# Start RStudio
# (Session -> New session, Environment -> Clear environment)

# Objects
y <- 5 # "assignment" (see Environment window)
y
y = 5
x <- 8 - y
x
y

# Functions
sqrt(16)
abs(-3)

# Data types
today <- "Monday"
today
a <- FALSE
a
!a

# Important data types: vectors and matrices
numbers <- c(5, 6, 4, 7, 3)  # c for concatenate
numbers
numbers[2]
numbers[3:5]
numbers[-4]
numbers + 3
numbers
# ranges
numbers <- 3:8
numbers <- seq(4, 11, 0.3)
# repeat
numbers <- rep(4, 3)
# concatenate
numbers <- c(numbers, 6:8)
# matrices
A <- matrix(1:12, 3, 4)
A
A[2, ]
A[, 3]
A[, 2:4]
A[3, 4] <- 100
A

# Getting help
help(matrix)
help.search("column sums")
colSums(A)
help.start() # (also New window -> An Intro to R)

# Graphics
x <- 0:20
y <- x^2
plot(x, y)

# Environment (workspaces) and History
# show in RStudio
ls()
# press Up-key
# Can save the workspace (or when quitting)

# Scripting
# Open new R script
# in new file window:
a <- 5
print(a)
# save file: "example"
# in console:
source("example.R")
# add comment in file:
# this prints "5"

# Data frames (important and very useful!)
# in file:
table <- data.frame(letter=c("a", "b", "c"), number=4:6, w=c(3, 5, 1))
# source file
# in console:
table

# vector and matrix computation
x <- c(1,2,3,4) #vector assignment (c=concatenate)
y<-1:8 #(vector assignment)
y*2
y <- 1:8 + 5
y*z #element-wise
y*x #x is 'helpful'
y>=6 #
(y>6 & y<=4)
(y>6 & z<=4)
sum(y<6)
y[y>=6]

# sample from normal distribution
sample<-rnorm(10)

# apply
test<-matrix(rnorm(30),ncol=3) #create a matrix from a vector
apply(test,2,mean) #apply mean to each column

# create functions
myfun<-function(x){
  y=2*x+3
  return(y)
}
myfun(5)
apply(test,1,myfun) #apply own function
apply(test,1,function(x) mean(x)-5) #define function directly
myfun <- function(x) 2*x+3

# matrix sub-setting again
test[1,1] #sub-setting
test[,2] #all elements in second column
test[,c(2,3)] #all elements in second and third column

# packages
lillie.test()
install.packages('nortest')
library(nortest)
lillie.test()

# more advanced function
result<-t.test(y,z) #two sample t.test
names(result)
result$p.value #extract p.value
?t.test #HELP

# for-loop
for (i in 1:10){
  print(i)
}