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trying to overcome error messagein github actions by moving R package to root.

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AG Damsbo 2022-12-21 08:29:00 +01:00
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commit 582a481942
33 changed files with 1 additions and 1276 deletions
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^.*\.Rproj$ ^.*\.Rproj$
^\.Rproj\.user$ ^\.Rproj\.user$
^\.travis\.yml$
^appveyor\.yml$
^data-raw$ ^data-raw$
^test-data$

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# R for travis: see documentation at https://docs.travis-ci.com/user/languages/r
before_install:
- cd R
language: R
r: devel
sudo: false
cache: packages

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REDCap Repeating Instrument Table Splitter
===========================================
Paul W. Egeler, M.S., GStat
Spectrum Health Office of Research Administration
13 July 2017
## Table of Contents
* [Table of Contents](#table-of-contents)
* [Description](#description)
* [Illustration](#illustration)
* [Supported Platforms](#supported-platforms)
* [Coming Soon](#coming-soon)
* [Instructions](#instructions)
* [R](#r)
* [Installation](#installation)
* [Usage](#usage)
* [Examples](#examples)
* [SAS](#sas)
* [Examples](#examples-1)
* [Issues](#issues)
* [About REDCap](#about-redcap)
* [References](#references)
## Description
So the new buzz in the REDCap world seems to be Repeating Instruments
and Events. Certainly there is potential for a lot of utility in this
feature and I was excited to try it out. I know I will be using this
feature a lot in the future.
Unfortunately, I was not very happy with the way the data was exported
either via CSV or API call. When you conceptualize the data model for
a Repeating Instrument, you probably think of a multi-table model. You
might expect that the non-repeating instruments may constitute one table
that would be related to Repeating Instruments tables via a one-to-many
relationship. In reality, the data is outputted as one table with all
possible fields; this has the effect of nesting the output table in a
way that is not useful in most analysis software.
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),
but this is a lot of extra steps that can make reproducible research more difficult.
REDCapRITS is a programmatic solution to handle the problem in both SAS and R.
### Illustration
For example, consider this mocked-up data exported from a REDCap project with repeating instruments.
The data contains information on a subset of cars in R's built-in `mtcars` dataset [1].
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 |
|------:|:------------------------|:----------------------|:--------|:-----------|:----|:---|:-------------------------|:--------|:-----|:--------|:-------------|
| 1| | |AMC |Javelin |15.2 |8 |1 | | | | |
| 1|sale |1 | | | | | |12000.50 |1 |Bob |0 |
| 1|sale |2 | | | | | |13750.77 |3 |Sue |2 |
| 1|sale |3 | | | | | |15004.57 |2 |Kim |0 |
| 2| | |Cadillac |Fleetwood |10.4 |8 |0 | | | | |
| 3| | |Camaro |Z28 |13.3 |8 |0 | | | | |
| 3|sale |1 | | | | | |7800.00 |2 |Janice |2 |
| 3|sale |2 | | | | | |8000.00 |3 |Tim |0 |
| 4| | |Chrysler |Imperial |14.7 |8 |0 | | | | |
| 4|sale |1 | | | | | |7500.00 |1 |Jim |2 |
| 5| | |Datsun |710 |22.8 |4 |0 | | | | |
| 6| | |Dodge |Challenger |15.5 |8 |0 | | | | |
| 7| | |Duster |360 |14.3 |8 |0 | | | | |
| 7|sale |1 | | | | | |8756.40 |4 |Sarah |1 |
| 7|sale |2 | | | | | |6800.88 |2 |Pablo |0 |
| 7|sale |3 | | | | | |8888.88 |1 |Erica |0 |
| 7|sale |4 | | | | | |970.00 |4 |Juan |0 |
| 8| | |Ferrari |Dino |19.7 |6 |0 | | | | |
| 9| | |Mazda |RX4 Wag |21 |6 |0 | | | | |
| 10| | |Merc |230 |22.8 |4 |0 | | | | |
| 10|sale |1 | | | | | |7800.98 |2 |Ted |0 |
| 10|sale |2 | | | | | |7954.00 |1 |Quentin |0 |
| 10|sale |3 | | | | | |6800.55 |3 |Sharon |2 |
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.
Normalization would result in two tables: 1) a *primary* table and 2) a *sale* table.
The normalized tables would look like this:
**Primary table**
| car_id|make |model |mpg |cyl |motor_trend_cars_complete |
|------:|:--------|:----------|:----|:---|:-------------------------|
| 1|AMC |Javelin |15.2 |8 |1 |
| 2|Cadillac |Fleetwood |10.4 |8 |0 |
| 3|Camaro |Z28 |13.3 |8 |0 |
| 4|Chrysler |Imperial |14.7 |8 |0 |
| 5|Datsun |710 |22.8 |4 |0 |
| 6|Dodge |Challenger |15.5 |8 |0 |
| 7|Duster |360 |14.3 |8 |0 |
| 8|Ferrari |Dino |19.7 |6 |0 |
| 9|Mazda |RX4 Wag |21 |6 |0 |
| 10|Merc |230 |22.8 |4 |0 |
**Sale table**
|car_id |redcap_repeat_instrument |redcap_repeat_instance |price |color |customer |sale_complete |
|:------|:------------------------|:----------------------|:--------|:-----|:--------|:-------------|
|1 |sale |1 |12000.50 |1 |Bob |0 |
|1 |sale |2 |13750.77 |3 |Sue |2 |
|1 |sale |3 |15004.57 |2 |Kim |0 |
|3 |sale |1 |7800.00 |2 |Janice |2 |
|3 |sale |2 |8000.00 |3 |Tim |0 |
|4 |sale |1 |7500.00 |1 |Jim |2 |
|7 |sale |1 |8756.40 |4 |Sarah |1 |
|7 |sale |2 |6800.88 |2 |Pablo |0 |
|7 |sale |3 |8888.88 |1 |Erica |0 |
|7 |sale |4 |970.00 |4 |Juan |0 |
|10 |sale |1 |7800.98 |2 |Ted |0 |
|10 |sale |2 |7954.00 |1 |Quentin |0 |
|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
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`
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 |
|------:|:--------|:--------|:--------|:-----|:--------|
| 1|AMC |Javelin |12000.50 |1 |Bob |
| 1|AMC |Javelin |13750.77 |3 |Sue |
| 1|AMC |Javelin |15004.57 |2 |Kim |
| 3|Camaro |Z28 |7800.00 |2 |Janice |
| 3|Camaro |Z28 |8000.00 |3 |Tim |
| 4|Chrysler |Imperial |7500.00 |1 |Jim |
| 7|Duster |360 |8756.40 |4 |Sarah |
| 7|Duster |360 |6800.88 |2 |Pablo |
| 7|Duster |360 |8888.88 |1 |Erica |
| 7|Duster |360 |970.00 |4 |Juan |
| 10|Merc |230 |7800.98 |2 |Ted |
| 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::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.
- R
- SAS
I have made some effort to replicate the
messiness of real-world data and have tried to include as many special cases and data types as possible.
However, this code may not account for all contingencies or changes in the native REDCap export format.
If you find a bug, please feel free to open an issue or pull request.
#### Coming Soon
Currently, we have given some consideration to expand the capabilities into the following languages.
- Python
- VBA
If you have some talents in these or other languages, please feel free to open a pull request! We
welcome your contributions!
## Instructions
### R
[![Travis-CI Build Status](https://travis-ci.org/SpectrumHealthResearch/REDCapRITS.svg?branch=master)](https://travis-ci.org/SpectrumHealthResearch/REDCapRITS)
[![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/SpectrumHealthResearch/REDCapRITS?branch=master&svg=true)](https://ci.appveyor.com/project/pegeler/REDCapRITS)
#### Installation
First you must install the package. To do so, execute the following in your R console:
```r
if (!require(devtools)) install.packages("devtools")
devtools::install_github("SpectrumHealthResearch/REDCapRITS/R")
```
#### Usage
After the package is installed, follow these instructions:
1. Download the record dataset and metadata (data dictionary). This can
be accomplished by several methods:
- Using the API. Check with your REDCap administrator for details.
- Exporting the data from the web interface by selecting *CSV / Microsoft Excel (raw data)*.
- Exporting the data from the web interface by selecting *R Statistical Software*.
If you use this method, you may run the R script supplied by REDCap prior to splitting the data.
- **Do NOT** export from the web interface with the *CSV / Microsoft Excel (labels)* option.
This will not work with REDCapRITS.
1. Call the function, pointing it to your record dataset and metadata
`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, # Supply your site-specific URI
token = api_token, # Supply your own 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 of the raw data (not labelled!) 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)
```
REDCapRITS also works with the data export script (a.k.a., *syntax file*) supplied by REDCap. Here is an example of its usage:
```r
# Run the data export script supplied by REDCap.
# This will create a data.frame of your records called 'data'
source("/path/to/data/ExampleProject_R_2018-06-03_1700.r", chdir = TRUE)
# Get the metadata
metadata <- read.csv("/path/to/metadata/ExampleProject_DataDictionary_2018-06-03.csv")
# Split the tables
REDCapRITS::REDCap_split(data, metadata)
```
### SAS
1. Download the data, SAS code to load the data, and the data dictionary from REDCap.
1. Run the SAS code provided by REDCap to import the data.
1. Run the RECapRITS macro definitions in the source editor or using `%include`.
1. Run the macro call `%REDCAP_READ_DATA_DICT()` to load the data dictionary into your SAS session, pointing to the file location of your REDCap data dictionary.
1. Run the macro call `%REDCAP_SPLIT()`. You will have an output dataset for
your main table as well as for each repeating instrument.
#### Examples
Please follow the instructions from REDCap on importing the data into SAS. REDCap provides the data in a *csv* format as well as *bat* and *sas* files. The instructions are available when exporting the data from the REDCap web interface. If you do not use the pathway mapper (*bat* file) provided, you will need to go into the *sas* file provided by REDCap and alter the file path in the `infile` statment (Line 2).
```sas
* Run the program to import the data file into a SAS dataset;
%INCLUDE "c:\path\to\data\ExampleProject_SAS_2018-06-04_0950.sas";
* Run the MACRO definitions from this repo;
%INCLUDE "c:\path\to\macro\REDCap_split.sas";
* Read in the data dictionary;
%REDCAP_READ_DATA_DICT(c:\path\to\data\ExampleProject_DataDictionary_2018-06-04.csv);
* Split the tables;
%REDCAP_SPLIT();
```
## Issues
Suggestions and contributions are more than welcome! Please feel free to create an issue or pull request.
## 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.
## References
[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.

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/********************************************************************************
*
* FILE: REDCap_split.sas
*
* VERSION: 0.1.0
*
* PURPOSE: Take a REDCap dataset with multiple events and make into several
* tables with primary and foreign keys
*
* AUTHOR: Paul W. Egeler, M.S., GStat
*
* DATE: 22JUN2017
*
*******************************************************************************
*
* INSTRUCTIONS:
*
* 1. Run the SAS code provided by REDCap to load the records into your SAS session.
*
* 2. Download the data dictionary for your project.
*
* 3. Run the macro definitions REDCAP_READ_DATA_DICT and REDCAP_SPLIT
*
* 4. Run the macro call for REDCAP_READ_DATA_DICT to load in the data dictionry.
* This is necessary to split the tables correctly.
*
* 5. Run the macro call for REDCAP_SPLIT. You will have an output dataset for
* your main table as well as for each repeating instrument.
*
********************************************************************************/
%MACRO REDCAP_READ_DATA_DICT(
DATA_DICTIONARY /* The file path for the data dictionary */
);
DATA REDCAP_DATA_DICTIONARY;
LENGTH VAR_NAME $ 200 FORM_NAME $ 200 SECTION_HEADER $ 200 FIELD_TYPE $ 200 X1-X14 $ 2250;
INFILE "&DATA_DICTIONARY" FIRSTOBS = 2 DSD DLM = "," LRECL=32767;
INPUT VAR_NAME $ FORM_NAME $ SECTION_HEADER $ FIELD_TYPE $ X1-X14 $;
IF FIELD_TYPE EQ "descriptive" THEN DELETE;
DROP SECTION_HEADER X1-X14;
RUN;
%MEND REDCAP_READ_DATA_DICT;
%MACRO REDCAP_SPLIT(
DATA_SET = REDCAP /* The name of the SAS dataset created by REDCap */,
DATA_DICTIONARY = REDCAP_DATA_DICTIONARY /* The name of the SAS dataset of the data dictionary */,
NUMERIC_SUBTABLES = N /* Y/N: Should the subtables be numbered (Y) or should they be based on the name of the repeating instrument (N)? */
);
/* Find the key that links the base table to child tables */
/* Also check that REDCAP_REPEAT_INSTRUMENT is present in the data */
%LET DSID = %SYSFUNC(OPEN(&DATA_SET));
%LET KEY = %SYSFUNC(VARNAME(&DSID, 1));
%LET CHECK = %SYSFUNC(VARNUM(&DSID, REDCAP_REPEAT_INSTRUMENT));
%LET RC = %SYSFUNC(CLOSE(&DSID));
%IF &CHECK EQ 0 %THEN %DO;
%PUT ERROR: The dataset &DATA_SET does not contain repeating instruments.;
%PUT ERROR: Stopping MACRO.;
%GOTO FINISH;
%END;
/* Remove formatting from repeat instrument field */
%PUT NOTE: Removing formatting from REDCAP_REPEAT_INSTRUMENT in &DATA_SET..;
DATA &DATA_SET.;
SET &DATA_SET.;
FORMAT REDCAP_REPEAT_INSTRUMENT;
RUN;
/* Find the subtable names and number of subtables */
PROC SQL NOPRINT;
SELECT DISTINCT
REDCAP_REPEAT_INSTRUMENT,
"'"!!trim(REDCAP_REPEAT_INSTRUMENT)!!"'" AS INSTRUMENT_QUOTED
INTO
:INSTRUMENTS SEPARATED BY ' ',
:INSTRUMENT_LIST SEPARATED BY ','
FROM &DATA_SET AS A
WHERE REDCAP_REPEAT_INSTRUMENT GT '';
%LET N_SUBTABLES = &SQLOBS;
QUIT;
%IF &N_SUBTABLES EQ 0 %THEN %DO;
%PUT WARNING: There were no records containing repeating instruments in the dataset %LEFT(&DATA_SET).;
%PUT WARNING: No action was taken.;
%GOTO FINISH;
%END;
%PUT N SUBTABLES: %LEFT(&N_SUBTABLES);
%PUT INSTRUMENTS: %LEFT(&INSTRUMENTS);
%PUT INSTRUMENT LIST: %LEFT(%BQUOTE(&INSTRUMENT_LIST));
/* Get information on the variables in the dataset */
PROC CONTENTS
DATA = &DATA_SET.
OUT = REDCAP_VARNAMES(KEEP=NAME VARNUM)
NOPRINT;
RUN;
/* Make a list of fields and their associated forms based on data dictionary */
DATA REDCAP_FIELDS(KEEP=VAR_NAME FORM_NAME);
SET &DATA_DICTIONARY.;
IF FIELD_TYPE EQ "checkbox" THEN DO;
BASENAME = VAR_NAME;
DO I = 1 TO N;
SET REDCAP_VARNAMES POINT=I NOBS=N;
IF PRXMATCH("/^"!!trim(BASENAME)!!"___.+$/", NAME) THEN DO;
VAR_NAME = NAME;
OUTPUT;
END;
END;
END;
ELSE OUTPUT;
RUN;
/* Add instrument status fields to list of fields */
PROC SQL;
CREATE TABLE REDCAP_INSTRUMENT_STATUS_FIELDS AS
SELECT DISTINCT TRIM(FORM_NAME)!!"_complete" AS VAR_NAME LENGTH=200, FORM_NAME
FROM &DATA_DICTIONARY.;
QUIT;
PROC APPEND
BASE=REDCAP_FIELDS
DATA=REDCAP_INSTRUMENT_STATUS_FIELDS;
RUN;
DATA REDCAP_FIELDS;
SET REDCAP_FIELDS;
IF LENGTH(VAR_NAME) GT 32 THEN DO;
PUT "WARNING: The variable " VAR_NAME "is too long (MAX 32 CHARACTERS).";
PUT "WARNING: " VAR_NAME "will not be included in the output dataset.";
DELETE;
END;
RUN;
/* Sort out the field names */
PROC SQL NOPRINT;
SELECT VAR_NAME
INTO :VARS_BASE SEPARATED BY ' '
FROM REDCAP_FIELDS AS A
WHERE FORM_NAME NOT IN (&INSTRUMENT_LIST);
%put Base vars: &VARS_BASE;
%DO I = 1 %TO &N_SUBTABLES;
%LET INSTRUMENT_I = %SCAN(&INSTRUMENTS,&I,%STR( ));
SELECT VAR_NAME
INTO :VARS_&I. SEPARATED BY ' '
FROM REDCAP_FIELDS AS A
WHERE FORM_NAME EQ "&INSTRUMENT_I.";
%PUT &INSTRUMENT_I. vars: &&VARS_&I;
%END;
QUIT;
/* Make new data sets based on field names above */
DATA &DATA_SET._BASE (KEEP = &VARS_BASE);
SET &DATA_SET;
IF MISSING(REDCAP_REPEAT_INSTRUMENT);
RUN;
%DO I = 1 %TO &N_SUBTABLES;
%LET INSTRUMENT_I = %SCAN(&INSTRUMENTS,&I,%STR( ));
/* Create either numeric table name or name based on repeating instrument name (see issue #12) */
%IF &NUMERIC_SUBTABLES EQ N %THEN %DO;
%LET SUBTABLE_NAME = &DATA_SET._&INSTRUMENT_I.;
%IF %LENGTH(&SUBTABLE_NAME) GT 32 %THEN %DO;
%PUT ERROR: The table name &SUBTABLE_NAME is %LENGTH(&SUBTABLE_NAME) characters long (MAX 32 CHARACTERS).;
%PUT ERROR: &SUBTABLE_NAME will be skipped. Consider setting NUMERIC_SUBTABLES = Y.;
%GOTO SKIP;
%END;
%END;
%ELSE %DO;
%LET SUBTABLE_NAME = &DATA_SET._&I.;
%END;
DATA &SUBTABLE_NAME (KEEP = &KEY redcap_repeat_instance &&VARS_&I);
SET &DATA_SET;
IF REDCAP_REPEAT_INSTRUMENT EQ "&INSTRUMENT_I.";
RUN;
%PUT NOTE: Records from instrument &INSTRUMENT_I have been placed in &SUBTABLE_NAME;
%SKIP:
%END;
/* Clean up temporary datasets */
PROC DATASETS MEMTYPE=DATA LIBRARY=WORK NOLIST;
DELETE REDCAP_FIELDS REDCAP_VARNAMES REDCAP_INSTRUMENT_STATUS_FIELDS;
RUN;
%FINISH:
%MEND REDCAP_SPLIT;

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# DO NOT CHANGE the "init" and "install" sections below
# https://github.com/krlmlr/r-appveyor/issues/34#issuecomment-302016795
before_build:
ps: |
cp ..\travis-tool.sh R\travis-tool.sh
cp travis-tool.sh.cmd R\travis-tool.sh.cmd
cd R
bash -c "echo '^travis-tool\.sh(\.cmd)?$' >> .Rbuildignore"
# Download script file from GitHub
init:
ps: |
$ErrorActionPreference = "Stop"
Invoke-WebRequest http://raw.github.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "..\appveyor-tool.ps1"
Import-Module '..\appveyor-tool.ps1'
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ps: Bootstrap
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R_VERSION: release
PKGTYPE: binary
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name: Logs
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name: Bits
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R/tests/testthat/test-primary-table-name.R → tests/testthat/test-primary-table-name.R
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