dplyr In a Nutshell
This is a minimal guide, mostly for myself, to remind me of the most import dplyr functions and how they relate to base R functions that I'm familiar with. Also check out tidyr In a Nutshell.
8 dplyr Functions to Rule the World
Speedy Table
tbl_df
The 5 Guys + 1
filterselectmutategroup_bysummarisearrange
Chaining (pronounced "then")
%>%
Relating the Functions
Speedy Table
tbl_df works similar to data.table in that it prints sensibly.
Relating the 5 Guys + 1 to base R
List of dplyr functions and the base functions they're related to:
| Base Function | dplyr Function(s) | Special Powers |
|---|---|---|
| subset | filter & select | filter rows & select columns |
| transform | mutate | operate with columns not yet created |
| split | group_by | splits without cutting |
| lapply + do.call | summarise | apply and bind in a single bound |
| order + with | arrange | "I only have to specify dataframe once?" |
Chaining
%>%... Do you know ggplot2's +? Same idea.
Basically previous input in chain supplied as argument 1 to function on right side.
Demos
Speedy Table
library(dplyr)
mtcars2 <- tbl_df(mtcars)The 5 Guys
filter(mtcars2[1:10, ], cyl == 8)Source: local data frame [2 x 11]
mpg cyl disp hp drat wt qsec vs am gear carb
1 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2
2 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4
select(mtcars2[1:10, ], mpg, cyl, hp:vs)Source: local data frame [10 x 7]
mpg cyl hp drat wt qsec vs
Mazda RX4 21.0 6 110 3.90 2.620 16.46 0
Mazda RX4 Wag 21.0 6 110 3.90 2.875 17.02 0
Datsun 710 22.8 4 93 3.85 2.320 18.61 1
Hornet 4 Drive 21.4 6 110 3.08 3.215 19.44 1
Hornet Sportabout 18.7 8 175 3.15 3.440 17.02 0
Valiant 18.1 6 105 2.76 3.460 20.22 1
Duster 360 14.3 8 245 3.21 3.570 15.84 0
Merc 240D 24.4 4 62 3.69 3.190 20.00 1
Merc 230 22.8 4 95 3.92 3.150 22.90 1
Merc 280 19.2 6 123 3.92 3.440 18.30 1
arrange(mtcars2[1:10, ], cyl, disp)Source: local data frame [10 x 11]
mpg cyl disp hp drat wt qsec vs am gear carb
1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
3 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
5 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
6 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
7 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
8 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
9 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
10 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
mutate(mtcars2[1:10, ], displ_l = disp / 61.0237, displ_l_add1 = displ_l + 1)Source: local data frame [10 x 13]
mpg cyl disp hp drat wt qsec vs am gear carb displ_l
1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2.622
2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2.622
3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 1.770
4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 4.228
5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 5.899
6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 3.687
7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 5.899
8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 2.404
9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 2.307
10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 2.746
Variables not shown: displ_l_add1 (dbl)
summarise(mtcars, mean(disp)) mean(disp)
1 230.7
Chaining
mtcars2 %>%
group_by(cyl) %>%
summarise(md=mean(disp), mh=mean(hp), mdh=mean(disp + hp))Source: local data frame [3 x 4]
cyl md mh mdh
1 4 105.1 82.64 187.8
2 6 183.3 122.29 305.6
3 8 353.1 209.21 562.3
mtcars2 %>%
group_by(cyl, gear) %>%
summarise(md=mean(disp), mh=mean(hp), mdh=mean(disp + hp)) %>%
arrange(-cyl, -gear)Source: local data frame [8 x 5]
Groups: cyl
cyl gear md mh mdh
1 8 5 326.0 299.5 625.5
2 8 3 357.6 194.2 551.8
3 6 5 145.0 175.0 320.0
4 6 4 163.8 116.5 280.3
5 6 3 241.5 107.5 349.0
6 4 5 107.7 102.0 209.7
7 4 4 102.6 76.0 178.6
8 4 3 120.1 97.0 217.1
## Use `%>%` with base functions too!!!
mtcars2 %>%
group_by(cyl, gear) %>%
summarise(md=mean(disp), mh=mean(hp), mdh=mean(disp + hp)) %>%
arrange(-cyl, -gear) %>%
head()Source: local data frame [6 x 5]
Groups: cyl
cyl gear md mh mdh
1 8 5 326.0 299.5 625.5
2 8 3 357.6 194.2 551.8
3 6 5 145.0 175.0 320.0
4 6 4 163.8 116.5 280.3
5 6 3 241.5 107.5 349.0
6 4 5 107.7 102.0 209.7
mtcars2 %>%
group_by(cyl) %>%
summarise(max(disp), hp[1])Source: local data frame [3 x 3]
cyl max(disp) hp[1]
1 4 146.7 93
2 6 258.0 110
3 8 472.0 175
mtcars2 %>%
group_by(cyl) %>%
summarise(n = n()) Source: local data frame [3 x 2]
cyl n
1 4 11
2 6 7
3 8 14
table(mtcars$cyl)
4 6 8
11 7 14