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Publication-Ready Tables

library(mlstats)
library(dplyr)
library(stringr)

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:

install.packages("gt")
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")
```

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.