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")