timing in loops vs apply functions...

loops_vs_apply.R

library(ggplot2)
# n is the number of iterations for the test
# j is the number of matrices to create
# m is the size of the square matrix to solve
loops <- function(n,j,m){
  for(i in 1:n){
    mylist <- list()
    for(x in 1:j){mylist[[x]]<- matrix(rnorm(m^2),nrow=m,ncol=m)}
     
    for(x in 1:j) {z <- solve(mylist[[x]])}
  
 }
}

applies <- function(n,j,m){
  for(i in 1:n){
    mylist <- list()
    for(x in 1:j){mylist[[x]]<- matrix(rnorm(m^2),nrow=m,ncol=m)}
    out <- lapply(mylist,solve)
  }
}

# Test with increasing matrix size from 2 to 100
# This may take some time
loop_time_mz <- 0
apply_time_mz <- 0
for(i in 2:100){
  loop_time_mz <- c(loop_time_mz,system.time(loops(100,10,i), gcFirst = TRUE)[3])
  apply_time_mz <-  c(apply_time_mz,system.time(applies(100,10,i), gcFirst = TRUE)[3])
  cat(i,"\n")
}

# test with increasing number of matrices, but a fixed matrix size
loop_time_nm <- 0
apply_time_nm <- 0
for(i in 2:100){
  loop_time_nm<- c(loop_time_nm,system.time(loops(100,i,10), gcFirst = TRUE)[3])
  apply_time_nm <-  c(apply_time_nm,system.time(applies(100,i,10), gcFirst = TRUE)[3])
  cat(i,"\n")
}



time_df <- data.frame(cbind(rep(1:100,4),c(loop_time_mz,apply_time_mz,loop_time_nm,apply_time_nm)))
time_df$fxn <- c(rep("Loops",100),rep("Apply",100),rep("Loops",100),rep("Apply",100))
time_df$type <- c(rep("Increasing matrix size",200),rep("Increasing number of matrices",200))
names(time_df) <- c("size","time","Function","type")
ggplot(time_df,aes(x=size,y=time,group=Function,colour=Function))+geom_path()+facet_grid(type~.)