diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..bc280ca Binary files /dev/null and b/.DS_Store differ diff --git a/R/.Rbuildignore b/.Rbuildignore similarity index 54% rename from R/.Rbuildignore rename to .Rbuildignore index 7f78a9d..380c182 100644 --- a/R/.Rbuildignore +++ b/.Rbuildignore @@ -1,5 +1,4 @@ ^.*\.Rproj$ ^\.Rproj\.user$ -^\.travis\.yml$ -^appveyor\.yml$ ^data-raw$ +^test-data$ diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 0052e7a..0000000 --- a/.travis.yml +++ /dev/null @@ -1,9 +0,0 @@ -# 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 diff --git a/R/DESCRIPTION b/DESCRIPTION similarity index 100% rename from R/DESCRIPTION rename to DESCRIPTION diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 94a9ed0..0000000 --- a/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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If not, see . - -Also add information on how to contact you by electronic and paper mail. - - If the program does terminal interaction, make it output a short -notice like this when it starts in an interactive mode: - - Copyright (C) - This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. - This is free software, and you are welcome to redistribute it - under certain conditions; type `show c' for details. - -The hypothetical commands `show w' and `show c' should show the appropriate -parts of the General Public License. Of course, your program's commands -might be different; for a GUI interface, you would use an "about box". - - You should also get your employer (if you work as a programmer) or school, -if any, to sign a "copyright disclaimer" for the program, if necessary. -For more information on this, and how to apply and follow the GNU GPL, see -. - - The GNU General Public License does not permit incorporating your program -into proprietary programs. If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. diff --git a/R/NAMESPACE b/NAMESPACE similarity index 100% rename from R/NAMESPACE rename to NAMESPACE diff --git a/R/NEWS.md b/NEWS.md similarity index 100% rename from R/NEWS.md rename to NEWS.md diff --git a/R/.gitignore b/R/.gitignore deleted file mode 100644 index 807ea25..0000000 --- a/R/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -.Rproj.user -.Rhistory -.RData diff --git a/R/R/REDCap_split.r b/R/REDCap_split.r similarity index 100% rename from R/R/REDCap_split.r rename to R/REDCap_split.r diff --git a/R/R/process_user_input.r b/R/process_user_input.r similarity index 100% rename from R/R/process_user_input.r rename to R/process_user_input.r diff --git a/R/R/sysdata.rda b/R/sysdata.rda similarity index 100% rename from R/R/sysdata.rda rename to R/sysdata.rda diff --git a/R/R/utils.r b/R/utils.r similarity index 100% rename from R/R/utils.r rename to R/utils.r diff --git a/README.md b/README.md deleted file mode 100644 index 26813a3..0000000 --- a/README.md +++ /dev/null @@ -1,302 +0,0 @@ -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. diff --git a/R/REDCapRITS.Rproj b/REDCapRITS.Rproj similarity index 100% rename from R/REDCapRITS.Rproj rename to REDCapRITS.Rproj diff --git a/SAS/REDCap_split.sas b/SAS/REDCap_split.sas deleted file mode 100644 index 5948d20..0000000 --- a/SAS/REDCap_split.sas +++ /dev/null @@ -1,229 +0,0 @@ -/******************************************************************************** -* -* 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; diff --git a/appveyor.yml b/appveyor.yml deleted file mode 100644 index f47b710..0000000 --- a/appveyor.yml +++ /dev/null @@ -1,57 +0,0 @@ -# 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' - -install: - ps: Bootstrap - -cache: - - C:\RLibrary - -# Adapt as necessary starting from here - -environment: - R_VERSION: release - PKGTYPE: binary - -build_script: - - travis-tool.sh install_deps - -test_script: - - travis-tool.sh run_tests - -on_failure: - - 7z a failure.zip *.Rcheck\* - - appveyor PushArtifact failure.zip - -artifacts: - - path: '*.Rcheck\**\*.log' - name: Logs - - - path: '*.Rcheck\**\*.out' - name: Logs - - - path: '*.Rcheck\**\*.fail' - name: Logs - - - path: '*.Rcheck\**\*.Rout' - name: Logs - - - path: '\*_*.tar.gz' - name: Bits - - - path: '\*_*.zip' - name: Bits diff --git a/R/data-raw/metadata_names.R b/data-raw/metadata_names.R similarity index 100% rename from R/data-raw/metadata_names.R rename to data-raw/metadata_names.R diff --git a/R/man/REDCap_split.Rd b/man/REDCap_split.Rd similarity index 100% rename from R/man/REDCap_split.Rd rename to man/REDCap_split.Rd diff --git a/R/tests/testthat.R b/tests/testthat.R similarity index 100% rename from R/tests/testthat.R rename to tests/testthat.R diff --git a/R/tests/testthat/data/ExampleProject_DATA_2018-06-07_1129.csv b/tests/testthat/data/ExampleProject_DATA_2018-06-07_1129.csv similarity index 100% rename from R/tests/testthat/data/ExampleProject_DATA_2018-06-07_1129.csv rename to tests/testthat/data/ExampleProject_DATA_2018-06-07_1129.csv diff --git a/R/tests/testthat/data/ExampleProject_DataDictionary_2018-06-07.csv b/tests/testthat/data/ExampleProject_DataDictionary_2018-06-07.csv similarity index 100% rename from R/tests/testthat/data/ExampleProject_DataDictionary_2018-06-07.csv rename to tests/testthat/data/ExampleProject_DataDictionary_2018-06-07.csv diff --git a/R/tests/testthat/data/ExampleProject_metadata.json b/tests/testthat/data/ExampleProject_metadata.json similarity index 100% rename from R/tests/testthat/data/ExampleProject_metadata.json rename to tests/testthat/data/ExampleProject_metadata.json diff --git a/R/tests/testthat/data/ExampleProject_records.json b/tests/testthat/data/ExampleProject_records.json similarity index 100% rename from R/tests/testthat/data/ExampleProject_records.json rename to tests/testthat/data/ExampleProject_records.json diff --git a/R/tests/testthat/data/WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv b/tests/testthat/data/WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv similarity index 100% rename from R/tests/testthat/data/WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv rename to tests/testthat/data/WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv diff --git a/R/tests/testthat/data/WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv b/tests/testthat/data/WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv similarity index 100% rename from R/tests/testthat/data/WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv rename to tests/testthat/data/WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv diff --git a/R/tests/testthat/data/create-ref-data.R b/tests/testthat/data/create-ref-data.R similarity index 100% rename from R/tests/testthat/data/create-ref-data.R rename to tests/testthat/data/create-ref-data.R diff --git a/R/tests/testthat/helper-ExampleProject_R_2018-06-07_1129.r b/tests/testthat/helper-ExampleProject_R_2018-06-07_1129.r similarity index 100% rename from R/tests/testthat/helper-ExampleProject_R_2018-06-07_1129.r rename to tests/testthat/helper-ExampleProject_R_2018-06-07_1129.r diff --git a/R/tests/testthat/helper-paths.R b/tests/testthat/helper-paths.R similarity index 100% rename from R/tests/testthat/helper-paths.R rename to tests/testthat/helper-paths.R diff --git a/R/tests/testthat/test-API.R b/tests/testthat/test-API.R similarity index 100% rename from R/tests/testthat/test-API.R rename to tests/testthat/test-API.R diff --git a/R/tests/testthat/test-csv-exports.R b/tests/testthat/test-csv-exports.R similarity index 100% rename from R/tests/testthat/test-csv-exports.R rename to tests/testthat/test-csv-exports.R diff --git a/R/tests/testthat/test-forms-arg.R b/tests/testthat/test-forms-arg.R similarity index 100% rename from R/tests/testthat/test-forms-arg.R rename to tests/testthat/test-forms-arg.R diff --git a/R/tests/testthat/test-longitudinal.R b/tests/testthat/test-longitudinal.R similarity index 100% rename from R/tests/testthat/test-longitudinal.R rename to tests/testthat/test-longitudinal.R diff --git a/R/tests/testthat/test-primary-table-name.R b/tests/testthat/test-primary-table-name.R similarity index 100% rename from R/tests/testthat/test-primary-table-name.R rename to tests/testthat/test-primary-table-name.R