2.1 Establish Datasets

library(magrittr)

path <- system.file("misc/example-data-1.csv", package="codified")
col_types <- readr::cols_only(
  record_id = readr::col_integer(),
  name_last = readr::col_character(),
  dob       = readr::col_date(format = ""),
  gender    = readr::col_integer(),
  race      = readr::col_integer(),
  ethnicity = readr::col_integer()
)
ds <- readr::read_csv(path, col_types=col_types) %>%
  dplyr::mutate(
    gender     = as.character(gender),
    race       = as.character(race),
    ethnicity  = as.character(ethnicity)
  )
ds %>% 
  head(10) %>% 
  knitr::kable(caption = "Observed Dataset (first ten rows)")

ds_lu_gender <- tibble::tribble(
  ~input,   ~displayed            ,
  "0"   ,  "Female"               , 
  "1"   ,  "Male"                 ,
  "U"   ,  "Unknown/Not Reported"
)
knitr::kable(ds_lu_gender, caption = "Gender Mapping")

ds_lu_race <- tibble::tribble(
  ~input , ~displayed                                   ,
  "1"    , "American Indian/Alaska Native"              ,
  "2"    , "Asian"                                      ,
  "3"    , "Native Hawaiian or Other Pacific Islander"  ,
  "4"    , "Black or African American"                  ,
  "5"    , "White"                                      ,
  "M"    , "More than One Race"                         ,
  "6"    , "Unknown or Not Reported"
)
knitr::kable(ds_lu_race, caption = "Race Mapping")

ds_lu_ethnicity <- tibble::tribble(
  ~input,   ~displayed                      ,
  "2"   ,  "Not Hispanic or Latino"         ,
  "1"   ,  "Hispanic or Latino"             ,
  "0"   ,  "Unknown/Not Reported Ethnicity"
)
knitr::kable(ds_lu_ethnicity, caption = "Ethnicity Mapping")

2.2 Apply Map to Observed Dataset

ds_summary_long <- codified::table_nih_enrollment(
  d              = ds,
  d_lu_gender    = ds_lu_gender,
  d_lu_race      = ds_lu_race,
  d_lu_ethnicity = ds_lu_ethnicity
)

knitr::kable(ds_summary_long, caption = "Counts of Each Subgroup")

2.3 Conform to NIH Cosmetics

codified::table_nih_enrollment_pretty(
  d              = ds,
  d_lu_gender    = ds_lu_gender,
  d_lu_race      = ds_lu_race,
  d_lu_ethnicity = ds_lu_ethnicity
)

3.1 Establish Datasets

First, install the REDCapR package if necessary, and then load it into memory.

if( !requireNamespace("REDCapR", quietly=T) )
  devtools::install_github("OuhscBbmc/REDCapR")

library(REDCapR)

Next, download the data from the REDCap database into the ds_2 data.frame. If you’re running the most recent version of REDCapR (available on GitHub), the code will be:

ds_2 <- REDCapR::redcap_read_oneshot(
  redcap_uri = "https://bbmc.ouhsc.edu/redcap/api/",  # URL of REDCap Server.
  token      = "F304DEC3793FECC3B6DEEFF66302CAD3",    # User-specific token/password.
  guess_type = FALSE                                  # Keep all variables as strings/characters.
)$data  

The following code is more complicated than normal, because the vignette has to account for different versions of REDCapR being installed on the machine.

if( !requireNamespace("REDCapR", quietly=TRUE) ) {
  ds <- ds_2 # If REDCapR isn't installed, use the previous version of the dataset.
} else if( "0.9.8" < packageVersion("REDCapR") ) {
  ds_2 <- REDCapR::redcap_read_oneshot(
    redcap_uri = "https://bbmc.ouhsc.edu/redcap/api/",  # URL of REDCap Server.
    token      = "F304DEC3793FECC3B6DEEFF66302CAD3",    # User-specific token/password.
    guess_type = FALSE                                  # Keep all variables as strings/characters.
  )$data  
} else {
  # Older versions of REDCapR don't have the `guess_type` parameter
  ds_2 <- REDCapR::redcap_read_oneshot(
    redcap_uri = "https://bbmc.ouhsc.edu/redcap/api/",  # URL of REDCap Server.
    token      = "F304DEC3793FECC3B6DEEFF66302CAD3"     # User-specific token/password.
  )$data %>% 
    dplyr::mutate(
      gender      = as.character(gender),
      race        = as.character(race),
      ethnicity   = as.character(ethnicity)
    )
}

## 500 records and 13 columns were read from REDCap in 1.3 seconds.  The http status code was 200.

3.2 Conform to NIH Cosmetics

Now, convert these demographic data into a properly formatted NIH enrollment table. Pass the ds_lu_gender, ds_lu_race, and ds_lu_ethnicity metadata, which was defined above. As a reminder, these translate values like 1 to Male and 3 to Native Hawaiian or Other Pacific Islander.

table_nih_enrollment_pretty(
  d               = ds_2,
  d_lu_gender     = ds_lu_gender,
  d_lu_race       = ds_lu_race,
  d_lu_ethnicity  = ds_lu_ethnicity
)