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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