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Adding more info in illustration within README.md

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@ -28,8 +28,9 @@ Therefore, I have made a programmatic solution to handle the problem in both SAS
### Illustration
For example, consider this mocked-up data involving some information about cars in
R's built-in `mtcars` dataset as well as some sales data for some of the cars.
For example, consider this mocked-up data involving some information about a subset
of cars in R's built-in `mtcars` dataset (1). Contained in the data is a repeating instrument,
*sales*, which contains sales transaction data for some of those cars.
| car_id|redcap_repeat_instrument |redcap_repeat_instance |make |model |mpg |cyl |motor_trend_cars_complete |price |color |customer |sale_complete |
|------:|:------------------------|:----------------------|:--------|:-----------|:----|:---|:-------------------------|:--------|:-----|:--------|:-------------|
@ -57,11 +58,10 @@ R's built-in `mtcars` dataset as well as some sales data for some of the cars.
| 10|sale |2 | | | | | |7954.00 |1 |Quentin |0 |
| 10|sale |3 | | | | | |6800.55 |3 |Sharon |2 |
*Data credit*: Henderson and Velleman (1981), Building multiple regression models interactively. *Biometrics*, **37**, 391--411.
**Modified with fake data for the purpose of illustration**
You can see that the data from the non-repeating forms (primary table) is interlaced with the data in the repeating forms,
creating a checkerboard pattern. In order to do analysis, the data must be normalized and then the tables rejoined.
Normalization would result in two tables: 1) a *primary* table and 2) a *sale* table.
The normalized tables would look like this:
**Primary table**
@ -79,7 +79,7 @@ The normalized tables would look like this:
| 9|Mazda |RX4 Wag |21 |6 |0 |
| 10|Merc |230 |22.8 |4 |0 |
**Child table**
**Sale table**
|car_id |redcap_repeat_instrument |redcap_repeat_instance |price |color |customer |sale_complete |
|:------|:------------------------|:----------------------|:--------|:-----|:--------|:-------------|
@ -97,7 +97,10 @@ The normalized tables would look like this:
|10 |sale |2 |7954.00 |1 |Quentin |0 |
|10 |sale |3 |6800.55 |3 |Sharon |2 |
After inner joining the primary table to the child table on `car_id` and selecting only the fields you are interested in,
Suppose you would like to do some analysis such as sale price by make of car or find
the most popular color for each model. To do so, you can join the tables together using
relational algebra. After inner joining the *primary* table to the *sale* table on `car_id`
and selecting only the fields you are interested in,
your resulting analytic dataset might look something like this:
| car_id|make |model |price |color |customer |
@ -116,6 +119,16 @@ your resulting analytic dataset might look something like this:
| 10|Merc |230 |7954.00 |1 |Quentin |
| 10|Merc |230 |6800.55 |3 |Sharon |
Such a join can be accomplished numerous ways. Just to name a few:
- SAS
- [`PROC SQL`](http://support.sas.com/documentation/cdl/en/proc/61895/HTML/default/viewer.htm#a002473709.htm)
- The [`MERGE`](http://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a000202970.htm) statement in a `DATA` step
- R
- [`dplyr::*_join`](https://www.rdocumentation.org/packages/dplyr/versions/0.7.5/topics/join)
- [`sqldf`](https://www.rdocumentation.org/packages/sqldf/versions/0.4-11/topics/sqldf)
- [`base::merge`](https://www.rdocumentation.org/packages/base/versions/3.5.0/topics/merge)
### Supported Platforms
Currently, the R and SAS code is well-tested with mocked-up data.
@ -260,8 +273,11 @@ Suggestions and contributions are more than welcome! Please feel free to create
## About REDCap
This code was written for [REDCap electronic data capture tools](https://projectredcap.org/).^1^ Code for this project was tested on the REDCap instance hosted at Spectrum Health, Grand Rapids, MI. REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources.
This code was written for [REDCap electronic data capture tools](https://projectredcap.org/)(2). Code for this project was tested on the REDCap instance hosted at Spectrum Health, Grand Rapids, MI. REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources.
## References
^1^Paul A. Harris, Robert Taylor, Robert Thielke, Jonathon Payne, Nathaniel Gonzalez, Jose G. Conde, Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377-81.
(1) Henderson and Velleman (1981), Building multiple regression models interactively. *Biometrics*, **37**, 391--411.
**Modified with fake data for the purpose of illustration**
(2) Paul A. Harris, Robert Taylor, Robert Thielke, Jonathon Payne, Nathaniel Gonzalez, Jose G. Conde, Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377-81.