# Introduction to R
# April 1, 2008
# Stat560

################################
# R as a calculator
################################

3^3
log10(100)
exp(4)

################################
# Creating objects and assigning values
# Objects are assigned using <-
# Note, R is case sensitive! x and X are different
################################
# a scalar
x <- 5
y <- 2
x * y
z <- "I_Heart_R"

# a vector created with the concatenation function (c)
# must consist of the same type of data (i.e., numeric, character, logical)
x12 <- c(10,6,8)
x12 * 10
people <- c(seq(1,6,1))
blood.gp <- c('A', 'B', 'AB', 'B', 'O', 'O')
sex <- c( rep('M', 3), rep('F', 3) )
# hey, what is seq and rep? Just ask R: at the prompt type ?rep and ?seq

# accessing specific elements
# the first element of the vector people
people[1]
# elements 1 through 3 of the vector people
people[1:3]
# the second element of the vector blood.gp
blood.gp[2]

# lists are similar to vectors but can have different data types
# formally described as An R list is an ob ject consisting of an ordered collection of ob jects known as its components.
doe <- list(name="john",age=28,married=F)
doe
names(doe)
doe$name
doe$age

# another example
Lst <- list(name="Fred", wife="Mary", no.children=3,  child.ages=c(4,7,9)) 
Lst$child.ages
Lst[[4]]

# Binding vectors together
dai <- c(0,1,2,5,10,20)
pinf <- c(0,0,0,5,25,80)
row.bind <- rbind(dai,pinf)
col.bind <- cbind(dai,pinf)

# accessing columns and rows
row.bind[1,1] # this gets the element in row 1 column 1
row.bind[,1] # this gets all elements in column 1
row.bind[1,] # this gets all elements in row 1


# Matrices
m1 <- matrix(1:9, byrow=T, ncol=3)
m2 <- matrix(1:9, byrow=F, ncol=3)
m1[,1]
m1[,2]
m1[1,]

# data frames are a useful object and can consist of data of different types arranged in
# a rectangular table. They are basically matrices that can be composed of different data types
df5 <- data.frame(dai,pinf)
clinic.1 <- data.frame(people, blood.gp, sex)
clinic.1$people
clinic.1$people[1:3]

# useful functions for working with dataframes
dim(clinic.1)
colnames(clinic.1)
attributes(clinic.1)

# Making an index
# Recall above we made the character vector sex, consisting of M's and F's
sex

# What if we wanted to just pull out the elements with an M
which(sex== 'M')
blokes <- which(sex== 'M')
blokes
sex[blokes]
# How would you get the sex of the female samples?

# modifying subsets of data 
my.vector <- c(seq(0,20,2))
my.vector
index <- which(my.vector < 5)
my.vector[index]
my.vector[index] <- my.vector[index] + .1
my.vector

# reading data into a file
data <- read.table( "filename.txt", header=T)
?read.table

# writing data to a file
write.table <- write.table(data, file = "new_file.txt", row.names = F, quote = F, sep="\t")

# see what objects exist
ls()

# removing objects
rm(blokes)

# saving objects for future R sessions
save.image()

# quit R
q()