#Sapply for loop in r how to#

Specifically, Chapter 17 demonstrates the strengths of the purrr package, and I highly recommend the exercises throughout the chapter if you would like practice on this topic. Some of the content presented below was adapted from R for Data Science by Hadley Wickham, which is an excellent resource. However, none of these come up anywhere else in this book, so all I wanted to do here is draw your attention to the fact that they exist.The contents of this document were presented as a talk to the Louisville R Users Group Meetup.

However, although I do make passing reference to the tapply() later on, I donвЂ™t make much use of them in this book.Д«efore moving on, I should mention that there are several other functions that work along similar lines, and have suspiciously similar names: lapply, mapply, apply, vapply, rapply and eapply. The tapply() and by() functions are quite handy things to know about, and are pretty widely used.

#Sapply for loop in r how to#

An example of how to use the by() function is shown in the following extract: by( data = age, INDICES = gender, FUN = mean ) # gender: female This time around the three arguments are called data, INDICES and FUN, but theyвЂ™re pretty much the same thing. It actually does the same thing as tapply(), but the output is formatted a bit differently. We then calculate the mean() of the ages, separately for the males and the females. In this extract, what weвЂ™re doing is using gender to define two different groups of people, and using their ages as the data.

Tapply( X = age, INDEX = gender, FUN = mean ) # female male This probably sounds a little abstract, so letвЂ™s consider a specific example, using the nightgarden.Rdata file that we used in Chapter 7. Each such value defines a group: the tapply() function constructs the subset of X that corresponds to that group, and then applies the function FUN to that subset of the data. 140 What the tapply() function does is loop over all of the different values that appear in the INDEX variable. However, there is also an INDEX argument which specifies a grouping variable. As before X specifies the data, and FUN specifies a function. The second of these functions is tapply(), which has three key arguments. The sapply() function has implicitly looped over the elements of words, and for each such element applied the nchar() function to calculate the number of letters in the corresponding word. Notice how similar this is to the second example of a for loop in Section 8.2.2. Sapply( X = words, FUN = nchar ) # along the loom of the land The following example illustrates the basics of how it works: words <- c('along', 'the', 'loom', 'of', 'the', 'land') The two most important arguments to this function are X, which specifies a vector containing the data, and FUN, which specifies the name of a function that should be applied to each element of the data vector. The first and simplest of these functions is sapply().

However, because you often see these functions referred to in other contexts, it may be useful to very briefly discuss a few of them. In most applications that beginners might want to undertake, this probably isnвЂ™t very important, since most beginners tend to start out working with fairly small data sets and donвЂ™t usually need to undertake extremely time consuming number crunching. The main reason why this can be handy is that вЂ“ due to the way that R is written вЂ“ these implicit looping functions are usually about to do the same calculations much faster than the corresponding explicit loops. However, instead of typing out the whole loop, the whole thing is done with a single command. What I mean by this is that these are functions that carry out operations very similar to those that youвЂ™d normally use a loop for.

In addition to providing the explicit looping structures via while and for, R also provides a collection of functions for implicit loops. ThereвЂ™s one last topic I want to discuss in this chapter.