Create and analyze data frames
TODO
- link to dfTransform
Install required packages
wants <- c("car")
has <- wants %in% rownames(installed.packages())
if(any(!has)) install.packages(wants[!has])Create and analyze data frames
Create data frames
From existing variables
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 2Analyze the structure of data frames
[1] 12 6[1] 12[1] 6 id sex group age IQ rating
Min. : 1.00 f:6 CG:4 Min. :21.00 Min. : 80.00 Min. :0.000
1st Qu.: 3.75 m:6 WL:4 1st Qu.:24.75 1st Qu.: 87.00 1st Qu.:2.000
Median : 6.50 T :4 Median :27.00 Median : 95.00 Median :3.500
Mean : 6.50 Mean :27.33 Mean : 94.75 Mean :3.167
3rd Qu.: 9.25 3rd Qu.:29.50 3rd Qu.: 98.25 3rd Qu.:4.250
Max. :12.00 Max. :34.00 Max. :116.00 Max. :5.000 'data.frame': 12 obs. of 6 variables:
$ id : int 1 2 3 4 5 6 7 8 9 10 ...
$ sex : Factor w/ 2 levels "f","m": 1 1 1 2 1 2 2 2 1 1 ...
$ group : Factor w/ 3 levels "CG","WL","T": 3 3 1 2 3 2 1 1 3 2 ...
$ age : int 25 24 27 26 21 31 34 28 24 29 ...
$ IQ : num 95 84 99 116 98 83 88 110 95 80 ...
$ rating: num 5 5 3 5 4 4 0 3 1 2 ... 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 id sex group age IQ rating
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 2 id sex group age IQ rating
4 4 m WL 26 116 5
6 6 m WL 31 83 4
8 8 m CG 28 110 3
9 9 f T 24 95 1
11 11 m CG 32 91 4Data types in data frames
fac <- c("CG", "T1", "T2")
DV1 <- c(14, 22, 18)
DV2 <- c("red", "blue", "blue")
## stringsAsFactors=FALSE is the default since R 4.0.0
## before, it was TRUE
myDf2 <- data.frame(fac, DV1, DV2, stringsAsFactors=FALSE)
str(myDf2)'data.frame': 3 obs. of 3 variables:
$ fac: chr "CG" "T1" "T2"
$ DV1: num 14 22 18
$ DV2: chr "red" "blue" "blue"'data.frame': 3 obs. of 3 variables:
$ fac: Factor w/ 3 levels "CG","T1","T2": 1 2 3
$ DV1: num 14 22 18
$ DV2: chr "red" "blue" "blue"Names of cases and variables
[[1]]
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
[[2]]
[1] "id" "sex" "group" "age" "IQ" "rating"[1] "id" "sex" "group" "age" "IQ" "rating" [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"Select and change observations
Basic indexing method
[1] T T CG WL T WL CG CG T WL CG WL
Levels: CG WL T[1] T
Levels: CG WL T [1] 5 5 3 5 4 4 0 3 1 2 4 2[1] 26[1] 27[1] WL
Levels: CG WL T id sex group age IQ rating
2 2 f T 24 84 5 [1] 25 24 27 26 21 31 34 28 24 29 32 27 age
1 25
2 24
3 27
4 26
5 21
6 31
7 34
8 28
9 24
10 29
11 32
12 27See dfTransform for selecting subsets of data more conveniently
Work with variables from a data frame
CG WL T
101.75 96.75 93.00 group
sex CG WL T
f 1 1 4
m 3 3 0Detach (automatically) loaded packages (if possible)
Get the article source from GitHub
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