The purpose of the page plan is to provide the option to split tables onto multiple pages. Currently, the page plan splits tables horizontally (i.e. row-wise), but in the future it is also planned to allow vertical (i.e. column-wise) splitting.
Pagination rules
The rules for how to split the table fall into 2 categories:
- Values-driven:
This feature involves splitting by the values of group
and/or label variables. It is specified via one or more
page_structure objects. Within the
page_structure, there are two possible methods for defining
the splits:
Split by every unique value of a
grouporlabelvariable. This can be done by passing the value “.default” as such:page_structure(group_val = ".default").Split by a specific value of a
grouporlabelvariable. This can be done by passing the specific value as such:page_structure(label_val = "n"). This will split after rows where label_val = “n”. If there are consecutive rows with label_val = “n”, it will split after the final row of that section of rows. This logic extends to instances where there are multiple sections with consecutive rows with label_val = “n”; there will be a split at the bottom of each section. Note: There can only be onepage_structurethat includes this level of specificity.
These methods can also be combined with a page_structure
or across multiple page_structures. For example,
page_structure(group_val = ".default", label_val = "n")
will split on every unique group_val, as well as every instance where
label_val = “n”.
If any of the page_structures contain a “.default”, it
may be desired to print a note indicating the grouping value for the
given page at the time of rendering. The location of this note can be
specified via the note_loc parameter in
page_plan. The functionality for note_loc may
be limited by the desired output type; for example, “preheader” is only
available for RTF outputs, while “source_note” and “subtitle” are
available for all output types.
- Max Rows-driven:
This feature, available via the max_rows argument in
page_plan, involves splitting based on the maximum number
of rows per table. Rows dedicated to group labels (without data) are
included in the row counts. If a set of rows within a single
group value are split apart, the group label will be
repeated for each page.
NOTE: If both max_rows and
page_structure are provided to the page_plan,
the table will first be split according to the
page_structure, follwed by the max_rows.
Examples of each of these approaches are below. To reduce the amount
of code displayed in the examples, following the initial table,
tfrmt’s layering functionality will be used to add the
page_plan. For more information about this, see the
“Layering tfrmts” vignette.
Values-driven splitting
Let’s take a subset of our example demography data.Expand for the code used to produce this subset
head(data_demog2)
#> # A tibble: 6 × 8
#> # Groups: rowlbl1 [1]
#> rowlbl1 rowlbl2 param grp ord1 ord2 column value
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 Age (y) n n cont 1 1 Placebo 86
#> 2 Age (y) n n cont 1 1 Xanomeline Low Dose 84
#> 3 Age (y) n n cont 1 1 Xanomeline High Dose 84
#> 4 Age (y) n n cont 1 1 Total 254
#> 5 Age (y) n p cont 1 1 p-value 0.593
#> 6 Age (y) Mean Mean cont 1 2 Placebo 75.2The formatted table as a single page is as follows:
base_tfrmt <- tfrmt(
# specify columns in the data
group = c(rowlbl1,grp),
label = rowlbl2,
column = column,
param = param,
value = value,
sorting_cols = c(ord1, ord2),
# specify value formatting
body_plan = body_plan(
frmt_structure(group_val = ".default", label_val = ".default", frmt_combine("{n} {pct}",
n = frmt("xxx"),
pct = frmt_when("==100" ~ "",
"==0" ~ "",
TRUE ~ frmt("(xx.x %)")))),
frmt_structure(group_val = ".default", label_val = "n", frmt("xxx")),
frmt_structure(group_val = ".default", label_val = c("Mean", "Median", "Min","Max"), frmt("xxx.x")),
frmt_structure(group_val = ".default", label_val = "SD", frmt("xxx.xx")),
frmt_structure(group_val = ".default", label_val = ".default", p = frmt("")),
frmt_structure(group_val = ".default", label_val = c("n","<65 yrs","<12 months","<25"), p = frmt_when(">0.99" ~ ">0.99",
"<0.001" ~ "<0.001",
TRUE ~ frmt("x.xxx", missing = "")))
),
# remove extra cols
col_plan = col_plan(-grp,
-starts_with("ord") ),
# Specify column styling plan
col_style_plan = col_style_plan(
col_style_structure(align = c(".",","," "), col = vars(Placebo, contains("Dose"), "Total", "p-value"))
),
# Specify row group plan
row_grp_plan = row_grp_plan(
row_grp_structure(group_val = ".default", element_block(post_space = " ")),
label_loc = element_row_grp_loc(location = "column")
)
)
base_tfrmt %>%
print_to_gt(data_demog2) | Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Age (y) | n | 86 | 84 | 84 | 254 | 0.593 |
| Mean | 75.2 | 75.7 | 74.4 | 75.1 | ||
| SD | 8.59 | 8.29 | 7.89 | 8.25 | ||
| Median | 76.0 | 77.5 | 76.0 | 77.0 | ||
| Min | 52.0 | 51.0 | 56.0 | 51.0 | ||
| Max | 89.0 | 88.0 | 88.0 | 89.0 | ||
| <65 yrs | 14 (16.3 %) | 8 ( 9.5 %) | 11 (13.1 %) | 33 (13.0 %) | 0.144 | |
| 65-80 yrs | 42 (48.8 %) | 47 (56.0 %) | 55 (65.5 %) | 144 (56.7 %) | ||
| >80 yrs | 30 (34.9 %) | 29 (34.5 %) | 18 (21.4 %) | 77 (30.3 %) | ||
| Sex | n | 86 | 84 | 84 | 254 | 0.141 |
| Male | 33 (38.4 %) | 34 (40.5 %) | 44 (52.4 %) | 111 (43.7 %) | ||
| Female | 53 (61.6 %) | 50 (59.5 %) | 40 (47.6 %) | 143 (56.3 %) | ||
| Race (Origin) | n | 86 | 84 | 84 | 254 | 0.648 |
| Caucasian | 75 (87.2 %) | 72 (85.7 %) | 71 (84.5 %) | 218 (85.8 %) | ||
| African Descent | 8 ( 9.3 %) | 6 ( 7.1 %) | 9 (10.7 %) | 23 ( 9.1 %) | ||
| Hispanic | 3 ( 3.5 %) | 6 ( 7.1 %) | 3 ( 3.6 %) | 12 ( 4.7 %) | ||
| Other | 1 ( 1.2 %) | 1 ( 0.4 %) | ||||
| MMSE | n | 86 | 84 | 84 | 254 | 0.595 |
| Mean | 18.0 | 17.9 | 18.5 | 18.1 | ||
| SD | 4.27 | 4.22 | 4.16 | 4.21 | ||
| Median | 19.5 | 18.0 | 20.0 | 19.0 | ||
| Min | 10.0 | 10.0 | 10.0 | 10.0 | ||
| Max | 23.0 | 24.0 | 24.0 | 24.0 | ||
| Duration of disease | n | 86 | 84 | 84 | 254 | 0.153 |
| Mean | 42.6 | 48.7 | 40.5 | 43.9 | ||
| SD | 30.24 | 29.58 | 24.69 | 28.40 | ||
| Median | 35.3 | 40.2 | 36.0 | 36.2 | ||
| Min | 7.2 | 7.8 | 2.2 | 2.2 | ||
| Max | 183.1 | 130.8 | 135.0 | 183.1 | ||
| <12 months | 5 ( 5.8 %) | 3 ( 3.6 %) | 4 ( 4.8 %) | 12 ( 4.7 %) | 0.789 | |
| >=12 months | 81 (94.2 %) | 81 (96.4 %) | 80 (95.2 %) | 242 (95.3 %) | ||
| Years of education | n | 86 | 84 | 84 | 254 | 0.388 |
| Mean | 12.6 | 13.2 | 12.5 | 12.8 | ||
| SD | 2.95 | 4.15 | 2.92 | 3.38 | ||
| Median | 12.0 | 12.0 | 12.0 | 12.0 | ||
| Min | 6.0 | 3.0 | 6.0 | 3.0 | ||
| Max | 21.0 | 24.0 | 20.0 | 24.0 | ||
| Baseline weight(kg) | n | 86 | 83 | 84 | 253 | 0.003 |
| Mean | 62.8 | 67.3 | 70.0 | 66.6 | ||
| SD | 12.77 | 14.12 | 14.65 | 14.13 | ||
| Median | 60.5 | 64.9 | 69.2 | 66.7 | ||
| Min | 34.0 | 45.4 | 41.7 | 34.0 | ||
| Max | 86.2 | 106.1 | 108.0 | 108.0 | ||
| Baseline height(cm) | n | 86 | 84 | 84 | 254 | 0.126 |
| Mean | 162.6 | 163.4 | 165.8 | 163.9 | ||
| SD | 11.52 | 10.42 | 10.13 | 10.76 | ||
| Median | 162.6 | 162.6 | 165.1 | 162.8 | ||
| Min | 137.2 | 135.9 | 146.1 | 135.9 | ||
| Max | 185.4 | 195.6 | 190.5 | 195.6 | ||
| Baseline BMI | n | 86 | 83 | 84 | 253 | 0.013 |
| Mean | 23.6 | 25.1 | 25.3 | 24.7 | ||
| SD | 3.67 | 4.27 | 4.16 | 4.09 | ||
| Median | 23.4 | 24.3 | 24.8 | 24.2 | ||
| Min | 15.1 | 17.7 | 13.7 | 13.7 | ||
| Max | 33.3 | 40.1 | 34.5 | 40.1 | ||
| <25 | 59 (68.6 %) | 47 (56.0 %) | 44 (52.4 %) | 150 (59.1 %) | 0.233 | |
| 25-<30 | 21 (24.4 %) | 27 (32.1 %) | 28 (33.3 %) | 76 (29.9 %) | ||
| >=30 | 6 ( 7.0 %) | 10 (11.9 %) | 12 (14.3 %) | 28 (11.0 %) | ||
Every unique value of 1 variable
Suppose we want to split the table by every unique value of
rowlbl1 and add a footnote indicating the table grouping.
We can drop rowlbl1 from the tables since its value will be
printed in the note. We will use the gt::grp_pull()
function to print the individual tables nicely in the vignette.
gts <- base_tfrmt %>%
layer_tfrmt(
tfrmt(
# page plan
page_plan = page_plan(
page_structure(group_val = list(rowlbl1 = ".default")),
note_loc = "source_note"
)
)
) %>%
print_to_gt(data_demog2)| Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Age (y) | n | 86 | 84 | 84 | 254 | 0.593 |
| Mean | 75.2 | 75.7 | 74.4 | 75.1 | ||
| SD | 8.59 | 8.29 | 7.89 | 8.25 | ||
| Median | 76.0 | 77.5 | 76.0 | 77.0 | ||
| Min | 52.0 | 51.0 | 56.0 | 51.0 | ||
| Max | 89.0 | 88.0 | 88.0 | 89.0 | ||
| <65 yrs | 14 (16.3 %) | 8 ( 9.5 %) | 11 (13.1 %) | 33 (13.0 %) | 0.144 | |
| 65-80 yrs | 42 (48.8 %) | 47 (56.0 %) | 55 (65.5 %) | 144 (56.7 %) | ||
| >80 yrs | 30 (34.9 %) | 29 (34.5 %) | 18 (21.4 %) | 77 (30.3 %) | ||
| rowlbl1: Age (y) | ||||||
| Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Sex | n | 86 | 84 | 84 | 254 | 0.141 |
| Male | 33 (38.4 %) | 34 (40.5 %) | 44 (52.4 %) | 111 (43.7 %) | ||
| Female | 53 (61.6 %) | 50 (59.5 %) | 40 (47.6 %) | 143 (56.3 %) | ||
| rowlbl1: Sex | ||||||
| Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Race (Origin) | n | 86 | 84 | 84 | 254 | 0.648 |
| Caucasian | 75 (87.2 %) | 72 (85.7 %) | 71 (84.5 %) | 218 (85.8 %) | ||
| African Descent | 8 ( 9.3 %) | 6 ( 7.1 %) | 9 (10.7 %) | 23 ( 9.1 %) | ||
| Hispanic | 3 ( 3.5 %) | 6 ( 7.1 %) | 3 ( 3.6 %) | 12 ( 4.7 %) | ||
| Other | 1 ( 1.2 %) | 1 ( 0.4 %) | ||||
| rowlbl1: Race (Origin) | ||||||
Every unique value of 2 variables
We could also choose to split on both grouping variables as such (showing first 3 tables only):
gts <- base_tfrmt %>%
layer_tfrmt(
tfrmt(
# page plan
page_plan = page_plan(
page_structure(group_val = ".default"),
note_loc = "source_note"
)
)
) %>%
print_to_gt(data_demog2) | Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Age (y) | n | 86 | 84 | 84 | 254 | 0.593 |
| Mean | 75.2 | 75.7 | 74.4 | 75.1 | ||
| SD | 8.59 | 8.29 | 7.89 | 8.25 | ||
| Median | 76.0 | 77.5 | 76.0 | 77.0 | ||
| Min | 52.0 | 51.0 | 56.0 | 51.0 | ||
| Max | 89.0 | 88.0 | 88.0 | 89.0 | ||
| rowlbl1: Age (y), grp: cont | ||||||
| Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Age (y) | <65 yrs | 14 (16.3 %) | 8 ( 9.5 %) | 11 (13.1 %) | 33 (13.0 %) | 0.144 |
| 65-80 yrs | 42 (48.8 %) | 47 (56.0 %) | 55 (65.5 %) | 144 (56.7 %) | ||
| >80 yrs | 30 (34.9 %) | 29 (34.5 %) | 18 (21.4 %) | 77 (30.3 %) | ||
| rowlbl1: Age (y), grp: cat | ||||||
| Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Sex | n | 86 | 84 | 84 | 254 | 0.141 |
| Male | 33 (38.4 %) | 34 (40.5 %) | 44 (52.4 %) | 111 (43.7 %) | ||
| Female | 53 (61.6 %) | 50 (59.5 %) | 40 (47.6 %) | 143 (56.3 %) | ||
| rowlbl1: Sex, grp: cat | ||||||
Specific value of a variable
Finally, we could split on a specific value observed in the data. For example, rowlbl1 = “Age (y)”.
gts <- base_tfrmt %>%
layer_tfrmt(
tfrmt(
# page plan
page_plan = page_plan(
page_structure(group_val = list(rowlbl1 = "Age (y)")),
note_loc = "source_note"
)
)
) %>%
print_to_gt(data_demog2) | Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Age (y) | n | 86 | 84 | 84 | 254 | 0.593 |
| Mean | 75.2 | 75.7 | 74.4 | 75.1 | ||
| SD | 8.59 | 8.29 | 7.89 | 8.25 | ||
| Median | 76.0 | 77.5 | 76.0 | 77.0 | ||
| Min | 52.0 | 51.0 | 56.0 | 51.0 | ||
| Max | 89.0 | 88.0 | 88.0 | 89.0 | ||
| <65 yrs | 14 (16.3 %) | 8 ( 9.5 %) | 11 (13.1 %) | 33 (13.0 %) | 0.144 | |
| 65-80 yrs | 42 (48.8 %) | 47 (56.0 %) | 55 (65.5 %) | 144 (56.7 %) | ||
| >80 yrs | 30 (34.9 %) | 29 (34.5 %) | 18 (21.4 %) | 77 (30.3 %) | ||
| Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Sex | n | 86 | 84 | 84 | 254 | 0.141 |
| Male | 33 (38.4 %) | 34 (40.5 %) | 44 (52.4 %) | 111 (43.7 %) | ||
| Female | 53 (61.6 %) | 50 (59.5 %) | 40 (47.6 %) | 143 (56.3 %) | ||
| Race (Origin) | n | 86 | 84 | 84 | 254 | 0.648 |
| Caucasian | 75 (87.2 %) | 72 (85.7 %) | 71 (84.5 %) | 218 (85.8 %) | ||
| African Descent | 8 ( 9.3 %) | 6 ( 7.1 %) | 9 (10.7 %) | 23 ( 9.1 %) | ||
| Hispanic | 3 ( 3.5 %) | 6 ( 7.1 %) | 3 ( 3.6 %) | 12 ( 4.7 %) | ||
| Other | 1 ( 1.2 %) | 1 ( 0.4 %) | ||||
| MMSE | n | 86 | 84 | 84 | 254 | 0.595 |
| Mean | 18.0 | 17.9 | 18.5 | 18.1 | ||
| SD | 4.27 | 4.22 | 4.16 | 4.21 | ||
| Median | 19.5 | 18.0 | 20.0 | 19.0 | ||
| Min | 10.0 | 10.0 | 10.0 | 10.0 | ||
| Max | 23.0 | 24.0 | 24.0 | 24.0 | ||
| Duration of disease | n | 86 | 84 | 84 | 254 | 0.153 |
| Mean | 42.6 | 48.7 | 40.5 | 43.9 | ||
| SD | 30.24 | 29.58 | 24.69 | 28.40 | ||
| Median | 35.3 | 40.2 | 36.0 | 36.2 | ||
| Min | 7.2 | 7.8 | 2.2 | 2.2 | ||
| Max | 183.1 | 130.8 | 135.0 | 183.1 | ||
| <12 months | 5 ( 5.8 %) | 3 ( 3.6 %) | 4 ( 4.8 %) | 12 ( 4.7 %) | 0.789 | |
| >=12 months | 81 (94.2 %) | 81 (96.4 %) | 80 (95.2 %) | 242 (95.3 %) | ||
| Years of education | n | 86 | 84 | 84 | 254 | 0.388 |
| Mean | 12.6 | 13.2 | 12.5 | 12.8 | ||
| SD | 2.95 | 4.15 | 2.92 | 3.38 | ||
| Median | 12.0 | 12.0 | 12.0 | 12.0 | ||
| Min | 6.0 | 3.0 | 6.0 | 3.0 | ||
| Max | 21.0 | 24.0 | 20.0 | 24.0 | ||
| Baseline weight(kg) | n | 86 | 83 | 84 | 253 | 0.003 |
| Mean | 62.8 | 67.3 | 70.0 | 66.6 | ||
| SD | 12.77 | 14.12 | 14.65 | 14.13 | ||
| Median | 60.5 | 64.9 | 69.2 | 66.7 | ||
| Min | 34.0 | 45.4 | 41.7 | 34.0 | ||
| Max | 86.2 | 106.1 | 108.0 | 108.0 | ||
| Baseline height(cm) | n | 86 | 84 | 84 | 254 | 0.126 |
| Mean | 162.6 | 163.4 | 165.8 | 163.9 | ||
| SD | 11.52 | 10.42 | 10.13 | 10.76 | ||
| Median | 162.6 | 162.6 | 165.1 | 162.8 | ||
| Min | 137.2 | 135.9 | 146.1 | 135.9 | ||
| Max | 185.4 | 195.6 | 190.5 | 195.6 | ||
| Baseline BMI | n | 86 | 83 | 84 | 253 | 0.013 |
| Mean | 23.6 | 25.1 | 25.3 | 24.7 | ||
| SD | 3.67 | 4.27 | 4.16 | 4.09 | ||
| Median | 23.4 | 24.3 | 24.8 | 24.2 | ||
| Min | 15.1 | 17.7 | 13.7 | 13.7 | ||
| Max | 33.3 | 40.1 | 34.5 | 40.1 | ||
| <25 | 59 (68.6 %) | 47 (56.0 %) | 44 (52.4 %) | 150 (59.1 %) | 0.233 | |
| 25-<30 | 21 (24.4 %) | 27 (32.1 %) | 28 (33.3 %) | 76 (29.9 %) | ||
| >=30 | 6 ( 7.0 %) | 10 (11.9 %) | 12 (14.3 %) | 28 (11.0 %) | ||
Splitting based on maximum rows
To instead limit the number of rows per page, we can set the
max_rows argument as such (showing first 3 tables
only):
gts <- base_tfrmt %>%
layer_tfrmt(
tfrmt(
# page plan
page_plan = page_plan(
max_rows = 20
)
)
) %>%
print_to_gt(data_demog2) | Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Age (y) | n | 86 | 84 | 84 | 254 | 0.593 |
| Mean | 75.2 | 75.7 | 74.4 | 75.1 | ||
| SD | 8.59 | 8.29 | 7.89 | 8.25 | ||
| Median | 76.0 | 77.5 | 76.0 | 77.0 | ||
| Min | 52.0 | 51.0 | 56.0 | 51.0 | ||
| Max | 89.0 | 88.0 | 88.0 | 89.0 | ||
| <65 yrs | 14 (16.3 %) | 8 ( 9.5 %) | 11 (13.1 %) | 33 (13.0 %) | 0.144 | |
| 65-80 yrs | 42 (48.8 %) | 47 (56.0 %) | 55 (65.5 %) | 144 (56.7 %) | ||
| >80 yrs | 30 (34.9 %) | 29 (34.5 %) | 18 (21.4 %) | 77 (30.3 %) | ||
| Sex | n | 86 | 84 | 84 | 254 | 0.141 |
| Male | 33 (38.4 %) | 34 (40.5 %) | 44 (52.4 %) | 111 (43.7 %) | ||
| Female | 53 (61.6 %) | 50 (59.5 %) | 40 (47.6 %) | 143 (56.3 %) | ||
| Race (Origin) | n | 86 | 84 | 84 | 254 | 0.648 |
| Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Race (Origin) | Caucasian | 75 (87.2 %) | 72 (85.7 %) | 71 (84.5 %) | 218 (85.8 %) | |
| African Descent | 8 ( 9.3 %) | 6 ( 7.1 %) | 9 (10.7 %) | 23 ( 9.1 %) | ||
| Hispanic | 3 ( 3.5 %) | 6 ( 7.1 %) | 3 ( 3.6 %) | 12 ( 4.7 %) | ||
| Other | 1 ( 1.2 %) | 1 ( 0.4 %) | ||||
| MMSE | n | 86 | 84 | 84 | 254 | 0.595 |
| Mean | 18.0 | 17.9 | 18.5 | 18.1 | ||
| SD | 4.27 | 4.22 | 4.16 | 4.21 | ||
| Median | 19.5 | 18.0 | 20.0 | 19.0 | ||
| Min | 10.0 | 10.0 | 10.0 | 10.0 | ||
| Max | 23.0 | 24.0 | 24.0 | 24.0 | ||
| Duration of disease | n | 86 | 84 | 84 | 254 | 0.153 |
| Mean | 42.6 | 48.7 | 40.5 | 43.9 | ||
| SD | 30.24 | 29.58 | 24.69 | 28.40 | ||
| Median | 35.3 | 40.2 | 36.0 | 36.2 | ||
| Min | 7.2 | 7.8 | 2.2 | 2.2 | ||
| Placebo | Xanomeline Low Dose | Xanomeline High Dose | Total | p-value | ||
|---|---|---|---|---|---|---|
| Duration of disease | Max | 183.1 | 130.8 | 135.0 | 183.1 | |
| <12 months | 5 ( 5.8 %) | 3 ( 3.6 %) | 4 ( 4.8 %) | 12 ( 4.7 %) | 0.789 | |
| >=12 months | 81 (94.2 %) | 81 (96.4 %) | 80 (95.2 %) | 242 (95.3 %) | ||
| Years of education | n | 86 | 84 | 84 | 254 | 0.388 |
| Mean | 12.6 | 13.2 | 12.5 | 12.8 | ||
| SD | 2.95 | 4.15 | 2.92 | 3.38 | ||
| Median | 12.0 | 12.0 | 12.0 | 12.0 | ||
| Min | 6.0 | 3.0 | 6.0 | 3.0 | ||
| Max | 21.0 | 24.0 | 20.0 | 24.0 | ||
| Baseline weight(kg) | n | 86 | 83 | 84 | 253 | 0.003 |
| Mean | 62.8 | 67.3 | 70.0 | 66.6 | ||
| SD | 12.77 | 14.12 | 14.65 | 14.13 | ||
| Median | 60.5 | 64.9 | 69.2 | 66.7 | ||
Notice that for groups (defined by the rowlbl1 variable) that are split up, the group header is repeated for each table.
Paginated outputs
When pagination is applied, the result of the
print_to_gt or print_mock_gt functions is a
gt_group object (collection of individual
gts). Passing this result to the gt::gtsave()
function will result in a multi-paged output.