About this vignette: All numeric values shown in this vignette are computed from the actual CCHS sample metadata files. Code is hidden by default for readability, but you can view the source .qmd file to see how values are calculated.
Overview
This example demonstrates generating mock Canadian Community Health Survey (CCHS) data using cchsflow metadata. The generated mock data can be used to test harmonization workflows before accessing real CCHS data.
Load metadata
Load the CCHS harmonization metadata files that define variables and their details across cycles.
# CCHS sample variables
variables <- read.csv(
system.file("extdata/cchs/variables_cchsflow_sample.csv", package = "MockData"),
header = TRUE,
check.names = FALSE,
na.strings = c("", "NA", "N/A"),
stringsAsFactors = FALSE
)
# CCHS sample variable details
variable_details <- read.csv(
system.file("extdata/cchs/variable_details_cchsflow_sample.csv", package = "MockData"),
header = TRUE,
check.names = FALSE,
na.strings = c("", "NA", "N/A"),
stringsAsFactors = FALSE
)The CCHS sample metadata includes 20 harmonized variables with 126 detail rows.
Extract available cycles
The sample metadata includes 8 CCHS cycles: cchs2003_p, cchs2005_p, cchs2007_2008_p, cchs2009_2010_p, cchs2011_2012_p, cchs2013_2014_p, cchs2001_p, cchs2003_p.
Understanding the metadata
The sample metadata includes 20 harmonized variables. For cchs2003_p, there are 20 harmonized variables available:
| variable | variable_raw | variableType | label |
|---|---|---|---|
| ADL_01 | RACC_6A | Categorical | Help preparing meals |
| ADL_02 | RACC_6B1 | Categorical | Help appointments/errands |
| ADL_03 | RACC_6C | Categorical | Help housework |
| ADL_04 | RACC_6E | Categorical | Help personal care |
| ADL_05 | RACC_6F | Categorical | Help move inside house |
| ADL_06 | RACC_6G | Categorical | Help personal finances |
| ADL_07 | RACC_6D | Categorical | Help heavy household chores |
| ADLF6R | RACCF6R | Categorical | Help tasks |
| ADL_der | NA | Categorical | Help tasks |
| ADM_RNO | ADMC_RNO | Continuous | Sequential record number |
Get raw variables to generate
For mock data generation, we need to create the raw source variables (before harmonization), not the harmonized variables.
# Get unique raw variables needed for this cycle (excludes derived variables)
raw_vars <- get_raw_variables(example_cycle, variables, variable_details,
include_derived = FALSE)This cycle requires 19 unique raw variables: 11 categorical and 8 continuous.
| variable_raw | variableType | harmonized_vars | n_harmonized |
|---|---|---|---|
| ADMC_RNO | Continuous | ADM_RNO | 1 |
| ALCC_1 | Categorical | ALC_1 | 1 |
| ALCC_5 | Categorical | ALW_1 | 1 |
| ALCC_5A1 | Continuous | ALW_2A1 | 1 |
| ALCC_5A2 | Continuous | ALW_2A2 | 1 |
| ALCC_5A3 | Continuous | ALW_2A3 | 1 |
| ALCC_5A4 | Continuous | ALW_2A4 | 1 |
| ALCC_5A5 | Continuous | ALW_2A5 | 1 |
| ALCC_5A6 | Continuous | ALW_2A6 | 1 |
| ALCC_5A7 | Continuous | ALW_2A7 | 1 |
| ALCCDTYP | Categorical | ALCDTTM | 1 |
| RACC_6A | Categorical | ADL_01 | 1 |
| RACC_6B1 | Categorical | ADL_02 | 1 |
| RACC_6C | Categorical | ADL_03 | 1 |
| RACC_6D | Categorical | ADL_07 | 1 |
| RACC_6E | Categorical | ADL_04 | 1 |
| RACC_6F | Categorical | ADL_05 | 1 |
| RACC_6G | Categorical | ADL_06 | 1 |
| RACCF6R | Categorical | ADLF6R | 1 |
Generate mock data for one cycle
Now let’s generate mock data for a single cycle.
# Configuration
n_records <- 100
target_cycle <- example_cycle
seed <- 12345
# Initialize data frame
df_mock <- data.frame(id = 1:n_records)We’ll generate 100 mock records for cchs2003_p.
Generate categorical variables
Generated 11 categorical variables. Data frame now has 12 columns.
Generate continuous variables
Generated 8 continuous variables. Final data frame has 20 columns.
Examine the result
Mock data structure:
'data.frame': 100 obs. of 20 variables:
$ id : int 1 2 3 4 5 6 7 8 9 10 ...
$ ALCC_1 : chr "2" "2" "2" "1" ...
$ ALCC_5 : chr "31" "27" "18" "35" ...
$ ALCCDTYP: chr "2" "3" "3" "2" ...
$ RACC_6A : chr "2" "2" "2" "1" ...
$ RACC_6B1: chr "1" "2" "2" "1" ...
$ RACC_6C : chr "1" "1" "1" "1" ...
$ RACC_6D : chr "1" "2" "1" "1" ...
$ RACC_6E : chr "2" "2" "2" "1" ...
$ RACC_6F : chr "2" "1" "1" "1" ...
$ RACC_6G : chr "2" "2" "2" "2" ...
$ RACCF6R : chr "2" "2" "1" "1" ...
$ ADMC_RNO: num 660337 104974 913583 245862 928899 ...
$ ALCC_5A1: num 15.7 12.3 27.5 34.1 22.9 ...
$ ALCC_5A2: num 12.99 41.72 2.23 16.19 41.72 ...
$ ALCC_5A3: num 14.21 15.41 35.32 8.22 3.53 ...
$ ALCC_5A4: num 32.52 17.32 6.43 35.39 19.99 ...
$ ALCC_5A5: num 42.96 14.85 8.18 11.72 6.64 ...
$ ALCC_5A6: num 35.2 20.7 16.4 42.4 11.7 ...
$ ALCC_5A7: num 46.1 20.1 10.1 32.9 22.5 ...First 5 rows:
| id | ALCC_1 | ALCC_5 | ALCCDTYP | RACC_6A | RACC_6B1 | RACC_6C | RACC_6D | RACC_6E | RACC_6F | RACC_6G | RACCF6R | ADMC_RNO | ALCC_5A1 | ALCC_5A2 | ALCC_5A3 | ALCC_5A4 | ALCC_5A5 | ALCC_5A6 | ALCC_5A7 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 31 | 2 | 2 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 660337.5 | 15.72437 | 12.99133 | 14.208899 | 32.515844 | 42.962386 | 35.19427 | 46.13716 |
| 2 | 2 | 27 | 3 | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 2 | 104973.9 | 12.28077 | 41.71837 | 15.410777 | 17.316266 | 14.849557 | 20.70368 | 20.08061 |
| 3 | 2 | 18 | 3 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 913582.7 | 27.53722 | 2.23390 | 35.316357 | 6.431974 | 8.183292 | 16.37728 | 10.13340 |
| 4 | 1 | 35 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 245862.2 | 34.09194 | 16.19133 | 8.218619 | 35.393032 | 11.720900 | 42.36451 | 32.92027 |
| 5 | 2 | 26 | 3 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 928898.9 | 22.91900 | 41.71608 | 3.526694 | 19.988267 | 6.644390 | 11.69989 | 22.53624 |
Missing values: No missing values
Summary
This example demonstrated generating mock CCHS data for testing cchsflow harmonization workflows. The generated data:
- Respects category ranges from variable_details
- Includes appropriate missing values
- Uses reproducible seeds
- Can be used to test harmonization functions before accessing real CCHS data
Next steps
- Test your cchsflow harmonization pipeline on this mock data
- Generate mock data for additional cycles as needed
- Calculate derived variables after harmonization (not during mock data generation)