multi_table {surveyexplorer}

Create a table of frequencies and counts for multiple-choice questions

Description

Generates a table presenting the distribution of responses for a specified multiple-choice question. If a grouping variable, group_by, is provided, the table extends to include row and column totals, along with additional count and frequency columns for each level of group_by (excluding specified subgroups, if any). When survey weights are specified with weights, the counts reflect the weighted values, and a note is appended at the bottom of the table.

Usage

multi_table(
  dataset,
  question,
  group_by = NULL,
  subgroups_to_exclude = NULL,
  weights = NULL,
  na.rm = FALSE
)

Arguments

dataset

The input dataframe (or tibble) of survey questions

question

The columns that contain each of the response options for a question, can be selected by using tidyselect semanatics or providing a vector of column names or numbers

group_by

Optional variable to group the analysis. If provided, the frequencies and counts will be calculated within each subgroup.

subgroups_to_exclude

Optional vector specifying subgroups to exclude from the analysis.

weights

Optional variable containing survey weights. If provided, frequencies and counts will be weighted accordingly.

na.rm

Logical indicating whether to remove NA values from question before analysis.

Value

A gt table displaying frequencies and counts for the specified multiple-choice question. If a grouping variable is provided, the table includes subgroups for a comprehensive analysis. If survey weights are specified, the table notes that frequencies and counts are weighted.

See Also

Other multiple-choice questions: multi_freq(), multi_summary()

Examples

#Basic Table
 multi_table(berlinbears, question = dplyr::starts_with('will_eat'))

#Use `group_by` to partition the question into several groups
 multi_table(berlinbears, question = dplyr::starts_with('will_eat'), group_by
 = gender)

#to ignore a subgroup, use `subgroups_to_exclude`
multi_table(berlinbears, question = dplyr::starts_with('will_eat'), group_by
= gender, subgroups_to_exclude = NA)

#Specifiy survey weights with `weights`
 multi_table(berlinbears, question = dplyr::starts_with('will_eat'), group_by
 = gender, weights = weights)