xlr (version 1.0.3)

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

Description

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 = ""
)

Value

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

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.

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")

# 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")

# 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)

# 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."))