xlr (version 1.1.1)

build_qtable: Summarize a Question Block

Description

Analyzes a block of related questions (such as matrix questions) and presents them in a single summary table. Optionally cross-tabulates results by other variables. All questions in the block must share the same response options.

Usage

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

Value

An xlr_table object. Write to Excel using write_xlsx(). See xlr_table for details.

Arguments

x

A data frame or tibble containing survey data.

block_cols

<tidyr_tidy_select> Columns that form the question block. All selected columns must have identical response options. Tip: Use starts_with('prefix') when block columns share a common prefix. See Examples.

cols

<tidyr_tidy_select> Optional column(s) to cross-tabulate against the question block (for example, demographics).

table_title

Character string. Title for the output table.

use_questions

Logical. If TRUE and data contains column labels (from .sav files), adds the full question text as a footnote. Default is FALSE.

use_NA

Logical. Whether to include NA values in the table. Default is TRUE. For advanced NA handling, use filter() before table creation.

wt

Column name (quoted or unquoted) for weighting variable. If NULL (default), no weighting is applied.

footnote

Character vector. Custom footnote text. When provided, overrides use_questions.

Details

This function works best with haven::labelled data, which is created when importing SPSS files (.sav) using haven::read_sav(). This format preserves question text and response option labels from survey platforms like Qualtrics.

Important: All questions in the block must have identical response options. The function uses the first question to determine valid response values. If you encounter errors, convert the block columns to factors beforehand to ensure consistency.

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.

See Also

build_table(), build_qtable()

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."))