Author: Tim Bergsma
Date: 2026-07-18

# An Introduction to Tablet for HTML

#### Tim Bergsma

#### 2026-07-18

# Motivation

Occasionally it is useful to generate a table of summary statistics for rows of a dataset, where such rows represent sampling units and and columns may be categorical or continuous. The excellent R package table1 does exactly this, and was the inspiration for `tablet`. `table1` however is optimized for html; `tablet` tries to provide a format-neutral implementation and relies on kableExtra to handle the rendering. Support for pdf (latex) is of particular interest, and is illustrated in the companion vignette. Here we convert that presentation to html for completeness. If you only need html output, you may prefer the interface, flexibility, and styling options of `table1`.

# Software

To support our examples, we load some other packages and in particular locate the melanoma dataset from boot.

    library(tidyr)
    library(dplyr)
    library(magrittr)
    library(kableExtra)
    library(boot)
    library(yamlet)
    library(tablet)

    x <- melanoma
    x %<>% select(-time, -year)

# Simple Case

For starters, we’ll just coerce two variables to factor to show that they are categorical, and then pass the whole thing to tablet(). Then we forward to kbl() for rendering (calls kableExtra::kbl and adds some magic).

    x %>%
      mutate(
        sex = factor(sex),
        ulcer = factor(ulcer)
      ) %>%
      tablet %>%
      kbl

| All (N = 205)
--- | ---
status
Mean (SD) | 1.79 (0.551)
Median (range) | 2 (1, 3)
Missing | 0
sex
0 | 126 (61.5%)
1 | 79 (38.5%)
age
Mean (SD) | 52.5 (16.7)
Median (range) | 54 (4, 95)
Missing | 0
thickness
Mean (SD) | 2.92 (2.96)
Median (range) | 1.94 (0.1, 17.4)
Missing | 0
ulcer
0 | 115 (56.1%)
1 | 90 (43.9%)

# With Metadata

Now we redefine the dataset, supplying metadata almost verbatim from `?melanoma`. This is fairly easy using package `yamlet`. Note that we reverse the authors’ factor order of 1, 0 for ulcer and move status ‘Alive’ to first position.

    x <- melanoma

    x %<>% decorate('
    time:      [ Survival Time Since Operation, day ]
    status:
     - End of Study Patient Status
     -
      - Alive: 2
      - Melanoma Death: 1
      - Unrelated Death: 3
    sex:       [ Sex, [ Male: 1, Female: 0 ]]
    age:       [ Age at Time of Operation, year ]
    year:      [ Year of Operation, year ]
    thickness: [ Tumor Thickness, mm ]
    ulcer:     [ Ulceration, [ Absent: 0, Present: 1 ]]
    ')
    x %<>% select(-time, -year)
    x %<>% group_by(status)
    x %<>% resolve

- group_by(status) causes statistics to be summarized in columns by group.

- resolve() disambiguates labels, units, and factor levels (actually creating factors where appropriate, such as for sex and ulcer).

Now we pass x to tablet() and kbl() for a more informative result.

    x %>% tablet %>% kbl

| Alive (N = 134) | Melanoma Death (N = 57) | Unrelated Death (N = 14) | All (N = 205)
--- | --- | --- | --- | ---
Sex
Male | 43 (32.1%) | 29 (50.9%) | 7 (50%) | 79 (38.5%)
Female | 91 (67.9%) | 28 (49.1%) | 7 (50%) | 126 (61.5%)
Age at Time of Operation (year)
Mean (SD) | 50 (15.9) | 55.1 (17.9) | 65.3 (10.9) | 52.5 (16.7)
Median (range) | 52 (4, 84) | 56 (14, 95) | 65 (49, 86) | 54 (4, 95)
Missing | 0 | 0 | 0 | 0
Tumor Thickness (mm)
Mean (SD) | 2.24 (2.33) | 4.31 (3.57) | 3.72 (3.63) | 2.92 (2.96)
Median (range) | 1.36 (0.1, 12.9) | 3.54 (0.32, 17.4) | 2.26 (0.16, 12.6) | 1.94 (0.1, 17.4)
Missing | 0 | 0 | 0 | 0
Ulceration
Absent | 92 (68.7%) | 16 (28.1%) | 7 (50%) | 115 (56.1%)
Present | 42 (31.3%) | 41 (71.9%) | 7 (50%) | 90 (43.9%)

Notice that:

- the order of variables down the left side is exactly their order in the dataset;
- the order of factor levels is exactly that in x;
- the order of groups across the top is exactly the levels (if any) of the grouping variable(s), and
- labels and titles (highest priority) are substituted for item names.

If you don’t particularly care for some aspect of the presentation, you can jump in between tablet() and kbl() to fix things up. For example, if you don’t want the “All” column you can just say

- `x %>% tablet %>% select(-All) %>% kbl`.

If you only want the the “All” column, you can just remove the group(s):

- `x %>% ungroup %>% select(-1) %>% tablet %>% kbl`.

By the way, you can also pass `all = NULL` to suppress the ‘All’ column.

# Grouped Columns

In tablet(), most columns are the consequences of a grouping variable. Not surprisingly, grouped columns are just a consequence of nested grouping variables. To illustrate, we follow the table1 vignette by adding a grouping variable that groups the two kinds of death.

    x %<>% mutate(class = status)                          # copy the current group
    x %<>% modify(class, label = 'class')                  # change its label
    levels(x$status) <- c('Alive','Melanoma','Unrelated')  # tweak current group
    levels(x$class)  <- c(' ',    'Death',   'Death')      # cluster groups
    x %<>% group_by(class, status)                         # nest groups
    x %>% tablet %>% kbl                              # render

| Death |
--- | --- | ---
| Alive (N = 134) | Melanoma (N = 57) | Unrelated (N = 14) | All (N = 205)
Sex
Male | 43 (32.1%) | 29 (50.9%) | 7 (50%) | 79 (38.5%)
Female | 91 (67.9%) | 28 (49.1%) | 7 (50%) | 126 (61.5%)
Age at Time of Operation (year)
Mean (SD) | 50 (15.9) | 55.1 (17.9) | 65.3 (10.9) | 52.5 (16.7)
Median (range) | 52 (4, 84) | 56 (14, 95) | 65 (49, 86) | 54 (4, 95)
Missing | 0 | 0 | 0 | 0
Tumor Thickness (mm)
Mean (SD) | 2.24 (2.33) | 4.31 (3.57) | 3.72 (3.63) | 2.92 (2.96)
Median (range) | 1.36 (0.1, 12.9) | 3.54 (0.32, 17.4) | 2.26 (0.16, 12.6) | 1.94 (0.1, 17.4)
Missing | 0 | 0 | 0 | 0
Ulceration
Absent | 92 (68.7%) | 16 (28.1%) | 7 (50%) | 115 (56.1%)
Present | 42 (31.3%) | 41 (71.9%) | 7 (50%) | 90 (43.9%)

# Transposed Groups

Categorical observations (in principle) and grouping variables are all factors, and are thus transposable. To illustrate, we drop the column group above and instead nest sex within status …

    x %<>% group_by(status, sex)
    x %<>% select(-class)
    x %>%
      tablet %>%
      kbl

| Alive | Melanoma | Unrelated |
--- | --- | --- | --- | ---
| Male (N = 43) | Female (N = 91) | Male (N = 29) | Female (N = 28) | Male (N = 7) | Female (N = 7) | All (N = 205)
Age at Time of Operation (year)
Mean (SD) | 52.5 (16.9) | 48.8 (15.4) | 53.9 (19.7) | 56.4 (16.2) | 62.4 (11.2) | 68.1 (10.6) | 52.5 (16.7)
Median (range) | 55 (12, 84) | 49 (4, 77) | 52 (19, 95) | 58 (14, 89) | 64 (49, 76) | 66 (54, 86) | 54 (4, 95)
Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0
Tumor Thickness (mm)
Mean (SD) | 2.73 (2.49) | 2.02 (2.22) | 4.63 (3.47) | 3.99 (3.71) | 4.83 (4.19) | 2.6 (2.84) | 2.92 (2.96)
Median (range) | 1.62 (0.16, 8.38) | 1.29 (0.1, 12.9) | 4.04 (0.81, 14.7) | 3.14 (0.32, 17.4) | 4.84 (0.65, 12.6) | 1.45 (0.16, 8.54) | 1.94 (0.1, 17.4)
Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0
Ulceration
Absent | 24 (55.8%) | 68 (74.7%) | 8 (27.6%) | 8 (28.6%) | 4 (57.1%) | 3 (42.9%) | 115 (56.1%)
Present | 19 (44.2%) | 23 (25.3%) | 21 (72.4%) | 20 (71.4%) | 3 (42.9%) | 4 (57.1%) | 90 (43.9%)

… or nest ulceration within status …

    x %<>% group_by(status, ulcer)
    x %>%
      tablet %>%
      kbl

| Alive | Melanoma | Unrelated |
--- | --- | --- | --- | ---
| Absent (N = 92) | Present (N = 42) | Absent (N = 16) | Present (N = 41) | Absent (N = 7) | Present (N = 7) | All (N = 205)
Sex
Male | 24 (26.1%) | 19 (45.2%) | 8 (50%) | 21 (51.2%) | 4 (57.1%) | 3 (42.9%) | 79 (38.5%)
Female | 68 (73.9%) | 23 (54.8%) | 8 (50%) | 20 (48.8%) | 3 (42.9%) | 4 (57.1%) | 126 (61.5%)
Age at Time of Operation (year)
Mean (SD) | 49.3 (15.4) | 51.6 (17.1) | 54.9 (19.9) | 55.1 (17.4) | 58.4 (8.66) | 72.1 (8.53) | 52.5 (16.7)
Median (range) | 50 (4, 83) | 54.5 (12, 84) | 59 (16, 83) | 56 (14, 95) | 56 (49, 71) | 72 (60, 86) | 54 (4, 95)
Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0
Tumor Thickness (mm)
Mean (SD) | 1.63 (1.93) | 3.58 (2.58) | 2.7 (3.35) | 4.94 (3.5) | 2.1 (1.93) | 5.34 (4.33) | 2.92 (2.96)
Median (range) | 1.13 (0.1, 12.9) | 3.06 (0.32, 12.2) | 1.94 (0.32, 14.7) | 4.04 (0.97, 17.4) | 1.45 (0.65, 6.12) | 4.84 (0.16, 12.6) | 1.94 (0.1, 17.4)
Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0

… or where it makes sense, use multiple levels of nesting.

    x %<>% group_by(status, ulcer, sex)
    x %>%
      tablet %>%
      kbl

| Alive | Melanoma | Unrelated |
--- | --- | --- | --- | ---
| Absent | Present | Absent | Present | Absent | Present |
| Male (N = 24) | Female (N = 68) | Male (N = 19) | Female (N = 23) | Male (N = 8) | Female (N = 8) | Male (N = 21) | Female (N = 20) | Male (N = 4) | Female (N = 3) | Male (N = 3) | Female (N = 4) | All (N = 205)
Age at Time of Operation (year)
Mean (SD) | 50.4 (17) | 48.9 (14.9) | 55.3 (16.9) | 48.7 (17) | 55.2 (22.2) | 54.6 (18.8) | 53.3 (19.2) | 57 (15.5) | 54.5 (7.14) | 63.7 (8.74) | 73 (2.65) | 71.5 (11.8) | 52.5 (16.7)
Median (range) | 54 (15, 83) | 49 (4, 77) | 56 (12, 84) | 48 (19, 75) | 56 (27, 83) | 59 (16, 77) | 52 (19, 95) | 58 (14, 89) | 52.5 (49, 64) | 66 (54, 71) | 72 (71, 76) | 70 (60, 86) | 54 (4, 95)
Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Tumor Thickness (mm)
Mean (SD) | 1.47 (1.72) | 1.69 (2) | 4.32 (2.42) | 2.97 (2.59) | 3.27 (4.68) | 2.14 (1.18) | 5.14 (2.86) | 4.72 (4.13) | 2.42 (2.5) | 1.67 (1.14) | 8.05 (4.02) | 3.3 (3.71) | 2.92 (2.96)
Median (range) | 0.97 (0.16, 7.09) | 1.29 (0.1, 12.9) | 3.87 (0.81, 8.38) | 1.94 (0.32, 12.2) | 1.78 (0.81, 14.7) | 2.02 (0.32, 3.56) | 4.83 (1.62, 12.9) | 3.54 (0.97, 17.4) | 1.46 (0.65, 6.12) | 1.45 (0.65, 2.9) | 6.76 (4.84, 12.6) | 2.26 (0.16, 8.54) | 1.94 (0.1, 17.4)
Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0

# Aesthetics

`tablet` tries to give rather exhaustive control over formatting. Much can be achieved by replacing elements of ‘fun’, ‘fac’, ‘num’, and ‘lab’ (see `?tablet.data.frame`). For finer control, you can replace these entirely. In this example, we will …

- ignore categoricals (other than groups) by replacing ‘fac’ with something of length zero,

- drop the ‘N =’ header material by substituting in ‘lab’, and

- switch to ‘(min - max)’ instead of ‘(min, max)’.

    x %<>% group_by(status)
    x %>%
      tablet(
        fac = NULL,
        lab ~ name,
        `Median (range)` ~ med + ' (' + min + ' - ' + max + ')'
      ) %>%
      kbl

| Alive | Melanoma | Unrelated | All
--- | --- | --- | --- | ---
Age at Time of Operation (year)
Mean (SD) | 50 (15.9) | 55.1 (17.9) | 65.3 (10.9) | 52.5 (16.7)
Median (range) | 52 (4 - 84) | 56 (14 - 95) | 65 (49 - 86) | 54 (4 - 95)
Missing | 0 | 0 | 0 | 0
Tumor Thickness (mm)
Mean (SD) | 2.24 (2.33) | 4.31 (3.57) | 3.72 (3.63) | 2.92 (2.96)
Median (range) | 1.36 (0.1 - 12.9) | 3.54 (0.32 - 17.4) | 2.26 (0.16 - 12.6) | 1.94 (0.1 - 17.4)
Missing | 0 | 0 | 0 | 0

# Note

The default presentation includes “N =” under the header, but also has percent characters in the table. Considerable gymnastics are required to make this work in latex. For html, we simply replace the newline character (supplied by ‘lab’) with a ‘br’ tag internally.

# Conclusion

`tablet` gives a flexible way of summarizing tables of observations. It reacts to numeric columns, factors, and grouping variables. Display order derives from the order of columns and factor levels in the data. Result columns can be grouped arbitrarily deep by supplying extra groups. Column labels and titles are respected. Rendering is largely the responsibility of `kableExtra` and can be extended. Further customization is possible by manipulating data after calling tablet() but before calling kbl(). Powerful results are possible with very little code.