1 Queries

Queries are crucial for the accuracy and reliability of a REDCap dataset. They help identify missing values, inconsistencies, and potential errors in the collected data. The rd_query() function allows you to generate queries using a specific expression.

To identify missing values in certain variables, simply provide the relevant information to the variables and expression arguments. In this scenario, the expression would be ‘is.na(x)’, where ‘x’ represents the variable itself:

example <- rd_query(covican_transformed,
                    variables = "copd",
                    expression = "is.na(x)")

Note: For variables with branching logic, the function will automatically apply the associated branching logic or at least report it.

Alternatively, to identify outliers or observations that meet a certain condition (for example, range):

example <- rd_query(covican_transformed,
                    variables = c("age", "potassium"),
                    expression = c("x > 80", "x > 4.2 & x < 4.3"),
                    event = "baseline_visit_arm_1")

Warning: The branching logic of the following variable was applied
automatically: potassium

In both cases, the function returns a list containing a data frame designed to aid you to locate each query in the REDCap project:

example$queries

And a summary of the generated queries per specified variable for each applied expression:

example$results

For longitudinal projects, the rd_event() allows you to check if a particular event is missing from a record in the exported data. This happens in REDCap when there is no collected data in a particular event from a record, as REDCap will not export the corresponding row. To identify these cases, you can use the following code:

example <- rd_event(covican_transformed,
                    event = "follow_up_visit_da_arm_1")

2 Control

After identifying queries, it is common practice to correct the original dataset in REDCap and re-run the query process for a new query dataset.

The check_queries() functiona allows you to compare the previous query dataset with the new one:

check <- check_queries(old = example$queries, 
                       new = new_example$queries)

The output, in addition to the query data frame, now includes a summary with the number of new, miscorrected, solved and pending queries:

# Print results
check$results

Note: The “Miscorrected” category includes queries that belong to the same combination of record identifier and variable in both the old and new reports, but with a different reason. For instance, if a variable had a missing value in the old report, but in the new report shows a value outside the established range, it would be classified as “Miscorrected”.

3 Export

With the help of the rd_export() function, you can export the identified queries to a .xlsx file of your choice:

rd_export(example)

This is the simplets way to use the function and will create a file named “example.xlsx” in your current working directory, but you can customise this exported file:

rd_export(queries = example$queries,
          column = "Link",
          sheet_name = "Queries - Proyecto",
          path = "C:/User/Desktop/queries.xlsx",
          password = "123") 

In both cases, a message will be generated in the console informing you that the file has been created and where it is located.

For more information, consult the complete vignette available at: https://bruigtp.github.io/REDCapDM/articles/REDCapDM.html