View source: R/multiplechoice.R

multi_table | R Documentation |

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.

```
multi_table(
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 |

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.

Other multiple-choice questions:
`multi_freq()`

,
`multi_summary()`

```
#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)
```

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