Aggregate data frames - base R
Simulate data
set.seed(123)
N <- 12
sex <- factor(sample(c("f", "m"), N, replace=TRUE), levels=c("f", "m"))
group <- factor(sample(rep(c("CG", "WL", "T"), 4), N, replace=FALSE), levels=c("CG", "WL", "T"))
age <- sample(18:35, N, replace=TRUE)
IQ <- round(rnorm(N, mean=100, sd=15))
rating <- round(runif(N, min=0, max=6))
(myDf1 <- data.frame(id=1:N, sex, group, age, IQ, rating)) id sex group age IQ rating
1 1 f T 25 95 5
2 2 f T 24 84 5
3 3 f CG 27 99 3
4 4 m WL 26 116 5
5 5 f T 21 98 4
6 6 m WL 31 83 4
7 7 m CG 34 88 0
8 8 m CG 28 110 3
9 9 f T 24 95 1
10 10 f WL 29 80 2
11 11 m CG 32 91 4
12 12 m WL 27 98 2Apply the same function to different variables in a data frame
$age
[1] 27.33333
$IQ
[1] 94.75
$rating
[1] 3.166667 age IQ rating
[1,] 21 80 0
[2,] 34 116 5 id sex group age IQ rating
TRUE FALSE FALSE TRUE TRUE TRUE id age IQ rating
1 1 25 95 5
2 2 24 84 5
3 3 27 99 3
4 4 26 116 5
5 5 21 98 4
6 6 31 83 4Apply the same function to pairs of variables from two data frames
N <- 100
x1 <- rnorm(N, 10, 10)
y1 <- rnorm(N, 10, 10)
x2 <- x1 + rnorm(N, 5, 4)
y2 <- y1 + rnorm(N, 10, 4)
tDf1 <- data.frame(x1, y1)
tDf2 <- data.frame(x2, y2) x1
statistic -3.659217
parameter 198
p.value 0.0001621933
conf.int Numeric,2
estimate Numeric,2
null.value 0
stderr 1.336687
alternative "less"
method " Two Sample t-test"
data.name "dots[[1L]][[1L]] and dots[[2L]][[1L]]"
y1
statistic -5.918708
parameter 198
p.value 7.044864e-09
conf.int Numeric,2
estimate Numeric,2
null.value 0
stderr 1.598287
alternative "less"
method " Two Sample t-test"
data.name "dots[[1L]][[2L]] and dots[[2L]][[2L]]"Separate descriptive statistics for each group for many variables
split() -> sapply()
$CG
id sex group age IQ rating
3 3 f CG 27 99 3
7 7 m CG 34 88 0
8 8 m CG 28 110 3
11 11 m CG 32 91 4
$WL
id sex group age IQ rating
4 4 m WL 26 116 5
6 6 m WL 31 83 4
10 10 f WL 29 80 2
12 12 m WL 27 98 2
$T
id sex group age IQ rating
1 1 f T 25 95 5
2 2 f T 24 84 5
5 5 f T 21 98 4
9 9 f T 24 95 1 CG WL T
97.00 94.25 93.00 Here equivalent to
CG WL T
97.00 94.25 93.00 aggregate()
numDf <- subset(myDf1, select=c("age", "IQ", "rating"))
aggregate(numDf, list(myDf1$sex, myDf1$group), FUN=mean) Group.1 Group.2 age IQ rating
1 f CG 27.00000 99.00000 3.000000
2 m CG 31.33333 96.33333 2.333333
3 f WL 29.00000 80.00000 2.000000
4 m WL 28.00000 99.00000 3.666667
5 f T 23.50000 93.00000 3.750000 sex group age IQ rating
1 f CG 27.00000 99.00000 3.000000
2 m CG 31.33333 96.33333 2.333333
3 f WL 29.00000 80.00000 2.000000
4 m WL 28.00000 99.00000 3.666667
5 f T 23.50000 93.00000 3.750000 age IQ rating
1 27.33333 94.75 3.166667Calculate different measures by group
sex group Freq
1 f CG 1
2 m CG 3
3 f WL 1
4 m WL 3
5 f T 4
6 m T 0 sex group age
1 f CG 27.00000
2 m CG 31.33333
3 f WL 29.00000
4 m WL 28.00000
5 f T 23.50000 sex group age
1 f CG NA
2 m CG 3.055050
3 f WL NA
4 m WL 2.645751
5 f T 1.732051Merge all results into one data frame
sex group age.M age.SD
1 f CG 27.00000 NA
2 f T 23.50000 1.732051
3 f WL 29.00000 NA
4 m CG 31.33333 3.055050
5 m WL 28.00000 2.645751 sex group age.M age.SD Freq
1 f CG 27.00000 NA 1
2 f T 23.50000 1.732051 4
3 f WL 29.00000 NA 1
4 m CG 31.33333 3.055050 3
5 m WL 28.00000 2.645751 3by()
: f
: CG
age IQ rating
27 99 3
------------------------------------------------------------
: m
: CG
age IQ rating
31.333333 96.333333 2.333333
------------------------------------------------------------
: f
: WL
age IQ rating
29 80 2
------------------------------------------------------------
: m
: WL
age IQ rating
28.000000 99.000000 3.666667
------------------------------------------------------------
: f
: T
age IQ rating
23.50 93.00 3.75
------------------------------------------------------------
: m
: T
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