I have a data frame with 10,000 rows and 40 columns. I am trying to apply a function to each of these rows. For each row, I am expecting to return a scalar which is the value of the statistic I am calculating in this function. Below is what I have done so far;
library(sandwich)
# Creating example data #
nrows=10000
ncols=40
n1=20
n2=20
df=data.frame(t(replicate(nrows, rnorm(ncols, 100, 3))))
cov=data.frame(group=as.factor(rep(c(1,2),c(n1,n2))))
# Function to evaluate on each row of df #
get_est= function(x){
mod = lm(x~cov$group)
vcov = vcovHC(mod)
coef = as.numeric(mod$coefficients[2])
se = sqrt(as.numeric(diag(vcov)[2]))
stats = coef/se
return(stats)
}
# Applying above function to full data #
t1=Sys.time()
estimates=apply(df, 1, function(x) get_est(x))
t2=Sys.time()-t1
# Time taken by apply function
Time difference of 32.10623 secs
Is there a way to significantly decrease the time taken to implement get_est() on the full data? The main reason I need to speed up the computation on a single df is because I have 1000 more data frames with the same dimension and I have to apply this function to each row to each of these data frames simultaneously. To illustrate, below is the broader situation I am dealing with;
# Creating example data
set.seed(1234)
nrows = 10000
ncols = 40
n1 = 20
n2 = 20
df.list = list()
for(i in 1:1000){
df.list[[i]] = data.frame(t(replicate(nrows, rnorm(ncols, 100, 3))))
}
# Applying get_est() to each row and to each of data frame in df.list #
all.est = foreach(j = 1:length(df.list), .combine = cbind, .packages = 'sandwich') %dopar% {
cov=data.frame(group=as.factor(rep(c(1,2),c(n1,n2))))
est = apply(df.list[[j]], 1, function(x) get_est(x))
return(est)
}
Even after parallelizing it is taking hours to finish. My ultimate objective is to significantly cut down the time to obtain "all.est" which will contain 10000 rows and 1000 columns where each column has the stats estimates for the respective data set. Any help is much appreciated!! Thanks in advance!