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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 asked to complete up to 14 daily surveys; as in most real mobile diary studies, not everyone completed every day, so T varies from 5 to 14 observations per person, 1,184 total across the N = 100 persons). 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   100   3.93  0.70 2.40–6.20     –    NA    NA    NA    NA  1.00
#> 2 Wellbeing    1,184   4.42  0.89 1.50–7.00  .47*     –  .42* -.40*  .51*   .50
#> 3 Screen time  1,184 132.05 39.01    15–246 -.66* -.32*     –  .29*  .54*   .34
#> 4 Stress       1,184   3.77  0.95       1–7 -.55* -.27*  .46*     –  -.02   .39
#> 5 Enjoyment    1,184   4.49  0.83 2.30–7.00  -.12  .40*  .32*   .15     –   .50
#>   ============ ===== ====== ===== ========= ===== ===== ===== ===== ===== =====
#> # ℹ Within-person correlations above, between-person correlations below the
#> #   diagonal.
#> # ℹ All correlations marked with a star are significant at p < .05.
#> # ℹ Based on 100 persons and 1,184 observations (median 12 per person; range:
#> #   5–14).
#> # 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.
Based on 100 persons and 1,184 observations (median 12 per person; range: 5–14).
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 100 3.93 0.70 2.40–6.20 – NA NA NA NA 1.00
2 Wellbeing 1,184 4.42 0.89 1.50–7.00 .47* – .42* -.40* .51* .50
3 Screen time 1,184 132.05 39.01 15–246 -.66* -.32* – .29* .54* .34
4 Stress 1,184 3.77 0.95 1–7 -.55* -.27* .46* – -.02 .39
5 Enjoyment 1,184 4.49 0.83 2.30–7.00 -.12 .40* .32* .15 – .50

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, group_size_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.",
  group_size_note  = "Based on 100 persons; observations per person varied due to non-response.",
  note_text        = "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. Simulated data.
Based on 100 persons; observations per person varied due to non-response.
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 100 3.93 0.70 2.40–6.20 – NA NA NA NA 1.00
2 Wellbeing 1,184 4.42 0.89 1.50–7.00 .47* – .42* -.40* .51* .50
3 Screen time 1,184 132.05 39.01 15–246 -.66* -.32* – .29* .54* .34
4 Stress 1,184 3.77 0.95 1–7 -.55* -.27* .46* – -.02 .39
5 Enjoyment 1,184 4.49 0.83 2.30–7.00 -.12 .40* .32* .15 – .50

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 100 3.93 0.70 2.40–6.20 – NA NA NA NA 1.00
2 Wellbeing 1,184 4.42 0.89 1.50–7.00 .47* – .42* -.40* .51* .50
3 Screen time 1,184 132.05 39.01 15–246 -.66* -.32* – .29* .54* .34
4 Stress 1,184 3.77 0.95 1–7 -.55* -.27* .46* – -.02 .39
5 Enjoyment 1,184 4.49 0.83 2.30–7.00 -.12 .40* .32* .15 – .50
Group-weighted multilevel descriptive statistics computed with mlstats.
Based on 100 persons and 1,184 observations (median 12 per person; range: 5–14).
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.
Based on 100 persons and 1,184 observations (median 12 per person; range: 5–14).
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 3.93 0.70 – NA NA NA NA 1.00
2 Wellbeing 4.42 0.89 .47* – .42* -.40* .51* .50
3 Screen time 132.05 39.01 -.66* -.32* – .29* .54* .34
4 Stress 3.77 0.95 -.55* -.27* .46* – -.02 .39
5 Enjoyment 4.49 0.83 -.12 .40* .32* .15 – .50

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.
Based on 100 persons and 1,184 observations (median 12 per person; range: 5–14).
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 100 3.93 0.70 2.40–6.20 – – – – – 1.00
2 Wellbeing 1,184 4.42 0.89 1.50–7.00 .47* – .42* -.40* .51* .50
3 Screen time 1,184 132.05 39.01 15–246 -.66* -.32* – .29* .54* .34
4 Stress 1,184 3.77 0.95 1–7 -.55* -.27* .46* – -.02 .39
5 Enjoyment 1,184 4.49 0.83 2.30–7.00 -.12 .40* .32* .15 – .50

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.
Based on 100 persons and 1,184 observations (median 12 per person; range: 5–14).
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 100 3.93 0.70 2.40–6.20 – NA NA NA NA 1.00
2 Daily wellbeing 1,184 4.42 0.89 1.50–7.00 .47* – .42* -.40* .51* .50
3 Screen time (min) 1,184 132.05 39.01 15–246 -.66* -.32* – .29* .54* .34
4 Perceived stress 1,184 3.77 0.95 1–7 -.55* -.27* .46* – -.02 .39
5 Media enjoyment 1,184 4.49 0.83 2.30–7.00 -.12 .40* .32* .15 – .50

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 = 5-14 daily observations per person (1,184 total). 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 = 5-14 daily observations per person (1,184 total). Self-control was measured as a trait (between-person only).
Based on 100 persons and 1,184 observations (median 12 per person; range: 5–14).
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 3.93 0.70 – – – – – 1.00
2 Daily wellbeing 4.42 0.89 .47* – .42* -.40* .51* .50
3 Screen time (min) 132.05 39.01 -.66* -.32* – .29* .54* .34
4 Perceived stress 3.77 0.95 -.55* -.27* .46* – -.02 .39
5 Media enjoyment 4.49 0.83 -.12 .40* .32* .15 – .50

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> = 5–14 daily observations per person (1,184 total)."
  ) |>
  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 3.93 0.70 – – – – – 1.00
2 Daily wellbeing 4.42 0.89 .47* – .42* -.40* .51* .50
3 Screen time (min) 132.05 39.01 -.66* -.32* – .29* .54* .34
4 Perceived stress 3.77 0.95 -.55* -.27* .46* – -.02 .39
5 Media enjoyment 4.49 0.83 -.12 .40* .32* .15 – .50
Note. N = 100, T = 5–14 daily observations per person (1,184 total).
Based on 100 persons and 1,184 observations (median 12 per person; range: 5–14).
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")
```