Best Practices for Bitfield Design

library(bitfield)
library(dplyr, warn.conflicts = FALSE)

Best Practices for Bitfield Design

This guide provides practical recommendations for effective bitfield design, protocol selection, and avoiding common pitfalls when using the bitfield package.

Planning Your Bitfield

Start with Clear Objectives

Before creating bitfields, identify what metadata needs to travel between workflows:

# Example: Quality assessment for agricultural data
bf_tbl
#> # A tibble: 9 × 5
#>       x     y commodity yield year 
#>   <dbl> <dbl> <fct>     <dbl> <chr>
#> 1  25.3  59.5 soybean   11.2  2021 
#> 2  27.9  58.1 maize     12.0  NA   
#> 3  27.8  57.8 soybean   13.2  2021r
#> 4  27    59.2 NA         4.43 2021 
#> 5 259   Inf   honey     13.0  2021 
#> 6  27.3  59.1 maize      8.55 2021 
#> 7  26.1  58.4 soybean   11.3  2021 
#> 8  26.5 NaN   maize     10.6  2021 
#> 9   0     0   soybean    9.01 2021

This dataset has several quality issues: - Missing values (NA in commodity and year columns) - Invalid coordinates (x=259, y=Inf, NaN) - Suspicious data points (x=0, y=0) - Mixed data formats (year “2021r”)

Choose Efficient Protocols

Select protocols that balance information content with bit efficiency:

reg <- bf_registry(name = "quality_check",
                   description = "Agricultural data quality assessment")

# Detect missing commodities (1 bit)
reg <- bf_map(protocol = "na", data = bf_tbl, x = commodity, registry = reg)

# Check for infinite coordinates (1 bit each)
reg <- bf_map(protocol = "inf", data = bf_tbl, x = x, registry = reg)
reg <- bf_map(protocol = "inf", data = bf_tbl, x = y, registry = reg)

# Check for suspicious zero coordinates (1 bit)
reg <- bf_map(protocol = "matches", data = bf_tbl, x = x, registry = reg, set = 0)

reg
#>   width 4
#>   flags 4  -|-|-|-
#> 
#>   pos encoding type    col
#>   1   0.0.1/0  na      commodity
#>   2   0.0.1/0  inf     x
#>   3   0.0.1/0  inf     y
#>   4   0.0.1/0  matches x

Protocol Selection Guidelines

Use Atomic Protocols

Each protocol should test exactly one concept for maximum reusability:

# Good: Separate protocols for different quality checks
reg2 <- bf_registry(name = "detailed_quality", description = "Detailed quality flags")

reg2 <- bf_map(protocol = "na", data = bf_tbl, x = commodity, registry = reg2)
reg2 <- bf_map(protocol = "range", data = bf_tbl, x = yield, registry = reg2,
               min = 0, max = 20, na.val = 1L)  # Reasonable yield range
reg2 <- bf_map(protocol = "grepl", data = bf_tbl, x = year, registry = reg2,
               pattern = "[0-9]{4}$", na.val = 0L)  # Valid 4-digit year

reg2
#>   width 3
#>   flags 3  -|-|-
#> 
#>   pos encoding type  col
#>   1   0.0.1/0  na    commodity
#>   2   0.0.1/0  range yield
#>   3   0.0.1/0  grepl year

Consider Downstream Needs

Design bitfields thinking about what information downstream users need:

# Encode with sufficient precision for downstream analysis
field <- bf_encode(registry = reg2)
decoded <- bf_decode(field, registry = reg2, verbose = FALSE)

# Combine with original data for downstream use
result <- bf_tbl %>%
  bind_cols(decoded) %>%
  bind_cols(bf_int = field$bf_int1)

head(result, 3)
#> # A tibble: 3 × 9
#>       x     y commodity yield year  na_commodity range_yield grepl_year bf_int
#>   <dbl> <dbl> <fct>     <dbl> <chr> <chr>        <chr>       <chr>       <int>
#> 1  25.3  59.5 soybean    11.2 2021  0            1           1               3
#> 2  27.9  58.1 maize      12.0 NA    0            1           0               2
#> 3  27.8  57.8 soybean    13.2 2021r 0            1           0               2

Common Pitfalls and Solutions

Pitfall 1: Over-Engineering Bitfields

Problem: Trying to encode too much information in a single bitfield.

Solution: Focus on essential metadata that enables informed decisions:

# Simple but effective approach
simple_reg <- bf_registry(name = "essential", description = "Essential quality flags")

simple_reg <- bf_map(protocol = "na", data = bf_tbl, x = commodity, registry = simple_reg)
simple_reg <- bf_map(protocol = "numeric", data = bf_tbl, x = yield, registry = simple_reg,
                     format = "half")  # Preserve yield with limited precision

simple_reg
#>   width 17
#>   flags 2  -|----------------
#> 
#>   pos encoding  type    col
#>   1   0.0.1/0   na      commodity
#>   2   1.5.10/15 numeric yield

Pitfall 2: Insufficient Testing

Problem: Not testing protocols with edge cases.

Solution: Always test with problematic data:

# Test with the problematic rows from bf_tbl
problematic_rows <- bf_tbl[c(4, 5, 8, 9), ]  # NA, Inf, NaN, zeros
print(problematic_rows)
#> # A tibble: 4 × 5
#>       x     y commodity yield year 
#>   <dbl> <dbl> <fct>     <dbl> <chr>
#> 1  27    59.2 NA         4.43 2021 
#> 2 259   Inf   honey     13.0  2021 
#> 3  26.5 NaN   maize     10.6  2021 
#> 4   0     0   soybean    9.01 2021

test_reg <- bf_registry(name = "edge_test", description = "Edge case testing")
test_reg <- bf_map(protocol = "na", data = problematic_rows, x = commodity, registry = test_reg)
test_reg <- bf_map(protocol = "inf", data = problematic_rows, x = x, registry = test_reg)

# Verify encoding/decoding works
test_field <- bf_encode(registry = test_reg)
test_decoded <- bf_decode(test_field, registry = test_reg, verbose = FALSE)
test_decoded
#> # A tibble: 4 × 2
#>   na_commodity inf_x
#>   <chr>        <chr>
#> 1 1            0    
#> 2 0            0    
#> 3 0            0    
#> 4 0            0

Pitfall 3: Ignoring Bit Budget

Problem: Using too many bits for unnecessary precision.

Solution: Match precision to actual needs:

# Compare different numeric encoding approaches
reg_precise <- bf_registry(name = "precise", description = "High precision encoding")
reg_efficient <- bf_registry(name = "efficient", description = "Efficient encoding")

# High precision (16 bits)
reg_precise <- bf_map(protocol = "numeric", data = bf_tbl, x = yield,
                      registry = reg_precise, format = "half")

# More efficient (using half precision)
reg_efficient <- bf_map(protocol = "numeric", data = bf_tbl, x = yield,
                        registry = reg_efficient, format = "half")

cat("Precise encoding:", reg_precise@width, "bits\n")
#> Precise encoding: 16 bits
cat("Efficient encoding:", reg_efficient@width, "bits\n")
#> Efficient encoding: 16 bits

Working with Categorical Data

Handle categorical variables efficiently:

# Check unique categories first
unique(bf_tbl$commodity)
#> [1] soybean maize   <NA>    honey  
#> Levels: honey maize soybean

# Encode categories (automatically determines bit needs)
cat_reg <- bf_registry(name = "categories", description = "Categorical encoding")
cat_reg <- bf_map(protocol = "category", data = bf_tbl, x = commodity,
                  registry = cat_reg, na.val = 0L)

cat_reg
#>   width 2
#>   flags 1  --
#> 
#>   pos encoding type     col
#>   1   0.0.2/0  category commodity

Validation and Documentation

Always Document Your Decisions

# Well-documented registry
final_reg <- bf_registry(
  name = "agricultural_qa",
  description = "Agricultural data quality assessment including missing value detection, coordinate validation, and yield encoding with 8-bit precision"
)

final_reg <- bf_map(protocol = "na", data = bf_tbl, x = commodity, registry = final_reg)
final_reg <- bf_map(protocol = "numeric", data = bf_tbl, x = yield, registry = final_reg,
                    format = "half")

final_reg
#>   width 17
#>   flags 2  -|----------------
#> 
#>   pos encoding  type    col
#>   1   0.0.1/0   na      commodity
#>   2   1.5.10/15 numeric yield

Verify Round-Trip Integrity

# Always test encode/decode cycle
final_field <- bf_encode(registry = final_reg)
final_decoded <- bf_decode(final_field, registry = final_reg, verbose = FALSE)

# Check that important information is preserved
original_na_count <- sum(is.na(bf_tbl$commodity))
decoded_na_count <- sum(final_decoded$na_commodity == "1")

cat("Original NA count:", original_na_count, "\n")
#> Original NA count: 1
cat("Decoded NA count:", decoded_na_count, "\n")
#> Decoded NA count: 1
cat("Information preserved:", original_na_count == decoded_na_count, "\n")
#> Information preserved: TRUE

Summary

• Plan first: Identify essential metadata before designing bitfields
• Use atomic protocols: One concept per protocol maximizes reusability
• Test thoroughly: Include edge cases and problematic data
• Match precision to needs: Don’t waste bits on unnecessary precision
• Document decisions: Clear descriptions help downstream users
• Validate round-trips: Always verify encode/decode preserves essential information

Following these practices ensures your bitfields effectively bridge workflows while maintaining data integrity and computational efficiency.