Create a one, two, three,..., n-way table

build_table creates a one, two, three, ..., n-way table. It should be used to calculate the count and percentage of different categorical variables. It gives the data back in a long format. The percentages calculated are the 'row' percentages.

Usage

build_table(
  x,
  cols,
  table_title = "",
  use_questions = FALSE,
  use_NA = FALSE,
  wt = NULL,
  footnote = ""
)

Arguments

x: a data frame or tidy object.
cols: <tidyr_tidy_select> These are the column(s) that we want to calculate the count and percentage of.
table_title: a string. The title of the table sheet.
use_questions: a logical. If the data has column labels convert the column label to a footnote with the question. See details for more information.
use_NA: a logical. Whether to include NA values in the table. For more complicated NA processing post creation, we recommend using filter.
wt: a quoted or unquote column name. Specify a weighting variable, if NULL no weight is applied.
footnote: a character vector. Optional parameter to pass a custom footnote to the question, this parameter overwrites use_questions.

Value

a xlr_table object. Use write_xlsx to write to an excel file. See xlr_table for more information.

Details

This function and its family (build_mtable, build_qtable) is designed to work with data with columns of type haven::labelled, which is the default format of data read with haven::read_sav/has the format of .sav. .sav is the default file function type of data from SPSS and can be exported from popular survey providers such as Qualtrics. When you read in data with haven::read_sav it imports data with the questions, labels for the response options etc.

By default this function converts labelled to a xlr_vector by default (and underlying it is a character() type).

See labelled and read_sav if you would like more details on the importing type.

Examples

library(xlr)

# You can use this function to calculate the number count and percentage
# of a categorical variable
build_table(
  clothes_opinions,
  gender,
  table_title = "The count of the gender groups")
#> 
#> ── The count of the gender groups ──────────────────────────────────────────────
#> # A xlr_table: 3 x 3
#>   gender           N Percent
#>   <x_vctr>   <x_int> <x_pct>
#> 1 female         464     46%
#> 2 male           461     46%
#> 3 non-binary      75      8%

# You must use a `tidyselect` statement, to select the columns that you wish to
# calculate the count, and group percentage.
# This will calculate the number of observations in each group of age and
# gender.
# The percentage will be the percentage of each age_group in each gender
# group (the row percentage).
build_table(
  clothes_opinions,
  c(gender,age_group),
  table_title = "This is the second example table")
#> 
#> ── This is the second example table ────────────────────────────────────────────
#> # A xlr_table: 12 x 4
#>    gender     age_group       N Percent
#>    <x_vctr>   <x_vctr>  <x_int> <x_pct>
#>  1 female     18-30         118     27%
#>  2 female     31-40          87     20%
#>  3 female     41-50          95     22%
#>  4 female     51-65         139     32%
#>  5 male       18-30         116     26%
#>  6 male       31-40          96     22%
#>  7 male       41-50          82     19%
#>  8 male       51-65         146     33%
#>  9 non-binary 18-30          18     25%
#> 10 non-binary 31-40          19     27%
#> 11 non-binary 41-50          18     25%
#> 12 non-binary 51-65          16     23%

# You can use more complicated tidy select statements if you have a large number
# of columns, but this is probably not recommended
#
# Using use_questions, if you have labelled data, it will take the label and
# include it as a footnote.
# This is useful for when you have exported data from survey platforms
# as a .sav, use `haven::read_sav` to load it into your R environment.
build_table(
  clothes_opinions,
  c(group:gender,Q1_1),
  table_title = "This is the third example table",
  use_questions = TRUE)
#> 
#> ── This is the third example table ─────────────────────────────────────────────
#> # A xlr_table: 30 x 5
#>    group    gender   Q1_1                    N Percent
#>    <x_vctr> <x_vctr> <x_vctr>          <x_int> <x_pct>
#>  1 a        female   Strongly Disagree      40     18%
#>  2 a        female   Disagree               39     18%
#>  3 a        female   Neutral                46     21%
#>  4 a        female   Agree                  53     24%
#>  5 a        female   Strongly Agree         41     19%
#>  6 a        male     Strongly Disagree      38     17%
#>  7 a        male     Disagree               44     20%
#>  8 a        male     Neutral                46     21%
#>  9 a        male     Agree                  44     20%
#> 10 a        male     Strongly Agree         46     21%
#> # ℹ 20 more rows
#> Questions
#> Pants are good to wear

# You can also use weights, these weights can be either doubles or integers
# based weights
# You can also set a footnote manually
build_table(
  clothes_opinions,
  age_group,
  table_title = "This is the fourth example table",
  wt = weight,
  footnote = paste0("This is a footnote, you can use it if you want",
                    "more detail in your table."))
#> 
#> ── This is the fourth example table ────────────────────────────────────────────
#> # A xlr_table: 4 x 3
#>   age_group         N Percent
#>   <x_vctr>    <x_dbl> <x_pct>
#> 1 18-30     260,187.0     27%
#> 2 31-40     202,597.0     21%
#> 3 41-50     207,621.0     21%
#> 4 51-65     302,365.0     31%
#> This is a footnote, you can use it if you wantmore detail in your table.