These functions allow crossing the data in a number of ways and avoid explicit use of loop constructs. In your case, you're getting the values 2 and 4 and then trying to index your vector again using its own values. Check if you have put an equal number of arguments in all c() functions that you assign to the vectors and that you have indicated strings of words with "".. Also, note that when you use the data.frame() function, character variables are imported as factors or categorical variables. lapply() sapply() tapply() These functions let you take data in batches and process the whole batch at once. But it looks to me a little bit "unnatural". Unlike the apply function, there is no margin argument when applying the lapply function to each component of the list. The l in front of apply … Let us take a list of 2 vectors and apply mean function to each element of list. Same function over multiple data frames in R, Make a list of data frames then use lapply to apply the function to them all. Doing this in base R is possible but far more difficult. We ended up building a function called timeStep() which timed a step-wise regression of a given size. Consider, however, returning a data.frame instead of a list: typical . where X is an input data object, MARGIN indicates how the function is applicable whether row-wise or column-wise, margin = 1 indicates row-wise and margin = 2 indicates column-wise, FUN points to an inbuilt or user-defined function.. I've got the working command below with lapply and rbind. Let us create a data frame first and then apply a sort() function on it using the lapply() function in R. In other words: The previous R syntax computed the row sums of each row of our data frame. lapply() function applies a function to a data frame. An interesting example of this is POSIXlt. Pay attention to usage of lapply function. There primary difference is in the object (such as list, matrix, data frame etc.) The problem is that you pass the condition as a string and not as a real condition, so R can't evaluate it when you want it to. unsplit returns a vector or data frame for which split(x, f) equals value. The lapply()function works on any list, not just a … I have the following lists of data frame: And a function that plot each individual data frame and named them based on list ID: At the end of the day it will have "FOO.png" and "BAR.png". The “apply family” of functions (apply, tapply, lapply and others) and related functions such as aggregate are central to using R.They provide an concise, elegant and efficient approach to apply (sometimes referred to as “to map”) a function to a set of cases, be they rows or columns in a matrix or data.frame, or elements in a list. We nest one lapply function inside another, but since lapply returns a list, we need to wrap the first lapply with as.data.frame. [R] lapply with data frame Noah Silverman noah at smartmediacorp.com Sun Feb 28 03:37:04 CET 2010. collapse is the Stata equivalent of R's aggregate function, which produces a new dataset from an input dataset by applying an aggregating function (or multiple aggregating functions, one per variable) to every variable in a dataset. We don’t use this extra power in this small example. That may seem needlessly heavy-weight, but it has a lot of down-stream advantages. lapply; Lapply in R. lapply function is used to apply a function on each element of a list and return a list. The microbenchmark suite runs an expression many times to get a distribution of run times (run times are notoriously unstable, so you should always report a distribution or summary of distribution of them). Also, thanks to akrun for the test data. Of course we can extend this to more dimensions too. If there are 3 dimensions use 3 as the second argument to apply the function over the third dimension. Assuming files is the vector of file names (as you imply above): import <- lapply(files, read.csv, header=FALSE) Then if you want to operate on each data.frame in the list... copy() is for copying data.table's. It's easier to think of it in terms of the two exposures that aren't used, rather than the five that are. The lapply function is best for working with data frames. Let us look at an example. In R the data frame is considered a list and the variables in the data frame are the elements of the list. Wadsworth & Brooks/Cole. Compare print(class(as.POSIXlt(Sys.time()))) print(class(data.frame(t=as.POSIXlt(Sys.time()))$t)), and d . lapply() can be used for other objects like data frames and lists. Working with Data Frames in R. Since data frames can be treated as a special case of lists, the functions lapply() and sapply() work in both cases. That said, here are some examples of how to do this with a for loop, with lapply(), and with purrr::map_dfr(). Try.. zz <- lapply(z,copy) zz[[1]][ , newColumn := 1 ] Using your original code, you will see that applying copy() to the list does not make a copy of the original data.table. Using lapply() Function In R. lapply() function is similar to the apply() function however it returns a list instead of a data frame. library(reshape2) #ggplot needs a dataframe data <- as.data.frame(data) #id variable for position in matrix data$id <- 1:nrow(data) #reshape to long format plot_data <- melt(data,id.var="id") #plot ggplot(plot_data, aes(x=id,y=value,group=variable,colour=variable)) + geom_point()+ geom_line(aes(lty=variable))... sapply iterates through the supplied vector or list and supplies each member in turn to the function. R Lapply Function To Data Frame Columns. 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