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114 lines
2.7 KiB
Markdown
114 lines
2.7 KiB
Markdown
# Split REDCap repeating instruments table into multiple tables
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This will take output from a REDCap export and split it into a base
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table and child tables for each repeating instrument. Metadata is used
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to determine which fields should be included in each resultant table.
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## Usage
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``` r
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REDCap_split(
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records,
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metadata,
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primary_table_name = "",
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forms = c("repeating", "all")
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)
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```
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## Arguments
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- records:
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Exported project records. May be a `data.frame`, `response`, or
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`character` vector containing JSON from an API call.
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- metadata:
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Project metadata (the data dictionary). May be a `data.frame`,
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`response`, or `character` vector containing JSON from an API call.
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- primary_table_name:
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Name given to the list element for the primary output table. Ignored
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if `forms = 'all'`.
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- forms:
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Indicate whether to create separate tables for repeating instruments
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only or for all forms.
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## Value
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A list of `"data.frame"`s. The number of tables will differ depending on
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the `forms` option selected.
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- `'repeating'`: one base table and one or more tables for each
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repeating instrument.
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- `'all'`: a data.frame for each instrument, regardless of whether it is
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a repeating instrument or not.
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## Author
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Paul W. Egeler
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## Examples
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``` r
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if (FALSE) { # \dontrun{
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# Using an API call -------------------------------------------------------
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library(RCurl)
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# Get the records
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records <- postForm(
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uri = api_url, # Supply your site-specific URI
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token = api_token, # Supply your own API token
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content = "record",
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format = "json",
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returnFormat = "json"
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)
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# Get the metadata
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metadata <- postForm(
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uri = api_url, # Supply your site-specific URI
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token = api_token, # Supply your own API token
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content = "metadata",
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format = "json"
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)
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# Convert exported JSON strings into a list of data.frames
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REDCapCAST::REDCap_split(records, metadata)
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# Using a raw data export -------------------------------------------------
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# Get the records
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records <- read.csv("/path/to/data/ExampleProject_DATA_2018-06-03_1700.csv")
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# Get the metadata
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metadata <- read.csv(
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"/path/to/data/ExampleProject_DataDictionary_2018-06-03.csv"
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)
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# Split the tables
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REDCapCAST::REDCap_split(records, metadata)
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# In conjunction with the R export script ---------------------------------
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# You must set the working directory first since the REDCap data export
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# script contains relative file references.
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old <- getwd()
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setwd("/path/to/data/")
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# Run the data export script supplied by REDCap.
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# This will create a data.frame of your records called 'data'
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source("ExampleProject_R_2018-06-03_1700.r")
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# Get the metadatan
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metadata <- read.csv("ExampleProject_DataDictionary_2018-06-03.csv")
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# Split the tables
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REDCapCAST::REDCap_split(data, metadata)
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setwd(old)
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} # }
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```
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