| title | subtitle | author | job | logo | framework | highlighter | hitheme | url | widgets | mode | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Introduction to the R Language |
Loop Functions - tapply |
Roger Peng, Associate Professor |
Johns Hopkins Bloomberg School of Public Health |
bloomberg_shield.png |
io2012 |
highlight.js |
tomorrow |
|
|
selfcontained |
tapply is used to apply a function over subsets of a vector. I don’t know why it’s called tapply.
> str(tapply)
function (X, INDEX, FUN = NULL, ..., simplify = TRUE)Xis a vectorINDEXis a factor or a list of factors (or else they are coerced to factors)FUNis a function to be applied- ... contains other arguments to be passed
FUN simplify, should we simplify the result?
Take group means.
> x <- c(rnorm(10), runif(10), rnorm(10, 1))
> f <- gl(3, 10)
> f
[1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3
[24] 3 3 3 3 3 3 3
Levels: 1 2 3
> tapply(x, f, mean)
1 2 3
0.1144464 0.5163468 1.2463678Take group means without simplification.
> tapply(x, f, mean, simplify = FALSE)
$‘1‘
[1] 0.1144464
$‘2‘
[1] 0.5163468
$‘3‘
[1] 1.246368Find group ranges.
> tapply(x, f, range)
$‘1‘
[1] -1.097309 2.694970
$‘2‘
[1] 0.09479023 0.79107293
$‘3‘
[1] 0.4717443 2.5887025split takes a vector or other objects and splits it into groups determined by a factor or list of factors.
> str(split)
function (x, f, drop = FALSE, ...)xis a vector (or list) or data framefis a factor (or coerced to one) or a list of factorsdropindicates whether empty factors levels should be dropped
> x <- c(rnorm(10), runif(10), rnorm(10, 1))
> f <- gl(3, 10)
> split(x, f)
$‘1‘
[1] -0.8493038 -0.5699717 -0.8385255 -0.8842019
[5] 0.2849881 0.9383361 -1.0973089 2.6949703
[9] 1.5976789 -0.1321970
$‘2‘
[1] 0.09479023 0.79107293 0.45857419 0.74849293
[5] 0.34936491 0.35842084 0.78541705 0.57732081
[9] 0.46817559 0.53183823
$‘3‘
[1] 0.6795651 0.9293171 1.0318103 0.4717443
[5] 2.5887025 1.5975774 1.3246333 1.4372701A common idiom is split followed by an lapply.
> lapply(split(x, f), mean)
$‘1‘
[1] 0.1144464
$‘2‘
[1] 0.5163468
$‘3‘
[1] 1.246368> library(datasets)
> head(airquality)
Ozone Solar.R Wind Temp Month Day
1 41 190 7.4 67 5 1
2 36 118 8.0 72 5 2
3 12 149 12.6 74 5 3
4 18 313 11.5 62 5 4
5 NA NA 14.3 56 5 5
6 28 NA 14.9 66 5 6> s <- split(airquality, airquality$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> 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
> 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> 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
10 Levels: 1.1 2.1 1.2 2.2 1.3 2.3 1.4 ... 2.5Interactions can create empty levels.
> str(split(x, list(f1, f2)))
List of 10
$ 1.1: num [1:2] -0.378 0.445
$ 2.1: num(0)
$ 1.2: num [1:2] 1.4066 0.0166
$ 2.2: num(0)
$ 1.3: num -0.355
$ 2.3: num 0.315
$ 1.4: num(0)
$ 2.4: num [1:2] -0.907 0.723
$ 1.5: num(0)
$ 2.5: num [1:2] 0.732 0.360Empty levels can be dropped.
> str(split(x, list(f1, f2), drop = TRUE))
List of 6
$ 1.1: num [1:2] -0.378 0.445
$ 1.2: num [1:2] 1.4066 0.0166
$ 1.3: num -0.355
$ 2.3: num 0.315
$ 2.4: num [1:2] -0.907 0.723
$ 2.5: num [1:2] 0.732 0.360