#######################
## Installation of R ##
#######################
## R: http://cran.at.r-project.org
## RStudio: http://www.rstudio.org/

## Installation of CRAN Packages
## Online
# install.packages(c("pkg1", "pkg2")) 

## Local
# install.packages("pkg.zip", repos=NULL)

## Installation of BioConductor Packages
# source("http://www.bioconductor.org/biocLite.R") 
# biocLite() 
# biocLite(c("pkg1", "pkg2"))

####################
## Getting Around ##
####################

## Create an object with the assignment operator ’<-’ (or ’=’) 
object <- ...

## List objects in current R session 
ls()

## Return content of current working directory 
dir()

## Return path of current working directory 
getwd()

## Change current working directory 
# setwd("/Users/tgirke/Desktop")

####################
## Basic R Syntax ##
####################
## General R command syntax 
object <- function(arguments) 
object <- object[arguments]

## Execute an R script 
source("my script.R")

## Execute an R script from command-line 
$ R CMD BATCH myscript.R 
$ R --slave < myscript.R
$ ./myscript.R

## Finding help 
?function

## Load a library 
library("grid")

## Summary of all functions within a library 
library(help="grid")

## Load library manual (PDF file) 
vignette("grid")

################
## Data Types ##
################
## Numeric data: 1, 2, 3 
x <- c(1, 2, 3)
x
is.numeric(x)
as.character(x)

## Character data: "a", "b", "c" 
x <- c("1", "2", "3")
x
is.character(x)
as.numeric(x)

## Complex data: 1, b, 3 
c(1, "b", 3)

## Logical data: TRUE, FALSE, TRUE 
x <- 1:10 < 5
x > !x

## Return indices for the ’TRUEs’ in logical vector 
which(x)

##################
## Data Objects ##
##################
## Vectors (1D) 
myVec <- 1:10
names(myVec) <- letters[1:10] 
myVec[1:5]
myVec[c(2,4,6,8)]
myVec[c("b", "d", "f")]

## Factors (1D): vectors with grouping information 
factor(c("dog", "cat", "mouse", "dog", "dog", "cat"))

## Matrices (2D), Data Frames (2D) and Arrays (≥2D) 
myMA <- matrix(1:30, 3, 10, byrow = T) 
myDF <- data.frame(Col1=1:10, Col2=10:1) 
myDF[1:4, ]
myDF[ ,c("Col2", "Col1", "Col1")]

## Lists: containers for any object type 
myL <- list(name="Fred", wife="Mary", no.children=3, child.ages=c(4,7,9)) 
myL[[4]][1:2]

## Functions: piece of code 
myfct <- function(arg1, arg2, ...) { 
        function body 
}

##############################
## General Subsetting Rules ##
##############################
## Subsetting by indices 
myVec <- 1:26
names(myVec) <- LETTERS 
myVec[1:4]

## Subsetting by same length logical vectors 
myLog <- myVec > 10 
myVec[myLog]

## Subsetting by field names 
myVec[c("B", "K", "M")]

## Special case 
iris$Species

######################################
## Basic Operators and Calculations ##
######################################
## Comparison operators: ==, ! =, <, >, <=, >= 
## Example: 
1==1

## Logical operators: AND: &, OR: |, NOT: ! 
## Example: 
x <- 1:10
y <- 10:1 
x > y & x > 5

## Calculations:
## Example: 
x + y
sum(x)
mean(x)
sd(x)
sqrt(x)
apply(iris[,1:3], 1, mean)

#######################################
## Reading and Writing External Data ##
#######################################
## Import Data into R 
myframe <- read.delim("http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/Samples/MolecularWeight_tair7.xls", sep="\t")
myframe[1:4,]

## Write Data from R to File 
write.table(myframe, file="myfile.xls", sep="\t", quote=F)

############################
## Some Great R Functions ##
############################
## The unique() function to make vector entries unique 
unique(iris$Species)
length(unique(iris$Species))

## The table() function counts the occurrences of entries 
table(iris$Species)

## The aggregate() function computes statistics of data aggregates 
aggregate(iris[,1:4], by=list(iris$Species), FUN=mean, na.rm=T)

## The %in% function returns the intersect between two vectors 
month.name[month.name %in% c("May", "July")]

## The merge() function joins data frames based on a common key column 
merge(frame1, frame2, by.x=1, by.y=1, all = TRUE)

## The apply() function simplifies the coding of iterative tasks (loops). 
## Creates a sample data set.
myMA <- matrix(rnorm(100000), 10000, 10, dimnames=list(1:10000, paste("C", 1:10, sep=""))) 
myMA[1:4,]

## Computes the mean for each row in myMA.
apply(myMA, 1, mean)[1:4] 

## Simplified computation on column aggregates 
source("http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/colAg.R") # Imports the colAg() function.
colAg(myMA=myMA, group=c(1,1,1,2,2,2,3,3,4,4), myfct=mean)[1:4,] 

############################
## Some Graphics Commands ##
############################
## Dot plots 
plot(1:10) 
plot(iris[,1:4])

## Barplot 
barplot(1:10)

## Help with plots 
?plot
?par