Publication-ready APA tables: from R to Word in 2 min (2025)

Getting started

Load the rempsyc package:

library(rempsyc)

Note: If you haven’t installed this packageyet, you will need to install it via the following command:install.packages("rempsyc"). Furthermore, you may be askedto install the following packages if you haven’t installed them already(you may decide to install them all now to avoid interrupting yourworkflow if you wish to follow this tutorial from beginning to end):

pkgs <- c("flextable", "broom", "report", "effectsize")install_if_not_installed(pkgs)

The function can be used on almost any dataframe (though it does notallow duplicate column names). Here’s a simple example using themtcars dataset, which comes with base R(meaning you can try this example too without downloading anything).

nice_table( mtcars[1:3, ], title = c("Table 1", "Motor Trend Car Road Tests"), note = c( "The data was extracted from the 1974 Motor Trend US magazine.", "* p < .05, ** p < .01, *** p < .001" ))

Custom table

# Standardize variables to get standardized coefficientsmtcars.std <- lapply(mtcars, scale)# Create a simple linear modelmodel <- lm(mpg ~ cyl + wt * hp, mtcars.std)# Gather summary statisticsstats.table <- as.data.frame(summary(model)$coefficients)# Get the confidence interval (CI) of the regression coefficientCI <- confint(model)# Add a row to join the variables names and CI to the statsstats.table <- cbind(row.names(stats.table), stats.table, CI)# Rename the columns appropriatelynames(stats.table) <- c("Term", "B", "SE", "t", "p", "CI_lower", "CI_upper")

The dataframe looks like this (notice the large number ofdecimals):

stats.table

## Term B SE t p## (Intercept) (Intercept) -0.1835269 0.08532112 -2.1510135 4.058431e-02## cyl cyl -0.1082286 0.15071576 -0.7180977 4.788652e-01## wt wt -0.6230206 0.10927573 -5.7013627 4.663587e-06## hp hp -0.2874898 0.11955935 -2.4045781 2.331865e-02## wt:hp wt:hp 0.2875867 0.08895462 3.2329593 3.221753e-03## CI_lower CI_upper## (Intercept) -0.3585914 -0.008462403## cyl -0.4174718 0.201014550## wt -0.8472358 -0.398805290## hp -0.5328053 -0.042174267## wt:hp 0.1050669 0.470106453

Now we can apply our function!

nice_table(stats.table)

Next, we can save the flextable as an object, which we can laterfurther edit, view, or export to another software (e.g., MicrosoftWord).

my_table <- nice_table(stats.table)

Breaking news! APA 7th edition actually advises against using beta(β) to represent standardized coefficients, but instead suggests to usea lowercase italic b followed by an asterisk: b*.rempsyc will follow this APA recommendation with version0.1.6.1 and going forward.

Opening/Saving table to Word

One can easily open the table in Word for quick copy-pasting.

print(my_table, preview = "docx")

Alternatively, one can save the table to Word by specifying theobject name and desired path.

flextable::save_as_docx(my_table, path = "nice_tablehere.docx")

Simply change the path to where you would like to save it. If youcopy-paste your path name, remember to use “R” slashes (‘/’ rather than‘\’). Also remember to specify the file name and its .docxextension.

That’s it! Simple eh?

Statistical formatting

Notice that if you provide the CI_lower andCI_upper column names, it will automatically and properlyformat your confidence interval column and remove the lower and upperbound columns.

You can also see that it automatically formats the df, b, t, and pvalues to italic. It also correctly rounded each row, and formatted pvalues as < .001 and stripped the leading zeros (it will do the samefor correlations r, R2, sr2).

Note: in order for this to work automatically, your columns must benamed correctly. Currently the function will make the followingconversions: p, t, SE, SD, M, W, N, n, z, F, b, r, and df to italic; R2and sr2 to italic squared, dR to italic R subscript, np2 to italic ηsubscript-p squared, ges to italic η subscript-G squared, and B tob* (and to β in version <= 0.1.6). Not seeing a symbol thatshould be there? Contact me and I’ll add it!

Let’s test this by simply changing our dataframe names for theexercise.

test <- head(mtcars, 3)names(test) <- c("dR", "N", "M", "SD", "W", "np2", "ges", "z", "r", "R2", "sr2")test[, 10:11] <- test[, 10:11] / 10nice_table(test)

Highlighting

You can also add an argument to highlight significant results forbetter visual discrimination, if you wish so.

nice_table(stats.table, highlight = TRUE)

Pro tip: You can instead provide thehighlight argument with a numeric value to set whatevercritical p-value check you want, likehighlight = .10, for “marginally significant” results, orhighlight = .01 if you want to be more conservative.

nice_table(stats.table, highlight = .01)

To remove the more traditional significance asterisks, you can setthe stars argument to FALSE.

nice_table(stats.table, stars = FALSE)

broom table

library(broom)model <- lm(mpg ~ cyl + wt * hp, mtcars)(stats.table <- tidy(model, conf.int = TRUE))

## # A tibble: 5 × 7## term estimate std.error statistic p.value conf.low conf.high## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>## 1 (Intercept) 49.5 3.66 13.5 1.58e-13 42.0 57.0 ## 2 cyl -0.365 0.509 -0.718 4.79e- 1 -1.41 0.678 ## 3 wt -7.63 1.52 -5.01 2.93e- 5 -10.7 -4.51 ## 4 hp -0.108 0.0298 -3.64 1.14e- 3 -0.169 -0.0473## 5 wt:hp 0.0258 0.00799 3.23 3.22e- 3 0.00944 0.0422

nice_table(stats.table, broom = "lm")

report table

library(report)model <- lm(mpg ~ cyl + wt * hp, mtcars)(stats.table <- report_table(model))

## Parameter | Coefficient | 95% CI | t(27) | p | Std. Coef. | Std. Coef. 95% CI | Fit## ------------------------------------------------------------------------------------------------------## (Intercept) | 49.49 | [ 41.97, 57.01] | 13.51 | < .001 | -0.18 | [-0.36, -0.01] | ## cyl | -0.37 | [ -1.41, 0.68] | -0.72 | 0.479 | -0.11 | [-0.42, 0.20] | ## wt | -7.63 | [-10.75, -4.51] | -5.01 | < .001 | -0.62 | [-0.85, -0.40] | ## hp | -0.11 | [ -0.17, -0.05] | -3.64 | 0.001 | -0.29 | [-0.53, -0.04] | ## wt × hp | 0.03 | [ 0.01, 0.04] | 3.23 | 0.003 | 0.29 | [ 0.11, 0.47] | ## | | | | | | | ## AIC | | | | | | | 147.01## AICc | | | | | | | 150.37## BIC | | | | | | | 155.80## R2 | | | | | | | 0.89## R2 (adj.) | | | | | | | 0.87## Sigma | | | | | | | 2.17

nice_table(stats.table, report = "lm")

The report package provides quite comprehensive tables,so one may request an abbreviated table with the shortargument.

nice_table(stats.table, report = "lm", short = TRUE)

rempsyc table

nice_table also integrates nicely with other functionsfrom the rempsyc package: nice_t_test, nice_mod,nice_slopes, nice_lm, andnice_lm_slopes, because they provide good defaultformats that include effect sizes. Let’s make a quick demo for some ofthem. The t-test function supports making several t-tests at once byspecifying the desired dependent variables.

stats.table <- nice_t_test( data = mtcars, response = c("mpg", "disp", "drat"), group = "am", warning = FALSE)stats.table

## Dependent Variable t df p d CI_lower## 1 mpg -3.767123 18.33225 1.373638e-03 -1.477947 -2.2659732## 2 disp 4.197727 29.25845 2.300413e-04 1.445221 0.6417836## 3 drat -5.646088 27.19780 5.266742e-06 -2.003084 -2.8592770## CI_upper## 1 -0.6705684## 2 2.2295594## 3 -1.1245499

nice_table(stats.table)

stats.table <- nice_mod( data = mtcars, response = "mpg", predictor = "gear", moderator = "wt")stats.table

## Dependent Variable Predictor df B t p## 1 mpg gear 28 -0.08718042 -0.7982999 4.314156e-01## 2 mpg wt 28 -0.94959988 -8.6037724 2.383144e-09## 3 mpg gear:wt 28 -0.23559962 -2.1551077 3.989970e-02## sr2 CI_lower CI_upper## 1 0.004805465 0.0000000000 0.02702141## 2 0.558188818 0.3142326391 0.80214500## 3 0.035022025 0.0003502202 0.09723370

nice_table(stats.table)

p-values

nice_table(test[8:11], col.format.p = 1:4)

r-values

The same goes for r-values. As you see below, you can alsooverwrite automatic default formatting.

nice_table(test[8:11], col.format.r = 1:4)

Custom functions

And one can even provide a custom function:

fun <- function(x) { x + 11.1}nice_table(test[8:11], col.format.custom = 2:4, format.custom = "fun")

fun <- function(x) { paste("×", x)}nice_table(test[8:11], col.format.custom = 2:4, format.custom = "fun")

fun <- function(x) { formatC(x, format = "f", digits = 0)}nice_table(test[3:6], col.format.custom = 1:4, format.custom = "fun")

fun <- function(x) { formatC(x, format = "f", digits = 5)}nice_table(test[3:6], col.format.custom = 1:4, format.custom = "fun")

library(dplyr)library(flextable)my_table %>% italic(j = 1, part = "body") %>% bg(bg = "gray", part = "header") %>% color(color = "blue", part = "header") %>% color(~ t > -3.5, ~ t + SE, color = "red") %>% bold(~ t > -3.5, ~ t + p, bold = TRUE) %>% set_header_labels( Term = "Model Term", B = "Standardized Beta", p = "p-value" )

# Setup example datasetdata <- cbind(iris[c(5, 1:3)], iris[1:3] + 1)names(data)[-1] <- c( paste0("T1.", names(data[2:4])), paste0("T2.", names(data[2:4])))# Get descriptive statisticslibrary(dplyr)descriptive.data <- data %>% group_by(Species) %>% summarize(across(T1.Sepal.Length:T2.Petal.Length, list(m = mean, sd = sd), .names = "{.col}.{.fn}" ))# Rename the columns so we can merge them laternames(descriptive.data) <- c( "Species", rep(c("T1.M", "T1.SD"), 3), rep(c("T2.M", "T2.SD"), 3))# Extract the data by variable and measurement timeT1.disp <- cbind( descriptive.data[1, 2:3], descriptive.data[2, 2:3], descriptive.data[3, 2:3])T1.hp <- cbind( descriptive.data[1, 4:5], descriptive.data[2, 4:5], descriptive.data[3, 4:5])T1.drat <- cbind( descriptive.data[1, 6:7], descriptive.data[2, 6:7], descriptive.data[3, 6:7])T2.disp <- cbind( descriptive.data[1, 8:9], descriptive.data[2, 8:9], descriptive.data[3, 8:9])T2.hp <- cbind( descriptive.data[1, 10:11], descriptive.data[2, 10:11], descriptive.data[3, 10:11])T2.drat <- cbind( descriptive.data[1, 12:13], descriptive.data[2, 12:13], descriptive.data[3, 12:13])# Combine Time 1 with Time 2T1 <- rbind(T1.disp, T1.hp, T1.drat)T2 <- rbind(T2.disp, T2.hp, T2.drat)wide.data <- cbind(Variable = names(iris[1:3]), T1, T2)# Rename variables to avoid duplicate names not allowednames(wide.data)[-1] <- paste0( rep(c("T1.", "T2."), each = 6), rep(descriptive.data$Species, times = 2, each = 2), paste0(c(".M", ".SD")))# Make preliminary nice_tablenice_table(wide.data)

nice_table(wide.data, separate.header = TRUE, italics = seq(wide.data))

Multilevel heading, with formatting

Another colleague asked, whether it was possible to use themultilevel headings, while still benefiting from the regular automaticformatting of the p-values, confidence intervals, etc. That was achallenging task to implement, but I think I’ve got something thatshould mostly work. Demo:

T1.mpg <- nice_t_test(data = mtcars, response = "mpg", group = "am")T2.mpg <- nice_t_test(data = mtcars, response = "mpg", group = "vs")T1.disp <- nice_t_test(data = mtcars, response = "disp", group = "am")T2.disp <- nice_t_test(data = mtcars, response = "disp", group = "vs")names(T1.mpg)[-1] <- paste0("T1.", names(T1.mpg)[-1])names(T2.mpg) <- paste0("T2.", names(T2.mpg))names(T1.disp)[-1] <- paste0("T1.", names(T1.disp)[-1])names(T2.disp) <- paste0("T2.", names(T2.disp))T1 <- rbind(T1.mpg, T1.disp)T2 <- rbind(T2.mpg, T2.disp)wide.data <- cbind(T1, T2[-(1)])nice_table(wide.data)

nice_table(wide.data, separate.header = TRUE, stars = FALSE)

Let’s test adding another level of heading for testing.

names(wide.data)[-1] <- paste0( rep(c("Early.", "Late."), each = 6), names(wide.data)[-1])nice_table(wide.data)

nice_table(wide.data, separate.header = TRUE, stars = FALSE)

Thanks for checking in

Make sure to check out this page again if you use the code after atime or if you encounter errors, as I periodically update or improve thecode. Feel free to contact me for comments, questions, or requests toimprove this function at https://github.com/rempsyc/rempsyc/issues. See alltutorials here: https://remi-theriault.com/tutorials.