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Publication-Ready Tables

Source: vignettes/tables.Rmd
tables.Rmd

mldesc() (for any method, including "bayes") returns a tibble that can be printed in three formats: a console-friendly default, a tinytable object, and a gt object. This vignette shows how to move from the default output to a fully-customised, journal-ready table.

Example data

We use media_diary, a simulated daily diary dataset included with mlstats (100 participants over 14 days; N = 100 persons, T = 1,400 daily observations). See ?media_diary for details.

data("media_diary")
vars <- c("self_control", "wellbeing", "screen_time", "stress", "enjoyment")
result <- mldesc(data = media_diary, group = "person", vars = vars)

Default console output

Simply printing the result gives a compact console-friendly view:

result
#> # Multilevel Descriptive Statistics
#>   ============ ===== ====== ===== ===== ===== ===== ===== ===== ===== =====
#>   variable     n_obs      m    sd range   `1`   `2`   `3`   `4`   `5`   icc
#>   ------------ ----- ------ ----- ----- ----- ----- ----- ----- ----- -----
#> 1 Self control 1,400   4.03  0.83   2–6     –    NA    NA    NA    NA  1.00
#> 2 Wellbeing    1,400   4.45  0.87   2–7  .61*     –  .42* -.43*  .45*   .46
#> 3 Screen time  1,400 128.66 42.29 0–272 -.67* -.34*     –  .29*  .57*   .45
#> 4 Stress       1,400   3.81  0.91   1–7 -.53* -.38*  .38*     –  .05*   .33
#> 5 Enjoyment    1,400   4.44  0.79   2–7  -.18 -.21*  .22*  .25*     –   .44
#>   ============ ===== ====== ===== ===== ===== ===== ===== ===== ===== =====
#> # ℹ Within-person correlations above, between-person correlations below the
#> #   diagonal.
#> # ℹ All correlations marked with a star are significant at p < .05.
#> # ℹ Correlations estimated via variance decomposition.
#> # ℹ Group-weighted multilevel descriptive statistics computed with mlstats.

tinytable format

tinytable is a lightweight table package included with mlstats (no extra installation needed). Pass format = "tt" to print():

print(result, format = "tt")
Multilevel Descriptive Statistics
Descriptives Correlationsa,b ICC
Variable Nobs M SD Range 1 2 3 4 5
Note. Group-weighted multilevel descriptive statistics computed with mlstats.
a Within-person correlations above, between-person correlations below the diagonal.
b All correlations marked with a star are significant at p < .05.
1 Self control 1,400 4.03 0.83 2–6 – NA NA NA NA 1.00
2 Wellbeing 1,400 4.45 0.87 2–7 .61* – .42* -.43* .45* .46
3 Screen time 1,400 128.66 42.29 0–272 -.67* -.34* – .29* .57* .45
4 Stress 1,400 3.81 0.91 1–7 -.53* -.38* .38* – .05* .33
5 Enjoyment 1,400 4.44 0.79 2–7 -.18 -.21* .22* .25* – .44

The result is a tinytable object that renders to HTML, PDF, or Word via Quarto/R Markdown (see below).

Custom title and notes

All print methods accept table_title, correlation_note, significance_note, and note_text:

print(result,
  format           = "tt",
  table_title      = "Daily diary study: descriptive statistics and multilevel correlations",
  correlation_note = "Within-person correlations above, between-person below the diagonal.",
  note_text        = "N = 100 persons, 14 daily observations each. Simulated data."
)
Daily diary study: descriptive statistics and multilevel correlations
Descriptives Correlationsa,b ICC
Variable Nobs M SD Range 1 2 3 4 5
Note. N = 100 persons, 14 daily observations each. Simulated data.
a Within-person correlations above, between-person below the diagonal.
b All correlations marked with a star are significant at p < .05.
1 Self control 1,400 4.03 0.83 2–6 – NA NA NA NA 1.00
2 Wellbeing 1,400 4.45 0.87 2–7 .61* – .42* -.43* .45* .46
3 Screen time 1,400 128.66 42.29 0–272 -.67* -.34* – .29* .57* .45
4 Stress 1,400 3.81 0.91 1–7 -.53* -.38* .38* – .05* .33
5 Enjoyment 1,400 4.44 0.79 2–7 -.18 -.21* .22* .25* – .44

gt format

gt produces richly formatted HTML tables and supports markdown in cells, footnotes, and fine typographic control. It must be installed separately:

print(result, format = "gt")
Multilevel Descriptive Statistics
Variable
Descriptives
Correlationsa,b
ICC
Nobs M SD Range 1 2 3 4 5
1 Self control 1,400 4.03 0.83 2–6 – NA NA NA NA 1.00
2 Wellbeing 1,400 4.45 0.87 2–7 .61* – .42* -.43* .45* .46
3 Screen time 1,400 128.66 42.29 0–272 -.67* -.34* – .29* .57* .45
4 Stress 1,400 3.81 0.91 1–7 -.53* -.38* .38* – .05* .33
5 Enjoyment 1,400 4.44 0.79 2–7 -.18 -.21* .22* .25* – .44
Group-weighted multilevel descriptive statistics computed with mlstats.
a Within-person correlations above, between-person correlations below the diagonal.
b All correlations marked with a star are significant at p < .05.

gt tables support further customisation via the gt package API after the initial print() call — see the gt documentation for details.

Manipulating the result before printing

Because mldesc() returns a tibble, standard dplyr operations work on it before printing.

Removing columns

Drop columns you don’t need in the final table:

result |>
  select(-n_obs, -range) |>
  print(format = "tt")
Multilevel Descriptive Statistics
Descriptives Correlationsa,b ICC
Variable M SD 1 2 3 4 5
Note. Group-weighted multilevel descriptive statistics computed with mlstats.
a Within-person correlations above, between-person correlations below the diagonal.
b All correlations marked with a star are significant at p < .05.
1 Self control 4.03 0.83 – NA NA NA NA 1.00
2 Wellbeing 4.45 0.87 .61* – .42* -.43* .45* .46
3 Screen time 128.66 42.29 -.67* -.34* – .29* .57* .45
4 Stress 3.81 0.91 -.53* -.38* .38* – .05* .33
5 Enjoyment 4.44 0.79 -.18 -.21* .22* .25* – .44

Replacing NA with a dash

self_control is a between-person-only trait: its within-person correlations are NA. Replace these with an em dash for cleaner output:

result |>
  mutate(across(everything(), ~ str_replace(as.character(.x), "^NA$", "–"))) |>
  print(format = "tt")
Multilevel Descriptive Statistics
Descriptives Correlationsa,b ICC
Variable Nobs M SD Range 1 2 3 4 5
Note. Group-weighted multilevel descriptive statistics computed with mlstats.
a Within-person correlations above, between-person correlations below the diagonal.
b All correlations marked with a star are significant at p < .05.
1 Self control 1,400 4.03 0.83 2–6 – – – – – 1.00
2 Wellbeing 1,400 4.45 0.87 2–7 .61* – .42* -.43* .45* .46
3 Screen time 1,400 128.66 42.29 0–272 -.67* -.34* – .29* .57* .45
4 Stress 1,400 3.81 0.91 1–7 -.53* -.38* .38* – .05* .33
5 Enjoyment 1,400 4.44 0.79 2–7 -.18 -.21* .22* .25* – .44

Renaming variables

Variable names are auto-formatted as sentence case. To customise them:

result |>
  mutate(variable = case_when(
    variable == "Self control" ~ "Trait self-control",
    variable == "Wellbeing"    ~ "Daily wellbeing",
    variable == "Screen time"  ~ "Screen time (min)",
    variable == "Stress"       ~ "Perceived stress",
    variable == "Enjoyment"    ~ "Media enjoyment"
  )) |>
  print(format = "tt", table_title = "Study variables: descriptive statistics")
Study variables: descriptive statistics
Descriptives Correlationsa,b ICC
Variable Nobs M SD Range 1 2 3 4 5
Note. Group-weighted multilevel descriptive statistics computed with mlstats.
a Within-person correlations above, between-person correlations below the diagonal.
b All correlations marked with a star are significant at p < .05.
1 Trait self-control 1,400 4.03 0.83 2–6 – NA NA NA NA 1.00
2 Daily wellbeing 1,400 4.45 0.87 2–7 .61* – .42* -.43* .45* .46
3 Screen time (min) 1,400 128.66 42.29 0–272 -.67* -.34* – .29* .57* .45
4 Perceived stress 1,400 3.81 0.91 1–7 -.53* -.38* .38* – .05* .33
5 Media enjoyment 1,400 4.44 0.79 2–7 -.18 -.21* .22* .25* – .44

Combining manipulations

All of the above can be chained. Here is an example of a polished table combining several customisations:

result |>
  select(-n_obs, -range) |>
  mutate(across(everything(), ~ str_replace(as.character(.x), "^NA$", "–"))) |>
  mutate(variable = case_when(
    variable == "Self control" ~ "Trait self-control",
    variable == "Wellbeing"    ~ "Daily wellbeing",
    variable == "Screen time"  ~ "Screen time (min)",
    variable == "Stress"       ~ "Perceived stress",
    variable == "Enjoyment"    ~ "Media enjoyment"
  )) |>
  print(
    format           = "tt",
    table_title      = "Descriptive statistics and multilevel correlations",
    correlation_note = "Within-person correlations above, between-person below the diagonal.",
    note_text        = "N = 100, T = 1,400 daily observations. Self-control was measured as a trait (between-person only)."
  )
Descriptive statistics and multilevel correlations
Descriptives Correlationsa,b ICC
Variable M SD 1 2 3 4 5
Note. N = 100, T = 1,400 daily observations. Self-control was measured as a trait (between-person only).
a Within-person correlations above, between-person below the diagonal.
b All correlations marked with a star are significant at p < .05.
1 Trait self-control 4.03 0.83 – – – – – 1.00
2 Daily wellbeing 4.45 0.87 .61* – .42* -.43* .45* .46
3 Screen time (min) 128.66 42.29 -.67* -.34* – .29* .57* .45
4 Perceived stress 3.81 0.91 -.53* -.38* .38* – .05* .33
5 Media enjoyment 4.44 0.79 -.18 -.21* .22* .25* – .44

For the equivalent using gt (which additionally supports footnotes and markdown-formatted cell content):

result |>
  select(-n_obs, -range) |>
  mutate(across(everything(), ~ str_replace(as.character(.x), "^NA$", "–"))) |>
  mutate(
    variable = case_when(
      variable == "Self control" ~ "Trait self-control<sup>c</sup>",
      variable == "Wellbeing"    ~ "Daily wellbeing",
      variable == "Screen time"  ~ "Screen time (min)",
      variable == "Stress"       ~ "Perceived stress",
      variable == "Enjoyment"    ~ "Media enjoyment"
    )
  ) |>
  print(
    format           = "gt",
    table_title      = "Descriptive statistics and multilevel correlations",
    correlation_note = "Within-person correlations above, between-person below the diagonal.",
    note_text        = "<i>Note</i>. <i>N</i> = 100, <i>T</i> = 1,400 daily observations."
  ) |>
  gt::tab_source_note(
    source_note = gt::html(
      "<sup>c</sup> Self-control was measured as a stable trait; no within-person correlations are available."
    )
  ) |>
  gt::fmt_markdown(columns = variable)
Descriptive statistics and multilevel correlations
Variable
Descriptives
Correlationsa,b
ICC
M SD 1 2 3 4 5
1 Trait self-controlc 4.03 0.83 – – – – – 1.00
2 Daily wellbeing 4.45 0.87 .61* – .42* -.43* .45* .46
3 Screen time (min) 128.66 42.29 -.67* -.34* – .29* .57* .45
4 Perceived stress 3.81 0.91 -.53* -.38* .38* – .05* .33
5 Media enjoyment 4.44 0.79 -.18 -.21* .22* .25* – .44
Note. N = 100, T = 1,400 daily observations.
a Within-person correlations above, between-person below the diagonal.
b All correlations marked with a star are significant at p < .05.
c Self-control was measured as a stable trait; no within-person correlations are available.

Embedding in Quarto documents

Word / DOCX output

Wrap the print() call in a Quarto code chunk with format: docx:

---
format: docx
---

```{r}
library(mlstats)
data("media_diary")

mldesc(
  data  = media_diary,
  group = "person",
  vars  = c("self_control", "wellbeing", "screen_time", "stress")
) |>
  print(format = "tt")
```

tinytable automatically converts to the appropriate format based on the output Quarto is rendering to.

HTML / PDF

Both tinytable and gt render natively to HTML and LaTeX. No extra setup is needed:

---
format: html   # or pdf
---

```{r}
library(mlstats)
data("media_diary")

mldesc(
  data  = media_diary,
  group = "person",
  vars  = c("self_control", "wellbeing", "screen_time", "stress")
) |>
  print(format = "tt")
```