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Overview

chmsflow uses two CSV metadata files to define how raw CHMS variables are harmonized. These files are bundled with the package in inst/extdata/ and are also available as data objects (variables and variable_details).

• variables.csv – lists every harmonized variable with its name, label, type, and unit
• variable-details.csv – defines the row-by-row recoding rules that rec_with_table() applies

This vignette is a column-by-column reference for both files. For an explanation of how these files fit into the harmonization workflow, see Methodology.

variables.csv

#> There are 211 variables, grouped in 24 subjects and 5 sections.

Columns

1. variable – the name of the harmonized variable.

2. label – a short label for the variable.

3. labelLong – a more detailed label for the variable.

4. section – the broad grouping where this variable belongs (e.g., sociodemographics, health behaviour, health status).

5. subject – the specific topic the variable pertains to (e.g., age, smoking, blood pressure).

6. variableType – whether the harmonized variable is Categorical or Continuous.

7. units – the units of the harmonized variable, or N/A if unitless.

8. databaseStart – the CHMS cycles that contain the variable, separated by commas.

9. variableStart – the source variable names as listed in each CHMS cycle. Uses the same format conventions as variable-details.csv (see below).

variable-details.csv

#> There are 1111 rows and 17 columns.

Row structure

Each row defines the recoding rule for one category of one variable. For a categorical variable with 4 categories, plus a not-applicable category, a missing category, and an else row, there are 7 rows.

Missing data rows use haven::tagged_na():

• NA::a – valid skip (not applicable)
• NA::b – missing (don’t know, refusal, not stated)

The else row catches values not matched by any other row.

Columns

We use clc_sex as a running example.

1. variable – name of the harmonized variable.

2. dummyVariable – dummy variable name for each category (categorical variables only; N/A for continuous).

3. typeEnd – variable type of the harmonized variable (cat or cont).

4. databaseStart – CHMS cycles containing this variable, separated by commas.

5. variableStart – source variable names in each CHMS cycle. Supports several formats:

6. typeStart – variable type in the source CHMS data (cat or cont).

7. recEnd – the value to recode each category to. Special values:

• copy – pass through unchanged (for continuous variables)
• NA::a – not applicable
• NA::b – missing
• Func::function_name – derived variable computed by the named function

8. numValidCat – number of non-missing categories (categorical only; N/A for continuous). Not used by rec_with_table().

9. catLabel – short label for the category.

10. catLabelLong – detailed label, matching CHMS documentation where possible.

11. units – units of the variable, or N/A. Must be consistent across all rows of the same variable.

12. recStart – the source value or range to match. Uses interval notation:

• [1, 4] – all integer values from 1 to 4
• [1, 2.5] – all values from 1 to 2.5 (2.55 would not match)
• else – all values not matched by other rows
• copy – combined with else, copies unmatched values unchanged

13. catStartLabel – label for the source category, matching CHMS documentation. For missing rows, describes each missing code and its value.

14. variableStartShortLabel – short label for the source variable.

15. variableStartLabel – detailed label for the source variable, matching CHMS documentation.

16. notes – relevant notes about changes between CHMS cycles, missing categories, or variable type changes.

Derived variables

Derived variables use two special column values:

• variableStart: DerivedVar::[var1, var2, var3] – lists the input variables
• recEnd: Func::function_name – names the R function that computes the derived variable

See Derived variables for details on how derived variables work.

Next steps

• See it in action – Follow the Analysis walkthrough to see how these metadata files drive a real analysis.
• Understand the methodology – For the design rationale behind the rules-as-data approach, see Methodology.
• Add your own variables – To extend the schema with custom variables, see How to add variables.

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They may not be fully stable and should be used with caution. We make no claims about them.
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