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@wibeasley
wibeasley committed Aug 11, 2022
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53 changes: 24 additions & 29 deletions vignettes/nih-enrollment-html.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -24,10 +24,9 @@ knitr::opts_chunk$set(echo = TRUE)
```

Install `codified` Package
=====================================================

First, install the `codified` package if necessary, and then [load it into memory](http://r-pkgs.had.co.nz/package.html#package).
-----------------------------------------------------

First, install the `codified` package if necessary, and then [load it into memory](http://r-pkgs.had.co.nz/package.html#package).

```{r install-codified}
if( !requireNamespace("codified", quietly=T) )
Expand All @@ -37,10 +36,9 @@ library(codified)
```

Local Data Source
=====================================================

## Establish Datasets
-----------------------------------------------------

### Establish Datasets

```{r local-establish}
library(magrittr)
Expand All @@ -60,13 +58,13 @@ ds <- readr::read_csv(path, col_types=col_types) %>%
race = as.character(race),
ethnicity = as.character(ethnicity)
)
ds %>%
head(10) %>%
ds %>%
head(10) %>%
knitr::kable(caption = "Observed Dataset (first ten rows)")
ds_lu_gender <- tibble::tribble(
~input, ~displayed ,
"0" , "Female" ,
"0" , "Female" ,
"1" , "Male" ,
"U" , "Unknown/Not Reported"
)
Expand All @@ -93,8 +91,7 @@ ds_lu_ethnicity <- tibble::tribble(
knitr::kable(ds_lu_ethnicity, caption = "Ethnicity Mapping")
```


## Apply Map to Observed Dataset
### Apply Map to Observed Dataset

```{r local-apply-map}
ds_summary_long <- codified::table_nih_enrollment(
Expand All @@ -107,8 +104,7 @@ ds_summary_long <- codified::table_nih_enrollment(
knitr::kable(ds_summary_long, caption = "Counts of Each Subgroup")
```


## Conform to NIH Cosmetics
### Conform to NIH Cosmetics

```{r local-cosmetically-format}
codified::table_nih_enrollment_pretty(
Expand All @@ -120,13 +116,13 @@ codified::table_nih_enrollment_pretty(
```

REDCap Data Source
=====================================================
-----------------------------------------------------

A hosted (fake) clinical trial dataset demonstrates how to extract demographic data from [REDCap](https://projectredcap.org/) and then present the demographic data in the NIH Inclusion Enrollment Report format.

## Establish Datasets
### Establish Datasets

First, install the `REDCapR` package if necessary, and then [load it into memory](http://r-pkgs.had.co.nz/package.html#package).
First, install the `REDCapR` package if necessary, and then [load it into memory](http://r-pkgs.had.co.nz/package.html#package).

```{r install-redcapr}
if( !requireNamespace("REDCapR", quietly=T) )
Expand All @@ -142,8 +138,9 @@ 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.
)$data
)$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.

```{r redcap-establish}
Expand All @@ -154,13 +151,13 @@ if( !requireNamespace("REDCapR", quietly=TRUE) ) {
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.
)$data
)$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.
)$data %>%
)$data %>%
dplyr::mutate(
gender = as.character(gender),
race = as.character(race),
Expand All @@ -169,8 +166,7 @@ if( !requireNamespace("REDCapR", quietly=TRUE) ) {
}
```


## Conform to NIH Cosmetics
### 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`.

Expand All @@ -183,9 +179,8 @@ table_nih_enrollment_pretty(
)
```


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 the`American Indian/Alaska Native` level, so it complies with the format of the NIH Enrollment table.

Expand All @@ -212,7 +207,7 @@ ds_3 <- tibble::tribble(
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"
8L, "Male" , "White" , "Not Hispanic or Latino"
)
table_nih_enrollment_pretty(
Expand All @@ -222,11 +217,11 @@ table_nih_enrollment_pretty(
```

Feedback Welcome
=====================================================
-----------------------------------------------------

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

**Upcoming Features**
### Upcoming Features

1. Create tables in a fresh docx file with [flextable](https://davidgohel.github.io/flextable/). [Issue #5](https://github.com/OuhscBbmc/codified/issues/5)
1. Create tables in a fresh pdf file [Issue #5](https://github.com/OuhscBbmc/codified/issues/5).
Expand All @@ -235,11 +230,11 @@ Please share your opinions with us by creating an issue at https://github.com/Ou
1. Allow variable names to change (*e.g.*, `Gender` instead of `gender`). [Issue #3](https://github.com/OuhscBbmc/codified/issues/3).
1. [pkgdown site](http://pkgdown.r-lib.org/), like [REDCapR](https://ouhscbbmc.github.io/REDCapR/).

**Group discussion**
### 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?
* IRB continuation tables?
* Graphs?

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