Skip to main content

Mastering Loop Functions in R: A Practical Guide to lapply and sapply

The Beginner’s Guide to Loop Functions in R:

Loop functions are essential tools for data manipulation and analysis in R programming. Two of the most commonly used loop functions in R are lapply and sapply. In this blog post, we'll explain what these functions are, how they work, and provide some practice material for beginners to intermediate level.


lapply: 

The 'l' stands for 'list'

lapply is a loop function in R that applies a function to each element of a list and returns the result as a list. Here's the basic syntax:

lapply(list, function)

The list argument is the list you want to apply the function to, and the function argument is the function you want to apply. For example, let's say we have a list of numbers and we want to apply the sqrt function to each element:

my_list <- list(1, 4, 9) 
lapply(my_list, sqrt)

This will return a list of the square roots of each element in my_list.

sapply: 

The 's' stands for 'simplify'

sapply is similar to lapply, but it simplifies the output to a vector or matrix if possible. Here's the basic syntax:

sapply(list, function)

The list argument and the function argument are the same as for lapply. For example, let's say we have a list of numbers and we want to apply the sqrt function and simplify the output to a vector:

my_list <- list(1, 4, 9) 
sapply(my_list, sqrt)

This will return a vector of the square roots of each element in my_list.

Practice Material

Here are some exercises to practice using lapply and sapply:

  • Use lapply to apply the sum function to each element of a list of vectors. For example, the list list(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9)) should return the list list(6, 15, 24).
  • Use sapply to apply the mean function and simplify the output to a vector for each column of a matrix. For example, the matrix matrix(c(1, 2, 3, 4, 5, 6), nrow = 2) should return the vector c(2, 4).
  • Use lapply and sapply to calculate the variance of each column of a matrix. For example, the matrix matrix(c(1, 2, 3, 4, 5, 6), nrow = 2) should return the vector c(2.5, 2.5).

  • For more practice you should start swirl's 11th lesson in R Programming. Complete download process of swirl and R Programming is here, click on the link!
  • You can look in to the practice and reading material that is provided in the text book, click here to download the textbook.
  • Lecture slides can be downloaded from here. It would be great if you go through them too.

In conclusion, lapply and sapply are powerful loop functions in R that can help you manipulate and analyze data efficiently. By understanding how these functions work and practicing using them with different data structures, you can improve your R programming skills and become a more effective data analyst.

Comments

Popular posts from this blog

What is Data? And What is Data Science Process?

The Beginner’s Guide to Data & Data Science Process About Data: In our First Video today we talked about Data and how the Cambridge English Dictionary and Wikipedia defines Data, then we looked on few forms of Data that are: Sequencing data   Population census data ( Here  is the US census website and  some tools to help you examine it , but if you aren’t from the US, I urge you to check out your home country’s census bureau (if available) and look at some of the data there!) Electronic medical records (EMR), other large databases Geographic information system (GIS) data (mapping) Image analysis and image extrapolation (A fun example you can play with is the  DeepDream software  that was originally designed to detect faces in an image, but has since moved on to more  artistic  pursuits.) Language and translations Website traffic Personal/Ad data (e.g.: Facebook, Netflix predictions, etc.) These data forms need a lot of preprocessin...

Efficient Data Manipulation with Loop Functions in R: A Deep Dive into apply and mapply

The Beginner’s Guide to Loop Functions in R: In addition to lapply and sapply , R also has apply and mapply , which are other loop functions that are commonly used for data manipulation and analysis. In this blog post, we'll explain what these functions are, how they work, and provide some practice material for beginners to intermediate level. apply:  Apply a Function to a Matrix or Array apply is a loop function in R that applies a function to either rows or columns of a matrix or array. Here's the basic syntax: apply(matrix/array, margin, function) The matrix/array argument is the matrix or array you want to apply the function to, and the margin argument specifies whether you want to apply the function to rows or columns. margin = 1 applies the function to rows, while margin = 2 applies the function to columns. The function argument is the function you want to apply. For example, let's say we have a matrix of numbers and we want to apply the sum function to each row:...

Optimization Example of Lexical Scoping in R: Exploring optim, optimize, and nlm Functions

The Beginner’s Guide to Optimization Example of Lexical Scoping in R: When it comes to optimization in R, lexical scoping can be a useful tool for optimizing complex functions that involve multiple variables. In this blog post, we will explore how lexical scoping can be used to optimize a function using the NLL (negative log-likelihood) function , and how the optim, optimize, and nlm functions can be used to perform optimization in R. Optimizing the NLL Function using Lexical Scoping The NLL function is a common function used in optimization problems. It is defined as the negative log of the likelihood function, which is used to estimate the parameters of a statistical model. In R, the NLL function can be defined using lexical scoping, which allows us to pass arguments to the function and access variables from within the function. Here is an example of how to define the NLL function using lexical scoping in R: nll <- function(data, parameters) {   # Define local variables ...