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Count How Many People Chose Each Option

Source: R/frequency.R
frequency.Rd

frequency() helps you understand categorical data by showing how many people chose each option. It's perfect for survey questions with fixed choices like education level, yes/no questions, or rating scales.

Think of it as creating a summary table that shows:

  • How many people chose each option

  • What percentage that represents

  • Running totals to see cumulative patterns

Usage

frequency(
  data,
  ...,
  weights = NULL,
  sort_frq = "none",
  show_na = TRUE,
  show_prc = TRUE,
  show_valid = TRUE,
  show_sum = TRUE,
  show_labels = "auto",
  show_unused = FALSE,
  sort.frq = NULL,
  show.na = NULL,
  show.prc = NULL,
  show.valid = NULL,
  show.sum = NULL,
  show.labels = NULL,
  show.unused = NULL
)

fre(data, ..., weights = NULL, sort_frq = "none", show_na = TRUE,
  show_prc = TRUE, show_valid = TRUE, show_sum = TRUE, show_labels = "auto",
  show_unused = FALSE, sort.frq = NULL, show.na = NULL, show.prc = NULL,
  show.valid = NULL, show.sum = NULL, show.labels = NULL, show.unused = NULL)

Arguments

data

Your survey data (a data frame or tibble)

...

The categorical variables you want to analyze. You can list multiple variables separated by commas, or use helpers like starts_with("trust")

weights

Optional survey weights for population-representative results. Without weights, you get sample frequencies. With weights, you get population estimates.

sort_frq

How to order the results:

  • "none" (default): Keep original order

  • "asc": Sort from lowest to highest frequency

  • "desc": Sort from highest to lowest frequency

show_na

Include missing values in the table? (Default: TRUE)

show_prc

Show raw percentages including missing values? (Default: TRUE)

show_valid

Show percentages excluding missing values? (Default: TRUE)

show_sum

Show cumulative totals? (Default: TRUE)

show_labels

Show category labels if available? (Default: "auto" - shows labels when they exist)

show_unused

Show all defined value labels, even those with zero observations? (Default: FALSE). When TRUE, values that have labels defined (e.g., from statistical software files) but no cases in the data are included with frequency 0. This is useful for labelled datasets where unused categories should still appear in the output. Automatically enables label display.

sort.frq, show.na, show.prc, show.valid, show.sum, show.labels, show.unused

Defunct dot-case argument names, removed in mariposa 0.6.9. Calling the function with any of them is an error; use the snake_case equivalents instead. (The formals are retained only so that the old names error clearly instead of being swallowed by ....)

Value

A frequency table showing counts and percentages for each category

Details

Understanding the Results

The frequency table shows:

  • Freq: Number of responses in each category

  • %: Percentage including missing values (use for "response rate")

  • Valid %: Percentage excluding missing values (use for "among those who answered")

  • Cum %: Running total percentage (helps identify cutoff points)

When to Use This

Use frequency() when you have:

  • Categorical variables (gender, region, education level)

  • Yes/No questions

  • Rating scales (satisfied/neutral/dissatisfied)

  • Any question with a fixed set of options

Weights Make a Difference

Without weights, you're describing your sample. With weights, you're estimating population values. Always use weights for population inference.

Tagged Missing Values

When data is imported with tagged NAs (e.g., via read_spss() with tag_na = TRUE, or read_stata(), read_sas(), read_xpt() with the tag_na parameter), frequency() automatically expands the missing value section to show each missing type individually (with its original missing value code and label), plus summary rows for Total Valid and Total Missing.

See also

table for base R frequency tables.

crosstab for cross-tabulation of two variables.

chi_square for testing relationships between categories.

describe for numeric variable summaries.

Other descriptive: crosstab(), describe()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic categorical analysis
survey_data %>% frequency(gender)
#> Frequency: gender
#>   2 categories, N valid = 2500, missing = 0
#> Use summary() for detailed output.

# Multiple variables with weights
survey_data %>% frequency(gender, region, weights = sampling_weight)
#> Frequency: gender [Weighted]
#>   2 categories, N valid = 2516, missing = 0
#> Frequency: region [Weighted]
#>   2 categories, N valid = 2516, missing = 0
#> Use summary() for detailed output.

# Grouped analysis by region
survey_data %>% 
  group_by(region) %>% 
  frequency(gender, weights = sampling_weight)
#> Frequency: gender [Weighted]
#>   [region = East] 2 categories, N valid = 509, missing = 0
#>   [region = West] 2 categories, N valid = 2007, missing = 0
#> Use summary() for detailed output.

# Education levels with sorting
survey_data %>% frequency(education, sort_frq = "desc")
#> Frequency: education
#>   4 categories, N valid = 2500, missing = 0
#> Use summary() for detailed output.

# Employment status with custom display options
survey_data %>% frequency(employment, weights = sampling_weight, 
                         show_na = TRUE, show_sum = TRUE)
#> Frequency: employment [Weighted]
#>   5 categories, N valid = 2516, missing = 0
#> Use summary() for detailed output.