It might take you a while to wrap your head around the idea, but it’s worth the investment. The idea of passing a function to another function is an extremely powerful idea, and it’s one of the behaviours that makes R a functional programming language. Rbind()ing in each iteration, save the output in a list, then useĭplyr::bind_rows(output) to combine the output into a single You might be generating a big data frame. Then combine that vector into a single string with Instead of paste()ing togetherĮach iteration with the previous, save the output in a character vector and A stricter option is to use purrr::flatten_dbl() - it will throw an error if the input isn’t a list of doubles. Here I’ve used unlist() to flatten a list of vectors into a single vector. Zero-length vector, seq_along() does the right thing: It’s a safe version of theįamiliar 1:length(l), with an important difference: if you have a You might not have seen seq_along() before. It’s useful to think of i as a pronoun, like “it”. This determines what to loop over:Įach run of the for loop will assign i to a different value from “integer”, “double”, “character”, etc) and the length of the vector. It has two arguments: the type of the vector (“logical”, The for loop at each iteration using c() (for example), your for loopĪ general way of creating an empty vector of given length is the vector()įunction. This is very important for efficiency: if you grow The output: output <- vector("double", length(x)).īefore you start the loop, you must always allocate sufficient spaceįor the output. Once you master the vocabulary of FP, you can solve many common iteration problems with less code, more ease, and fewer errors. Functional programming (FP) offers tools to extract out this duplicated code, so each common for loop pattern gets its own function.
#For loop in r data frame code
However, for loops are quite verbose, and require quite a bit of bookkeeping code that is duplicated for every for loop. On the imperative side you have tools like for loops and while loops, which are a great place to start because they make iteration very explicit, so it’s obvious what’s happening. In this chapter you’ll learn about two important iteration paradigms: imperative programming and functional programming.
Another tool for reducing duplication is iteration, which helps you when you need to do the same thing to multiple inputs: repeating the same operation on different columns, or on different datasets. One tool for reducing duplication is functions, which reduce duplication by identifying repeated patterns of code and extract them out into independent pieces that can be easily reused and updated. You’re likely to have fewer bugs because each line of code is Remembering to change every place that you copied-and-pasted the
As your needsĬhange, you only need to make changes in one place, rather than It’s easier to respond to changes in requirements. It’s easier to see the intent of your code, because your eyes areĭrawn to what’s different, not what stays the same. Reducing code duplication has three main benefits: Is there a way were I can determine the number of users_ids (in retweeters) that overlaps with the user_ids (in Followers)? Taking into consideration to compare user 1 in retweeters with user 1 in followers and so on.In functions, we talked about how important it is to reduce duplication in your code by creating functions instead of copying-and-pasting. Rtwts] <- search_tweets(x, n = 500)ĪA2 <- rbind.fill(lapply(AA, as.ame))īut this is combining all the users in one dataframe (but separating with with an extra column that identifies the user number, such as user 1, 2, 3. # execute `i` loops (one for each obs in users)įlws] <- get_followers(users, n = "all", page = "-1", parse = TRUE, token = NULL)
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