Throughout the examples, we will use the person_record
sample dataset, which is included in the tsg package. This
dataset contains demographic information about individuals, including
person_id, sex, age,
marital_status, employed status, and
functional difficulties.
dim(person_record)
#> [1] 2918 11
head(person_record)
#> person_id age sex marital_status employed seeing hearing walking remembering
#> 1 1 46 1 2 1 1 1 1 1
#> 2 2 44 2 2 1 1 1 1 1
#> 3 3 10 1 1 NA 1 1 1 1
#> 4 4 65 2 2 1 1 1 1 1
#> 5 5 65 1 2 1 1 1 1 1
#> 6 6 28 2 1 2 1 1 1 1
#> self_caring communicating
#> 1 1 1
#> 2 1 1
#> 3 1 1
#> 4 1 1
#> 5 1 1
#> 6 1 1The generate_frequency() function creates frequency
tables for one or more categorical variables in a data frame. It
supports a variety of enhancements, such as sorting, adding totals and
percentages, handling missing values, and customizing labels. This
function is highly versatile and can work with grouped data, outputting
either a single table or a list of tables.
If you pass multiple variables, it will generate frequency tables for each variable separately in a list.
person_record |>
generate_frequency(sex, age, marital_status)
#> $Sex
#> # A tibble: 3 × 3
#> category frequency percent
#> <int+lbl> <int> <dbl>
#> 1 1 [Male] 1516 52.0
#> 2 2 [Female] 1402 48.0
#> 3 0 [Total] 2918 100
#>
#> $Age
#> # A tibble: 96 × 3
#> category frequency percent
#> <chr> <int> <dbl>
#> 1 16 82 2.81
#> 2 15 75 2.57
#> 3 12 74 2.54
#> 4 13 70 2.40
#> 5 20 68 2.33
#> 6 14 66 2.26
#> 7 19 66 2.26
#> 8 11 61 2.09
#> 9 24 61 2.09
#> 10 18 59 2.02
#> # ℹ 86 more rows
#>
#> $`Marital status`
#> # A tibble: 6 × 3
#> category frequency percent
#> <int+lbl> <int> <dbl>
#> 1 1 [Single/never married] 1544 52.9
#> 2 2 [Married] 769 26.4
#> 3 3 [Common law/live-in] 424 14.5
#> 4 4 [Widowed] 138 4.73
#> 5 6 [Separated] 43 1.47
#> 6 0 [Total] 2918 100
#>
#> attr(,"class")
#> [1] "tsg" "tsgf" "list"You can also specify grouping using the group_by() from
dplyr and it will calculate the frequency table for each
group.
person_record |>
dplyr::group_by(sex) |>
generate_frequency(marital_status)
#> # A tibble: 12 × 4
#> sex category frequency percent
#> <int+lbl> <int+lbl> <int> <dbl>
#> 1 1 [Male] 1 [Single/never married] 859 56.7
#> 2 1 [Male] 2 [Married] 387 25.5
#> 3 1 [Male] 3 [Common law/live-in] 211 13.9
#> 4 1 [Male] 4 [Widowed] 40 2.64
#> 5 1 [Male] 6 [Separated] 19 1.25
#> 6 1 [Male] 0 [Total] 1516 100
#> 7 2 [Female] 1 [Single/never married] 685 48.9
#> 8 2 [Female] 2 [Married] 382 27.2
#> 9 2 [Female] 3 [Common law/live-in] 213 15.2
#> 10 2 [Female] 4 [Widowed] 98 6.99
#> 11 2 [Female] 6 [Separated] 24 1.71
#> 12 2 [Female] 0 [Total] 1402 100By default, the function will generate a single frequency table for
the grouped data. If you want to generate a list of frequency tables for
each group, you can set group_as_list = TRUE.
person_record |>
dplyr::group_by(sex) |>
generate_frequency(marital_status, group_as_list = TRUE)
#> $Male
#> # A tibble: 6 × 4
#> sex category frequency percent
#> <int+lbl> <int+lbl> <int> <dbl>
#> 1 1 [Male] 1 [Single/never married] 859 56.7
#> 2 1 [Male] 2 [Married] 387 25.5
#> 3 1 [Male] 3 [Common law/live-in] 211 13.9
#> 4 1 [Male] 4 [Widowed] 40 2.64
#> 5 1 [Male] 6 [Separated] 19 1.25
#> 6 1 [Male] 0 [Total] 1516 100
#>
#> $Female
#> # A tibble: 6 × 4
#> sex category frequency percent
#> <int+lbl> <int+lbl> <int> <dbl>
#> 1 2 [Female] 1 [Single/never married] 685 48.9
#> 2 2 [Female] 2 [Married] 382 27.2
#> 3 2 [Female] 3 [Common law/live-in] 213 15.2
#> 4 2 [Female] 4 [Widowed] 98 6.99
#> 5 2 [Female] 6 [Separated] 24 1.71
#> 6 2 [Female] 0 [Total] 1402 100
#>
#> attr(,"groups")
#> [1] "sex"
#> attr(,"group_attrs")
#> attr(,"group_attrs")$sex
#> attr(,"group_attrs")$sex$labels
#> Male Female
#> 1 2
#>
#> attr(,"group_attrs")$sex$label
#> [1] "Sex"
#>
#> attr(,"group_attrs")$sex$class
#> [1] "haven_labelled" "vctrs_vctr" "integer"
#>
#>
#> attr(,"class")
#> [1] "tsg" "tsgf" "list"By default, the output is sorted by frequency in descending order. If
sort_value is set to FALSE, the output will be
sorted by the variable values in ascending order.
person_record |>
generate_frequency(age, sort_value = TRUE)
#> # A tibble: 96 × 3
#> category frequency percent
#> <chr> <int> <dbl>
#> 1 16 82 2.81
#> 2 15 75 2.57
#> 3 12 74 2.54
#> 4 13 70 2.40
#> 5 20 68 2.33
#> 6 14 66 2.26
#> 7 19 66 2.26
#> 8 11 61 2.09
#> 9 24 61 2.09
#> 10 18 59 2.02
#> # ℹ 86 more rows
person_record |>
generate_frequency(age, sort_value = FALSE)
#> # A tibble: 96 × 3
#> category frequency percent
#> <chr> <int> <dbl>
#> 1 0 32 1.10
#> 2 1 42 1.44
#> 3 2 44 1.51
#> 4 3 41 1.41
#> 5 4 44 1.51
#> 6 5 54 1.85
#> 7 6 44 1.51
#> 8 7 47 1.61
#> 9 8 56 1.92
#> 10 9 48 1.64
#> # ℹ 86 more rowsIf multiple variables are specified, you can indicate which
variable/s is/are excluded from sorting using the
sort_except argument.
person_record |>
generate_frequency(
sex,
age,
marital_status,
# vector of variable names (character) to exclude from sorting
sort_except = "age"
)
#> $Sex
#> # A tibble: 3 × 3
#> category frequency percent
#> <int+lbl> <int> <dbl>
#> 1 1 [Male] 1516 52.0
#> 2 2 [Female] 1402 48.0
#> 3 0 [Total] 2918 100
#>
#> $Age
#> # A tibble: 96 × 3
#> category frequency percent
#> <chr> <int> <dbl>
#> 1 0 32 1.10
#> 2 1 42 1.44
#> 3 2 44 1.51
#> 4 3 41 1.41
#> 5 4 44 1.51
#> 6 5 54 1.85
#> 7 6 44 1.51
#> 8 7 47 1.61
#> 9 8 56 1.92
#> 10 9 48 1.64
#> # ℹ 86 more rows
#>
#> $`Marital status`
#> # A tibble: 6 × 3
#> category frequency percent
#> <int+lbl> <int> <dbl>
#> 1 1 [Single/never married] 1544 52.9
#> 2 2 [Married] 769 26.4
#> 3 3 [Common law/live-in] 424 14.5
#> 4 4 [Widowed] 138 4.73
#> 5 6 [Separated] 43 1.47
#> 6 0 [Total] 2918 100
#>
#> attr(,"class")
#> [1] "tsg" "tsgf" "list"n valuesYou can specify the top n most frequent values to
display in the frequency table, if sort_value is
TRUE. By default, it will show top-n values plus the
remaining values grouped into “Others”.
person_record |>
generate_frequency(
marital_status,
#top_n = 3
)
#> # A tibble: 6 × 3
#> category frequency percent
#> <int+lbl> <int> <dbl>
#> 1 1 [Single/never married] 1544 52.9
#> 2 2 [Married] 769 26.4
#> 3 3 [Common law/live-in] 424 14.5
#> 4 4 [Widowed] 138 4.73
#> 5 6 [Separated] 43 1.47
#> 6 0 [Total] 2918 100If you want to show only the top-n values and exclude the rest, set
top_n_only = TRUE.
person_record |>
generate_frequency(
marital_status,
#top_n = 3,
#top_n_only = TRUE
)
#> # A tibble: 6 × 3
#> category frequency percent
#> <int+lbl> <int> <dbl>
#> 1 1 [Single/never married] 1544 52.9
#> 2 2 [Married] 769 26.4
#> 3 3 [Common law/live-in] 424 14.5
#> 4 4 [Widowed] 138 4.73
#> 5 6 [Separated] 43 1.47
#> 6 0 [Total] 2918 100You can also specify whether to include or exclude NAs
(missing values) from the frequency table.
person_record |>
generate_frequency(
employed,
include_na = TRUE # default
)
#> # A tibble: 4 × 3
#> category frequency percent
#> <int+lbl> <int> <dbl>
#> 1 2 [No] 1186 40.6
#> 2 1 [Yes] 922 31.6
#> 3 9 [Not reported] 810 27.8
#> 4 0 [Total] 2918 100
# Exclude NA values
person_record |>
generate_frequency(
employed,
include_na = FALSE
)
#> # A tibble: 3 × 3
#> category frequency percent
#> <int+lbl> <int> <dbl>
#> 1 2 [No] 1186 56.3
#> 2 1 [Yes] 922 43.7
#> 3 0 [Total] 2108 100If the all variables passed to generate_frequency() are
of the same structure (i.e. have the same number of levels or
categories), you can collapse them into a single frequency table by
setting collapse_list = TRUE.
person_record |>
generate_frequency(
seeing,
hearing,
walking,
remembering,
self_caring,
communicating,
collapse_list = TRUE
)
#> # A tibble: 6 × 13
#> category frequency_0 frequency_1 frequency_2 frequency_3 frequency_4
#> <chr> <int> <int> <int> <int> <int>
#> 1 Seeing, even if w… 2918 2623 82 9 1
#> 2 Hearing, even if … 2918 2666 43 5 1
#> 3 Walking or climbi… 2918 2658 48 7 2
#> 4 Remembering or co… 2918 2680 28 7 0
#> 5 Self-caring (such… 2918 2671 37 5 2
#> 6 Communicating usi… 2918 2683 25 5 2
#> # ℹ 7 more variables: frequency_9 <int>, percent_0 <dbl>, percent_1 <dbl>,
#> # percent_2 <dbl>, percent_3 <dbl>, percent_4 <dbl>, percent_9 <dbl>Or equivalently using the collapse_list() helper
function.
person_record |>
generate_frequency(
seeing,
hearing,
walking,
remembering,
self_caring,
communicating
) |>
collapse_list()
#> # A tibble: 6 × 13
#> category frequency_0 frequency_1 frequency_2 frequency_3 frequency_4
#> <chr> <int> <int> <int> <int> <int>
#> 1 Seeing, even if w… 2918 2623 82 9 1
#> 2 Hearing, even if … 2918 2666 43 5 1
#> 3 Walking or climbi… 2918 2658 48 7 2
#> 4 Remembering or co… 2918 2680 28 7 0
#> 5 Self-caring (such… 2918 2671 37 5 2
#> 6 Communicating usi… 2918 2683 25 5 2
#> # ℹ 7 more variables: frequency_9 <int>, percent_0 <dbl>, percent_1 <dbl>,
#> # percent_2 <dbl>, percent_3 <dbl>, percent_4 <dbl>, percent_9 <dbl>You can also add cumulative frequency and percentage to the frequency table.
person_record |>
generate_frequency(
sex,
add_cumulative = TRUE,
add_cumulative_percent = TRUE
)
#> # A tibble: 3 × 5
#> category frequency percent cumulative cumulative_percent
#> <int+lbl> <int> <dbl> <int> <dbl>
#> 1 1 [Male] 1516 52.0 1516 52.0
#> 2 2 [Female] 1402 48.0 2918 100
#> 3 0 [Total] 2918 100 NA NAYou can also specify whether to express the value as a proportion.
person_record |>
generate_frequency(
marital_status,
as_proportion = TRUE
)
#> # A tibble: 6 × 3
#> category frequency proportion
#> <int+lbl> <int> <dbl>
#> 1 1 [Single/never married] 1544 0.529
#> 2 2 [Married] 769 0.264
#> 3 3 [Common law/live-in] 424 0.145
#> 4 4 [Widowed] 138 0.0473
#> 5 6 [Separated] 43 0.0147
#> 6 0 [Total] 2918 1You can also position the total row at the top of the table.
person_record |>
generate_frequency(
marital_status,
position_total = "top"
)
#> # A tibble: 6 × 3
#> category frequency percent
#> <int+lbl> <int> <dbl>
#> 1 0 [Total] 2918 100
#> 2 1 [Single/never married] 1544 52.9
#> 3 2 [Married] 769 26.4
#> 4 3 [Common law/live-in] 424 14.5
#> 5 4 [Widowed] 138 4.73
#> 6 6 [Separated] 43 1.47NOTE: For labelled data, the value for the row total is automatically
set the lowest numeric value. The default label for the total row is
“Total”; if you want to set a custom label for the total row, you can
use the label_total argument.
The generate_crosstab() function allows you to create
cross-tabulations between two variables, which is useful for exploring
relationships between categorical variables.
person_record |>
generate_crosstab(marital_status, sex)
#> # A tibble: 6 × 6
#> category total frequency_1 frequency_2 percent_1 percent_2
#> <int+lbl> <int> <int> <int> <dbl> <dbl>
#> 1 1 [Single/never married] 1544 859 685 55.6 44.4
#> 2 2 [Married] 769 387 382 50.3 49.7
#> 3 3 [Common law/live-in] 424 211 213 49.8 50.2
#> 4 4 [Widowed] 138 40 98 29.0 71.0
#> 5 6 [Separated] 43 19 24 44.2 55.8
#> 6 0 [Total] 2918 1516 1402 52.0 48.0NOTE: If you pass only one variable, it will fall back to
generate_frequency() and generate a frequency table for
variable specified.
If you pass mutliple variables, it will generate cross-tabulations for each pair of variables separately in a list.
person_record |>
generate_crosstab(
sex,
seeing,
hearing,
walking,
remembering,
self_caring,
communicating
)
#> $`Seeing, even if wearing glasses`
#> # A tibble: 3 × 12
#> category total frequency_1 frequency_2 frequency_3 frequency_4 frequency_NA
#> <int+lbl> <int> <int> <int> <int> <int> <int>
#> 1 1 [Male] 1516 1369 40 4 0 103
#> 2 2 [Female] 1402 1254 42 5 1 100
#> 3 0 [Total] 2918 2623 82 9 1 203
#> # ℹ 5 more variables: percent_1 <dbl>, percent_2 <dbl>, percent_3 <dbl>,
#> # percent_4 <dbl>, percent_NA <dbl>
#>
#> $`Hearing, even if using hearing aid`
#> # A tibble: 3 × 12
#> category total frequency_1 frequency_2 frequency_3 frequency_4 frequency_NA
#> <int+lbl> <int> <int> <int> <int> <int> <int>
#> 1 1 [Male] 1516 1386 23 4 0 103
#> 2 2 [Female] 1402 1280 20 1 1 100
#> 3 0 [Total] 2918 2666 43 5 1 203
#> # ℹ 5 more variables: percent_1 <dbl>, percent_2 <dbl>, percent_3 <dbl>,
#> # percent_4 <dbl>, percent_NA <dbl>
#>
#> $`Walking or climbing steps, even if with cane or artificial leg`
#> # A tibble: 3 × 12
#> category total frequency_1 frequency_2 frequency_3 frequency_4 frequency_NA
#> <int+lbl> <int> <int> <int> <int> <int> <int>
#> 1 1 [Male] 1516 1381 27 4 1 103
#> 2 2 [Female] 1402 1277 21 3 1 100
#> 3 0 [Total] 2918 2658 48 7 2 203
#> # ℹ 5 more variables: percent_1 <dbl>, percent_2 <dbl>, percent_3 <dbl>,
#> # percent_4 <dbl>, percent_NA <dbl>
#>
#> $`Remembering or concentrating`
#> # A tibble: 3 × 10
#> category total frequency_1 frequency_2 frequency_3 frequency_NA percent_1
#> <int+lbl> <int> <int> <int> <int> <int> <dbl>
#> 1 1 [Male] 1516 1393 17 3 103 91.9
#> 2 2 [Female] 1402 1287 11 4 100 91.8
#> 3 0 [Total] 2918 2680 28 7 203 91.8
#> # ℹ 3 more variables: percent_2 <dbl>, percent_3 <dbl>, percent_NA <dbl>
#>
#> $`Self-caring (such as wasking all over or dressing)`
#> # A tibble: 3 × 12
#> category total frequency_1 frequency_2 frequency_3 frequency_4 frequency_NA
#> <int+lbl> <int> <int> <int> <int> <int> <int>
#> 1 1 [Male] 1516 1391 19 2 1 103
#> 2 2 [Female] 1402 1280 18 3 1 100
#> 3 0 [Total] 2918 2671 37 5 2 203
#> # ℹ 5 more variables: percent_1 <dbl>, percent_2 <dbl>, percent_3 <dbl>,
#> # percent_4 <dbl>, percent_NA <dbl>
#>
#> $`Communicating using his/her casual (customary) language`
#> # A tibble: 3 × 12
#> category total frequency_1 frequency_2 frequency_3 frequency_4 frequency_NA
#> <int+lbl> <int> <int> <int> <int> <int> <int>
#> 1 1 [Male] 1516 1396 13 3 1 103
#> 2 2 [Female] 1402 1287 12 2 1 100
#> 3 0 [Total] 2918 2683 25 5 2 203
#> # ℹ 5 more variables: percent_1 <dbl>, percent_2 <dbl>, percent_3 <dbl>,
#> # percent_4 <dbl>, percent_NA <dbl>
#>
#> attr(,"class")
#> [1] "tsg" "tsgc" "list"You can also specify grouping with group_by() from
dplyr and it will calculate the cross-tabulation for each
group.
person_record |>
dplyr::group_by(sex) |>
generate_crosstab(marital_status, employed)
#> # A tibble: 12 × 9
#> sex category total frequency_1 frequency_2 frequency_NA percent_1
#> <int+lbl> <int+lbl> <int> <int> <int> <int> <dbl>
#> 1 1 [Male] 1 [Single/ne… 859 127 330 402 14.8
#> 2 1 [Male] 2 [Married] 387 274 108 5 70.8
#> 3 1 [Male] 3 [Common la… 211 169 41 1 80.1
#> 4 1 [Male] 4 [Widowed] 40 18 21 1 45
#> 5 1 [Male] 6 [Separated] 19 11 8 0 57.9
#> 6 1 [Male] 0 [Total] 1516 599 508 409 39.5
#> 7 2 [Female] 1 [Single/ne… 685 63 234 388 9.20
#> 8 2 [Female] 2 [Married] 382 155 218 9 40.6
#> 9 2 [Female] 3 [Common la… 213 67 142 4 31.5
#> 10 2 [Female] 4 [Widowed] 98 26 72 0 26.5
#> 11 2 [Female] 6 [Separated] 24 12 12 0 50
#> 12 2 [Female] 0 [Total] 1402 323 678 401 23.0
#> # ℹ 2 more variables: percent_2 <dbl>, percent_NA <dbl>If you want to generate a list of cross-tabulations for each group,
you can set group_as_list = TRUE.
person_record |>
dplyr::group_by(sex) |>
generate_crosstab(marital_status, employed, group_as_list = TRUE)
#> $Male
#> # A tibble: 6 × 9
#> sex category total frequency_1 frequency_2 frequency_NA percent_1
#> <int+lbl> <int+lbl> <int> <int> <int> <int> <dbl>
#> 1 1 [Male] 1 [Single/neve… 859 127 330 402 14.8
#> 2 1 [Male] 2 [Married] 387 274 108 5 70.8
#> 3 1 [Male] 3 [Common law/… 211 169 41 1 80.1
#> 4 1 [Male] 4 [Widowed] 40 18 21 1 45
#> 5 1 [Male] 6 [Separated] 19 11 8 0 57.9
#> 6 1 [Male] 0 [Total] 1516 599 508 409 39.5
#> # ℹ 2 more variables: percent_2 <dbl>, percent_NA <dbl>
#>
#> $Female
#> # A tibble: 6 × 9
#> sex category total frequency_1 frequency_2 frequency_NA percent_1
#> <int+lbl> <int+lbl> <int> <int> <int> <int> <dbl>
#> 1 2 [Female] 1 [Single/nev… 685 63 234 388 9.20
#> 2 2 [Female] 2 [Married] 382 155 218 9 40.6
#> 3 2 [Female] 3 [Common law… 213 67 142 4 31.5
#> 4 2 [Female] 4 [Widowed] 98 26 72 0 26.5
#> 5 2 [Female] 6 [Separated] 24 12 12 0 50
#> 6 2 [Female] 0 [Total] 1402 323 678 401 23.0
#> # ℹ 2 more variables: percent_2 <dbl>, percent_NA <dbl>
#>
#> attr(,"groups")
#> [1] "sex"
#> attr(,"class")
#> [1] "tsg" "tsgc" "list"You can specify whether to calculate the percentage or proportion by
row or column using the percent_by_column argument. If it
is set to TRUE, the percentage will be calculated by
column; if set to FALSE, it will be calculated by row. The
default is FALSE.
person_record |>
generate_crosstab(
marital_status,
sex,
percent_by_column = TRUE
)
#> # A tibble: 6 × 7
#> category frequency_total frequency_1 frequency_2 percent_total percent_1
#> <int+lbl> <int> <int> <int> <dbl> <dbl>
#> 1 1 [Single/nev… 1544 859 685 52.9 56.7
#> 2 2 [Married] 769 387 382 26.4 25.5
#> 3 3 [Common law… 424 211 213 14.5 13.9
#> 4 4 [Widowed] 138 40 98 4.73 2.64
#> 5 6 [Separated] 43 19 24 1.47 1.25
#> 6 0 [Total] 2918 1516 1402 100 100
#> # ℹ 1 more variable: percent_2 <dbl>Just like generate_frequency(), you can also specify
whether to express the value as a proportion.
person_record |>
generate_crosstab(
marital_status,
sex,
as_proportion = TRUE
)
#> # A tibble: 6 × 6
#> category total frequency_1 frequency_2 proportion_1 proportion_2
#> <int+lbl> <int> <int> <int> <dbl> <dbl>
#> 1 1 [Single/never marri… 1544 859 685 0.556 0.444
#> 2 2 [Married] 769 387 382 0.503 0.497
#> 3 3 [Common law/live-in] 424 211 213 0.498 0.502
#> 4 4 [Widowed] 138 40 98 0.290 0.710
#> 5 6 [Separated] 43 19 24 0.442 0.558
#> 6 0 [Total] 2918 1516 1402 0.520 0.480You can also position the total row at the top of the table.
person_record |>
generate_crosstab(
marital_status,
sex,
position_total = "top"
)
#> # A tibble: 6 × 6
#> category total frequency_1 frequency_2 percent_1 percent_2
#> <int+lbl> <int> <int> <int> <dbl> <dbl>
#> 1 0 [Total] 2918 1516 1402 52.0 48.0
#> 2 1 [Single/never married] 1544 859 685 55.6 44.4
#> 3 2 [Married] 769 387 382 50.3 49.7
#> 4 3 [Common law/live-in] 424 211 213 49.8 50.2
#> 5 4 [Widowed] 138 40 98 29.0 71.0
#> 6 6 [Separated] 43 19 24 44.2 55.8You can export your frequency table or cross-tabulation to Excel
using the write_xlsx().
You can add a title and subtitle to your table using the
add_table_title() and add_table_subtitle()
functions.
person_record |>
generate_crosstab(marital_status, sex) |>
add_table_title("Marital Status by Sex") |>
add_table_subtitle("Sample dataset: person_record") |>
write_xlsx(path = "table-02.xlsx")You can also add end notes to your table using the
add_source_note() and add_footnote()
functions.
person_record |>
generate_crosstab(marital_status, sex) |>
add_table_title("Marital Status by Sex") |>
add_table_subtitle("Sample dataset: person_record") |>
add_source_note("Source: person_record dataset") |>
add_footnote("This is a footnote for the table") |>
write_xlsx(path = "table-03.xlsx")Alternatively, you can directly add table title, subtitle,
source_note, and footnotes by specifying them in the arguments of the
write_xlsx() function.
You can use the add_facade() function to apply a facade
to your table. A facade is a set of styling options that can be applied
to the table to customize its appearance.
person_record |>
generate_frequency(sex) |>
add_facade(
table.offsetRow = 2,
table.offsetCol = 1
) |>
write_xlsx(
path = "table-04.xlsx",
# Using built-in facade
facade = get_tsg_facade("yolo")
)If you want to further customize the appearance of your table, you
can use the facade argument to specify a YAML facade file.
The facade file contains styling options for the table, such as font
size, border style, background color, and text alignment.
person_record |>
generate_frequency(sex) |>
write_xlsx(
path = "table-05.xlsx",
# Using built-in facade
facade = get_tsg_facade("yolo")
)You can generate a template facade file using the
generate_template() function and then customize it to your
needs.
generate_output() functiongenerate_output() can be used to generate and save the
output file in the specified format (e.g., Excel, HTML, PDF, Word). It
supports various formats and can handle different data structures.
NOTE: At the moment, it only supports Excel output. The other formats are not yet implemented.