Select Variables in a Dictionary — select

Keep, drop, or reorder variables in a DataDictionary using tidyselect semantics, similar to dplyr::select() for data frames. This is useful for narrowing a dictionary down to the variables relevant to a particular dataset or analysis, or for resolving ambiguity (e.g. dropping one of two variables that happen to share a label) before using functions like index_rows().

Usage

select_variables(dictionary, ...)

Arguments

dictionary: A data dictionary object
...: One or more unquoted variable names, tidyselect helpers (e.g. starts_with(), ends_with()), or negated selections (e.g. -age) identifying which variables to keep. The order of the selection determines the variable order in the returned dictionary.

Value

A data dictionary object containing only the selected variables, in selection order.

Examples


dd <- as_data_dictionary(iris)

# keep only Species and columns ending in "Length"
select_variables(dd, Species, ends_with("Length"))
#> Data Dictionary:
#> # A tibble: 3 × 8
#>   name         type    label description units divby_modeling category_levels   
#>   <chr>        <chr>   <chr> <chr>       <chr> <chr>          <chr>             
#> 1 Species      Nominal none  none        none  none           setosa, versicolo…
#> 2 Sepal.Length Numeric none  none        none  none           none              
#> 3 Petal.Length Numeric none  none        none  none           none              
#> # ℹ 1 more variable: category_labels <chr>

# drop a variable
select_variables(dd, -Species)
#> Data Dictionary:
#> # A tibble: 4 × 8
#>   name         type    label description units divby_modeling category_levels
#>   <chr>        <chr>   <chr> <chr>       <chr> <chr>          <chr>          
#> 1 Sepal.Length Numeric none  none        none  none           none           
#> 2 Sepal.Width  Numeric none  none        none  none           none           
#> 3 Petal.Length Numeric none  none        none  none           none           
#> 4 Petal.Width  Numeric none  none        none  none           none           
#> # ℹ 1 more variable: category_labels <chr>

# resolve a label collision by dropping one of the colliding variables
# before ordering rows with index_rows()
dd_dup <- data_dictionary(
  numeric_variable("age_baseline", label = "Age"),
  numeric_variable("age_followup", label = "Age")
)

select_variables(dd_dup, -age_followup)
#> Data Dictionary:
#> # A tibble: 1 × 8
#>   name         type    label description units divby_modeling category_levels
#>   <chr>        <chr>   <chr> <chr>       <chr> <chr>          <chr>          
#> 1 age_baseline Numeric Age   none        none  none           none           
#> # ℹ 1 more variable: category_labels <chr>