rcoder outlines a lightweight data structure that captures categorical codings and easily converts them to other implementations.
rcoder’s main functions are coding() and
code(). code() maps a character-valued key to
some value to represent a categorical level.
library(rcoder)
library(magrittr)
code("Yes", 1)
#> <Code>
#> label: 'Yes'
#> value: 1code() objects can hold an arbitrary amount of metadata.
Useful pieces of information to know are descriptions of what levels
represent (in case the label doesn’t have enough information) and
whether or not the value represents a missing value.
code(
  "No response",
  -99,
  description = "Surveyed individual ignored question when asked",
  missing = TRUE
)
#> <Code>
#> label: 'No response'
#> value: -99
#> description: 'Surveyed individual ignored question when asked'
#> Represents a missing valuecoding() is a collection of code() objects
that represents the full set of values for a categorical variable.
coding(
  code("Don't know", 0, missing = TRUE),
  code("Never", 1),
  code("Rarely", 2),
  code("Sometimes", 3),
  code("Frequently", 4),
  code("Always", 5),
  code("No response", -99, missing = TRUE),
  code("Refused", -88, missing = TRUE),
  code("Absent", -77, missing = TRUE)
)
#> <Coding>
#> # A tibble: 9 × 5
#>   link        label       value description missing
#>   <chr>       <chr>       <dbl> <chr>       <lgl>  
#> 1 Don't know  Don't know      0 Don't know  TRUE   
#> 2 Never       Never           1 Never       FALSE  
#> 3 Rarely      Rarely          2 Rarely      FALSE  
#> 4 Sometimes   Sometimes       3 Sometimes   FALSE  
#> 5 Frequently  Frequently      4 Frequently  FALSE  
#> 6 Always      Always          5 Always      FALSE  
#> 7 No response No response   -99 No response TRUE   
#> 8 Refused     Refused       -88 Refused     TRUE   
#> 9 Absent      Absent        -77 Absent      TRUEcoding() objects are designed to be an intermediate
representation of categorical data so that they can be converted into
different representations on the fly, e.g. ODK XLSForm choices and
STATA/SPSS columns via haven.
coding(
  code("Never", 1),
  code("Rarely", 2),
  code("Sometimes", 3),
  code("Frequently", 4),
  code("Always", 5),
  .label = "frequency"
) %>%
  coding_to_odk()
#> # A tibble: 5 × 3
#>   list_name  name label     
#>   <chr>     <dbl> <chr>     
#> 1 frequency     1 Never     
#> 2 frequency     2 Rarely    
#> 3 frequency     3 Sometimes 
#> 4 frequency     4 Frequently
#> 5 frequency     5 AlwaysTo facilitate recoding, coding() objects link to one
another through the code() labels. If multiple values are
collapsed into one, use the links_from parameter to
identify which values are combined into one.
original_coding <- coding(
  code("No", 0L),
  code("Yes", 1L),
  code("No response", -99L, missing = TRUE),
  code("Refused", -88L, missing = TRUE),
  code("Absent", -77L, missing = TRUE)
)
new_coding <- coding(
  code("No", 0L),
  code("Yes", 1L),
  code("Missing", NA, links_from = c("No response", "Refused", "Absent"))
)
new_coding %>%
  link_codings(original_coding)
#>          link label_to value_to     label_1 value_1
#> 1      Absent  Missing       NA      Absent     -77
#> 2          No       No        0          No       0
#> 3 No response  Missing       NA No response     -99
#> 4     Refused  Missing       NA     Refused     -88
#> 5         Yes      Yes        1         Yes       1These linked codings can be converted into a function that accepts a vector and returns a recoded vector.
new_coding %>%
  link_codings(original_coding) %>%
  make_recode_query()
#> function (x) 
#> {
#>     attrs <- attributes(x)
#>     x <- dplyr::case_when(x == -77L ~ NA_integer_, x == 0L ~ 
#>         0L, x == -99L ~ NA_integer_, x == -88L ~ NA_integer_, 
#>         x == 1L ~ 1L)
#>     attributes(x) <- attrs
#>     x
#> }
#> <environment: 0x7faab3ea3298>Install rcoder from CRAN with:
install.packages("rcoder")To get the latest stable changes that may not be on CRAN, install from the nyuglobalties r-universe with:
install.packages("rcoder", repos = "https://nyuglobalties.r-universe.dev")Finally, get the development version straight from GitHub:
# install.packages("remotes")
remotes::install_github("nyuglobalties/rcoder")