Introduction to Efficient Coding in R

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As a developer, it’s essential to write efficient code that meets the requirements of your project while minimizing computational time and resources. In this article, we’ll explore how to improve the given R code by leveraging for-loops, applying functional programming principles, and utilizing built-in functions like apply and rowSums.

Understanding the Original Code

The original code creates 18 different triangular distributions using the dtriang() function from the mc2d package. Each distribution is defined by its minimum value (min), maximum value (max), and mode value (mode). The code combines these distributions using the + operator to create a new, cumulative distribution.

library(mc2d)
x <- seq(from=0.5, to=6, by=0.001)
june.cool <- dtriang(x, min=1,   max=2, mode=1) +
             dtriang(x, min=1,   max=4, mode=2) +
             dtriang(x, min=0.5, max=1, mode=1) +
             dtriang(x, min=2,   max=4, mode=3) +
             dtriang(x, min=0.25,max=1, mode=1) +
             dtriang(x, min=1,   max=3, mode=2) +
             dtriang(x, min=0.5, max=2, mode=1) +
             dtriang(x, min=1,   max=5, mode=2.5) +
             dtriang(x, min=1,   max=6, mode=4)

Identifying Areas for Improvement

Upon examining the original code, we notice that:

• The x vector is created using a sequence of values with a small step size (0.001).
• Each distribution is defined separately and then combined using the + operator.
• The apply function is used to apply the dtriang function to each row of a matrix.

Applying Functional Programming Principles

One way to improve the code is to leverage functional programming principles, such as composition and currying. Instead of creating separate distributions using the + operator, we can define a single function that takes a vector of arguments (min, max, mode) and returns the corresponding triangular distribution.

# Define a function to create a triangular distribution
triangulate <- function(x, min_val, max_val, mode_val) {
  dtriang(x, min=min_val, max=max_val, mode=mode_val)
}

# Create a vector of arguments for each distribution
args <- list(
  c(1, 2, 1),
  c(1, 4, 2),
  c(0.5, 1, 1),
  c(2, 4, 3),
  c(0.25, 1, 1),
  c(1, 3, 2),
  c(0.5, 2, 1),
  c(1, 5, 2.5),
  c(1, 6, 4)
)

# Apply the function to each argument and sum the results
june.cool <- rowSums(
  apply(args, 1, function(arg) triangulate(x, arg[1], arg[2], arg[3]))
)

Using apply and rowSums

Another way to improve the code is to use the built-in apply and rowSums functions. These functions can simplify the process of applying a function to each row of a matrix.

# Apply the function to each argument and sum the results using apply and rowSums
june.cool <- rowSums(
  apply(args, 1, function(arg) {
    dtriang(x, min=arg[1], max=arg[2], mode=arg[3])
  })
)

Performance Considerations

When it comes to performance, the choice of function can make a significant difference. In this case, using apply and rowSums may be faster than defining a custom function.

# Use benchmarking to compare the performance of different approaches
library microrma
set.seed(123)
args <- list(
  c(1, 2, 1),
  c(1, 4, 2),
  c(0.5, 1, 1),
  c(2, 4, 3),
  c(0.25, 1, 1),
  c(1, 3, 2),
  c(0.5, 2, 1),
  c(1, 5, 2.5),
  c(1, 6, 4)
)

# Define a custom function
triangulate_custom <- function(x, min_val, max_val, mode_val) {
  dtriang(x, min=min_val, max=max_val, mode=mode_val)
}

# Define the original code as a benchmark
original_code <- expression(
  seq(from=0.5, to=6, by=0.001),
  dtriang(seq(from=0.5, to=6, by=0.001), min=1, max=2, mode=1) +
    dtriang(seq(from=0.5, to=6, by=0.001), min=1, max=4, mode=2) +
    ...
)

# Benchmark the custom function
custom_benchmark <- benchmark(
  triangulate_custom,
  original_code,
  times = 10,
  verbose = TRUE
)

# Benchmark the apply and rowSums approach
apply_benchmark <- benchmark(
  apply(args, 1, function(arg) {
    dtriang(seq(from=0.5, to=6, by=0.001), min=arg[1], max=arg[2], mode=arg[3])
  }),
  times = 10,
  verbose = TRUE
)

Conclusion

In this example, we applied functional programming principles and used built-in functions like apply and rowSums to simplify the code. We also considered performance when choosing between different approaches.

By applying these techniques, you can write more concise, readable, and efficient code in R. Remember to always consider performance when writing R code, especially for large datasets or high-traffic applications.

Last modified on 2024-08-14