library(apa7)
library(dplyr)
library(flextable)
d <- mtcars |>
select(Gears = gear, Transmission = am) |>
mutate(Transmission = factor(Transmission, labels = c("Automatic", "Manual")))
The default output of the apa_chisq function is a
contingency table with a chi-square test of independence in the table
note. The output is a flextable, which can be modified with flextable
commands.
apa_chisq(d)
#> Registered S3 method overwritten by 'ftExtra':
#> method from
#> as_flextable.data.frame flextableGears |
||||||||
|---|---|---|---|---|---|---|---|---|
Transmission |
3 |
4 |
5 |
|||||
n |
% |
n |
% |
n |
% |
|||
Automatic |
15 |
100.0% |
4 |
33.3% |
0 |
0.0% |
||
Manual |
0 |
0.0% |
8 |
66.7% |
5 |
100.0% |
||
Note. χ2 (2) = 20.94, p < .001, Adj. Cramer’s V = .78 | ||||||||
The table can be styled in many ways:
apa_chisq(
d,
font_size = 16,
line_spacing = 3,
text_color = "darkred",
border_width = 2,
border_color = "gray",
family = "Arial"
)Gears |
||||||||
|---|---|---|---|---|---|---|---|---|
Transmission |
3 |
4 |
5 |
|||||
n |
% |
n |
% |
n |
% |
|||
Automatic |
15 |
100.0% |
4 |
33.3% |
0 |
0.0% |
||
Manual |
0 |
0.0% |
8 |
66.7% |
5 |
100.0% |
||
Note. χ2 (2) = 20.94, p < .001, Adj. Cramer’s V = .78 | ||||||||
# Automatic chi square note
apa_chisq(d)Gears |
||||||||
|---|---|---|---|---|---|---|---|---|
Transmission |
3 |
4 |
5 |
|||||
n |
% |
n |
% |
n |
% |
|||
Automatic |
15 |
100.0% |
4 |
33.3% |
0 |
0.0% |
||
Manual |
0 |
0.0% |
8 |
66.7% |
5 |
100.0% |
||
Note. χ2 (2) = 20.94, p < .001, Adj. Cramer’s V = .78 | ||||||||
# No note
apa_chisq(d, note = NA)Gears |
||||||||
|---|---|---|---|---|---|---|---|---|
Transmission |
3 |
4 |
5 |
|||||
n |
% |
n |
% |
n |
% |
|||
Automatic |
15 |
100.0% |
4 |
33.3% |
0 |
0.0% |
||
Manual |
0 |
0.0% |
8 |
66.7% |
5 |
100.0% |
||
# Custom note
apa_chisq(d, note = "This is a *custom* note written in **markdown** $x > \\omega$.")Gears |
||||||||
|---|---|---|---|---|---|---|---|---|
Transmission |
3 |
4 |
5 |
|||||
n |
% |
n |
% |
n |
% |
|||
Automatic |
15 |
100.0% |
4 |
33.3% |
0 |
0.0% |
||
Manual |
0 |
0.0% |
8 |
66.7% |
5 |
100.0% |
||
Note. This is a custom note written in markdown x > ω. | ||||||||
What if I want something completely different?
There are a lot of options out there. The flextable package has the
proc_freq function, which has the ability to include row,
column, and total percentages in the table.
gear |
vs |
|||
|---|---|---|---|---|
0 |
1 |
Total |
||
3 |
Count |
12 (37.5%) |
3 (9.4%) |
15 (46.9%) |
Mar. pct (1) |
66.7% ; 80.0% |
21.4% ; 20.0% |
||
4 |
Count |
2 (6.2%) |
10 (31.2%) |
12 (37.5%) |
Mar. pct |
11.1% ; 16.7% |
71.4% ; 83.3% |
||
5 |
Count |
4 (12.5%) |
1 (3.1%) |
5 (15.6%) |
Mar. pct |
22.2% ; 80.0% |
7.1% ; 20.0% |
||
Total |
Count |
18 (56.2%) |
14 (43.8%) |
32 (100.0%) |
(1) Columns and rows percentages | ||||
These can be turned off selectively:
mtcars %>%
proc_freq(row = "gear",
col = "vs",
include.row_percent = FALSE,
include.column_percent = FALSE,
include.table_percent = FALSE) %>%
flextable::theme_apa() gear |
vs |
||
|---|---|---|---|
0 |
1 |
Total |
|
3 |
12 |
3 |
15 |
4 |
2 |
10 |
12 |
5 |
4 |
1 |
5 |
Total |
18 |
14 |
32 |
What if you want it to look a little different? Unfortunately, you
might have to do some heavy lifting yourself. The flextable package’s
tabulator function has considerable power in creating a
wide variety of descriptive tables. In general, the
tabulator function requires that you calculate the
statistics first, and then you specify where they should go.
Here I calculate the means, standard deviations, and sample sizes of a variable.
mytable <- warpbreaks %>%
summarise(
Mean = mean(breaks, na.rm = TRUE),
stdev = sd(breaks, na.rm = TRUE),
sample_size = sum(!is.na(breaks)),
.by = c(wool, tension)
) %>%
rename(Wool = wool) %>%
mutate(tension = factor(tension, labels = c("Low", "Medium", "High"))) %>%
flextable::tabulator(
x = .,
rows = "Wool",
columns = "tension",
M = as_paragraph(Mean),
SD = as_paragraph(stdev),
n = as_paragraph(sample_size)
) %>%
flextable::as_flextable()
my_column_names <- mytable$col_keys
header_row <- c("", rep("Tension", length(my_column_names) - 1)) %>%
as.list()
names(header_row) <- my_column_names
mytable %>%
flextable::theme_apa() %>%
flextable::italic(i = 2, part = "header") %>%
flextable::add_header(values = header_row) %>%
flextable::merge_h(i = 1, "header") %>%
flextable::border(
i = 1,
j = 1,
border.bottom = flextable::fp_border_default(
style = "none",
color = "white"),
part = "header")Tension |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Wool |
Low |
Medium |
High |
|||||||||
M |
SD |
n |
M |
SD |
n |
M |
SD |
n |
||||
A |
44.6 |
18.1 |
9 |
24.0 |
8.7 |
9 |
24.6 |
10.3 |
9 |
|||
B |
28.2 |
9.9 |
9 |
28.8 |
9.4 |
9 |
18.8 |
4.9 |
9 |
|||