R: Refactoring to dplyr

I’ve been looking back over some of the early code I wrote using R before I knew about the dplyr library and thought it’d be an interesting exercise to refactor some of the snippets.

We’ll use the following data frame for each of the examples:

library(dplyr)
 
data = data.frame(
  letter = sample(LETTERS, 50000, replace = TRUE),
  number = sample (1:10, 50000, replace = TRUE)
  )

Take {n} rows

> data[1:5,]
  letter number
1      R      7
2      Q      3
3      B      8
4      R      3
5      U      2

becomes:

> data %>% head(5)
  letter number
1      R      7
2      Q      3
3      B      8
4      R      3
5      U      2

Order by numeric value descending

> data[order(-(data$number)),][1:5,]
   letter number
14      H     10
17      G     10
63      L     10
66      W     10
73      R     10

becomes:

> data %>% arrange(desc(number)) %>% head(5)
  letter number
1      H     10
2      G     10
3      L     10
4      W     10
5      R     10

Count number of items

> length(data[,1])
[1] 50000

becomes:

> data %>% count()
Source: local data frame [1 x 1]
 
      n
1 50000

Filter by column value

> length(subset(data, number == 1)[, 1])
[1] 4928

becomes:

> data %>% filter(number == 1) %>% count()
Source: local data frame [1 x 1]
 
     n
1 4928

Group by variable and count

> aggregate(data, by= list(data$number), function(x) length(x))
   Group.1 letter number
1        1   4928   4928
2        2   5045   5045
3        3   5064   5064
4        4   4823   4823
5        5   5032   5032
6        6   5163   5163
7        7   4945   4945
8        8   5077   5077
9        9   5025   5025
10      10   4898   4898

becomes:

> data %>% count(number)
Source: local data frame [10 x 2]
 
   number    n
1       1 4928
2       2 5045
3       3 5064
4       4 4823
5       5 5032
6       6 5163
7       7 4945
8       8 5077
9       9 5025
10     10 4898

Select a range of rows

> data[4:5,]
  letter number
4      R      3
5      U      2

becomes:

> data %>% slice(4:5)
  letter number
1      R      3
2      U      2

There’s certainly more code in some of the dplyr examples but I find it easier to remember how the dplyr code works when I come back to it and hence tend to favour that approach.