But once, they were created I could use the lapply and sapply functions to ‘apply’ each function: > largeplans=c(61,63,65) Standard lapply or sapply functions work very nice for this but operate only on single function. For example assume that we want to calculate minimum, maximum and mean value of each variable in data frame. Use lapply() twice to call select_el() over all elements in split_low: once with the index equal to 1 and a second time with the index equal to 2. sapply is a user-friendly version and wrapper of lapply by default returning a vector, matrix or, if simplify = "array", an array if appropriate, by applying simplify2array(). First I had to create a few pretty ugly functions. Assign the result to names and years, respectively. combined_files <- bind_rows(lapply(files, fread)) Here, I’m using the bind_rows function from the tidyverse libraries. mapply is a multivariate version of sapply. The hardest part of using lapply() is writing the function that is to be applied to each piece. Here are some examples: vars1<-c(5,6,7) vars2<-c(10,20,30) myFun <-function(var1,var2) So we can use lapply() to go through the numbers 3 through 8 and do the same thing each time. sapply is a user-friendly version and is a wrapper of lapply. The problem is that I often want to calculate several diffrent statistics of the data. This is the first cut at parallelizing R scripts. The "mc" stands for "multicore," and as you might gather, this function distributes the lapply tasks across multiple CPU cores to be executed in parallel. Step 4: Combine the files using the bind_rows function from the dplyr library and the lapply and fread functions. In this exercise, we will generate four bootstrap linear regression models and combine the summaries of these models into a single data frame. R matrix function tutorial covers matrix functions in R; apply function and sapply function with uses and examples to understand the concept thoroughly. lapply returns a list of the same length as X , each element of which is the result of applying FUN to the corresponding element of X . The parallel library, which comes with R as of version 2.14.0, provides the mclapply() function which is a drop-in replacement for lapply. The Family of Apply functions pertains to the R base package, and is populated with functions to manipulate slices of data from matrices, arrays, lists and data frames in a repetitive way.Apply Function in R are designed to avoid explicit use of loop constructs. Apply a function to multiple list or vector arguments Description. result <-lapply (x, f) #apply f to x using a single core and lapply library (multicore) result <-mclapply (x, f) #same thing using all the cores in your machine tapply and aggregate In the case above, we had naturally “split” data; we had a vector of city names that led to a list of different data.frames of weather data. It combines a list of data frames together (the same thing as the do.call(rbind, dfs) function). By default, sapply returns a vector, matrix or an array. The Apply family comprises: apply, lapply , sapply, vapply, mapply, rapply, and tapply. mapply applies FUN to the first elements of each ... argument, the second elements, the third elements, and so on. It is a dimension preserving variant of “sapply” and “lapply”. In our case, the variables of interest are stored in columns 3 through 8 of our data frame. Arguments are recycled if necessary. 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