R vs Python: Technical Feature Comparison

Sustainable Heart Rate Analysis

Author

Claude (Anthropic)

Published

December 27, 2025

Overview

This document compares how R and Python handle specific programming patterns used in the sustainable heart rate calculation pipeline. Rather than declaring winners, we examine the trade-offs and design philosophies behind each approach.

The complete workflow on the webpage includes:

Processing second-by-second heart rate streams for individual activities
Calculating rolling maximum values over 6, 20, 40, and 60-minute time windows
Aggregating across activities within each year for each athlete
Computing medians of yearly maximums to establish sustainable benchmarks
Parallelizing across hundreds of athletes
Visualizing the distribution of ratios across the athlete population

The code demonstrates several key patterns: functional iteration with map(), nested group operations with nest(), pattern-based column selection with across(), error handling with possibly(), parallelization with furrr, and seamless integration with ggplot2 for visualization.

1. Pipeline Composition: The Pipe Operator

How It Works in R

acts |>
  filter(athlete_id == athlete) |>
  group_by(year) |>
  nest() |>
  mutate(results = map(data, \(x) many_windows(x))) |>
  unnest(results)

The |> operator passes the result of each expression as the first argument to the next function. It works with any function, not just methods.

Key characteristic: Universal - you can pipe into tidyverse verbs, base R functions, or your own custom functions interchangeably.

How It Works in Python

Pandas (method chaining):

(acts
 .query('athlete_id == @athlete')
 .groupby('year')
 .apply(lambda x: many_windows(x))
 .reset_index(drop=True))

Polars (method chaining):

(acts
 .filter(pl.col('athlete_id') == athlete)
 .group_by('year')
 .map_groups(lambda x: many_windows(x)))

Method chaining works only with DataFrame methods. For custom functions, you need: - .apply() (Pandas) - wraps function call - .map_groups() (Polars) - similar wrapper - .pipe() - can call any function but less commonly used

Key characteristic: Method-based - limited to the DataFrame’s API unless you use wrappers.

Technical Trade-offs

R’s universal pipe:

✓ Consistent syntax for all operations
✓ Easy to mix library functions with custom code
✓ Natural for functional programming
✓ Positron IDE now provides autocomplete for piped operations
✗ Limited autocomplete in RStudio (shows function args but not what to pipe to next)

Python’s method chaining:

✓ IDE autocomplete shows available methods
✓ Clear that you’re working with a DataFrame
✓ Type checkers can validate method calls
✗ Custom functions break the chain or require wrappers
✗ Less flexible for non-method operations

Where This Matters

In the sustainable HR code, the pipeline flows into nest() and then uses map() on the nested data. In Python, this requires breaking the chain or using .apply() which is less natural. For workflows that heavily mix DataFrame operations with custom functions, R’s approach is more fluid.

2. Functional Iteration: `map()` vs List Comprehensions

How It Works in R

# Map over time windows, return numeric vector
results <- map_dbl(windows, \(w) {
  rolled <- rollmean(streams$heartrate, w, partial = FALSE)
  max_val <- max(rolled, na.rm = TRUE)
  if_else(is.finite(max_val), max_val, NA_real_)
})

# Map over two vectors simultaneously
map2_df(acts$athlete_id, acts$id, safe_fetch)

The purrr library provides:

Type-specific variants: map_dbl(), map_chr(), map_int(), map_lgl()
Multi-argument maps: map2(), pmap()
DataFrame output: map_df(), map_dfr(), map_dfc()

Type safety: If a function returns the wrong type, you get an immediate error. map_dbl() guarantees a numeric vector.

How It Works in Python

List comprehension:

results = [
    max_val if np.isfinite(max_val := max(
        pd.Series(streams['heartrate']).rolling(w).mean().dropna()
    )) else np.nan
    for w in windows
]

Using map:

def calc_window(w):
    rolled = pd.Series(streams['heartrate']).rolling(w).mean().dropna()
    max_val = rolled.max()
    return max_val if np.isfinite(max_val) else np.nan

results = list(map(calc_window, windows))

Multiple iterables:

# Need zip for multiple iterables
results = [safe_fetch(a, i) for a, i in zip(athlete_ids, activity_ids)]

# Or with map
results = list(map(safe_fetch, athlete_ids, activity_ids))

Type safety: Returns a generic list. No guarantees about element types until runtime.

Technical Trade-offs

R’s map family:

✓ Type-safe variants prevent runtime errors
✓ Clear intent (map_dbl says “I expect numbers”)
✓ Parallel variants (future_map_dbl) work identically
✓ Multi-argument versions are explicit
✗ More functions to learn
✗ Less familiar to programmers from other languages

Python’s approach:

✓ List comprehensions are very readable for simple cases
✓ Pythonic idiom, widely taught
✓ Walrus operator (:=) can capture intermediate values
✗ No type guarantees
✗ Complex comprehensions become hard to read
✗ Multiple iterables require zip() boilerplate

Where This Matters

The sustainable HR code uses map_dbl() to calculate max values for each time window. The type safety catches errors where a window calculation might return something unexpected. For complex multi-step transformations within iterations, R’s approach is clearer. For simple transformations, Python’s comprehensions work well.

3. Nested Group Operations: `nest()` vs `groupby()`

How It Works in R

acts |>
  group_by(year) |>
  nest() |>
  mutate(results = map(data, \(x) many_windows(x))) |>
  unnest(results)

This creates a tibble where:

Each row represents one year
The data column contains a tibble of all activities for that year (list-column)
You can then map() over the data column to apply functions
unnest() flattens the results back out

Key characteristic: Explicit nested structure - each group’s data is literally stored as a data frame in a column.

How It Works in Python

Pandas:

# GroupBy creates an opaque grouping
grouped = acts.groupby('year')

# Apply function to each group
results = grouped.apply(lambda x: many_windows(x))

# May need to reset index and wrangle structure

Polars:

(acts
 .group_by('year')
 .map_groups(lambda x: many_windows(x)))

Key characteristic: Implicit grouping - the GroupBy object doesn’t create a visible nested structure, it’s an instruction to apply operations per group.

Technical Trade-offs

R’s nest():

✓ Explicit structure you can inspect (View() shows nested data)
✓ Natural to apply multiple operations per group
✓ Easy to store multiple results (multiple map columns)
✓ List-columns are a general pattern, not group-specific
✗ Concept takes time to learn
✗ Can be memory-intensive for huge datasets

Python’s groupby:

✓ Familiar to SQL users
✓ Memory-efficient (doesn’t materialize groups)
✓ Standard pattern across many languages
✗ Less transparent what the structure is
✗ Harder to apply multiple different operations
✗ Results need to be wrangled back to proper DataFrame

Where This Matters

The sustainable HR code processes each year independently (calculate windows for all activities in that year, then get max), then aggregates across years (median of yearly maxes). The nested structure makes this two-step process explicit - one row per year with nested data, then one row per athlete with aggregated results.

4. Column Selection and Transformation: Tidyselect vs Manual Selection

How It Works in R

# Select columns by pattern, transform, and rename in one step
summarise(
  across(starts_with("HR_"), 
         \(x) max(x, na.rm = TRUE), 
         .names = "max_{str_remove(.col, 'HR_')}")
)

# Then select the new columns
summarise(
  across(starts_with("max_"), \(x) median(x, na.rm = TRUE))
)

This uses:

Tidyselect helpers: starts_with(), ends_with(), contains(), matches(), where(), everything()
across(): Apply function to multiple columns
.names: Template for renaming results

Key characteristic: Declarative column selection with natural language predicates.

How It Works in Python

Pandas:

# Select columns manually
hr_cols = [c for c in df.columns if c.startswith('HR_')]

# Transform
max_vals = df[hr_cols].max()

# Rename
max_vals.index = [f"max_{c.replace('HR_', '')}" for c in hr_cols]

# Then repeat for median
max_cols = [c for c in df.columns if c.startswith('max_')]
median_vals = df[max_cols].median()

Polars:

# Can use regex selector
(df.select([
    pl.col('^HR_.*$')  # Regex pattern
    .max()
    .name.map(lambda c: f"max_{c.replace('HR_', '')}")
]))

Key characteristic: Explicit selection via comprehensions or regex, separate renaming step.

Technical Trade-offs

R’s tidyselect:

✓ Reads like natural language (starts_with, contains)
✓ Combines selection, transformation, and renaming
✓ .names templating is very concise
✓ Same helpers work across all tidyverse functions
✓ Standard modern R approach
✗ Less explicit about what’s being selected

Python’s approach:

✓ Explicit - you see the list comprehension
✓ Full regex power in Polars
✓ Familiar list operations
✗ More verbose for common patterns
✗ Selection and transformation separated
✗ Renaming requires additional code

Where This Matters

The sustainable HR code selects all columns starting with “HR_”, takes the max of each, and renames them with a template. In R this is one across() call. In Python it’s 2-3 steps. For analytical code that frequently operates on column patterns, R’s approach is much more concise.

5. Error Handling: Functional Wrappers vs Try-Except

How It Works in R

# Wrap a function to return default value on error
safe_fetch <- possibly(
  \(athlete, act_id) fetch_streams(athlete, act_id) |> hr_windows(),
  otherwise = list(HR_6 = NA_real_, HR_20 = NA_real_, 
                   HR_40 = NA_real_, HR_60 = NA_real_)
)

# Use it directly
map2_df(acts$athlete_id, acts$id, safe_fetch)

Other purrr adverbs:

safely() - returns list(result = ..., error = ...)
quietly() - suppresses messages
insistently() - retries with backoff

Key characteristic: Functional composition - you transform the function itself, then use it normally.

How It Works in Python

Try-except:

def safe_fetch(athlete, act_id):
    try:
        return fetch_streams(athlete, act_id) | hr_windows()
    except Exception:
        return {'HR_6': np.nan, 'HR_20': np.nan, 
                'HR_40': np.nan, 'HR_60': np.nan}

Decorator pattern (closer to R’s approach):

from functools import wraps

def possibly(default):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            try:
                return func(*args, **kwargs)
            except:
                return default
        return wrapper
    return decorator

@possibly(default={'HR_6': np.nan, ...})
def safe_fetch(athlete, act_id):
    return fetch_streams(athlete, act_id) | hr_windows()

Key characteristic: Imperative error handling or custom decorators.

Technical Trade-offs

R’s adverbs:

✓ No control flow in business logic
✓ Compose once, use everywhere
✓ Works naturally with map/pipeline
✓ Standard library (part of purrr)
✗ Less explicit about what errors are caught
✗ Another abstraction to understand

Python’s try-except:

✓ Very explicit about error handling
✓ Can catch specific exception types
✓ Familiar to all Python developers
✗ Clutters the function body
✗ Decorator pattern requires setup

Where This Matters

The sustainable HR code processes thousands of activities, some of which might have missing data or fail to fetch. The possibly() wrapper keeps the main pipeline code clean - no try-except blocks scattered throughout. For code that prefers explicit error handling in each function, Python’s approach is clearer. For cleaner pipelines with abstracted error handling, R’s approach works better.

6. Parallelization Strategies

How It Works in R

library(furrr)
plan(multisession, workers = 8)

# Change map to future_map - that's it
result <- future_map_dfr(
  unique(acts$athlete_id),
  safe_athlete
)

Two changes from sequential:

Set the plan
Add future_ prefix to map function

Key characteristic: Drop-in parallelism for existing sequential code.

How It Works in Python

Multiprocessing:

from multiprocessing import Pool

with Pool(processes=8) as pool:
    results = pool.map(safe_athlete, athlete_ids)
    df = pd.concat(results, ignore_index=True)

Polars (automatic):

# Parallelizes automatically - no code changes
(pl.scan_csv('data.csv')
 .group_by('athlete_id')
 .agg([...])  # Runs in parallel
 .collect())

Key characteristic: - Multiprocessing: Explicit parallel setup - Polars: Automatic parallelization

Technical Trade-offs

R’s furrr:

✓ Minimal code change
✓ Same type guarantees as sequential version
✓ Automatic result collection
✗ Limited to embarrassingly parallel problems
✗ No query optimization

Python multiprocessing:

✓ Full control over processes
✓ Standard library
✗ Verbose setup
✗ Pickle overhead
✗ Manual result aggregation

Polars automatic:

✓ Zero code changes
✓ Query optimization too
✓ Uses all cores automatically
✗ Less control
✗ Only for Polars operations

Where This Matters

The sustainable HR code parallelizes across athletes (processing each athlete’s data independently). R’s furrr makes this straightforward - change map_dfr to future_map_dfr. Polars would parallelize the aggregations automatically, which requires no explicit code but means rewriting the entire pipeline in Polars.

7. Missing Value Handling

How It Works in R

max(x, na.rm = TRUE)
median(x, na.rm = TRUE)
min(streams$heartrate, na.rm = TRUE)

Every aggregation function requires explicit handling of NA values.

Default behavior: If you forget na.rm, the function returns NA if any input is NA. This makes missing data issues obvious.

How It Works in Python

# Skip NaN by default
df.max()
df.median()
df['heartrate'].min()

# Can be explicit if desired
df.max(skipna=True)   # Default behavior
df.max(skipna=False)  # Propagate NaN

Default behavior: Most operations skip NaN silently.

Technical Trade-offs

R’s explicit approach:

✓ Forces consideration of NAs on every operation
✓ Self-documenting (code shows NA strategy)
✓ Forgotten parameter produces NA (bug is obvious)
✗ Verbose and repetitive
✗ Tedious for clean data

Python’s implicit approach:

✓ Less typing
✓ Usually does what you want
✗ Easy to miss NA issues
✗ Silent failures possible

Where This Matters

Heart rate data from wearables has missing values (sensor dropouts, strap issues, data quality problems). Explicit na.rm = TRUE documents that you’ve thought about this. For clean data, Python’s approach is more convenient. For messy real-world sensor data, R’s explicitness catches bugs.

8. Visualization Integration

The webpage demonstrates how the analysis flows directly into visualization. After calculating the sustainable HR ratios, the code creates a boxplot showing the distribution across athletes:

How It Works in R

all_athletes |>
  pivot_longer(cols = c(ratio_6, ratio_20, ratio_40), 
               names_to = "variable", values_to = "value") |>
  ggplot(aes(x = variable, y = value)) +
    geom_boxplot() +
    geom_point(data = overlay_point, 
               aes(x = variable, y = point_value),
               color = "red", size = 3, shape = 18) +
    theme_minimal()

This demonstrates several key features:

Data reshaping (pivot_longer) flows into plotting
Layer-based graphics (boxplot + overlay points)
Clean syntax for publication-quality output
Single pipeline from analysis to visualization

Key characteristic: The data manipulation pipeline flows directly into ggplot2. Data reshaping and plotting are part of the same workflow.

How It Works in Python

Matplotlib (imperative):

# Reshape first
long_df = pd.melt(all_athletes, 
                  value_vars=['ratio_6', 'ratio_20', 'ratio_40'])

# Then plot imperatively
fig, ax = plt.subplots()
bp = ax.boxplot([long_df[long_df['variable']==v]['value'] 
                  for v in ['ratio_6', 'ratio_20', 'ratio_40']])
ax.scatter([1,2,3], overlay_values, color='red')

Plotnine (ggplot2 port):

(ggplot(long_df, aes(x='variable', y='value'))
 + geom_boxplot()
 + geom_point(data=overlay_point, aes(x='variable', y='point_value'),
              color='red', size=3))

Technical Trade-offs

ggplot2:

✓ Concise layer-based syntax
✓ Better defaults
✓ Integrates with tidyverse workflow
✓ Consistent across plot types

matplotlib:

✓ Maximum control
✓ Mature and well-documented
✗ Verbose
✗ Imperative style breaks flow
✗ Requires manual data wrangling

plotnine:

✓ Nearly identical to ggplot2
✗ Less mature
✗ Smaller community
✗ Occasional bugs

Where This Matters

The sustainable HR analysis produces statistical graphics for publication/presentation. ggplot2’s integration with the data pipeline and better defaults make this more efficient. For analytical workflows that produce many plots as part of the analysis, ggplot2’s approach reduces friction.

Performance Reality Check

For the Crickles dataset (~1-2 million activities), comparing different implementations:

R tidyverse + furrr (8 workers): Baseline (current implementation)
R data.table: Could be faster for some operations, but syntax much less readable
Python Pandas: Similar speed to single-threaded tidyverse; slower than furrr
Python Polars (auto-parallel): ~2-3x faster than furrr

Key insight: The code already uses parallelization via furrr with 8 workers. Polars’ advantage comes from:

Query optimization (processes only what’s needed)
Rust implementation (no interpreter overhead)
Better memory layout (columnar, cache-friendly)
Automatic SIMD vectorization

However: The sustainable HR calculation runs overnight when training models. Processing time is not the bottleneck - model development and validation time is.

Polars’ speed advantage would matter if:

Dataset grew 10x+ (tens of millions of activities)
Real-time processing was needed
Batch jobs were missing SLAs

For current scale and use case, code readability and maintainability matter more than a 2-3x speed difference.

Readability Comparison: A Polars Rewrite

How would the code compare if the sustainable HR pipeline were rewritten in Polars?

Current R Implementation

The code on the webpage shows this structure:

athlete_windows <- function(acts, athlete) {
  acts |>
    filter(athlete_id == athlete) |>
    group_by(year) |>
    nest() |>
    mutate(results = map(data, \(x) suppressWarnings(many_windows(x)))) |>
    unnest(results) |>
    # Step 1: Max per year
    summarise(
      across(starts_with("HR_"), \(x) max(x, na.rm = TRUE), 
             .names = "max_{str_remove(.col, 'HR_')}")
    ) |>
    # Step 2: Median of yearly maxes 
    summarise(
      across(starts_with("max_"), \(x) median(x, na.rm = TRUE))
    ) |>
    mutate(
      ratio_6  = max_60 / max_6,
      ratio_20 = max_60 / max_20,
      ratio_40 = max_60 / max_40
    )
}

Equivalent Polars Implementation

def athlete_windows_polars(acts: pl.DataFrame, athlete: str) -> pl.DataFrame:
    """Process one athlete's data - Polars version"""
    
    # Filter for athlete
    athlete_data = acts.filter(pl.col('athlete_id') == athlete)
    
    # Can't use nest() - need to manually process by year
    yearly_results = []
    for (year,), year_data in athlete_data.group_by('year'):
        try:
            windows = many_windows(year_data)
            yearly_results.append(windows)
        except Exception as e:
            print(f"Error processing year {year}: {e}")
            continue
    
    if not yearly_results:
        return pl.DataFrame()
    
    # Combine all years
    all_yearly = pl.concat(yearly_results)
    
    # Step 1: Max per year - need to select HR columns with regex
    max_per_year = (all_yearly
        .select([
            pl.col('^HR_\d+$').max().name.map(
                lambda c: f"max_{c.replace('HR_', '')}"
            )
        ]))
    
    # Step 2: Median of yearly maxes
    medians = (max_per_year
        .select([
            pl.col('^max_\d+$').median()
        ]))
    
    # Step 3: Calculate ratios
    result = medians.with_columns([
        (pl.col('max_60') / pl.col('max_6')).alias('ratio_6'),
        (pl.col('max_60') / pl.col('max_20')).alias('ratio_20'),
        (pl.col('max_60') / pl.col('max_40')).alias('ratio_40')
    ])
    
    return result

Line-by-Line Comparison

Operation	R (tidyverse)	Polars	Readability
Filter for athlete	`filter(athlete_id == athlete)`	`filter(pl.col('athlete_id') == athlete)`	R slightly cleaner
Group by year + process	`group_by(year) \|> nest() \|> mutate(...)`	`for (year,), data in ... group_by('year'):`	R much clearer
Error handling	`possibly()` wrapper (done once)	`try-except` in loop	R cleaner
Select HR columns	`across(starts_with("HR_"))`	`pl.col('^HR_\d+$')`	R more readable
Max with renaming	`.names = "max_{str_remove(.col, 'HR_')}"`	`.name.map(lambda c: f"max_{c.replace('HR_', '')}")`	R cleaner
Second aggregation	Another `summarise(across(...))`	Another `select([pl.col(...)...])`	R cleaner
Final ratios	`mutate(ratio_6 = max_60 / max_6, ...)`	`with_columns([(pl.col('max_60') / pl.col('max_6')).alias('ratio_6'), ...])`	R more concise

Readability Assessment

R version: - 15 lines in a single pipeline - Reads top to bottom - Each step is one verb - Natural language selectors (starts_with) - Error handling abstracted away

Polars version: - ~35-40 lines with explicit loops - Pipeline broken by loop - Regex instead of natural language - Error handling inline - More explicit but more verbose

Verdict: The Polars version would be approximately 30-40% more code and noticeably less clear, primarily due to:

No nest() equivalent - forces manual grouping loop
Regex for column selection - '^HR_\d+$' vs starts_with("HR_")
Verbose renaming - .name.map(lambda ...) vs .names template
Breaking the pipeline - the two-step aggregation becomes separate operations

What About Performance?

Yes, the Polars version would be 2-3x faster. But for this use case: - The current code runs overnight (acceptable for batch processing) - The performance gain doesn’t solve a problem - The readability cost is significant for long-term maintenance

When Would Polars Make Sense?

Rewriting in Polars would be worthwhile if:

Dataset grew 10x+ and became genuinely slow
Real-time processing was required
The project was starting from scratch (no existing R codebase)
The team was more comfortable with Python

For this application, the readability cost outweighs the performance gain.

Language Philosophy Differences

These technical differences reflect deeper design philosophies:

R (tidyverse) Philosophy

Analysis as conversation: Code should read like describing your analysis
Modern R standard: The tidyverse approach is how R data analysis is done today
Pipeline thinking: Data flows through transformations
Explicit is better: Force consideration of edge cases (NA handling)
Functional: Transform data, don’t mutate it
Consistent verbs: Same patterns across all data operations

Python Philosophy

General-purpose first: Familiar patterns from broader programming
Explicit is better than implicit: (but different things are explicit!)
There should be one obvious way: (though Pandas often violates this)
Readability counts: But through standard programming idioms
Pragmatic: Use whatever style fits the task

Summary: Technical Trade-offs for This Code

The sustainable heart rate calculation uses several patterns where R’s approach is more concise:

Where R is more concise:

Piping into any function (not just methods)
Type-safe functional iteration
Nested group operations with list-columns
Pattern-based column selection and transformation
Functional error handling

Where Python has advantages:

Raw performance (Polars is 2-3x faster than furrr parallelized code)
Type checking with mypy
IDE autocomplete for methods (though Positron now provides this for R too)
Broader ecosystem for non-analytical tasks

Where they’re similar:

Both can accomplish the task
Both have mature testing frameworks
Both integrate with version control
Both can be deployed (Shiny vs web frameworks)

The choice depends on:

Which style matches the team’s mental model of the task
Team expertise and preferences
Whether performance is genuinely a bottleneck
Integration requirements with other systems

Prepared by Claude (Anthropic AI) for the Crickles sustainable heart rate analysis

Overview

1. Pipeline Composition: The Pipe Operator

How It Works in R

How It Works in Python

Technical Trade-offs

Where This Matters

2. Functional Iteration: map() vs List Comprehensions

How It Works in R

How It Works in Python

Technical Trade-offs

Where This Matters

3. Nested Group Operations: nest() vs groupby()

How It Works in R

How It Works in Python

Technical Trade-offs

Where This Matters

4. Column Selection and Transformation: Tidyselect vs Manual Selection

How It Works in R

How It Works in Python

Technical Trade-offs

Where This Matters

5. Error Handling: Functional Wrappers vs Try-Except

How It Works in R

How It Works in Python

Technical Trade-offs

Where This Matters

6. Parallelization Strategies

How It Works in R

How It Works in Python

Technical Trade-offs

Where This Matters

7. Missing Value Handling

How It Works in R

How It Works in Python

Technical Trade-offs

Where This Matters

8. Visualization Integration

How It Works in R

How It Works in Python

Technical Trade-offs

Where This Matters

Performance Reality Check

Readability Comparison: A Polars Rewrite

Current R Implementation

Equivalent Polars Implementation

Line-by-Line Comparison

Readability Assessment

What About Performance?

When Would Polars Make Sense?

Language Philosophy Differences

R (tidyverse) Philosophy

Python Philosophy

Summary: Technical Trade-offs for This Code

2. Functional Iteration: `map()` vs List Comprehensions

3. Nested Group Operations: `nest()` vs `groupby()`