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

Getting started

Load the rempsyc package:

library(rempsyc)

Note: If you haven’t installed this package yet, you will need to install it via the following command: install.packages("rempsyc"). Furthermore, you may be asked to install the following packages if you haven’t installed them already (you may decide to install them all now to avoid interrupting your workflow 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 not allow duplicate column names). Here’s a simple example using the mtcars 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 coefficients
mtcars.std <- lapply(mtcars, scale)
# Create a simple linear model
model <- lm(mpg ~ cyl + wt * hp, mtcars.std)
# Gather summary statistics
stats.table <- as.data.frame(summary(model)$coefficients)
# Get the confidence interval (CI) of the regression coefficient
CI <- confint(model)
# Add a row to join the variables names and CI to the stats
stats.table <- cbind(row.names(stats.table), stats.table, CI)
# Rename the columns appropriately
names(stats.table) <- c("Term", "B", "SE", "t", "p", "CI_lower", "CI_upper")

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

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 later further edit, view, or export to another software (e.g., Microsoft Word).

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 use a lowercase italic b followed by an asterisk: b*. rempsyc will follow this APA recommendation with version 0.1.6.1 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 the object 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 you copy-paste your path name, remember to use “R” slashes (‘/’ rather than ‘\’). Also remember to specify the file name and its .docx extension.

That’s it! Simple eh?

Statistical formatting

Notice that if you provide the CI_lower and CI_upper column names, it will automatically and properly format your confidence interval column and remove the lower and upper bound columns.

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

Note: in order for this to work automatically, your columns must be named correctly. Currently the function will make the following conversions: p, t, SE, SD, M, W, N, n, z, F, b, r, and df to italic; R2 and sr2 to italic squared, dR to italic R subscript, np2 to italic η subscript-p squared, ges to italic η subscript-G squared, and B to b* (and to β in version <= 0.1.6). Not seeing a symbol that should be there? Contact me and I’ll add it!

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

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

Highlighting

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

nice_table(stats.table, highlight = TRUE)

Pro tip: You can instead provide the highlight argument with a numeric value to set whatever critical p-value check you want, like highlight = .10, for “marginally significant” results, or highlight = .01 if you want to be more conservative.

nice_table(stats.table, highlight = .01)

To remove the more traditional significance asterisks, you can set the 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 short argument.

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

rempsyc table

nice_table also integrates nicely with other functions from the rempsyc package: nice_t_test, nice_mod, nice_slopes, nice_lm, and nice_lm_slopes, because they provide good default formats that include effect sizes. Let’s make a quick demo for some of them. The t-test function supports making several t-tests at once by specifying 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 also overwrite 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")

Multilevel heading, with formatting

Another colleague asked, whether it was possible to use the multilevel headings, while still benefiting from the regular automatic formatting of the p-values, confidence intervals, etc. That was a challenging task to implement, but I think I’ve got something that should 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 a time or if you encounter errors, as I periodically update or improve the code. Feel free to contact me for comments, questions, or requests to improve this function at https://github.com/rempsyc/rempsyc/issues. See all tutorials here: https://remi-theriault.com/tutorials.