build_qtable: Summarise a question block

In xlr: Create Table Summaries and Export Neat Tables to 'Excel'

Summarise a question block

Description

This function helps analyse a block of questions or matrix questions into a single table. It also lets the user cut these questions by other questions in the data. The block of questions mush have the same response options.

Usage

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

Arguments

x	a data frame or tidy object
block_cols	<tidyr_tidy_select> statement. These are the columns that make up the question block, they must have the same response option. Most question block columns start with the same piece of text, so you should use starts_with('column_text'). See the Examples below.
cols	<tidyr_tidy_select> statement. These are the column(s) that we want to cut the questions in the question block by.
table_title	a string. The title of the table sheet
use_questions	a logical. If the data has column labels (was a imported .sav) file, convert the column label to a footnote with the question.
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_table, 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.

Value

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

Examples

library(xlr)

# You can use this function to get a block of questions
build_qtable(
  clothes_opinions,
  starts_with("Q1"),
  table_title = "This is an example table")

# Another way you could select the same columns
build_qtable(
  clothes_opinions,
  c(Q1_1,Q1_2,Q1_3,Q1_4),
  table_title = "This is an example table")

# Yet another way to select the same columns
build_qtable(
  clothes_opinions,
  all_of(c("Q1_1","Q1_2","Q1_3","Q1_4")),
  table_title = "This is an example table")
# You can also cut all questions in the block by a single column
build_qtable(
  clothes_opinions,
  starts_with("Q1"),
  gender2,
  table_title = "This is the second example table")

# You can also cut all questions in the block by a multiple columns
# By setting `use_questions=TRUE` then the footnote will be the questions
# labels, for the cut questions
build_qtable(
  clothes_opinions,
  starts_with("Q1"),
  c(gender2,age_group),
  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
build_qtable(
  clothes_opinions,
  starts_with("Q1"),
  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."))

xlr documentation built on April 3, 2025, 6:07 p.m.