multi_freq {surveyexplorer}

Generate an UpSet plot for multiple-choice questions

Description

Visualize multiple-choice question responses with an upset plot, a visual tool for exploring the overlap and distribution of multiple-choice question responses. The function supports optional subgrouping of data using the group_by variable, exclusion of specific subgroups with 'subgroups_to_exclude,' and data weighting with the 'weights' parameter. Users can also choose to exclude NA values from the questions prior to analysis using the 'na.rm' parameter.

Usage

multi_freq(
  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

An upset plot visualizing the distribution of responses to the multiple-choice question.

See Also

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

Examples

#Use dplyr to select questions
library(dplyr)

#Basic Upset plot

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

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

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