Combining multiple function arguments inside list2serv(lapply(),) - r

Question

I'm working with ten training datasets, train1 through train10, and would like to repeat the following statements for 1 through 10 with a single block of code:
train_y_1 <- c(train1$y)
train1$y <-NULL
train_x_1 <- data.matrix(train1)
olsfit_1 <- cv.glmnet(y=train_y_1, x=train_x_1, alpha=1, family="gaussian")
I've read in the forums that lapply() is preferable to for loops. My code:
# Create empty data frames and list (to be populated with values in main program)
list2env(setNames(lapply(1:10, function(i) data.frame()), paste0('train_y_', 1:10)), envir=.GlobalEnv)
list2env(setNames(lapply(1:10, function(i) data.frame()), paste0('train_x_', 1:10)), envir=.GlobalEnv)
list2env(setNames(lapply(1:10, function(i) list()), paste0('lasso_', 1:10)), envir=.GlobalEnv)
# Create y and x input matrices and run ten lasso regressions
list2env(lapply(mget(paste0('train', 1:10)), mget(paste0('train_y_', 1:10)), mget(paste0('train_x_', 1:10)), mget(paste0('lasso_', 1:10)),
function(a,b,c,d)
{
b <- c(a$y);
a$y <- NULL;
c <- data.matrix(a);
d <- cv.glmnet(y=b, x=c, alpha=1, family="gaussian");
}), envir=.GlobalEnv)
which produces the error message:
Error in match.fun(FUN) :
'mget(paste0("train_y_", 1:10))' is not a function, character or symbol
So it looks like R is confused by the four mget() functions which I intended to be reading in values for the a,b,c,d arguments, but I'm not sure how to proceed next.
Any suggestions?

Answer 1

You want to keep all your data in lists whenever possible, avoiding polluting the global environment with a bunch of variables. This isn't tested, and train is missing, but should be a similar list of your train data. Then, you could do something like,
trainy <- setNames(lapply(1:10, function(i) data.frame()), paste0('train_y_', 1:10))
trainx <- setNames(lapply(1:10, function(i) data.frame()), paste0('train_x_', 1:10))
lasso <- setNames(lapply(1:10, function(i) list()), paste0('lasso_', 1:10))
f <- function(a,b,c,d) {
b <- c(a$y);
a$y <- NULL;
c <- data.matrix(a);
d <- cv.glmnet(y=b, x=c, alpha=1, family="gaussian");
}
mapply(f, train, trainy, trainx, lasso, SIMPLIFY=F)
Although, since your lists are just initializing variables, you probably just want to loop (apply) over a list of your training data,
lapply(train, function(x) {
... # the statements you want to repeat
list(...) # return a list of the three data.frames
})

Answer 2

We can achieve this with the following code.
# Load libraries
library(dplyr);library(glmnet)
# Gather all the variables in global into a list
fit = mget(paste0("train", 1:10), envir = .GlobalEnv) %>%
# Pipe each element of the list into `cv.glmnet` function
lapply(function(dat) {cv.glmnet(y = dat$y,
x = data.matrix(dat %>% mutate(y = NULL)),
alpha = 1,
family = "gaussian")})
Your output will be neatly stored in fit, which is a list with 10 elements. You can call each element with fit[[i]]. For example coef(fit[[1]]) pulls out the coefs for train1 and lapply(fit, coef) pulls the coef for all 10 models and stores them in a list.