3	24.4	4	146.7	62	3.69	3.19	20	1	0	4	2
4	22.8	4	140.8	95	3.92	3.15	22.9	1	0	4	2
5	21	6	160	110	3.9	2.875	17.02	0	1	4	4

Using Multiple Assignments

library(dplyr)
a <- filter(df,mpg > 20)
b <- sample_n(a,size = 5)
c <- arrange(b,desc(mpg))
c

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
1	30.4	4	95.1	113	3.77	1.513	16.9	1	1	5	2
2	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2
3	24.4	4	146.7	62	3.69	3.19	20	1	0	4	2
4	21.5	4	120.1	97	3.7	2.465	20.01	1	0	3	1
5	21.4	4	121	109	4.11	2.78	18.6	1	1	4	2

Using the Pipe Operator

library(dplyr)
df %>% filter(mpg > 20) %>% sample_n(size = 5) %>% arrange(desc(mpg))

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
1	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2
2	26	4	120.3	91	4.43	2.14	16.7	0	1	5	2
3	24.4	4	146.7	62	3.69	3.19	20	1	0	4	2
4	22.8	4	108	93	3.85	2.32	18.61	1	1	4	1
5	21	6	160	110	3.9	2.62	16.46	0	1	4	4

Hope you find this helpful when performing various operations on your data!