Easily make nice APA tables

Make nice APA tables easily through a wrapper around the flextable package with sensical defaults and automatic formatting features.

Usage

nice_table(
  data,
  highlight = FALSE,
  stars = TRUE,
  italics,
  col.format.p,
  col.format.r,
  col.format.ci,
  format.custom,
  col.format.custom,
  width = NULL,
  spacing = 2,
  broom = NULL,
  report = NULL,
  short = FALSE,
  title,
  note,
  separate.header
)

Arguments

data: The data frame, to be converted to a flextable. The data frame cannot have duplicate column names.
highlight: Highlight rows with statistically significant results? Requires a column named "p" containing p-values. Can either accept logical (TRUE/FALSE) OR a numeric value for a custom critical p-value threshold (e.g., 0.10 or 0.001).
stars: Logical. Whether to add asterisks for significant p values.
italics: Which columns headers should be italic? Useful for column names that should be italic but that are not picked up automatically by the function. Select with numerical range, e.g., 1:3.
col.format.p: Applies p-value formatting to columns that cannot be named "p" (for example for a data frame full of p-values, also because it is not possible to have more than one column named "p"). Select with numerical range, e.g., 1:3.
col.format.r: Applies r-value formatting to columns that cannot be named "r" (for example for a data frame full of r-values, also because it is not possible to have more than one column named "r"). Select with numerical range, e.g., 1:3.
col.format.ci: Applies 95% confidence interval formatting to selected columns (e.g., when reporting more than one interval).
format.custom: Applies custom formatting to columns selected via the col.format.custom argument. This is useful if one wants custom formatting other than for p- or r-values. It can also be used to transform (e.g., multiply) certain values or print a specific symbol along the values for instance.
col.format.custom: Which columns to apply the custom function to. Select with numerical range, e.g., 1:3.
width: Width of the table, in percentage of the total width, when exported e.g., to Word. For full width, use width = 1.
spacing: Spacing of the rows (1 = single space, 2 = double space)
broom: If providing a tidy table produced with the broom package, which model type to use if one wants automatic formatting (options are "t.test", "lm", "cor.test", and "wilcox.test").
report: If providing an object produced with the report package, which model type to use if one wants automatic formatting (options are "t.test", "lm", and "cor.test").
short: Logical. Whether to return an abbreviated version of the tables made by the report package.
title: Optional, to add a table header, if desired.
note: Optional, to add one or more table footnote (APA note), if desired.
separate.header: Logical, whether to separate headers based on name delimiters (i.e., periods ".").

Value

An APA-formatted table of class "flextable"

Details

The resulting flextable objects can be opened in Word with print(table, preview ="docx"), or saved to Word with the flextable::save_as_docx() function.

Examples

# Make the basic table
my_table <- 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"
  )
)
my_table
Table 1
Motor Trend Car Road Tests
mpg cyl disp hp drat wt qsec vs am gear carb
21.00 6.00 160.00 110.00 3.90 2.62 16.46 0.00 1.00 4.00 4.00
21.00 6.00 160.00 110.00 3.90 2.88 17.02 0.00 1.00 4.00 4.00
22.80 4.00 108.00 93.00 3.85 2.32 18.61 1.00 1.00 4.00 1.00
Note. The data was extracted from the 1974 Motor Trend US magazine.
* p < .05, ** p < .01, *** p < .001

# \donttest{
# Save table to word
mypath <- tempfile(fileext = ".docx")
flextable::save_as_docx(my_table, path = mypath)
# }

# Publication-ready tables
mtcars.std <- lapply(mtcars, scale)
model <- lm(mpg ~ cyl + wt * hp, mtcars.std)
stats.table <- as.data.frame(summary(model)$coefficients)
CI <- confint(model)
stats.table <- cbind(
  row.names(stats.table),
  stats.table, CI
)
names(stats.table) <- c(
  "Term", "B", "SE", "t", "p",
  "CI_lower", "CI_upper"
)
nice_table(stats.table, highlight = TRUE)
Term b* SE t p 95% CI
(Intercept) -0.18 0.09 -2.15 .041* [-0.36, -0.01]
cyl -0.11 0.15 -0.72 .479 [-0.42, 0.20]
wt -0.62 0.11 -5.70 < .001*** [-0.85, -0.40]
hp -0.29 0.12 -2.40 .023* [-0.53, -0.04]
wt × hp 0.29 0.09 3.23 .003** [0.11, 0.47]

# Test different column names
test <- head(mtcars)
names(test) <- c(
  "dR", "N", "M", "SD", "b", "np2",
  "ges", "p", "r", "R2", "sr2"
)
test[, 10:11] <- test[, 10:11] / 10
nice_table(test)
dR N M SD b ηp2 ηG2 p r R2 sr2
21.00 6 160.00 110.00 3.90 2.62 16.46 < .001*** 1.0 .40 .40
21.00 6 160.00 110.00 3.90 2.88 17.02 < .001*** 1.0 .40 .40
22.80 4 108.00 93.00 3.85 2.32 18.61 1.00 1.0 .40 .10
21.40 6 258.00 110.00 3.08 3.21 19.44 1.00 .00 .30 .10
18.70 8 360.00 175.00 3.15 3.44 17.02 < .001*** .00 .30 .20
18.10 6 225.00 105.00 2.76 3.46 20.22 1.00 .00 .30 .10

# Custom cell formatting (such as p or r)
nice_table(test[8:11], col.format.p = 2:4, highlight = .001)
p r R2 sr2
< .001*** 1.00 .400 .400
< .001*** 1.00 .400 .400
1.00 1.00 .400 .100
1.00 < .001*** .300 .100
< .001*** < .001*** .300 .200
1.00 < .001*** .300 .100

nice_table(test[8:11], col.format.r = 1:4)
p r R2 sr2
.00 1.0 .40 .40
.00 1.0 .40 .40
1.0 1.0 .40 .10
1.0 .00 .30 .10
.00 .00 .30 .20
1.0 .00 .30 .10

# Apply custom functions to cells
fun <- function(x) {
  x + 11.1
}
nice_table(test[8:11], col.format.custom = 2:4, format.custom = "fun")
#> Error: object 'fun' not found

fun <- function(x) {
  paste("x", x)
}
nice_table(test[8:11], col.format.custom = 2:4, format.custom = "fun")
#> Error: object 'fun' not found

# Separate headers based on periods
header.data <- structure(
  list(
    Variable = c(
      "Sepal.Length",
      "Sepal.Width", "Petal.Length"
    ), setosa.M = c(
      5.01, 3.43,
      1.46
    ), setosa.SD = c(0.35, 0.38, 0.17), versicolor.M =
      c(5.94, 2.77, 4.26), versicolor.SD = c(0.52, 0.31, 0.47)
  ),
  row.names = c(NA, -3L), class = "data.frame"
)
nice_table(header.data,
  separate.header = TRUE,
  italics = 2:4
)
Variable setosa versicolor
M SD M SD
Sepal.Length 5.01 0.35 5.94 0.52
Sepal.Width 3.43 0.38 2.77 0.31
Petal.Length 1.46 0.17 4.26 0.47

Table 1
Motor Trend Car Road Tests
mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
21.00	6.00	160.00	110.00	3.90	2.62	16.46	0.00	1.00	4.00	4.00
21.00	6.00	160.00	110.00	3.90	2.88	17.02	0.00	1.00	4.00	4.00
22.80	4.00	108.00	93.00	3.85	2.32	18.61	1.00	1.00	4.00	1.00
Note. The data was extracted from the 1974 Motor Trend US magazine.
* p < .05, p < .01, * p < .001

Term	b*	SE	t	p	95% CI
(Intercept)	-0.18	0.09	-2.15	.041*	[-0.36, -0.01]
cyl	-0.11	0.15	-0.72	.479	[-0.42, 0.20]
wt	-0.62	0.11	-5.70	< .001***	[-0.85, -0.40]
hp	-0.29	0.12	-2.40	.023*	[-0.53, -0.04]
wt × hp	0.29	0.09	3.23	.003**	[0.11, 0.47]

p	r	R2	sr2
< .001***	1.00	.400	.400
< .001***	1.00	.400	.400
1.00	1.00	.400	.100
1.00	< .001***	.300	.100
< .001***	< .001***	.300	.200
1.00	< .001***	.300	.100

p	r	R2	sr2
.00	1.0	.40	.40
.00	1.0	.40	.40
1.0	1.0	.40	.10
1.0	.00	.30	.10
.00	.00	.30	.20
1.0	.00	.30	.10

Variable	setosa		versicolor
Variable	M	SD	M	SD
Sepal.Length	5.01	0.35	5.94	0.52
Sepal.Width	3.43	0.38	2.77	0.31
Petal.Length	1.46	0.17	4.26	0.47

p	r	R2	sr2
.00	1.0	.40	.40
.00	1.0	.40	.40
1.0	1.0	.40	.10
1.0	.00	.30	.10
.00	.00	.30	.20
1.0	.00	.30	.10

Usage

Arguments

Value

Details

See also

Examples

p	r	R2	sr2
.00	1.0	.40	.40
.00	1.0	.40	.40
1.0	1.0	.40	.10
1.0	.00	.30	.10
.00	.00	.30	.20
1.0	.00	.30	.10