customizing-vectors-r

name

customizing-vectors-r

description

Type-stable vector operations and custom vector classes using vctrs. Use this skill when building R packages with custom types, need guaranteed output types regardless of input values, implementing consistent coercion/casting rules, or creating vector classes that work seamlessly with data frames. Covers when to use vctrs vs base R, building custom vector classes, coercion methods, and testing vctrs classes.

Customizing Vectors R

This skill covers type-stable operations and custom vector class design using the vctrs package.

Core Benefits

• Type stability - Predictable output types regardless of input values
• Size stability - Predictable output sizes from input sizes
• Consistent coercion rules - Single set of rules applied everywhere
• Robust class design - Proper S3 vector infrastructure

When to Use vctrs

Use vctrs when:

1. Building custom vector classes - Data frame compatibility, subsetting
2. Type-stable functions in packages - Guaranteed output types
3. Consistent coercion/casting - Explicit rules, predictable behavior
4. Size/length stability - Predictable sizing from inputs

Don't use vctrs when:

• Simple one-off analyses - Base R is sufficient
• No custom classes needed - Standard types work fine
• Performance critical + simple operations - Base R may be faster
• External API constraints - Must return base R types

vctrs vs Base R Decision Matrix

Use Case	Base R	vctrs	When to Choose vctrs
Simple combining	c()	vec_c()	Need type stability, consistent rules
Custom classes	S3 manually	new_vctr()	Want data frame compatibility, subsetting
Type conversion	as.*()	vec_cast()	Need explicit, safe casting
Finding common type	Not available	vec_ptype_common()	Combining heterogeneous inputs
Size operations	length()	vec_size()	Working with non-vector objects

Building Custom Vector Classes

See custom-vector-class.md for the complete pattern:

• Constructor (low-level)
• Helper (user-facing)
• Format method

Type-Stable Functions

See type-stable-functions.md for:

• Guaranteed output types with vec_cast()
• Avoiding type-depends-on-data patterns

Coercion and Casting

See coercion-casting.md for:

• Explicit casting with clear rules
• Common type finding with vec_ptype_common()
• Self-coercion methods
• Cross-type coercion methods

Implementation Patterns

Coercion Methods

Define vec_ptype2.* methods for type compatibility:

vec_ptype2.pkg_percent.pkg_percent <- function(x, y, ...) new_percent()
vec_ptype2.pkg_percent.double <- function(x, y, ...) double()

Casting Methods

Define vec_cast.* methods for conversion:

vec_cast.pkg_percent.double <- function(x, to, ...) new_percent(x)
vec_cast.double.pkg_percent <- function(x, to, ...) vec_data(x)

See coercion-methods.md for complete examples.

Performance Considerations

When vctrs Adds Overhead

• Simple operations - vec_c(1, 2) vs c(1, 2)
• One-off scripts - Type safety less critical
• Small vectors - Overhead may outweigh benefits

When vctrs Improves Performance

• Package functions - Type stability prevents expensive re-computation
• Complex classes - Consistent behavior reduces debugging
• Data frame operations - Robust column type handling
• Repeated operations - Predictable types enable optimization

Package Development

Exports and Dependencies

# DESCRIPTION
Imports: vctrs

# NAMESPACE - Import what you need
importFrom(vctrs, vec_assert, new_vctr, vec_cast, vec_ptype_common)

Testing vctrs Classes

See testing-vctrs.md for:

• Type stability tests
• Coercion tests

Key Insight

vctrs is most valuable in package development where type safety, consistency, and extensibility matter more than raw speed for simple operations.

source: Sarah Johnson's gist https://gist.github.com/sj-io/3828d64d0969f2a0f05297e59e6c15ad