Improved instructions for R package in README.md.

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Egeler, Paul W 2018-06-03 17:09:26 -04:00
parent 2cf5c8e019
commit a1297903c2

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@ -35,6 +35,8 @@ a solution to handle the problem in both SAS and R.
## Instructions ## Instructions
### R ### R
#### Installation
First you must install the package. To do so, execute the following in your R console: First you must install the package. To do so, execute the following in your R console:
```r ```r
@ -42,14 +44,57 @@ if (!require(devtools)) install.packages("devtools")
devtools::install_github("SpectrumHealthResearch/REDCapRITS/R") devtools::install_github("SpectrumHealthResearch/REDCapRITS/R")
``` ```
#### Usage
After the package is installed, follow these instructions: After the package is installed, follow these instructions:
1. Download the record dataset and metadata. This can 1. Download the record dataset and metadata. This can
be accomplished either by traditional methods or using the API. The be accomplished either by traditional methods or using the API.
`read.csv()` function should be able to handle newline characters within
records, so no pre-processing of metadata csv is needed.
1. Call the function, pointing it to your record dataset and metadata 1. Call the function, pointing it to your record dataset and metadata
`data.frame`s or JSON character vectors. `data.frame`s or JSON character vectors. You may need to load the package via
`library()` or `require()`.
#### Examples
Here is an example usage in conjuction with an API call to your REDCap instance:
```r
library(RCurl)
# Get the records
records <- postForm(
uri = api_url, # Supply your site-specific URI
token = api_token, # Supply your own API token
content = 'record',
format = 'json',
returnFormat = 'json'
)
# Get the metadata
metadata <- postForm(
uri = api_url,
token = api_token,
content = 'metadata',
format = 'json'
)
# Convert exported JSON strings into a list of data.frames
REDCapRITS::REDCap_split(records, metadata)
```
And here is an example of usage when downloading a REDCap export manually from your REDCap web interface:
```r
# Get the records
records <- read.csv("/path/to/data/ExampleProject_DATA_2018-06-03_1700.csv")
# Get the metadata
metadata <- read.csv("/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv")
# Split the tables
REDCapRITS::REDCap_split(records, metadata)
```
### SAS ### SAS