NIH Enrollment Tables in HTML

0.1 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)

0.2 Local Data Source

0.2.1 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

0.2.2 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")

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

0.2.3 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
)

	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

0.3 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.

0.3.1 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())
  )

0.3.2 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
)

	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

0.4 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

0.5 Feedback Welcome

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

0.5.1 Upcoming Features

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

0.5.2 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?

NIH Enrollment Tables in HTML

Will Beasley and Peter Higgins

2022-08-12

0.1 Install `codified` Package

0.2 Local Data Source

0.2.1 Establish Datasets

0.2.2 Apply Map to Observed Dataset

0.2.3 Conform to NIH Cosmetics

0.3 REDCap Data Source

0.3.1 Establish Datasets

0.3.2 Conform to NIH Cosmetics

0.4 Collapsing Levels

0.5 Feedback Welcome

0.5.1 Upcoming Features

0.5.2 Group discussion

NIH Enrollment Tables in HTML

Will Beasley and Peter Higgins

2022-08-12

0.1 Install codified Package

0.2 Local Data Source

0.2.1 Establish Datasets

0.2.2 Apply Map to Observed Dataset

0.2.3 Conform to NIH Cosmetics

0.3 REDCap Data Source

0.3.1 Establish Datasets

0.3.2 Conform to NIH Cosmetics

0.4 Collapsing Levels

0.5 Feedback Welcome

0.5.1 Upcoming Features

0.5.2 Group discussion

0.1 Install `codified` Package