lapply

Is there a way to use mapply on two vectors to construct a named list? The first vector would be of type character and contain the names used for the list while the second contains the values. So far, the only solution I have is: > dummyList = list() > addToList <- function(name, value) { + dummyList[[name]] <- value + } > mapply(addToList, c("foo", "bar"), as.list(c(1, 2)) $foo `1` $bar `2` This seems like a rather contrived solution, but I can't figure out how to do it otherwise. The problems I have with it are: It requires the creation of dummyList even though dummyList is never changed and

This is very similar to a question applying a common function to multiple columns of a data.table uning .SDcols answered thoroughly here . The difference is that I would like to simultaneously apply a different function on another column which is not part of the .SD subset. I post a simple example below to show my attempt to solve the problem: dt = data.table(grp = sample(letters[1:3],100, replace = TRUE), v1 = rnorm(100), v2 = rnorm(100), v3 = rnorm(100)) sd.cols = c("v2", "v3") dt.out = dt[, list(v1 = sum(v1), lapply(.SD,mean)), by = grp, .SDcols = sd.cols] Yields the following error: Error

I'm partitioning a data frame with split() in order to use parLapply() to call a function on each partition in parallel. The data frame has 1.3 million rows and 20 cols. I'm splitting/partitioning by two columns, both character type. Looks like there are ~47K unique IDs and ~12K unique codes, but not every pairing of ID and code are matched. The resulting number of partitions is ~250K. Here is the split() line: system.time(pop_part <- split(pop, list(pop$ID, pop$code))) The partitions will then be fed into parLapply() as follows: cl <- makeCluster(detectCores()) system.time(par_pop <-

I've encounter a weird behavior when calling lm within a lapply using the weights argument. My code consist of a list of formula on which I run a linear model that I call in lapply . So far it was working: dd <- data.frame(y = rnorm(100), x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100), x4 = rnorm(100), wg = runif(100,1,100)) ls.form <- list( formula(y~x1+x2), formula(y~x3+x4), formula(y~x1|x2|x3), formula(y~x1+x2+x3+x4) ) res.no.wg <- lapply(ls.form, lm, data = dd) However, when I add the weights argument, I get a weird error: res.with.wg <- lapply(ls.form, lm, data = dd, weights = dd[,"wg"

I have data like the SampleData below, which has lists of different length that I'd like to combine in to a data frame like the Desired Result below. I've tried using lapply and cbind.na from the qpcR package like the example below, but for some reason it won't let me turn the result into a data frame. If I just used two of the lists and cbind.na it will combine them and add the NA to the end like I want, but when I try using it in lapply it just leaves them as a list of different length lists. Any tips are greatly appreciated. SampleData<-list(list(1,2,3),list(1,2),list(3,4,6,7)) Desired

I have generated a list of models, and would like to create a summary table. As and example, here are two models: x <- seq(1:10) y <- sin(x)^2 model1 <- lm(y ~ x) model2 <- lm(y ~ x + I(x^2) + I(x^3)) and two formulas, the first generating the equation from components of formula get.model.equation <- function(x) { x <- as.character((x$call)$formula) x <- paste(x[2],x[1],x[3]) } and the second generating the name of model as a string get.model.name <- function(x) { x <- deparse(substitute(x)) } With these, I create a summary table model.list <- list(model1, model2) AIC.data <- lapply(X = model

I am trying to come up with a variant of mapply (call it xapply for now) that combines the functionality (sort of) of expand.grid and mapply . That is, for a function FUN and a list of arguments L1 , L2 , L3 , ... of unknown length , it should produce a list of length n1*n2*n3 (where ni is the length of list i ) which is the result of applying FUN to all combinations of the elements of the list. If expand.grid worked to generate lists of lists rather than data frames, one might be able to use it, but I have in mind that the lists may be lists of things that won't necessarily fit into a data