Recreating APA Manual Table 7.12 in R with apa7

Making tables in APA style (Part 12 of 24)

In this 24-part series, each of the tables in Chapter 7 of the Publication Manual of the American Psychological Association (7th Edition) is recreated with apa7, flextable, easystats, and tidyverse functions.

NoteHighlights

• Selective indentation with flextable::padding
• Aligned significance stars with add_star_column

library(apa7)
library(flextable)
library(ftExtra)
library(tidyverse)
set_flextable_defaults(theme_fun = theme_apa, 
                       font.family = "Times New Roman")

Figure 1

Screenshot of the APA Manual’s Table 7.12

To make the process more realistic, I simulated data using the means and standard deviations from Figure 1, calculated descriptive statistics, conducted one-way ANOVAs, and extracted significance tests and effect sizes.

Note that the F column’s stars were added using the add_star_column function, which looks for a column called p, and converts p-values to stars.

set.seed(123)

# Make data
d_descriptives <- "
Measure                 Urban_M Urban_SD        Rural_M     Rural_SD
Self-esteem             2.91        0.49        3.35        0.35
Social support          4.22        1.50        5.56        1.20
Threat                  2.78        0.87        1.99        0.88
Challenge               2.48        0.88        2.83        1.20
Self-efficacy           2.65        0.79        3.53        0.92" |>
  readr::read_tsv() |>
  suppressMessages() |> 
  pivot_longer(-Measure) |> 
  separate(name, c("type", "name")) |> 
  pivot_wider() |> 
  mutate(data = map2(M, SD, \(m,sd) rnorm(148, m, sd))) |> 
  select(Measure, type, data) |> 
  unnest(data) |> 
  rename(y = data) |> 
  nest(.by = Measure)

# Analysis
d_analysis <- d_descriptives |> 
  mutate(
    msd = map(data, \(d) {
      d |> 
        summarise(M = mean(y),
                  SD = sd(y), 
                  .by = type) 
    }),
    fit = map(data, \(d) aov(y ~ type, data = d)),
    mp = map(fit, \(fit) {
      parameters::model_parameters(fit) |>
        as_tibble() |>
        slice(1)
      }
      ),
    `F` = map_dbl(mp, "F"),
    p = map_dbl(mp, "p"),
    eta2 = map_dbl(fit, \(fit) {
      eta2 <- suppressMessages(
        effectsize::effectsize(fit, type = "eta2" ))
      
      as_tibble(eta2)$Eta2
        
        }))

# Get residual degrees of freedom
df_residual <- d_analysis$fit[[1]]$df.residual

# Format data
d_formatted <- d_analysis |> 
  unnest(msd) |> 
  pivot_wider_name_first(names_from = type, 
                         values_from = c(M, SD)) |> 
  select(Measure, contains("_"), `F`, p, eta2)  |> 
  mutate(`F` = align_chr(`F`)) |> 
  add_star_column(`F`, merge = TRUE) |> 
  select(-p) |> 
  rename_with(\(x) ifelse(x == "F", 
                          paste0("*F*(1,", df_residual, ")"), 
                          x)) |> 
  dplyr::add_row(Measure = "Cognitive appraisals", .after = 2) 

# Make table
d_formatted |> 
  apa_flextable() |> 
  padding(i = 4:6, j = 1, padding.left = 20) |> 
  width(width = c(1.65, .8, .8, .05, .8, .8, .9, .7))

Table 1

Means, Standard Deviations, and One-Way Analyses of Variance in Psychological and Social Resources and Cognitive Appraisals

Measure	Urban			Rural		F(1,294)	η2
	M	SD		M	SD
Self-esteem	2.90	0.46		3.38	0.33	109.38***	.27
Social support	4.20	1.55		5.61	1.13	79.88***	.21
Cognitive appraisals
Threat	2.71	0.88		2.09	0.95	34.57***	.11
Challenge	2.51	0.93		2.82	1.13	6.61*	.02
Self-efficacy	2.66	0.79		3.50	0.89	72.78***	.20

* p < .05; ** p < .01 *** p < .001

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