NIH Enrollment Tables in HTML

Install codified Package

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

# if( !requireNamespace("codified", quietly = TRUE) )
#   remotes::install_github(repo = "OuhscBbmc/codified")

library(codified)

Local Data Source

Establish Datasets

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)")
Observed Dataset (first ten rows)
record_id name_last dob ethnicity race gender
1 Cornel 1991-05-13 1 4 0
2 Wojdyla 1931-10-14 1 4 0
3 Stunkard 1964-09-15 1 3 1
4 Cimochowski 1969-05-03 1 4 1
5 Woodland 1969-11-22 1 4 0
6 Pistoia 1943-08-26 1 4 1
7 Ahlquist 1944-01-22 1 4 0
8 Ashlin 1931-04-14 1 4 1
9 Djokovic 1962-07-07 1 1 1
10 Mosses 1978-06-20 1 3 0 
ds_lu_gender <- tibble::tribble(
  ~input,   ~displayed            ,
  "0"   ,  "Female"               ,
  "1"   ,  "Male"                 ,
  "U"   ,  "Unknown/Not Reported"
)
knitr::kable(ds_lu_gender, caption = "Gender Mapping")
Gender Mapping
input displayed
0 Female
1 Male
U Unknown/Not Reported 
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")
Race Mapping
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 
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")
Ethnicity Mapping
input displayed
2 Not Hispanic or Latino
1 Hispanic or Latino
0 Unknown/Not Reported Ethnicity

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")
Counts of Each Subgroup
gender race ethnicity n
Female American Indian/Alaska Native Not Hispanic or Latino 1
Female American Indian/Alaska Native Hispanic or Latino 8
Female American Indian/Alaska Native Unknown/Not Reported Ethnicity 0
Female Asian Not Hispanic or Latino 0
Female Asian Hispanic or Latino 0
Female Asian Unknown/Not Reported Ethnicity 0
Female Native Hawaiian or Other Pacific Islander Not Hispanic or Latino 1
Female Native Hawaiian or Other Pacific Islander Hispanic or Latino 28
Female Native Hawaiian or Other Pacific Islander Unknown/Not Reported Ethnicity 3
Female Black or African American Not Hispanic or Latino 1
Female Black or African American Hispanic or Latino 172
Female Black or African American Unknown/Not Reported Ethnicity 14
Female White Not Hispanic or Latino 1
Female White Hispanic or Latino 22
Female White Unknown/Not Reported Ethnicity 2
Female More than One Race Not Hispanic or Latino 0
Female More than One Race Hispanic or Latino 0
Female More than One Race Unknown/Not Reported Ethnicity 0
Female Unknown or Not Reported Not Hispanic or Latino 0
Female Unknown or Not Reported Hispanic or Latino 7
Female Unknown or Not Reported Unknown/Not Reported Ethnicity 0
Male American Indian/Alaska Native Not Hispanic or Latino 0
Male American Indian/Alaska Native Hispanic or Latino 10
Male American Indian/Alaska Native Unknown/Not Reported Ethnicity 0
Male Asian Not Hispanic or Latino 0
Male Asian Hispanic or Latino 0
Male Asian Unknown/Not Reported Ethnicity 0
Male Native Hawaiian or Other Pacific Islander Not Hispanic or Latino 1
Male Native Hawaiian or Other Pacific Islander Hispanic or Latino 22
Male Native Hawaiian or Other Pacific Islander Unknown/Not Reported Ethnicity 1
Male Black or African American Not Hispanic or Latino 5
Male Black or African American Hispanic or Latino 154
Male Black or African American Unknown/Not Reported Ethnicity 6
Male White Not Hispanic or Latino 0
Male White Hispanic or Latino 33
Male White Unknown/Not Reported Ethnicity 1
Male More than One Race Not Hispanic or Latino 0
Male More than One Race Hispanic or Latino 0
Male More than One Race Unknown/Not Reported Ethnicity 0
Male Unknown or Not Reported Not Hispanic or Latino 0
Male Unknown or Not Reported Hispanic or Latino 7
Male Unknown or Not Reported Unknown/Not Reported Ethnicity 0
Unknown/Not Reported American Indian/Alaska Native Not Hispanic or Latino 0
Unknown/Not Reported American Indian/Alaska Native Hispanic or Latino 0
Unknown/Not Reported American Indian/Alaska Native Unknown/Not Reported Ethnicity 0
Unknown/Not Reported Asian Not Hispanic or Latino 0
Unknown/Not Reported Asian Hispanic or Latino 0
Unknown/Not Reported Asian Unknown/Not Reported Ethnicity 0
Unknown/Not Reported Native Hawaiian or Other Pacific Islander Not Hispanic or Latino 0
Unknown/Not Reported Native Hawaiian or Other Pacific Islander Hispanic or Latino 0
Unknown/Not Reported Native Hawaiian or Other Pacific Islander Unknown/Not Reported Ethnicity 0
Unknown/Not Reported Black or African American Not Hispanic or Latino 0
Unknown/Not Reported Black or African American Hispanic or Latino 0
Unknown/Not Reported Black or African American Unknown/Not Reported Ethnicity 0
Unknown/Not Reported White Not Hispanic or Latino 0
Unknown/Not Reported White Hispanic or Latino 0
Unknown/Not Reported White Unknown/Not Reported Ethnicity 0
Unknown/Not Reported More than One Race Not Hispanic or Latino 0
Unknown/Not Reported More than One Race Hispanic or Latino 0
Unknown/Not Reported More than One Race Unknown/Not Reported Ethnicity 0
Unknown/Not Reported Unknown or Not Reported Not Hispanic or Latino 0
Unknown/Not Reported Unknown or Not Reported Hispanic or Latino 0
Unknown/Not Reported Unknown or Not Reported Unknown/Not Reported Ethnicity 0

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
)
Ethnic Categories
Not Hispanic or Latino
Hispanic or Latino
Unknown/Not Reported Ethnicity
Racial Categories Female Male Unknown/
Not Reported 		Female Male Unknown/
Not Reported 		Female Male Unknown/
Not Reported
American Indian/Alaska Native 1 0 0 8 10 0 0 0 0
Asian 0 0 0 0 0 0 0 0 0
Native Hawaiian or Other Pacific Islander 1 1 0 28 22 0 3 1 0
Black or African American 1 5 0 172 154 0 14 6 0
White 1 0 0 22 33 0 2 1 0
More than One Race 0 0 0 0 0 0 0 0 0
Unknown or Not Reported 0 0 0 7 7 0 0 0 0

REDCap Data Source

A hosted (fake) clinical trial dataset demonstrates how to extract demographic data from REDCap and then present the demographic data in the NIH Inclusion Enrollment Report format.

Establish Datasets

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

# if( !requireNamespace("REDCapR", quietly = TRUE) )
#   remotes::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(
  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

However CRAN policy understandably discourages vignettes from using “Internet resources” so this vignette mimics the code above with this local call. On your own computer, feel free to call that demonstration REDCap project.

ds_2 <-
  readr::read_csv(
    file      = system.file("misc/example-data-2.csv", package = "codified"),
    col_types = readr::cols(.default = readr::col_character())
  )

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
)
Ethnic Categories
Not Hispanic or Latino
Hispanic or Latino
Unknown/Not Reported Ethnicity
Racial Categories Female Male Unknown/
Not Reported 		Female Male Unknown/
Not Reported 		Female Male Unknown/
Not Reported
American Indian/Alaska Native 1 0 0 8 10 0 0 0 0
Asian 0 0 0 0 0 0 0 0 0
Native Hawaiian or Other Pacific Islander 1 1 0 28 22 0 3 1 0
Black or African American 1 5 0 172 154 0 14 6 0
White 1 0 0 22 33 0 2 1 0
More than One Race 0 0 0 0 0 0 0 0 0
Unknown or Not Reported 0 0 0 7 7 0 0 0 0

Collapsing Levels

Many observed datasets may collect race with a different set of levels. For instance in ds_3, the American Indian level is separate from the Alaska Native level. In the first and second rows in the metadata below, the two levels are effectively combined into theAmerican Indian/Alaska Native level, so it complies with the format of the NIH Enrollment table.

ds_lu_race_3 <- tibble::tribble(
  ~input                      , ~displayed                                  ,
  "American Indian"           , "American Indian/Alaska Native"             , # Combine w/ Alaska Native
  "Alaska Native"             , "American Indian/Alaska Native"             , # Combine w/ American Indian
  "Asian"                     , "Asian"                                     ,
  "Native Hawaiian"           , "Native Hawaiian or Other Pacific Islander" , # Combine w/ Pacific Islanders
  "Pacific Islander"          , "Native Hawaiian or Other Pacific Islander" , # Combine w/ Hawaiian
  "Black or African American" , "Black or African American"                 ,
  "White"                     , "White"                                     ,
  "More than One Race"        , "More than One Race"                        ,
  "Unknown or Not Reported"   , "Unknown or Not Reported"
)

ds_3 <- tibble::tribble(
  ~subject_id,   ~gender  , ~race            ,  ~ethnicity                 ,
           1L,   "Female" , "American Indian",  "Not Hispanic or Latino"   ,
           2L,   "Male"   , "American Indian",  "Not Hispanic or Latino"   ,
           3L,   "Male"   , "American Indian",  "Not Hispanic or Latino"   ,
           4L,   "Female" , "Alaska Native"  ,  "Not Hispanic or Latino"   ,
           5L,   "Male"   , "Alaska Native"  ,  "Not Hispanic or Latino"   ,
           6L,   "Male"   , "Alaska Native"  ,  "Not Hispanic or Latino"   ,
           7L,   "Male"   , "White"          ,  "Not Hispanic or Latino"   ,
           8L,   "Male"   , "White"          ,  "Not Hispanic or Latino"
)

table_nih_enrollment_pretty(
  d               = ds_3,
  d_lu_race       = ds_lu_race_3
)
Ethnic Categories
Not Hispanic or Latino
Hispanic or Latino
Unknown/Not Reported Ethnicity
Racial Categories Female Male Unknown/
Not Reported 		Female Male Unknown/
Not Reported 		Female Male Unknown/
Not Reported
American Indian/Alaska Native 2 4 0 0 0 0 0 0 0
Asian 0 0 0 0 0 0 0 0 0
Native Hawaiian or Other Pacific Islander 0 0 0 0 0 0 0 0 0
Black or African American 0 0 0 0 0 0 0 0 0
White 0 2 0 0 0 0 0 0 0
More than One Race 0 0 0 0 0 0 0 0 0
Unknown or Not Reported 0 0 0 0 0 0 0 0 0

Feedback Welcome

Please share your opinions with us by creating an issue at https://github.com/OuhscBbmc/codified/issues.

Upcoming Features

  1. Create tables in a fresh docx file with flextable. Issue #5
  2. Create tables in a fresh pdf file Issue #5.
  3. Populate tables in an existing pdf file with staplr?
  4. The ‘totals’ column & row are calculated. The NIH form does this already, so this is mostly for the purpose of checking. Issue #4.
  5. Allow variable names to change (e.g., Gender instead of gender). Issue #3.
  6. pkgdown site, like REDCapR.

Group discussion

  • Any holes in the functionality or concept?
  • What else (e.g., features or explanation) do you think would be helpful?
  • Are people able to run this package? Is the vignette clear?
  • What other standardized tables/graphs/whatever could this be applied to?
    • IRB continuation tables?
    • Graphs?