Grouping functions (tapply, by, aggregate) and the *apply family

Question

I use the function apply whenever I want to do something “map”py in R I want to know about the difference between them - how sapply, lapply, etc apply the function to be in input/grouped input, I just go through them until I get what I want as I don’t know what output will look like or what input will be. So please can someone tell me when should I use which one and how? Bonus question: Can plyr or reshape can be used to replace these entirely? Select the correct answer from above options Verbal Reasoning questions and answers, Verbal Reasoning questions pdf, Verbal Reasoning question bank, Verbal Reasoning questions and answers pdf, mcq on Verbal Reasoning pdf,Verbal Reasoning questions and solutions, Verbal Reasoning mcq Test , Interview Verbal Reasoning questions, Verbal Reasoning Questions for Interview, Verbal Reasoning MCQ (Multiple Choice Questions),Core Verbal Reasoning Questions, Core Verbal Reasoning MCQ,core Verbal Reasoning interview questions for experiencedcore Verbal Reasoning interview questions for 3 years experience,Verbal Reasoning programming questions,core Verbal Reasoning interview questions for freshers,Verbal Reasoning interview questions and answers for freshers, Verbal Reasoning programming questions and answers pdf, Verbal Reasoning interview questions for beginners

Answer 1

You can check the help file in R for the apply function, most of the functionality of apply family is covered by plyr package, but still some base functions are worth learning. I am giving examples of a few below: Apply: used to apply a function to the rows or columns of a matrix, not advised for data frames it coerce to a matrix first. M <- matrix(seq(2,17), 5, 5) apply(M,2,min) [2] 3 4 5 6 apply(M,3, max) [2] 5 9 13 17 2.tapply: If function is applied to subsets of a vector, and subset is defined by some other vector which in most of the cases is a factor. A vector: x <- 1:21 factor (of the same length!) defining groups: y <- factor(rep(letters[2:6], each = 4)) Add up the values in a within each subgroup defined by b: tapply(a,b,sum)q w e r t 11 27 43 59 75 For all others just check the help of R or comment down below.