Lazy Bayesian

Continuing on with the discussion of for loops and apply functions bring us to another set of apply functions used to, well, apply a function to data in different ways. In this post, I will be: Discussing the arrays or data arrangements for which the different apply functions are designed. That is, when to use each one. Comparing for loops to tapply, lapply, and sapply. I will write for loops for each so you can better familiarize yourself with for loops and situations where you can use the apply functions, instead. The data I will be using for this is the same data set that I used for the apply function post . This is some code I used to prepare the data to get it to its current state. Some of which I will be discussing later. I mostly provide this for the sake of disclosure and clarity. lapply and sapply: Apply a function over a Vector or List This is the most apparent and obvious replacement to a for loop. You give lapply the information set that you wish to iterat

When analyzing data you often have to iterate through a set of values, or apply the same function to to that set. While R does not have a great reputation for iterative processes, the apply functions are a way around writing a slow for loop. Mastering the use of the apply functions will make your coding much more efficient and versatile. In this post, I will discuss the following: for loops. When and how to use them. I'll also briefly mention while loops. How to use apply . I will address  tapply ,  lapply , and  sapply in a subsequent post. To help demonstrate how the apply function can be used instead of a for loop, I will carry out the same task using both methods. Before I do that, I am going to go over some looping basics for those who may be unfamiliar or may need a review. For Loop Basics A for loop iterates through the elements of a vector (a set of values), where at each iteration will be represented by the object provided in the for statement. One conven