Rhe JackHo327

Tabs and Windows

Function	Shortcut
Fullscreen	`⌘`+ `Enter`
Previous Tab	`⌘`+ `Left Arrow`
Next Tab	`⌘`+ `Right Arrow`
Go to Tab	`⌘` + `Number`
Go to Window	`⌘` + `Option` + `Number`
Go to Split Pane by Direction	`⌘` + `Option` + `Arrow`

There are many Git workflows out there, I heavily suggest also reading the atlassian.com [Git Workflow][article] article as there is more detail then presented here.

The two prevailing workflows are [Gitflow][gitflow] and [feature branches][feature]. IMHO, being more of a subscriber to continuous integration, I feel that the feature branch workflow is better suited.

When using Bash in the command line, it leaves a bit to be desired when it comes to awareness of state. I would suggest following these instructions on [setting up GIT Bash autocompletion][git-auto].

Basic branching

When working with a centralized workflow the concepts are simple, master represented the official history and is always deployable. With each now scope of work, aka feature, the developer is to create a new branch. For clarity, make sure to use descriptive names like transaction-fail-message or github-oauth for your branches.

	> library(parallel) # load the library
	# create a cluster, which is made up by "detectCores()" of workers
	> cl <- makePSOCKcluster(rep("localhost",detectCores()))
	> use_multi_core_computing <- function(mat, threshold){
	result <- parSapply(cl,mat, function(x){ # parSapply() can be treated as a multi-core version sapply()
	if(x > threshold){
	x ** 2
	}else{
	sqrt(x)
	}

	> set.seed(123)
	> N <- 10
	> fake.vector <- runif(N, min = 0, max = 10)

	# and this time, let's tested 1000 times
	> microbenchmark(use_for_loop(fake.vector,5),use_apply_family_funcs(fake.vector,5),times = running_times)
	Unit: microseconds
	expr min lq mean median uq max neval cld
	use_for_loop(fake.vector, 5) 15.396 18.160 22.09172 19.343 27.239 62.372 1000 a
	use_apply_family_funcs(fake.vector, 5) 21.712 24.081 28.98297 25.660 34.542 138.165 1000 b

	> set.seed(123)
	> N <- 10000
	> fake.vector <- runif(N, min = 0, max = 10)

	# Here we want to use fake.vector to generate a new vector, without modifying the fake.vector
	> use_for_loop <- function(mat, threshold) {
	result <- numeric(length(mat))
	for(i in 1:length(mat)) {
	if(mat[i] > threshold){
	result[i] <- mat[i] ** 2

	> ret <- numeric(100000)
	> for_fun <- function(x){
	for(i in 1:x){
	ret[i] <- i^2
	}
	}

	# firstly, type "?microbenchmark" to check its user doc
	> microbenchmark(for_fun(100000), times = 100)
	Unit: milliseconds

	> ret <- numeric(100000)
	> for_fun <- function(x){
	for(i in 1:x){
	ret[i] <- i^2
	}
	}

	> system.time(for_fun(100000))
	user system elapsed
	0.13 0.00 0.13