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cleaning up illustration in README.md

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@ -24,13 +24,15 @@ way that is not useful in most analysis software.
The normalized data can be retrieved by downloading repeating instruments individually then doing a little The normalized data can be retrieved by downloading repeating instruments individually then doing a little
data munging or by writing a few custom parameters in a series of API calls (then doing more data munging), data munging or by writing a few custom parameters in a series of API calls (then doing more data munging),
but this is a lot of extra steps that can make reproducible research more difficult. but this is a lot of extra steps that can make reproducible research more difficult.
Therefore, I have made a programmatic solution to handle the problem in both SAS and R.
REDCapRITS is a programmatic solution to handle the problem in both SAS and R.
### Illustration ### Illustration
For example, consider this mocked-up data involving some information about a subset For example, consider this mocked-up data exported from a REDCap project with repeating instruments.
of cars in R's built-in `mtcars` dataset (1). Contained in the data is a repeating instrument, The data contains information on a subset of cars in R's built-in `mtcars` dataset [1].
*sales*, which contains sales transaction data for some of those cars. Within the table there is also a repeating instrument, *sales*, which has 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 | | car_id|redcap_repeat_instrument |redcap_repeat_instance |make |model |mpg |cyl |motor_trend_cars_complete |price |color |customer |sale_complete |
|------:|:------------------------|:----------------------|:--------|:-----------|:----|:---|:-------------------------|:--------|:-----|:--------|:-------------| |------:|:------------------------|:----------------------|:--------|:-----------|:----|:---|:-------------------------|:--------|:-----|:--------|:-------------|
@ -59,7 +61,7 @@ of cars in R's built-in `mtcars` dataset (1). Contained in the data is a repeati
| 10|sale |3 | | | | | |6800.55 |3 |Sharon |2 | | 10|sale |3 | | | | | |6800.55 |3 |Sharon |2 |
You can see that the data from the non-repeating forms (primary table) is interlaced with the data in the repeating forms, You can see that the data from the non-repeating form (primary table) is interlaced with the data in the repeating form,
creating a checkerboard pattern. In order to do analysis, the data must be normalized and then the tables rejoined. 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. Normalization would result in two tables: 1) a *primary* table and 2) a *sale* table.
The normalized tables would look like this: The normalized tables would look like this:
@ -98,7 +100,7 @@ The normalized tables would look like this:
|10 |sale |3 |6800.55 |3 |Sharon |2 | |10 |sale |3 |6800.55 |3 |Sharon |2 |
Suppose you would like to do some analysis such as sale price by make of car or find 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 the most popular color for each model. To do so, you can join the tables together with
relational algebra. After inner joining the *primary* table to the *sale* table on `car_id` relational algebra. After inner joining the *primary* table to the *sale* table on `car_id`
and selecting only the fields you are interested in, and selecting only the fields you are interested in,
your resulting analytic dataset might look something like this: your resulting analytic dataset might look something like this:
@ -126,7 +128,7 @@ Such a join can be accomplished numerous ways. Just to name a few:
- The [`MERGE`](http://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a000202970.htm) statement in a `DATA` step - The [`MERGE`](http://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a000202970.htm) statement in a `DATA` step
- R - R
- [`dplyr::*_join`](https://www.rdocumentation.org/packages/dplyr/versions/0.7.5/topics/join) - [`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) - [`sqldf::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) - [`base::merge`](https://www.rdocumentation.org/packages/base/versions/3.5.0/topics/merge)
### Supported Platforms ### Supported Platforms
@ -273,11 +275,11 @@ Suggestions and contributions are more than welcome! Please feel free to create
## About REDCap ## About REDCap
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. 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 ## References
(1) Henderson and Velleman (1981), Building multiple regression models interactively. *Biometrics*, **37**, 391--411. [1] Henderson and Velleman (1981), Building multiple regression models interactively. *Biometrics*, **37**, 391--411.
**Modified with fake data for the purpose of illustration** **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. [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.