```
# Create a matrix
= matrix(c(1, 2, 3, 4, 5, 6), nrow = 2)
m
print(m)
```

```
[,1] [,2] [,3]
[1,] 1 3 5
[2,] 2 4 6
```

Learn how to use functional programming in R to write more efficient, maintainable, and elegant code. This blog post covers the basics of functional programming, from passing functions as arguments to creating reusable, interconnected functions. Perfect for data scientists and R programmers looking to level up their coding skills and expand their data science toolkit.

programming

functional programming

r

technical

Author

Published

July 10, 2023

Ready for a quick dive into the wonderful world of functional programming? Whether you’re a seasoned programmer or just starting your journey with R for some data science project, getting a grasp on functional programming will not only add a new weapon to your data science arsenal but also make your code more efficient and readable. So, without further ado, let’s get started!

Functional programming, put simply, is a programming style that mimics mathematical functions. You might be thinking, “Isn’t all programming based on functions?” Well, yes, but functional programming takes this concept to the next level. It’s all about structuring your code as a series of reusable, interconnected functions, each doing one specific task and doing it well. In this blog post, we’ll focus on functions as arguments to other functions.

Now, why should you, an R programmer, care about this? Well, R might not be a pure functional programming language, but it does offer strong functional programming capabilities. Mastering them can make your code more effective and attractive. Plus, functional programming principles can help us eliminate redundant code and make our scripts more maintainable.

It’s a bit like building with Lego blocks. Each block (or function) has its own shape and purpose, and you can connect them in various ways to create whatever you want. And the best part? You can disassemble and reassemble them without affecting the individual blocks!

Let’s start with a powerful feature that many data science workflows heavily benefit from: passing functions as arguments to other functions. This concept might seem a bit tricky at first, but once you get the hang of it, you’ll see how it adds a whole new level of flexibility and efficiency to your code.

In R, functions are *“first-class citizens”*, meaning they can be treated just like any other object or data type. They can be assigned to variables, stored in lists, and most importantly for our current discussion, passed as arguments to other functions. This feature allows us to create more general, reusable functions and reduce code repetition.

`apply()`

functionOne of the most common examples of passing a function as an argument is using the `apply()`

function in R. The `apply()`

applies a function to the rows or columns of a matrix or data frame. Here’s how it works.

We will first set the stage and initialize a \(2 \times 3\) matrix `m`

:

```
[,1] [,2] [,3]
[1,] 1 3 5
[2,] 2 4 6
```

Now, suppose we want to calculate the sum of each row in `m`

. We could do that manually via:

```
# Initialize a vector to store the row sums
row_sums = numeric(nrow(m))
for (i in seq_len(nrow(m))){
row_sums[i] = sum(m[i, ])
}
print(row_sums)
```

`[1] 9 12`

But this code is pretty clunky for such a simple task and, more importantly, hard to maintain. What if we wanted to compute the sum of each **column**? We would need to

- rename
`row_sums`

to`col_nums`

both at the initialization and within the for loop; - carefully adjust the iterator
`i`

so that it iterates over`[1, ..., ncol(m)]`

; and finally - adjust the matrix slicing to
`m[, i]`

.

I don’t know about you, but I would probably miss at least one of those adjustments.

So let’s rewrite `row_sums`

as a one-liner via functional programming. We use the function `apply(X, MARGIN, FUN)`

, which works as follows:

`X`

: The object that we operate on. In our case, we want to apply some function on parts of the matrix`m`

, so`X = m`

.`MARGIN`

: You can select whether you want to apply the function to each row (`MARGIN = 1`

), to each column (`MARGIN=2`

), or to rows and columns combined (`MARGIN=c(1,2)`

). We want to compute the sum of each row, so we choose`MARGIN = 1`

.`FUN`

: The actual function that you want to apply across the rows or columns. In our case, it’s the`sum`

function.

```
# Apply the sum function to the rows of the matrix
row_sums = apply(m, MARGIN=1, FUN=sum)
print(row_sums)
```

`[1] 9 12`

If you want to take your first steps towards functional programming, go ahead and try those two exercises.

Exercise 1

Change the example code so that it calculates the **column sums** of the matrix `m`

instead of the row sums. The answer should be `[3, 7, 11]`

.

Exercise 2

Change the example code so that calculates the **mean** of each column in `m`

instead of the sum. The answer should be `[1.5, 3.5, 5.5]`

.

`FUN`

– ignore missing valuesSuppose we have a matrix of data, and some of the values are missing, as represented by `NA`

:

```
[,1] [,2] [,3]
[1,] 1 NA 5
[2,] 2 4 6
```

If we simply execute the code from the previous example, we will get `NA`

in the row with a missing value:

`[1] NA 12`

If we think about it, it’s not surprising: Behind the scenes, we execute `sum(c(1, NA, 5))`

and that’s `NA`

. If you are a bit into data science with R, you might know that `sum(c(1, NA, 5), na.rm=TRUE)`

would ignore the `NA`

value and return the sum of the non-NA values, which is `6`

. So we need a way to funnel `na.rm`

through the `apply`

function into `sum`

. Lucky for us, there is a straightforward way. We can simply add any additional arguments in our call to `apply`

as follows:

```
# Compute the sum of each row, removing NA values
row_sums <- apply(m, 1, sum, na.rm=TRUE)
print(row_sums)
```

`[1] 6 12`

In this example, we’re passing the `sum`

function as an argument to the `apply`

function as usual. But we’re also passing `na.rm=TRUE`

as an additional argument to the `sum`

function. This means that when `apply`

calls the `sum`

function for each row, it’s actually calling `sum(row, na.rm=TRUE)`

. Perfect!

Suppose you are writing some machine learning algorithm that aims to predict the true value for four test cases. But your algorithm tries to quantify some notion of uncertainty around that prediction. Therefore, it outputs three values for each case:

```
# Define a true vector and a predicted matrix
true <- c(1, 3, 2, 2)
predicted <- matrix(c(1, 2, 3, 2, 3, 4, 1, 3, 3, 2, 2, 2), nrow = 4)
for (i in seq_along(true)){
cat("True:", true[i], "| predictions:", predicted[i, ], "\n")
}
```

```
True: 1 | predictions: 1 3 3
True: 3 | predictions: 2 4 2
True: 2 | predictions: 3 1 2
True: 2 | predictions: 2 3 2
```

Now, you want to **quantify** the error for each case with some metric to know how good the set of predictions is for each case. In this example, we’ll first use the Mean Squared Error (MSE) as our metric. MSE is a common metric for regression problems, measuring the average squared differences between the true and predicted values. Here, it will quantify the error between the set of predictions \(\hat{y}_1, \ldots, \hat{y}_N\) and the true value \(y^*\) for a single case, \(MSE=\frac{1}{N}\sum\limits_{i=1}^N(y^*-\hat{y}_i)^2\).

If we want to know how well the algorithm predicts the first test case, we can simply call:

This is the prediction error for the first case, and we could compute such an error for every case in the test set. Now, let’s create a function that takes the entire `true`

vector, the entire `predicted`

matrix, and an error metric function as arguments. It will apply the error metric function to the true vector and each column of the predicted matrix. It might seem like a bit of an overkill for now, but it will be worth it very soon:

```
# Create a function that computes the error between a true vector and each column of a predicted matrix
compute_errors <- function(true, predicted, error_func) {
# Initialize a vector to store the errors
errors <- numeric(ncol(predicted))
# For each column in the predicted matrix
for (i in seq_len(ncol(predicted))) {
# Compute the error between the true vector and the current column
errors[i] <- error_func(true, predicted[,i])
}
return(errors)
}
```

Finally, let’s use our `compute_error`

function to compute the MSE between the true vector and each column of the prediction matrix:

```
# Use the custom function to compute the MSEs
errors <- compute_errors(true, predicted, error_func=mse)
print(errors)
```

`[1] 0.50 1.75 1.25`

In this example, we compute the MSE between the true vector and each column of the predicted matrix. We achieve this by passing the `mse`

function as an argument to the compute_errors function. This demonstrates how passing functions as arguments can make your code more flexible and modular, enabling you to easily switch between different error metrics. It was a bit of upfront work, but it reduces the actual call to compute the errors down to a single line of code.

But this functional programming paradigm really shines when we want to **replace the error function** that we apply! Let’s compute the Mean Average Error (MAE) instead of the MSE. No problem with our functional programming solution. After all, the concrete error function just an argument to our `compute_errors`

wrapper that implements all the logic. We just define the `mae`

function \(\frac{1}{N}\sum\limits_{i=1}^N | y^*-\hat{y}_i|\) and plug it into the `compute_errors`

wrapper:

```
# Define a function to compute the Mean Absolute Error
mae <- function(true, predicted) {
return (mean(abs(true - predicted)))
}
# Use the custom function to compute the MAEs
errors <- compute_errors(true, predicted, error_func=mae)
print(errors)
```

`[1] 0.50 1.25 0.75`

Boom, we compute the MAE between each case from the true vector and each corresponding set of predictions from the predicted matrix. All we had to do is pass the `mae`

function as an argument to the `compute_errors`

function. Most importantly, we did not need to make any changes to the `compute_errors`

function – instead, we just provided a drop-in replacement for the `error_func`

that `compute_errors`

applies internally.

Exercise 3

Adjust the MAE example code so that `NA`

values are ignored and the mean is computed with respect to all non-NA values.

`{purrr}`

For me personally, one of the most fascinating aspects of functional programming in R is the concept of ‘partial functions’. This concept allows us to create new functions from existing ones by pre-filling some of the arguments. Think of it as a customizable tool that you can tweak to fit your specific needs.

In part 2 of this blog post series, we’ll be taking a closer look at partial functions in R. We’ll understand how they work, why they’re useful, and how you can use them to make your R code even more efficient, elegant, and re-usable.

So, stay tuned for the next blog post. If you want to be notified via email when the next blog post is published, consider subscribing to my slow mailing list **The Training Loop** below.

Cheers!

Do you enjoy my blog? Subscribe here to get notifications and updates (it's free!):

```
---
title: "Functional Programming in R, Part 1: Basics"
description: "Learn how to use functional programming in R to write more efficient, maintainable, and elegant code. This blog post covers the basics of functional programming, from passing functions as arguments to creating reusable, interconnected functions. Perfect for data scientists and R programmers looking to level up their coding skills and expand their data science toolkit."
date: 07-10-2023
categories:
- programming
- functional programming
- r
- technical
image: thumbnail.png
draft: false
number-sections: false
format:
html:
fig-cap-location: bottom
include-before-body: ../../html/margin_image.html
include-after-body: ../../html/blog_footer.html
comments: false
editor:
markdown:
wrap: sentence
---
# What is functional programming?
Ready for a quick dive into the wonderful world of functional programming? Whether you're a seasoned programmer or just starting your journey with R for some data science project, getting a grasp on functional programming will not only add a new weapon to your data science arsenal but also make your code more efficient and readable. So, without further ado, let's get started!
Functional programming, put simply, is a programming style that mimics mathematical functions. You might be thinking, "Isn't all programming based on functions?" Well, yes, but functional programming takes this concept to the next level. It's all about structuring your code as a series of reusable, interconnected functions, each doing one specific task and doing it well. In this blog post, we'll focus on functions as arguments to other functions.
Now, why should you, an R programmer, care about this? Well, R might not be a pure functional programming language, but it does offer strong functional programming capabilities. Mastering them can make your code more effective and attractive. Plus, functional programming principles can help us eliminate redundant code and make our scripts more maintainable.
It's a bit like building with Lego blocks. Each block (or function) has its own shape and purpose, and you can connect them in various ways to create whatever you want. And the best part? You can disassemble and reassemble them without affecting the individual blocks!
# Basics in R: Passing functions as arguments
Let's start with a powerful feature that many data science workflows heavily benefit from: passing functions as arguments to other functions. This concept might seem a bit tricky at first, but once you get the hang of it, you'll see how it adds a whole new level of flexibility and efficiency to your code.
In R, functions are *"first-class citizens"*, meaning they can be treated just like any other object or data type. They can be assigned to variables, stored in lists, and most importantly for our current discussion, passed as arguments to other functions. This feature allows us to create more general, reusable functions and reduce code repetition.
# Example 1: Using the `apply()` function
One of the most common examples of passing a function as an argument is using the `apply()` function in R. The `apply()` applies a function to the rows or columns of a matrix or data frame. Here's how it works.
We will first set the stage and initialize a $2 \times 3$ matrix `m`:
```{r}
# Create a matrix
m = matrix(c(1, 2, 3, 4, 5, 6), nrow = 2)
print(m)
```
Now, suppose we want to calculate the sum of each row in `m`. We could do that manually via:
```{r}
# Initialize a vector to store the row sums
row_sums = numeric(nrow(m))
for (i in seq_len(nrow(m))){
row_sums[i] = sum(m[i, ])
}
print(row_sums)
```
But this code is pretty clunky for such a simple task and, more importantly, hard to maintain. What if we wanted to compute the sum of each **column**? We would need to
1. rename `row_sums` to `col_nums` both at the initialization and within the for loop;
2. carefully adjust the iterator `i` so that it iterates over `[1, ..., ncol(m)]`; and finally
3. adjust the matrix slicing to `m[, i]`.
I don't know about you, but I would probably miss at least one of those adjustments.
![Example: Using the `apply` function to calculate row sums.](function_as_argument_apply.png){width=100%}
So let's rewrite `row_sums` as a one-liner via functional programming. We use the function `apply(X, MARGIN, FUN)`, which works as follows:
- `X`: The object that we operate on. In our case, we want to apply some function on parts of the matrix `m`, so `X = m`.
- `MARGIN`: You can select whether you want to apply the function to each row (`MARGIN = 1`), to each column (`MARGIN=2`), or to rows and columns combined (`MARGIN=c(1,2)`). We want to compute the sum of each row, so we choose `MARGIN = 1`.
- `FUN`: The actual function that you want to apply across the rows or columns. In our case, it's the `sum` function.
```{r}
# Apply the sum function to the rows of the matrix
row_sums = apply(m, MARGIN=1, FUN=sum)
print(row_sums)
```
If you want to take your first steps towards functional programming, go ahead and try those two exercises.
::: {.callout-note}
## Exercise 1
Change the example code so that it calculates the **column sums** of the matrix `m` instead of the row sums. The answer should be `[3, 7, 11]`.
:::
::: {.callout-note}
## Exercise 2
Change the example code so that calculates the **mean** of each column in `m` instead of the sum. The answer should be `[1.5, 3.5, 5.5]`.
:::
# Example 2: Additional arguments to `FUN` -- ignore missing values
Suppose we have a matrix of data, and some of the values are missing, as represented by `NA`:
```{r}
# Create a matrix
m = matrix(c(1, 2, NA, 4, 5, 6), nrow = 2)
print(m)
```
If we simply execute the code from the previous example, we will get `NA` in the row with a missing value:
```{r}
# Compute row sums (without handling NA)
row_sums = apply(m, MARGIN=1, FUN=sum)
print(row_sums)
```
If we think about it, it's not surprising: Behind the scenes, we execute `sum(c(1, NA, 5))` and that's `NA`. If you are a bit into data science with R, you might know that `sum(c(1, NA, 5), na.rm=TRUE)` would ignore the `NA` value and return the sum of the non-NA values, which is `6`. So we need a way to funnel `na.rm` through the `apply` function into `sum`. Lucky for us, there is a straightforward way. We can simply add any additional arguments in our call to `apply` as follows:
```{r}
# Compute the sum of each row, removing NA values
row_sums <- apply(m, 1, sum, na.rm=TRUE)
print(row_sums)
```
In this example, we're passing the `sum` function as an argument to the `apply` function as usual. But we're also passing `na.rm=TRUE` as an additional argument to the `sum` function. This means that when `apply` calls the `sum` function for each row, it's actually calling `sum(row, na.rm=TRUE)`. Perfect!
# Example 3: Custom functions -- computing error metrics
Suppose you are writing some machine learning algorithm that aims to predict the true value for four test cases. But your algorithm tries to quantify some notion of uncertainty around that prediction. Therefore, it outputs three values for each case:
```{r}
# Define a true vector and a predicted matrix
true <- c(1, 3, 2, 2)
predicted <- matrix(c(1, 2, 3, 2, 3, 4, 1, 3, 3, 2, 2, 2), nrow = 4)
for (i in seq_along(true)){
cat("True:", true[i], "| predictions:", predicted[i, ], "\n")
}
```
Now, you want to **quantify** the error for each case with some metric to know how good the set of predictions is for each case. In this example, we'll first use the Mean Squared Error (MSE) as our metric. MSE is a common metric for regression problems, measuring the average squared differences between the true and predicted values. Here, it will quantify the error between the set of predictions $\hat{y}_1, \ldots, \hat{y}_N$ and the true value $y^*$ for a single case, $MSE=\frac{1}{N}\sum\limits_{i=1}^N(y^*-\hat{y}_i)^2$.
```{r}
# Define a function to compute the Mean Squared Error
mse <- function(true, predicted) {
return (mean((true - predicted) ** 2))
}
```
If we want to know how well the algorithm predicts the first test case, we can simply call:
```{r}
mse(true[1], predicted[1, ])
```
This is the prediction error for the first case, and we could compute such an error for every case in the test set.
Now, let's create a function that takes the entire `true` vector, the entire `predicted` matrix, and an error metric function as arguments. It will apply the error metric function to the true vector and each column of the predicted matrix. It might seem like a bit of an overkill for now, but it will be worth it very soon:
```{r}
# Create a function that computes the error between a true vector and each column of a predicted matrix
compute_errors <- function(true, predicted, error_func) {
# Initialize a vector to store the errors
errors <- numeric(ncol(predicted))
# For each column in the predicted matrix
for (i in seq_len(ncol(predicted))) {
# Compute the error between the true vector and the current column
errors[i] <- error_func(true, predicted[,i])
}
return(errors)
}
```
Finally, let's use our `compute_error` function to compute the MSE between the true vector and each column of the prediction matrix:
```{r}
# Use the custom function to compute the MSEs
errors <- compute_errors(true, predicted, error_func=mse)
print(errors)
```
In this example, we compute the MSE between the true vector and each column of the predicted matrix. We achieve this by passing the `mse` function as an argument to the compute_errors function. This demonstrates how passing functions as arguments can make your code more flexible and modular, enabling you to easily switch between different error metrics. It was a bit of upfront work, but it reduces the actual call to compute the errors down to a single line of code.
But this functional programming paradigm really shines when we want to **replace the error function** that we apply! Let's compute the Mean Average Error (MAE) instead of the MSE. No problem with our functional programming solution. After all, the concrete error function just an argument to our `compute_errors` wrapper that implements all the logic. We just define the `mae` function $\frac{1}{N}\sum\limits_{i=1}^N | y^*-\hat{y}_i|$ and plug it into the `compute_errors` wrapper:
```{r}
# Define a function to compute the Mean Absolute Error
mae <- function(true, predicted) {
return (mean(abs(true - predicted)))
}
# Use the custom function to compute the MAEs
errors <- compute_errors(true, predicted, error_func=mae)
print(errors)
```
Boom, we compute the MAE between each case from the true vector and each corresponding set of predictions from the predicted matrix. All we had to do is pass the `mae` function as an argument to the `compute_errors` function. Most importantly, we did not need to make any changes to the `compute_errors` function -- instead, we just provided a drop-in replacement for the `error_func` that `compute_errors` applies internally.
::: {.callout-note}
## Exercise 3
Adjust the MAE example code so that `NA` values are ignored and the mean is computed with respect to all non-NA values.
:::
# Next up: partial functions with `{purrr}`
For me personally, one of the most fascinating aspects of functional programming in R is the concept of 'partial functions'. This concept allows us to create new functions from existing ones by pre-filling some of the arguments. Think of it as a customizable tool that you can tweak to fit your specific needs.
In part 2 of this blog post series, we'll be taking a closer look at partial functions in R. We'll understand how they work, why they're useful, and how you can use them to make your R code even more efficient, elegant, and re-usable.
So, stay tuned for the next blog post. If you want to be notified via email when the next blog post is published, consider subscribing to my slow mailing list [**The Training Loop**](https://marvinschmitt.beehiiv.com/subscribe) below.
Cheers!
```