Compute weighted frequencies, optionally grouped and/or across multiple variables

get_freqs() computes weighted frequency tables for survey-style data. It supports:

Plain data frames with an optional weight column
survey.design and svyrep.design objects (from the survey/srvyr ecosystem)
Single-variable or multi-variable inputs
Optional grouping variables
Optional inclusion/exclusion of zero-count levels (see Limitations for survey multi-variable)

For single-variable inputs, the response column in the output retains the original variable name. For multi-variable inputs, responses are pivoted to long format using names_to and values_to.

Usage

get_freqs(
  data,
  x,
  group = NULL,
  wt = NULL,
  names_to = "name",
  values_to = "value",
  name_label = NULL,
  keep = NULL,
  drop_zero = FALSE,
  decimals = 1,
  na.rm = TRUE
)

Arguments

data

A data frame/tibble, survey.design, or svyrep.design object.

x

<tidy-select> Columns selecting one or more variables. You can pass:

A bare column name (e.g., x = q1)
A tidyselect expression (e.g., x = tidyselect::starts_with("q"))
A vector of strings or symbols c("q1", "q2") or c(q1, q2).
An object containing a vector tidyselect::all_of(variables).

group

<tidy-select> A selection of columns to group the data. This operates very similarly to .by from dplyr (for more info on that see ?dplyr_by). It can also be a character vector. If using an external character vector it must be wrapped in curly brackets ({{}}).

In addition, grouped data can be piped in via dplyr::group_by() or srvyr::group_by(). If data is a grouped_df and group is provided, get_means() will combine the variable(s) used in either group_by function and the variable(s) supplied in group to calculate frequencies.

wt

Optional weight column (numeric). Ignored for survey.design or svyrep.design inputs, where weights come from the design. If omitted for data frames, will output unweighted frequencies.

names_to

A string specifying the name of the column to create to hold the variable names (or labels) when x selects multiple variables. Default is "name".

values_to

A string specifying the name of the column to create to hold the responses when x selects multiple variables. Default is "value".

name_label

Optional label to attach to the names_to column in multi-variable outputs (e.g., a question preface). If missing, will check the data for a question_preface attribute.

keep

Optional post-aggregation filter applied only to multi-variable outputs:

character vector: keep only rows where values_to is in this set
function: predicate on the values_to vector; returns logical mask (length nrow or scalar TRUE)
tidy expression: a dplyr-style filter expression evaluated in the result context

drop_zero

Logical; whether to drop zero-count rows from the output. If FALSE, zero-count levels will be included by using tidyr::complete(). Default is FALSE.

decimals

Number of decimal places for rounding counts (n). Percent (pct) is rounded to decimals + 2 so that it contains the right number of decimals when multiplying by 100.

na.rm

Logical; whether to remove rows with missing values in x and group before computing frequencies. Default is TRUE.

Value

A tibble with columns:

For single-variable inputs: [x variable], n, pct, and any grouping columns.
For multi-variable inputs: [grouping columns if any], [names_to], [values_to], n, pct.

The result has class "adlgraphs_freqs" and common attributes:

attr(., "dataset"): the original dataset
attr(., "variable_label"), attr(., "variable_name")
For grouped outputs: attr(., "group_names") and attr(., "group_labels")
For multi-variable: attr(., "item_names"), attr(., "item_labels"), attr(., "x_expr")

Details

The keep argument is applied after aggregation in multi-variable outputs to filter rows of the result based on the response column (values_to). It is ignored for single-variable calls. Accepted forms:

Character vector: keep = c("yes", "no")
Function: keep = \(v) v %in% c("yes", "no") or any predicate that returns a logical vector of length nrow(result) or a single TRUE (no filtering). NA entries are dropped.
Tidy expression: keep = .data[[values_to]] %in% c("yes","no") or simply resp != "skip" when values_to = "resp".

Note: For tidy expressions, the expression is evaluated in the context of the result tibble.

Methods

get_freqs.default(): Operates on data frames/tibbles. If wt is omitted, simple unweighted frequencies are reported. Calculation is from dplyr::count() with .drop = drop_zero; zero-count levels can be included when drop_zero = FALSE and the variables are factors with unused levels.
get_freqs.survey.design(): Operates on survey.design objects. Weights are taken from the design. Grouping is honored inside low-level survey computations.
get_freqs.svyrep.design(): Operates on svyrep.design objects.

Examples


# here's a basic unweighted frequency for satisfaction_service
get_freqs(basic_df, x = satisfaction_service)
#> # A tibble: 5 × 3
#>   satisfaction_service     n   pct
#>   <fct>                <dbl> <dbl>
#> 1 Very Dissatisfied        0 0    
#> 2 Dissatisfied             2 0.167
#> 3 Neutral                  3 0.25 
#> 4 Satisfied                4 0.333
#> 5 Very Satisfied           3 0.25 

# now check it with weights
get_freqs(basic_df, x = satisfaction_service, wt = wts)
#> # A tibble: 5 × 3
#>   satisfaction_service     n   pct
#>   <fct>                <dbl> <dbl>
#> 1 Very Dissatisfied        0 0    
#> 2 Dissatisfied             2 0.105
#> 3 Neutral                  3 0.158
#> 4 Satisfied                9 0.474
#> 5 Very Satisfied           5 0.263

# now check grouped
get_freqs(basic_df, x = satisfaction_service, group = grp, wt = wts)
#> # A tibble: 15 × 4
#>    grp   satisfaction_service     n   pct
#>    <fct> <fct>                <dbl> <dbl>
#>  1 A     Very Dissatisfied        0 0    
#>  2 A     Dissatisfied             1 0.2  
#>  3 A     Neutral                  1 0.2  
#>  4 A     Satisfied                1 0.2  
#>  5 A     Very Satisfied           2 0.4  
#>  6 B     Very Dissatisfied        0 0    
#>  7 B     Dissatisfied             0 0    
#>  8 B     Neutral                  1 0.143
#>  9 B     Satisfied                5 0.714
#> 10 B     Very Satisfied           1 0.143
#> 11 C     Very Dissatisfied        0 0    
#> 12 C     Dissatisfied             1 0.143
#> 13 C     Neutral                  1 0.143
#> 14 C     Satisfied                3 0.429
#> 15 C     Very Satisfied           2 0.286

# groups can also be added by using `group_by()` ahead of time
basic_df |>
  dplyr::group_by(grp) |>
  get_freqs(x = satisfaction_service, wt = wts)
#> # A tibble: 15 × 4
#>    grp   satisfaction_service     n   pct
#>    <fct> <fct>                <dbl> <dbl>
#>  1 A     Very Dissatisfied        0 0    
#>  2 A     Dissatisfied             1 0.2  
#>  3 A     Neutral                  1 0.2  
#>  4 A     Satisfied                1 0.2  
#>  5 A     Very Satisfied           2 0.4  
#>  6 B     Very Dissatisfied        0 0    
#>  7 B     Dissatisfied             0 0    
#>  8 B     Neutral                  1 0.143
#>  9 B     Satisfied                5 0.714
#> 10 B     Very Satisfied           1 0.143
#> 11 C     Very Dissatisfied        0 0    
#> 12 C     Dissatisfied             1 0.143
#> 13 C     Neutral                  1 0.143
#> 14 C     Satisfied                3 0.429
#> 15 C     Very Satisfied           2 0.286

# Now check with multiple x variables
get_freqs(
  basic_df,
  x = c("x1", "x2"),
  wt = wts,
  keep = "Yes",
  na.rm = TRUE
)
#> # A tibble: 2 × 4
#>   name     value     n   pct
#>   <fct>    <fct> <dbl> <dbl>
#> 1 Q1. Blue Yes       8 0.444
#> 2 Q2. Red  Yes       7 0.412

# rename the outputs
get_freqs(
  basic_df,
  x = c("x1", "x2"),
  wt = wts,
  names_to = "item",
  values_to = "resp",
  na.rm = TRUE
)
#> # A tibble: 4 × 4
#>   item     resp      n   pct
#>   <fct>    <fct> <dbl> <dbl>
#> 1 Q1. Blue Yes       8 0.444
#> 2 Q1. Blue No       10 0.556
#> 3 Q2. Red  Yes       7 0.412
#> 4 Q2. Red  No       10 0.588

# now check multiple x variables with a grouping variable
get_freqs(
  basic_df,
  x = c("x1", "x2"),
  group = "grp",
  wt = wts,
  na.rm = TRUE
)
#> # A tibble: 12 × 5
#>    grp   name     value     n   pct
#>    <fct> <fct>    <fct> <dbl> <dbl>
#>  1 A     Q1. Blue Yes       2 0.5  
#>  2 A     Q1. Blue No        2 0.5  
#>  3 A     Q2. Red  Yes       3 0.6  
#>  4 A     Q2. Red  No        2 0.4  
#>  5 B     Q1. Blue Yes       1 0.143
#>  6 B     Q1. Blue No        6 0.857
#>  7 B     Q2. Red  Yes       1 0.2  
#>  8 B     Q2. Red  No        4 0.8  
#>  9 C     Q1. Blue Yes       5 0.714
#> 10 C     Q1. Blue No        2 0.286
#> 11 C     Q2. Red  Yes       3 0.429
#> 12 C     Q2. Red  No        4 0.571


# ---- keep examples (multi-variable, data frame) ----

# 1) keep as a character vector: retain only "yes" responses across items
get_freqs(
  basic_df,
  x = c("x1", "x2"),
  wt = wts,
  keep = c("Yes"),
  na.rm = TRUE
)
#> # A tibble: 2 × 4
#>   name     value     n   pct
#>   <fct>    <fct> <dbl> <dbl>
#> 1 Q1. Blue Yes       8 0.444
#> 2 Q2. Red  Yes       7 0.412

# Survey design  ------------
basic_df_svy <- srvyr::as_survey_design(
   ids = id,
   strata = strata,
   weights = "wts",
   .data = basic_df
 )

# basic example
get_freqs(basic_df_svy, x = satisfaction_service)
#> # A tibble: 5 × 3
#>   satisfaction_service     n   pct
#>   <fct>                <dbl> <dbl>
#> 1 Dissatisfied             2 0.105
#> 2 Neutral                  3 0.158
#> 3 Satisfied                9 0.474
#> 4 Very Dissatisfied        0 0    
#> 5 Very Satisfied           5 0.263

# multi-variable example
get_freqs(basic_df_svy, c(x1, x2))
#> # A tibble: 4 × 4
#>   name     value     n   pct
#>   <fct>    <fct> <dbl> <dbl>
#> 1 Q1. Blue Yes       8 0.444
#> 2 Q1. Blue No       10 0.556
#> 3 Q2. Red  Yes       7 0.412
#> 4 Q2. Red  No       10 0.588

# grouped example
get_freqs(basic_df_svy, satisfaction_service, grp)
#> # A tibble: 15 × 4
#>    grp   satisfaction_service     n   pct
#>    <chr> <fct>                <dbl> <dbl>
#>  1 A     Dissatisfied             1 0.2  
#>  2 A     Neutral                  1 0.2  
#>  3 A     Satisfied                1 0.2  
#>  4 A     Very Dissatisfied        0 0    
#>  5 A     Very Satisfied           2 0.4  
#>  6 B     Dissatisfied             0 0    
#>  7 B     Neutral                  1 0.143
#>  8 B     Satisfied                5 0.714
#>  9 B     Very Dissatisfied        0 0    
#> 10 B     Very Satisfied           1 0.143
#> 11 C     Dissatisfied             1 0.143
#> 12 C     Neutral                  1 0.143
#> 13 C     Satisfied                3 0.429
#> 14 C     Very Dissatisfied        0 0    
#> 15 C     Very Satisfied           2 0.286

# grouped example with multi-variable
get_freqs(basic_df_svy, c(x1, x2), grp)
#> # A tibble: 12 × 5
#>    grp   name     value     n   pct
#>    <chr> <fct>    <fct> <dbl> <dbl>
#>  1 A     Q1. Blue Yes       2 0.5  
#>  2 A     Q1. Blue No        2 0.5  
#>  3 A     Q2. Red  Yes       3 0.6  
#>  4 A     Q2. Red  No        2 0.4  
#>  5 B     Q1. Blue Yes       1 0.143
#>  6 B     Q1. Blue No        6 0.857
#>  7 B     Q2. Red  Yes       1 0.2  
#>  8 B     Q2. Red  No        4 0.8  
#>  9 C     Q1. Blue Yes       5 0.714
#> 10 C     Q1. Blue No        2 0.286
#> 11 C     Q2. Red  Yes       3 0.429
#> 12 C     Q2. Red  No        4 0.571