# Formatting, printing and exporting tables

## The difference between a dataframe and its render

Most of objects encountered throughout the easystats packages are “tables”, i.e., a 2D matrix with columns and rows. In R, these objects are often, at their core, data frames. Let’s create one to use as an example:

library(insight)

df <- data.frame(
Variable = c(1, 3, 5, 3, 1),
Group = c("A", "A", "A", "B", "B"),
CI = c(0.95, 0.95, 0.95, 0.95, 0.95),
CI_low = c(3.35, 2.425, 6.213, 12.1, 1.23),
CI_high = c(4.23, 5.31, 7.123, 13.5, 3.61),
p = c(0.001, 0.0456, 0.45, 0.0042, 0.34)
)

df
#>   Variable Group   CI CI_low CI_high      p
#> 1        1     A 0.95  3.350   4.230 0.0010
#> 2        3     A 0.95  2.425   5.310 0.0456
#> 3        5     A 0.95  6.213   7.123 0.4500
#> 4        3     B 0.95 12.100  13.500 0.0042
#> 5        1     B 0.95  1.230   3.610 0.3400

When I display in in the console (calling an object - e.g. df - is actually equivalent to calling print(df)), the output looks alright, but it could be improved. Some packages, such as knitr, have functions to create a nicer output. For instance, in markdown, so that it can be nicely rendered in markdown documents when copied:

knitr::kable(df, format = "markdown")
| Variable|Group |   CI| CI_low| CI_high|      p|
|--------:|:-----|----:|------:|-------:|------:|
|        1|A     | 0.95|  3.350|   4.230| 0.0010|
|        3|A     | 0.95|  2.425|   5.310| 0.0456|
|        5|A     | 0.95|  6.213|   7.123| 0.4500|
|        3|B     | 0.95| 12.100|  13.500| 0.0042|
|        1|B     | 0.95|  1.230|   3.610| 0.3400|

Or HTML, which again makes it look great in HTML files (such as this webpage you’re reading):

knitr::kable(df, format = "html")
Variable Group CI CI_low CI_high p
1 A 0.95 3.350 4.230 0.0010
3 A 0.95 2.425 5.310 0.0456
5 A 0.95 6.213 7.123 0.4500
3 B 0.95 12.100 13.500 0.0042
1 B 0.95 1.230 3.610 0.3400

## The insight workflow

The insight package also contains function to improve the “printing”, or rendering, of tables. Its design dissociates two separate and independent steps: formatting and exporting.

### Formatting

The purpose of formatting is to improve a given table, while still keeping it as a regular R data frame, so that it can be for instance further modified by the user.

format_table(df)
#>   Variable Group         95% CI     p
#> 1     1.00     A [ 3.35,  4.23] 0.001
#> 2     3.00     A [ 2.42,  5.31] 0.046
#> 3     5.00     A [ 6.21,  7.12] 0.450
#> 4     3.00     B [12.10, 13.50] 0.004
#> 5     1.00     B [ 1.23,  3.61] 0.340

As you can see, format_table() modifies columns, turning number into characters (so that it has the same amount of digits), and detecting confidence intervals. This is usually combined with column-specific formatting functions, like format_p():

df %>%
mutate(p = format_p(p, stars = TRUE)) %>%
format_table()
#>   Variable Group         95% CI           p
#> 1     1.00     A [ 3.35,  4.23] p = 0.001**
#> 2     3.00     A [ 2.42,  5.31] p = 0.046*
#> 3     5.00     A [ 6.21,  7.12] p = 0.450
#> 4     3.00     B [12.10, 13.50] p = 0.004**
#> 5     1.00     B [ 1.23,  3.61] p = 0.340

## Using unicode symbols as effect size names

With use_symbols = TRUE, it is possible to render certain effect size names as symbols, if these are used as column names. Note that this only works on OS X or Linux, or on Windows from R 4.2 or higher.

x <- data.frame(
Glass_delta = 0.4,
Epsilon2 = 0.7,
R2 = 0.4
)

# standard output
format_table(x)
#>   Phi (adj.) Glass' delta Epsilon2   R2
#> 1       0.30         0.40     0.70 0.40

# column names of effect sizes as symbols
format_table(x, use_symbols = TRUE)
#>   ϕ (adj.) Glass' Δ   ε²   R²
#> 1     0.30     0.40 0.70 0.40

In combination with export_table() (see next section), this will give you nicely formatted tables.

export_table(format_table(x, use_symbols = TRUE))
#> ϕ (adj.) | Glass' Δ |   ε² |   R²
#> ---------------------------------
#> 0.30     |     0.40 | 0.70 | 0.40

### Exporting

The next step is exporting, which takes a data frame and renders it in a given format, so that it looks good in the console, or in markdown, HTML or latex.

export_table(df)
#> Variable | Group |   CI | CI_low | CI_high |        p
#> -----------------------------------------------------
#>        1 |     A | 0.95 |   3.35 |    4.23 | 1.00e-03
#>        3 |     A | 0.95 |   2.42 |    5.31 |     0.05
#>        5 |     A | 0.95 |   6.21 |    7.12 |     0.45
#>        3 |     B | 0.95 |  12.10 |   13.50 | 4.20e-03
#>        1 |     B | 0.95 |   1.23 |    3.61 |     0.34

For markdown or HTML, simply change the format argument to markdown (“md”)…

export_table(df, format = "md")
Variable Group CI CI_low CI_high p
1 A 0.95 3.35 4.23 1.00e-03
3 A 0.95 2.42 5.31 0.05
5 A 0.95 6.21 7.12 0.45
3 B 0.95 12.10 13.50 4.20e-03
1 B 0.95 1.23 3.61 0.34

…or HTML format.

export_table(df, format = "html")
Variable CI CI_low CI_high p
A
1 0.95 3.35 4.23 1.00e-03
3 0.95 2.42 5.31 0.05
5 0.95 6.21 7.12 0.45
B
3 0.95 12.10 13.50 4.20e-03
1 0.95 1.23 3.61 0.34

This can be combined with format_table().

df %>%
format_table(ci_brackets = c("(", ")")) %>%
export_table(format = "html")
Variable 95% CI p
A
1.00 ( 3.35, 4.23) 0.001
3.00 ( 2.42, 5.31) 0.046
5.00 ( 6.21, 7.12) 0.450
B
3.00 (12.10, 13.50) 0.004
1.00 ( 1.23, 3.61) 0.340