# sqlcaseR: Building long CASE WHEN statements for SQL interfaces in R

Version 0.2.1

Leoson Hoay
Last Updated: 8 Sep 2025 (0.2.0 -> 0.2.1)

## Introduction

This module was born out of my genuine frustration while constructing an extremely long CASE WHEN…THEN statement to re-label categorical variables. It is most helpful for folks who intend to work with SQL directly in the R environment, likely with a SQL connector such as RODBC or RSQLite.

The package provides three main functions for generating SQL statements: - `casewhen()` - Creates CASE WHEN…THEN statements for value mapping - `inlist()` - Creates IN() statements for filtering - `updatetable()` - Creates UPDATE statements for bulk data updates

All functions now support flexible column selection, robust error handling, different quote types, and various options for handling NULL/NA values.

Go from this:

Hotel/Motel | Living in Shelter/Hotel/Motel
Homeless Shelter | Living in Shelter/Hotel/Motel
Homeless Status Not Applicable | Not Homeless
N/A | Not Homeless
No | Not Homeless
Homeless, Doubled-Up | Doubled Up

To this:

    CASE WHEN 'Hotel/Motel' THEN 'Living in Shelter/Hotel/Motel'
     WHEN 'Homeless Shelter' THEN 'Living in Shelter/Hotel/Motel'
     WHEN 'Homeless Status Not Applicable' THEN 'Not Homeless'
     WHEN 'N/A' THEN 'Not Homeless'
     WHEN 'No' THEN 'Not Homeless'
     WHEN 'Homeless, Doubled-Up' THEN 'Doubled Up'

The package also supports the creation of long SQL IN() lists via the inlist() function, and UPDATE statements via the updatetable() function. The IN() functionality was inspired by reading about Kevin Flerlage’s Excel implementation.

## Demonstration

    library(sqlcaser)

The package assumes that the user has a mapping CSV file or an R dataframe similar to the example below:

    samp <- system.file("extdata", "sample.csv", package = "sqlcaser")
    mapping <- read.csv(samp)
    mapping
    #>             Homeless..Hotel.or.Motel Living.in.Shelter.Hotel.Motel
    #> 1              Homeless, Hotel/Motel Living in Shelter/Hotel/Motel
    #> 2        Homeless, Living in Shelter Living in Shelter/Hotel/Motel
    #> 3                  Homeless Shelters Living in Shelter/Hotel/Motel
    #> 4                        Hotel/motel Living in Shelter/Hotel/Motel
    #> 5                        Hotel/Motel Living in Shelter/Hotel/Motel
    #> 6                   Homeless Shelter Living in Shelter/Hotel/Motel
    #> 7     Homeless Status Not Applicable                  Not Homeless
    #> 8                                N/A                  Not Homeless
    #> 9                                 No                  Not Homeless
    #> 10              Homeless, Doubled-Up                    Doubled Up
    #> 11 Homeless, In Doubled-Up Residence                    Doubled Up
    #> 12             Homeless, Unsheltered                   Unsheltered

The function casewhen() takes an R dataframe or the file path of the mapping file as input,and returns the CASE statement as a string, while printing it to the console as well.

    statement <- casewhen(samp)
    #>
    #> CASE WHEN 'Homeless, Hotel or Motel' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless, Hotel/Motel' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless, Living in Shelter' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless Shelters' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Hotel/motel' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Hotel/Motel' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless Shelter' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless Status Not Applicable' THEN 'Not Homeless'
    #>  WHEN 'N/A' THEN 'Not Homeless'
    #>  WHEN 'No' THEN 'Not Homeless'
    #>  WHEN 'Homeless, Doubled-Up' THEN 'Doubled Up'
    #>  WHEN 'Homeless, In Doubled-Up Residence' THEN 'Doubled Up'
    #>  WHEN 'Homeless, Unsheltered' THEN 'Unsheltered'

The user can then easily include it as part of the SQL query:

    query <- paste("SELECT id, ", statement, " END AS status "," \nFROM table;")
    cat(query)
    #> SELECT id,
    #> CASE WHEN 'Homeless, Hotel or Motel' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless, Hotel/Motel' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless, Living in Shelter' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless Shelters' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Hotel/motel' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Hotel/Motel' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless Shelter' THEN 'Living in Shelter/Hotel/Motel'
    #>  WHEN 'Homeless Status Not Applicable' THEN 'Not Homeless'
    #>  WHEN 'N/A' THEN 'Not Homeless'
    #>  WHEN 'No' THEN 'Not Homeless'
    #>  WHEN 'Homeless, Doubled-Up' THEN 'Doubled Up'
    #>  WHEN 'Homeless, In Doubled-Up Residence' THEN 'Doubled Up'
    #>  WHEN 'Homeless, Unsheltered' THEN 'Unsheltered'
    #>   END AS status
    #> FROM table;

## Advanced Examples

### Using Named Columns and ELSE Clause

    # Create sample data with named columns
    data <- data.frame(
      status = c("Active", "Inactive", "Pending"),
      display = c("Currently Active", "Not Active", "Under Review")
    )

    # Use column names instead of positions, add ELSE clause
    advanced_case <- casewhen(data,
                             when_col = "status",
                             then_col = "display",
                             else_value = "Unknown Status")
    #>
    #> CASE WHEN 'Active' THEN 'Currently Active'
    #>  WHEN 'Inactive' THEN 'Not Active'
    #>  WHEN 'Pending' THEN 'Under Review'
    #>  ELSE 'Unknown Status'

### Working with Numeric Data and Auto-Quoting

    # Create data with numeric values
    scores <- data.frame(
      grade = c("A", "B", "C"),
      points = c(90, 80, 70)
    )

    # Auto-quote mode handles numeric values without quotes
    numeric_case <- casewhen(scores,
                            when_col = "grade",
                            then_col = "points",
                            quote_type = "auto")
    #>
    #> CASE WHEN 'A' THEN 90
    #>  WHEN 'B' THEN 80
    #>  WHEN 'C' THEN 70

### Creating IN Lists with Duplicate Removal

    # Create sample data with duplicates
    categories <- data.frame(
      category = c("Sales", "Marketing", "Sales", "IT", "Marketing", "HR")
    )

    # Remove duplicates automatically
    unique_list <- inlist(categories,
                         value_col = "category",
                         distinct = TRUE)
    #> Removed 2 duplicate values.
    #>
    #> IN('Sales', 'Marketing', 'IT', 'HR')

### Bulk UPDATE Statements

    # Create sample update data
    updates <- data.frame(
      id = c(1, 2, 3),
      name = c("John Doe", "Jane Smith", "Bob Wilson"),
      department = c("Engineering", "Sales", "Marketing"),
      salary = c(75000, 65000, 60000)
    )

    # Generate UPDATE statements using ID as key
    update_statements <- updatetable(updates,
                                    tablename = "employees",
                                    key_col = "id")
    #>
    #> UPDATE employees
    #> SET name = 'John Doe', department = 'Engineering', salary = 75000
    #> WHERE id = 1;
    #>
    #> UPDATE employees
    #> SET name = 'Jane Smith', department = 'Sales', salary = 65000
    #> WHERE id = 2;
    #>
    #> UPDATE employees
    #> SET name = 'Bob Wilson', department = 'Marketing', salary = 60000
    #> WHERE id = 3;

## Sample Data

A sample mapping file is provided in this package. The file path can be accessed as follows:

    samplepath <- system.file("extdata", "sample.csv", package = "sqlcaser")

## Function Reference

### `casewhen()` - CASE Statement Generator

Description

Constructs CASE WHEN…THEN statements from mapping data with flexible column selection and robust options.

Usage

    casewhen(inputfile = NULL, header = FALSE, when_col = 1, then_col = 2,
             else_value = NULL, quote_type = "single", handle_nulls = "skip")

Arguments

- `inputfile` - R dataframe or path to mapping file
- `when_col` - Column name/index for WHEN values (default: 1)
- `then_col` - Column name/index for THEN values (default: 2)
- `else_value` - Optional ELSE value for the CASE statement
- `quote_type` - “single”, “double”, or “auto” (default: “single”)
- `handle_nulls` - “skip”, “null”, or “error” (default: “skip”)

### `inlist()` - IN Statement Generator

Description

Creates SQL IN() statements with duplicate removal and flexible column selection.

Usage

    inlist(inputfile = NULL, header = FALSE, value_col = 1,
           quote_type = "single", handle_nulls = "skip", distinct = TRUE)

Arguments

- `inputfile` - R dataframe or path to mapping file
- `value_col` - Column name/index for IN values (default: 1)
- `quote_type` - “single”, “double”, or “auto” (default: “single”)
- `distinct` - Remove duplicates if TRUE (default: TRUE)

### `updatetable()` - UPDATE Statement Generator

Description

Generates bulk UPDATE statements with flexible column selection and key specification.

Usage

    updatetable(inputfile = NULL, tablename = NULL, key_col = 1, update_cols = NULL,
                quote_type = "auto", handle_nulls = "skip", batch_updates = TRUE)

Arguments

- `inputfile` - R dataframe or path to mapping file
- `tablename` - Name of SQL table to update
- `key_col` - Column name/index for WHERE clause (default: 1)
- `update_cols` - Specific columns to update (default: all except key_col)
- `quote_type` - “single”, “double”, or “auto” (default: “auto”)

## Common Parameters Explained

Quote Types: - `"single"` - Use single quotes for all values: `'value'` - `"double"` - Use double quotes for all values: `"value"` - `"auto"` - Smart quoting: numeric values unquoted, text values single-quoted

NULL Handling: - `"skip"` - Skip rows/values with NA (default, safest) - `"null"` - Convert NA to SQL NULL in output - `"error"` - Stop execution when NA encountered

## Installation

Install using:

    devtools::install_github("leosonh/sqlcaseR")

## Acknowledgments

Much thanks to a couple of my prior colleagues at Learning Collider - Nitya Raviprakash and Jasmin Dial - who provided healthy discussion around my misery of constructing long SQL queries. Credit is also due to Kevin Flerlage, whose efforts in automating this process in Excel are commendable and partially inspired this package.

## Citation and License

If desired, cite the package using:

    citation("sqlcaseR")

License: MIT License