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Adding illustration to README.md
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Egeler, Paul W
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README.md
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README.md
@ -19,19 +19,126 @@ might expect that the non-repeating instruments may constitute one table
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that would be related to Repeating Instruments tables via a one-to-many
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relationship. In reality, the data is outputted as one table with all
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possible fields; this has the effect of nesting the output table in a
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way that is not useful in most analysis software. Therefore, I have made
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a solution to handle the problem in both SAS and R.
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way that is not useful in most analysis software.
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## Supported Platforms
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The normalized data can be retrieved by downloading repeating instruments individually then doing a little
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data munging or by writing a few custom parameters in a series of API calls (then doing more data munging),
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but this is a lot of extra steps that can make reproducible research more difficult.
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Therefore, I have made a programmatic solution to handle the problem in both SAS and R.
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### Illustration
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For example, consider this mocked-up data involving some information about cars in
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R's built-in `mtcars` dataset as well as some sales data for some of the cars.
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| car_id|redcap_repeat_instrument |redcap_repeat_instance |make |model |mpg |cyl |motor_trend_cars_complete |price |color |customer |sale_complete |
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|------:|:------------------------|:----------------------|:--------|:-----------|:----|:---|:-------------------------|:--------|:-----|:--------|:-------------|
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| 1| | |AMC |Javelin |15.2 |8 |1 | | | | |
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| 1|sale |1 | | | | | |12000.50 |1 |Bob |0 |
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| 1|sale |2 | | | | | |13750.77 |3 |Sue |2 |
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| 1|sale |3 | | | | | |15004.57 |2 |Kim |0 |
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| 2| | |Cadillac |Fleetwood |10.4 |8 |0 | | | | |
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| 3| | |Camaro |Z28 |13.3 |8 |0 | | | | |
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| 3|sale |1 | | | | | |7800.00 |2 |Janice |2 |
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| 3|sale |2 | | | | | |8000.00 |3 |Tim |0 |
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| 4| | |Chrysler |Imperial |14.7 |8 |0 | | | | |
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| 4|sale |1 | | | | | |7500.00 |1 |Jim |2 |
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| 5| | |Datsun |710 |22.8 |4 |0 | | | | |
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| 6| | |Dodge |Challenger |15.5 |8 |0 | | | | |
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| 7| | |Duster |360 |14.3 |8 |0 | | | | |
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| 7|sale |1 | | | | | |8756.40 |4 |Sarah |1 |
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| 7|sale |2 | | | | | |6800.88 |2 |Pablo |0 |
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| 7|sale |3 | | | | | |8888.88 |1 |Erica |0 |
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| 7|sale |4 | | | | | |970.00 |4 |Juan |0 |
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| 8| | |Ferrari |Dino |19.7 |6 |0 | | | | |
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| 9| | |Mazda |RX4 Wag |21 |6 |0 | | | | |
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| 10| | |Merc |230 |22.8 |4 |0 | | | | |
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| 10|sale |1 | | | | | |7800.98 |2 |Ted |0 |
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| 10|sale |2 | | | | | |7954.00 |1 |Quentin |0 |
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| 10|sale |3 | | | | | |6800.55 |3 |Sharon |2 |
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*Data credit*: Henderson and Velleman (1981), Building multiple regression models interactively. *Biometrics*, **37**, 391--411.
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**Modified with fake data for the purpose of illustration**
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You can see that the data from the non-repeating forms (primary table) is interlaced with the data in the repeating forms,
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creating a checkerboard pattern. In order to do analysis, the data must be normalized and then the tables rejoined.
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The normalized tables would look like this:
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**Primary table**
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| car_id|make |model |mpg |cyl |motor_trend_cars_complete |
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|------:|:--------|:----------|:----|:---|:-------------------------|
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| 1|AMC |Javelin |15.2 |8 |1 |
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| 2|Cadillac |Fleetwood |10.4 |8 |0 |
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| 3|Camaro |Z28 |13.3 |8 |0 |
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| 4|Chrysler |Imperial |14.7 |8 |0 |
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| 5|Datsun |710 |22.8 |4 |0 |
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| 6|Dodge |Challenger |15.5 |8 |0 |
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| 7|Duster |360 |14.3 |8 |0 |
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| 8|Ferrari |Dino |19.7 |6 |0 |
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| 9|Mazda |RX4 Wag |21 |6 |0 |
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| 10|Merc |230 |22.8 |4 |0 |
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**Child table**
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|car_id |redcap_repeat_instrument |redcap_repeat_instance |price |color |customer |sale_complete |
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|:------|:------------------------|:----------------------|:--------|:-----|:--------|:-------------|
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|1 |sale |1 |12000.50 |1 |Bob |0 |
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|1 |sale |2 |13750.77 |3 |Sue |2 |
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|1 |sale |3 |15004.57 |2 |Kim |0 |
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|3 |sale |1 |7800.00 |2 |Janice |2 |
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|3 |sale |2 |8000.00 |3 |Tim |0 |
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|4 |sale |1 |7500.00 |1 |Jim |2 |
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|7 |sale |1 |8756.40 |4 |Sarah |1 |
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|7 |sale |2 |6800.88 |2 |Pablo |0 |
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|7 |sale |3 |8888.88 |1 |Erica |0 |
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|7 |sale |4 |970.00 |4 |Juan |0 |
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|10 |sale |1 |7800.98 |2 |Ted |0 |
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|10 |sale |2 |7954.00 |1 |Quentin |0 |
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|10 |sale |3 |6800.55 |3 |Sharon |2 |
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After inner joining the primary table to the child table on `car_id` and selecting only the fields you are interested in,
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your resulting analytic dataset might look something like this:
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| car_id|make |model |price |color |customer |
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|------:|:--------|:--------|:--------|:-----|:--------|
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| 1|AMC |Javelin |12000.50 |1 |Bob |
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| 1|AMC |Javelin |13750.77 |3 |Sue |
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| 1|AMC |Javelin |15004.57 |2 |Kim |
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| 3|Camaro |Z28 |7800.00 |2 |Janice |
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| 3|Camaro |Z28 |8000.00 |3 |Tim |
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| 4|Chrysler |Imperial |7500.00 |1 |Jim |
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| 7|Duster |360 |8756.40 |4 |Sarah |
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| 7|Duster |360 |6800.88 |2 |Pablo |
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| 7|Duster |360 |8888.88 |1 |Erica |
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| 7|Duster |360 |970.00 |4 |Juan |
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| 10|Merc |230 |7800.98 |2 |Ted |
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| 10|Merc |230 |7954.00 |1 |Quentin |
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| 10|Merc |230 |6800.55 |3 |Sharon |
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### Supported Platforms
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Currently, the R and SAS code is well-tested with mocked-up data.
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- R
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- SAS
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### Coming Soon
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I have made some effort to replicate the
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messiness of real-world data and have tried to include as many special cases and data types as possible.
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However, this code may not account for all contingencies or changes in the native REDCap export format.
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If you find a bug, please feel free to open an issue or pull request.
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#### Coming Soon
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Currently, we have given some consideration to expand the capabilities into the following languages.
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- Python
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- VBA
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If you have some talents in these or other languages, please feel free to open a pull request! We
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welcome your contributions!
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## Instructions
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### R
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@ -48,7 +155,7 @@ devtools::install_github("SpectrumHealthResearch/REDCapRITS/R")
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After the package is installed, follow these instructions:
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1. Download the record dataset and metadata. This can
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1. Download the record dataset and metadata (data dictionary). This can
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be accomplished by several methods:
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- Using the API. Check with your REDCap administrator for details.
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- Exporting the data from the web interface by selecting *CSV / Microsoft Excel (raw data)*.
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