View source: R/multiplechoice.R

multi_freq | R Documentation |

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.

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

`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 |

`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 |

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

Other multiple-choice questions:
`multi_summary()`

,
`multi_table()`

```
#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)
```

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