00pen

It is written after reading Efficient R programming book website.(https://csgillespie.github.io/efficientR/)

1. Prerequisites

library("microbenchmark")
library("profvis")
library("ggplot2")

#install 3 packages and attach by using install.packages() and library()

2. profiling

profvis( expr = { any code })

# this code can show you how long each code takes and how much each code takes by percent

3. system and Ram

Sys.info()

#see your OS and its version etc.

# Note: uses 2+ GB RAM and several seconds or more depending on hardware

# 1: Create large dataset

X = as.data.frame(matrix(rnorm(1e8), nrow = 1e7))

# 2: Find the median of each column using a single core

r1 = lapply(X, median)

# 3: Find the median of each column using many cores

r2 = parallel::mclapply(X, median)

#mclapply only work in parallel on Mac or Linux. we must use parLapply() on Windows.

4. packages

install.packages('installr')

installr::updateR()

#if R return FALSE, your Rversion is uptodate.

pkgs = c('raster', 'leaflet', 'rgeos')

install.packages(pkgs)

inst = lapply(pkgs, library, characher.only = TRUE)

#to shorten code, install packages in only two line of code and use lapply

#to make library(pkgs[i]).

#we use library instead of require because the former returns error when pkg is not installed.

update.packages()

#default of ask parameter is TRUE, change it to FASLE if you want.

#if you want this code run automatically when starting R

#you can add update.packages(ask = FALSE) in .Rprofile startup file