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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
)

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)

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.

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 Analysis Results
#> --------------------------
#> 
#> gender (Gender)
#> # total N=2500 valid N=2500 mean=NA sd=NA skewness=NA
#> 
#> +--------+--------+--------+--------+--------+--------+
#> |  Value |  Label |      N |  Raw % |Valid % | Cum. % |
#> +--------+--------+--------+--------+--------+--------+
#> |   Male |   Male |   1194 |  47.76 |  47.76 |  47.76 |
#> | Female | Female |   1306 |  52.24 |  52.24 | 100.00 |
#> +--------+--------+--------+--------+--------+--------+
#> |  Total |        |   2500 | 100.00 | 100.00 |        |
#> +--------+--------+--------+--------+--------+--------+
#> 

# Multiple variables with weights
survey_data %>% frequency(gender, region, weights = sampling_weight)
#> 
#> Weighted Frequency Analysis Results
#> -----------------------------------
#> 
#> gender (Gender)
#> # total N=2516 valid N=2516 mean=NA sd=NA skewness=NA
#> 
#> +--------+--------+--------+--------+--------+--------+
#> |  Value |  Label |      N |  Raw % |Valid % | Cum. % |
#> +--------+--------+--------+--------+--------+--------+
#> |   Male |   Male |   1195 |  47.48 |  47.48 |  47.48 |
#> | Female | Female |   1321 |  52.52 |  52.52 | 100.00 |
#> +--------+--------+--------+--------+--------+--------+
#> |  Total |        |   2516 | 100.00 | 100.00 |        |
#> +--------+--------+--------+--------+--------+--------+
#> 
#> 
#> region (Region (East/West))
#> # total N=2516 valid N=2516 mean=NA sd=NA skewness=NA
#> 
#> +--------+--------+--------+--------+--------+--------+
#> |  Value |  Label |      N |  Raw % |Valid % | Cum. % |
#> +--------+--------+--------+--------+--------+--------+
#> |   East |   East |    509 |  20.23 |  20.23 |  20.23 |
#> |   West |   West |   2007 |  79.77 |  79.77 | 100.00 |
#> +--------+--------+--------+--------+--------+--------+
#> |  Total |        |   2516 | 100.00 | 100.00 |        |
#> +--------+--------+--------+--------+--------+--------+
#> 

# Grouped analysis by region
survey_data %>% 
  group_by(region) %>% 
  frequency(gender, weights = sampling_weight)
#> 
#> Weighted Frequency Analysis Results
#> -----------------------------------
#> 
#> gender (Gender)
#> 
#> Group: region = East
#> --------------------
#> # total N=509 valid N=509 mean=NA sd=NA skewness=NA
#> 
#> +--------+--------+--------+--------+--------+--------+
#> |  Value |  Label |      N |  Raw % |Valid % | Cum. % |
#> +--------+--------+--------+--------+--------+--------+
#> |   Male |   Male |    249 |  49.01 |  49.01 |  49.01 |
#> | Female | Female |    260 |  50.99 |  50.99 | 100.00 |
#> +--------+--------+--------+--------+--------+--------+
#> |  Total |        |    509 | 100.00 | 100.00 |        |
#> +--------+--------+--------+--------+--------+--------+
#> 
#> 
#> Group: region = West
#> --------------------
#> # total N=2007 valid N=2007 mean=NA sd=NA skewness=NA
#> 
#> +--------+--------+--------+--------+--------+--------+
#> |  Value |  Label |      N |  Raw % |Valid % | Cum. % |
#> +--------+--------+--------+--------+--------+--------+
#> |   Male |   Male |    945 |  47.09 |  47.09 |  47.09 |
#> | Female | Female |   1062 |  52.91 |  52.91 | 100.00 |
#> +--------+--------+--------+--------+--------+--------+
#> |  Total |        |   2007 | 100.00 | 100.00 |        |
#> +--------+--------+--------+--------+--------+--------+
#> 

# Education levels with sorting
survey_data %>% frequency(education, sort.frq = "desc")
#> 
#> Frequency Analysis Results
#> --------------------------
#> 
#> education (Highest educational attainment)
#> # total N=2500 valid N=2500 mean=NA sd=NA skewness=NA
#> 
#> +------------------------+------------------------+--------+--------+--------+--------+
#> |                  Value |                  Label |      N |  Raw % |Valid % | Cum. % |
#> +------------------------+------------------------+--------+--------+--------+--------+
#> |             University |             University |    399 |  15.96 |  15.96 | 100.00 |
#> | Intermediate Secondary | Intermediate Secondary |    629 |  25.16 |  25.16 |  58.80 |
#> |        Basic Secondary |        Basic Secondary |    841 |  33.64 |  33.64 |  33.64 |
#> |     Academic Secondary |     Academic Secondary |    631 |  25.24 |  25.24 |  84.04 |
#> +------------------------+------------------------+--------+--------+--------+--------+
#> |                  Total |                        |   2500 | 100.00 | 100.00 |        |
#> +------------------------+------------------------+--------+--------+--------+--------+
#> 

# Employment status with custom display options
survey_data %>% frequency(employment, weights = sampling_weight, 
                         show.na = TRUE, show.sum = TRUE)
#> 
#> Weighted Frequency Analysis Results
#> -----------------------------------
#> 
#> employment (Employment status)
#> # total N=2516 valid N=2516 mean=NA sd=NA skewness=NA
#> 
#> +------------+------------+--------+--------+--------+--------+
#> |      Value |      Label |      N |  Raw % |Valid % | Cum. % |
#> +------------+------------+--------+--------+--------+--------+
#> |    Student |    Student |     80 |   3.18 |   3.18 |   3.18 |
#> |   Employed |   Employed |   1603 |  63.71 |  63.71 |  66.89 |
#> | Unemployed | Unemployed |    184 |   7.32 |   7.32 |  74.21 |
#> |    Retired |    Retired |    534 |  21.21 |  21.21 |  95.41 |
#> |      Other |      Other |    115 |   4.59 |   4.59 | 100.00 |
#> +------------+------------+--------+--------+--------+--------+
#> |      Total |            |   2516 | 100.00 | 100.00 |        |
#> +------------+------------+--------+--------+--------+--------+
#>