table_nih_enrollment {codified} | R Documentation |
Produce an NIH-compliant enrollment table.
Description
Produce an NIH enrollment table, leveraging metadata to adapt to the observed data.frame.
Usage
table_nih_enrollment(
d,
d_lu_gender = NULL,
d_lu_race = NULL,
d_lu_ethnicity = NULL,
variable_gender = "gender",
variable_race = "race",
variable_ethnicity = "ethnicity"
)
Arguments
d |
data.frame of observed values in the investigation. Required. |
d_lu_gender |
data.frame that maps the observed levels of gender to the NIH-recommended levels of gender. Required only if the levels are not the same. |
d_lu_race |
data.frame that maps the observed levels of gender to the NIH-recommended levels of gender. Required only if the levels are not the same. |
d_lu_ethnicity |
data.frame that maps the observed levels of gender to the NIH-recommended levels of gender. Required only if the levels are not the same. |
variable_gender |
name of the gender variable in the |
variable_race |
name of the race variable in the |
variable_ethnicity |
name of the ethnicity variable in the |
Details
https://grants.nih.gov/grants/how-to-apply-application-guide/forms-d/general/g.500-phs-inclusion-enrollment-report.htm
Value
Table for publication
Author(s)
Will Beasley, Peter Higgins, Andrew Peters, Sreeharsha Mandem
Examples
ds_1 <- tibble::tribble(
~subject_id, ~gender , ~race , ~ethnicity ,
1L, "Male" , "Black or African American", "Not Hispanic or Latino" ,
2L, "Male" , "Black or African American", "Not Hispanic or Latino" ,
3L, "Female" , "Black or African American", "Unknown/Not Reported Ethnicity",
4L, "Male" , "White" , "Not Hispanic or Latino" ,
5L, "Male" , "White" , "Not Hispanic or Latino" ,
6L, "Female" , "White" , "Not Hispanic or Latino" ,
7L, "Male" , "White" , "Hispanic or Latino" ,
8L, "Male" , "White" , "Hispanic or Latino"
)
table_nih_enrollment(ds_1)
table_nih_enrollment_pretty(ds_1)
table_nih_enrollment(ds_1) |>
tidyr::pivot_wider(names_from = gender, values_from = n)
table_nih_enrollment(ds_1) |>
dplyr::mutate(
gender_ethnicity = paste0(gender, " by ", ethnicity)
) |>
dplyr::select(-gender, -ethnicity) |>
tidyr::pivot_wider(names_from = gender_ethnicity, values_from = n)
ds_2 <- tibble::tribble(
~subject_id, ~gender , ~race , ~ethnicity ,
1L, "Male" , "Black or African American", "Not Latino" ,
2L, "Male" , "Black or African American", "Not Latino" ,
3L, "Female", "Black or African American", "Unknown" ,
4L, "Male" , "White" , "Not Latino" ,
5L, "Male" , "White" , "Not Latino" ,
6L, "Female", "White" , "Not Latino" ,
7L, "Male" , "White" , "Latino" ,
8L, "Male" , "White" , "Latino"
)
ds_lu_ethnicity <- tibble::tribble(
~input , ~displayed ,
"Not Latino", "Not Hispanic or Latino" ,
"Latino" , "Hispanic or Latino" ,
"Unknown" , "Unknown/Not Reported Ethnicity"
)
table_nih_enrollment(ds_2, d_lu_ethnicity = ds_lu_ethnicity)
table_nih_enrollment_pretty(ds_2, d_lu_ethnicity = ds_lu_ethnicity)
## Read a 500-patient fake dataset
path <- system.file("misc/example-data-1.csv", package = "codified")
ds_3 <- readr::read_csv(path) |>
dplyr::mutate(
gender = as.character(gender),
race = as.character(race),
ethnicity = as.character(ethnicity)
)
ds_lu_gender <- tibble::tribble(
~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"
)
ds_lu_ethnicity <- tibble::tribble(
~input, ~displayed ,
"2" , "Not Hispanic or Latino" ,
"1" , "Hispanic or Latino" ,
"0" , "Unknown/Not Reported Ethnicity"
)
table_nih_enrollment(
d = ds_3,
d_lu_gender = ds_lu_gender,
d_lu_race = ds_lu_race,
d_lu_ethnicity = ds_lu_ethnicity
)
table_nih_enrollment_pretty(
d = ds_3,
d_lu_gender = ds_lu_gender,
d_lu_race = ds_lu_race,
d_lu_ethnicity = ds_lu_ethnicity
)