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split()

split takes a vector or other objects and splits it into groups determined by a factor or a list of factors.

> str(split)
function (x, f, drop = FALSE, …)

Arguments:

  • x is a vector (or list) or data frame
  • f is a factor (or coerced to one) or a list of factors
  • drop indicates whether empty factor levels should be dropped

Example:

> x <- c(rnorm(10), runif(10), rnorm(10, 1))
> f <- gl(3, 10)
> split(x, f)
$`1`
 [1] -0.5615980 -1.0227400  2.3743946 -1.1828731
 [5] -0.4588891  1.3406054 -1.2529312 -1.5232924
 [9] -0.9483891  0.5974399

$`2`
 [1] 0.28819663 0.24568527 0.08535357 0.64725198
 [5] 0.55680796 0.99364603 0.25011440 0.90709685
 [9] 0.20941979 0.63402239

$`3`
 [1]  0.6468613 -0.3783117 -0.8270978  0.3052268
 [5]  0.9999587  1.0533331  2.4046919  1.3490653
 [9]  0.6020979  1.3048118

A common idiom is split() followed by an lapply()

> lapply(split(x, f), mean)
$`1`
[1] -0.2638273

$`2`
[1] 0.4817595

$`3`
[1] 0.7460637

Splitting a Data Frame

> library(datasets)
> head(airquality)
  Ozone Solar.R Wind Temp Month Day
1    41     190  7.4   67     5   1
2    36     118  8.0   72     5   2
3    12     149 12.6   74     5   3
4    18     313 11.5   62     5   4
5    NA      NA 14.3   56     5   5
6    28      NA 14.9   66     5   6

> s <- split(airquality, airquality$Month)
> lapply(s, function(x) colMeans(x[, c("Ozone", "Solar.R", "Wind")]))
$`5`
   Ozone  Solar.R     Wind 
      NA       NA 11.62258 

$`6`
    Ozone   Solar.R      Wind 
       NA 190.16667  10.26667 

$`7`
     Ozone    Solar.R       Wind 
        NA 216.483871   8.941935 

$`8`
   Ozone  Solar.R     Wind 
      NA       NA 8.793548 

$`9`
   Ozone  Solar.R     Wind 
      NA 167.4333  10.1800

You can also use sapply() here.

> sapply(s, function(x) colMeans(x[, c("Ozone", "Solar.R", "Wind")]))
               5         6          7        8        9
Ozone         NA        NA         NA       NA       NA
Solar.R       NA 190.16667 216.483871       NA 167.4333
Wind    11.62258  10.26667   8.941935 8.793548  10.1800

Remove NAs

> sapply(s, function(x) colMeans(x[, c("Ozone", "Solar.R", "Wind")], na.rm  = TRUE))
                5         6          7          8         9
Ozone    23.61538  29.44444  59.115385  59.961538  31.44828
Solar.R 181.29630 190.16667 216.483871 171.857143 167.43333
Wind     11.62258  10.26667   8.941935   8.793548  10.18000

Splitting on More than One Level

> x <- rnorm(10)
> f1 <- gl(2, 5)
> f2 <- gl(5, 2)
> f1
 [1] 1 1 1 1 1 2 2 2 2 2
Levels: 1 2
> f2
 [1] 1 1 2 2 3 3 4 4 5 5
Levels: 1 2 3 4 5
> interaction(f1, f2)
 [1] 1.1 1.1 1.2 1.2 1.3 2.3 2.4 2.4 2.5 2.5
Levels: 1.1 2.1 1.2 2.2 1.3 2.3 1.4 2.4 1.5 2.5

Interactions can create empty levels.

> str(split(x, list(f1, f2)))
List of 10
 $ 1.1: num [1:2] 1.829 -0.808
 $ 2.1: num(0) 
 $ 1.2: num [1:2] -1.329 0.544
 $ 2.2: num(0) 
 $ 1.3: num 0.477
 $ 2.3: num -0.108
 $ 1.4: num(0) 
 $ 2.4: num [1:2] -0.44 0.394
 $ 1.5: num(0) 
 $ 2.5: num [1:2] 1.333 -0.414

Use drop = TRUE to get rid of empty levels.

> str(split(x, list(f1, f2), drop = TRUE))
List of 6
 $ 1.1: num [1:2] 1.829 -0.808
 $ 1.2: num [1:2] -1.329 0.544
 $ 1.3: num 0.477
 $ 2.3: num -0.108
 $ 2.4: num [1:2] -0.44 0.394
 $ 2.5: num [1:2] 1.333 -0.414