Overview | 1. Load data | 2. Recode medications | 3. Derive hypertension status | 4. Inspect results | 5. Combine cycles | Next steps

Introduction | How to use derived variables | Creating a derived variable | Next steps

1. Introduction | What chmsflow provides | Typical workflow | 2. Installation | 3. Quick start | 4. Variable types | 4.1 Source variables (direct mapping) | 4.2 Transformed variables (continuous to categorical) | 4.3 Derived variables (computed by functions) | 5. Next steps

Introduction | How to add existing CHMS variables to chmsflow | Example of an existing CHMS variable: Age | Specifying the variable on variable_details.csv | Columns | Specifying the variable on variables.csv | How to create derived variables and add them to chmsflow | Example of a derived variable: Smoking pack-years | Step 1. Creating a derived function | Steps 2 and 3. Specifying pack-years in variable_details.csv and variables.csv | Adding labels to a derived variable | Creating a derived variable using derived variables | Next steps

Why CHMS harmonization is non-trivial | Variable naming changes | Coding scheme differences | Medication data format changes | How chmsflow works | Rules as data, derivation as code | The variableStart column | The recStart and recEnd columns | Missing data semantics | Derived variables | Known limits | Next steps | References

Missing data (NA --> tagged_na) | CHMS approach to coding missing data | Example haven::tagged_na() | Using tagged_na for creating derived variables | Next steps

1. Introduction | Available medication variables | Cycle differences | 2. When to use medication recoding | 3. Workflow | 3.1 Cycles 1--2 | 3.2 Cycles 3--6 | 4. Advanced: using individual classification functions | Cycle format differences | Next steps

Overview | Workflow | Setup inside the RDC | 1. Set the library path | 2. Load packages | 3. Load additional scripts and worksheets | 4. Load CHMS data | 5. Run your analysis | Tips | Next steps

Overview | variables.csv | Columns | variable-details.csv | Row structure | Derived variables | Next steps

Introduction | Example of a derived function | 1. Create and load a customized function for your derived variables. | 2. Add the derived variable to the variable_details and variables worksheets. | 3. Recode the derived variable

Install and load recodeflow | Install recodeflow | Load recodeflow | Additional packages

Example 1. Recode a single variable from a single dataset | Example 2. Recode a single variable across multiple datasets | Example 3. Recode a single variable, with different categories, from multiple datasets | Option 1: recode category agegrp variable into a common variable for only datasets with the same category responses | Option 2: recode the categorical agegrp variable into a continuous age_cont variable | Option 3: recode the categorical agegrp variable into a harmonized categorical variable | Example 4. Recode multiple variables from multiple datasets | Example 5. Recode all variables in the variables worksheet | Example 6: Add the data origin in combined datasets | Example 7. Recode derived variables

Example data | How to create the variable_details worksheet | Rows | Columns (14) | variable_details for dervived variables | How to create the variables worksheet variables

What is the function rec_with_table? | Output - Warning messages | Output - Notes | Arguments - Required | Arguments - Default

Working with template variables

Introduction | Structure of the worksheet variable_details | Naming convention for not applicable and missing values | Rows | Columns | Derived Variables | Tables

Introduction | How the variables.csv sheet is organized | Derived Variables

recodeflow approach to coding missing data | Example haven::tagged_na()