Frequency tables
Install required packages
wants <- c("DescTools", "dplyr")
has <- wants %in% rownames(installed.packages())
if(any(!has)) install.packages(wants[!has])Category frequencies for one variable
Absolute frequencies
[1] "C" "C" "B" "B" "C" "E" "D" "A" "B" "C" "E" "C"myLetters
A B C D E
1 3 5 1 2 [1] "A" "B" "C" "D" "E"B
3 
(Cumulative) relative frequencies
myLetters
A B C D E
0.08333333 0.25000000 0.41666667 0.08333333 0.16666667 A B C D E
0.08333333 0.33333333 0.75000000 0.83333333 1.00000000 Counting non-existent categories
[1] C C B B C E D A B C E C
Levels: A B C D E QletFac
A B C D E Q
1 3 5 1 2 0 Counting runs
[1] "f" "m" "m" "m" "f" "f" "m" "m" "m" "m" "f" "m" "m"Run Length Encoding
lengths: int [1:6] 1 3 2 4 1 2
values : chr [1:6] "f" "m" "f" "m" "f" "m"[1] 6 [1] "f" "m" "m" "m" "f" "f" "m" "m" "m" "m" "f" "m" "m"Contingency tables for two or more variables
Absolute frequencies using table()
[1] f f m f f f f m m f
Levels: f m [1] office home office home office office home home home home
Levels: home office work
sex home office
f 4 3
m 2 1Number of cases in table: 10
Number of factors: 2
Test for independence of all factors:
Chisq = 0.07937, df = 1, p-value = 0.7782
Chi-squared approximation may be incorrect
Using xtabs()
sex work counts
1 f office 3
2 f home 1
3 m office 4
4 f home 0
5 f office 0
6 f office 1
7 f home 2
8 m home 3
9 m home 4
10 f home 4 work
sex home office
f 4 3
m 2 1 work
sex home office
f 7 4
m 7 4Marginal sums and means
f m
7 3 home office
3 2 Margins computed over dimensions
in the following order:
1: sex
2: work work
sex home office mean
f 4.0 3.0 3.5
m 2.0 1.0 1.5
mean 3.0 2.0 2.5Relative frequencies
work
sex home office
f 0.4 0.3
m 0.2 0.1Conditional relative frequencies
work
sex home office
f 0.5714286 0.4285714
m 0.6666667 0.3333333 work
sex home office
f 0.6666667 0.7500000
m 0.3333333 0.2500000Flat contingency tables for more than two variables
[1] A B B A B A A B B A
Levels: A B sex f m
group A B A B
work
home 3 1 0 2
office 2 1 0 1Recovering the original data from contingency tables
Individual-level data frame
sex work
1 f home
2 f home
3 f home
4 f home
5 m home
6 m home
7 f office
8 f office
9 f office
10 m officeGroup-level data frame
sex work Freq
1 f home 4
2 m home 2
3 f office 3
4 m office 1Percentile rank
[1] 0.90 0.88 0.82 0.69 0.55 -0.06 -0.31 -0.38 -0.69 -0.21 [1] 100 90 80 70 60 50 30 20 10 40Using base R
[1] 1.0 0.9 0.8 0.7 0.6 0.5 0.3 0.2 0.1 0.4[1] 50 [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 [1] -0.69 -0.38 -0.31 -0.21 -0.06 0.55 0.69 0.82 0.88 0.90
Using package dplyr
Data set
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)Absolute frequencies
count() gives frequencies using new variable n (by default).
sex group n
1 f CG 1
2 f WL 1
3 f T 3
4 m CG 3
5 m WL 3
6 m T 1By default, count() drops groups / combinations of groups with no entries. Use option .drop=FALSE to include entries with frequency 0.
sex group n
1 f CG 1
2 f WL 1
3 f T 3
4 m CG 3
5 m WL 3
6 m T 1Add corresponding count to existing data frame.
id sex group age IQ rating n
1 1 m WL 29 106 1 3
2 2 m T 31 112 3 1
3 3 f T 20 98 2 3
4 4 f T 31 97 4 3
5 5 m CG 24 86 2 3
6 6 f WL 20 89 2 1
7 7 m CG 32 79 3 3
8 8 m CG 22 109 4 3
9 9 f CG 25 100 1 1
10 10 m WL 27 90 2 3
11 11 m WL 35 96 2 3
12 12 f T 27 88 4 3Relative frequencies
sex group n freq_rel
1 f CG 1 0.08333333
2 f WL 1 0.08333333
3 f T 3 0.25000000
4 m CG 3 0.25000000
5 m WL 3 0.25000000
6 m T 1 0.08333333 id sex group age IQ rating n freq_rel
1 1 m WL 29 106 1 3 0.25000000
2 2 m T 31 112 3 1 0.08333333
3 3 f T 20 98 2 3 0.25000000
4 4 f T 31 97 4 3 0.25000000
5 5 m CG 24 86 2 3 0.25000000
6 6 f WL 20 89 2 1 0.08333333
7 7 m CG 32 79 3 3 0.25000000
8 8 m CG 22 109 4 3 0.25000000
9 9 f CG 25 100 1 1 0.08333333
10 10 m WL 27 90 2 3 0.25000000
11 11 m WL 35 96 2 3 0.25000000
12 12 f T 27 88 4 3 0.25000000Conditional relative frequencies
myDf1 %>%
count(sex, group, name="n_sex_group") %>%
group_by(sex) %>%
mutate(n_sex=sum(n_sex_group),
freq_cond_rel=n_sex_group / n_sex) %>%
ungroup()# A tibble: 6 x 5
sex group n_sex_group n_sex freq_cond_rel
<fct> <fct> <int> <int> <dbl>
1 f CG 1 5 0.2
2 f WL 1 5 0.2
3 f T 3 5 0.6
4 m CG 3 7 0.429
5 m WL 3 7 0.429
6 m T 1 7 0.143myDf1 %>%
add_count(sex, name="n_sex") %>%
add_count(sex, group, name="n_sex_group") %>%
mutate(freq_cond_rel=n_sex_group / n_sex) %>%
select(id, sex, group, n_sex, n_sex_group, freq_cond_rel) id sex group n_sex n_sex_group freq_cond_rel
1 1 m WL 7 3 0.4285714
2 2 m T 7 1 0.1428571
3 3 f T 5 3 0.6000000
4 4 f T 5 3 0.6000000
5 5 m CG 7 3 0.4285714
6 6 f WL 5 1 0.2000000
7 7 m CG 7 3 0.4285714
8 8 m CG 7 3 0.4285714
9 9 f CG 5 1 0.2000000
10 10 m WL 7 3 0.4285714
11 11 m WL 7 3 0.4285714
12 12 f T 5 3 0.6000000Percent rank
id sex group age IQ rating rating_pr
1 1 m WL 29 106 1 0.00000
2 2 m T 31 112 3 63.63636
3 3 f T 20 98 2 18.18182
4 4 f T 31 97 4 81.81818
5 5 m CG 24 86 2 18.18182
6 6 f WL 20 89 2 18.18182
7 7 m CG 32 79 3 63.63636
8 8 m CG 22 109 4 81.81818
9 9 f CG 25 100 1 0.00000
10 10 m WL 27 90 2 18.18182
11 11 m WL 35 96 2 18.18182
12 12 f T 27 88 4 81.81818Detach (automatically) loaded packages (if possible)
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