Chapter 1 R 101

1.1 Working Directory

getwd() allows us to get our current Working directory.

setwd() allows us to set out working directory to which ever location we want it to be. Preferably to set this to the same directory as the project folder is.

getwd()
setwd("C:/Users/Logan/Documents")

1.2 Basic Arithmetic

At the heart of R programming, its a big calculator.

Arithmetic Operators like +, -, *, /.

Addition

4 + 2

## [1] 6

Subtraction

4 - 2

## [1] 2

Multiplication

4 * 2

## [1] 8

Division

9 / 2

## [1] 4.5

Combination

(4 * 3) / (2 + 4)

## [1] 2

3 + 4 * 3

## [1] 15

Remainder

14%%5

## [1] 4

Quotient

14%/%5

## [1] 2

Other generic mathematical function like log and square root are also available.

Power

2^69

## [1] 5.902958e+20

Log

log(13)

## [1] 2.564949

Square Root

sqrt(144)

## [1] 12

1.3 Defining Variables & Objects

Assignment operator <- is used to assigned the value to a variable It sets an object(everything) to a certain variable.

new_variable <- "Hello World!"
new_variable

## [1] "Hello World!"

x <- 5 + 6
x

## [1] 11

Importance of spacing

y<-3
y< -3

## [1] FALSE

1.4 Vector

Vectors are created using c() function. In R, vector are just an array of number. Also known as row matrices in mathematics.

Create a vector.

apple <- c('red','green',"yellow")
apple

## [1] "red"    "green"  "yellow"

1.5 Types of object

Like many other programming language, R have various types of object as well.

numeric
complex
integer
logical
character

We can check its type using the class() function

Numeric

# creates a vector of double (decimal) values
a <- c(8.9, 1.0, 1.4)   
class(a)

## [1] "numeric"

Complex

# creates a vector of imaginary values
b <- c(1 + 2i, 4 - 8i) 
class(b)

## [1] "complex"

Integer

# creates a vector of integer (whole numbers) values
p <- c(23L, 44L)        
class(p)

## [1] "integer"

Logical

# creates a vector of true or false
q <- 4 == 5  
class(q)

## [1] "logical"

Character

# creates a vector of character values
r <- "Data"  
class(r)

## [1] "character"

Vector can only store object of the same type. Storing multiple types of objects will convert them to a single type.

my_vec <- c("My", 10)
my_vec

## [1] "My" "10"

As we can see, 10 has been converted from a numeric type to a character type as seen in the output surounded by quotation mark.

1.6 List

List are created using the list() function. List, as opposed to vector, can store objects of multiple types.

my_list <- list(42, "cd", FALSE, 1 + 3i)
my_list

## [[1]]
## [1] 42
## 
## [[2]]
## [1] "cd"
## 
## [[3]]
## [1] FALSE
## 
## [[4]]
## [1] 1+3i

1.7 Matrices

Matrices are 2 dimensional vector which can only accept a single or objects of the same type.

Matrix

my_matrix <- matrix(1:6, nrow = 3, ncol = 2) # ":" stands for 1 to 6
my_matrix

##      [,1] [,2]
## [1,]    1    4
## [2,]    2    5
## [3,]    3    6

Matrices have various attribute associated to it.

Dimension – dim()
Number of Rows – nrow()
Number of Columns == ncol()
Row names – rownames()
Column Names – colnames
Structure of the data – str()
Attributes – attributes()

Dimension

dim(my_matrix)

## [1] 3 2

Number of Rows

nrow(my_matrix)

## [1] 3

Number of Columns

ncol(my_matrix)

## [1] 2

Row Names

rownames(my_matrix)

## NULL

Column Names

colnames(my_matrix)

## NULL

Structure

str(my_matrix)

##  int [1:3, 1:2] 1 2 3 4 5 6

Attributes

attributes(my_matrix)

## $dim
## [1] 3 2

To extract certain elements from a Matrices, we have to use the square bracket [,] instead.

Anything to the left of the "," is to extract the rows.

Anything to the right of the "," is to extract the columns.

Extracts 2nd column

my_matrix[,2]

## [1] 4 5 6

Extracts 1st column

my_matrix[,1]

## [1] 1 2 3

Extracts 2nd row

my_matrix[2,]

## [1] 2 5

Extracts 1st row

my_matrix[1,]

## [1] 1 4

Extracts the intersection of 3rd row and 2nd column

my_matrix[3,2]

## [1] 6

1.8 Data Frame

Data Frame are 2 dimensional list. They can store multiple types of objects as opposed to matrix

Data Frame

my_dataframe <- data.frame(name = c("Ali", "Bala", "Chong", "Dom"), 
                           score = c(65, 57, 89, 92))
my_dataframe

##    name score
## 1   Ali    65
## 2  Bala    57
## 3 Chong    89
## 4   Dom    92

Data Frame also have various attribute associated to it.

Dimension – dim()
Number of Rows – nrow()
Number of Columns == ncol()
Row names – rownames()
Column Names – colnames
Structure of the data – str()
Attributes – `attributes()

Dimension

dim(my_dataframe)

## [1] 4 2

Number of Rows

nrow(my_dataframe)

## [1] 4

Number of Columns

ncol(my_dataframe)

## [1] 2

Row Names

rownames(my_dataframe)

## [1] "1" "2" "3" "4"

Column Names

colnames(my_dataframe)

## [1] "name"  "score"

Structure

str(my_dataframe)

## 'data.frame':    4 obs. of  2 variables:
##  $ name : Factor w/ 4 levels "Ali","Bala","Chong",..: 1 2 3 4
##  $ score: num  65 57 89 92

Attributes

attributes(my_dataframe)

## $names
## [1] "name"  "score"
## 
## $row.names
## [1] 1 2 3 4
## 
## $class
## [1] "data.frame"

To extract certain elements from a Data Frame, we have to use the square bracket [,] too

Anything to the left of the "," is to extract the rows.

Anything to the right of the "," is to extract the columns.

Extracts 2nd column

my_dataframe[,2]

## [1] 65 57 89 92

Extracts 1st column

my_dataframe[,1]

## [1] Ali   Bala  Chong Dom  
## Levels: Ali Bala Chong Dom

Extracts 2nd row

my_dataframe[2,]

##   name score
## 2 Bala    57

Extracts 1st row

my_dataframe[1,]

##   name score
## 1  Ali    65

Extracts the intersection of 3rd row and 2nd column

my_dataframe[3,2]

## [1] 89

1.9 Packages

Most of time you will be using various 3rd party packages that have ben develop by other users in the community. Inorder to use these packages, we first have to install them. we can do that with the install.packages() function.

# installing packages
install.packages("ggplot2")

Once the packages have been installed, we will have to load it into rstudio b4 we can use its functions. we can load it by calling the library() function

# load the package into R studio
library(ggplot2)

1.10 Control Structures

In R, there are a few types of control structure.

They are mainly:

Decision Making
- if … else statement
- nested if … else statement
Loops
- While Loop
Combination of both

1.10.1 Decision Making

if … else Statement

# First, create a variable
age <- 15

if (age < 21){
  print("You are NOT an adult yet!")
} else {
  print("You ARE an adult yet!")
}

## [1] "You are NOT an adult yet!"

Nested if … else Statement

if (age < 12) {
  print("You are a children!")
} else if (age < 21) {
  print("You are a teenager!")
} else {
  print("You are an adult!")
}

## [1] "You are a teenager!"

1.10.2 Loops

While loop

# First, create a variable
age <- 15

# Execute the print statements as long as the condition is fulfilled
while (age < 21) {
  print(age)
  print("You are not an adult yet!")
  age <- age + 1
}

## [1] 15
## [1] "You are not an adult yet!"
## [1] 16
## [1] "You are not an adult yet!"
## [1] 17
## [1] "You are not an adult yet!"
## [1] 18
## [1] "You are not an adult yet!"
## [1] 19
## [1] "You are not an adult yet!"
## [1] 20
## [1] "You are not an adult yet!"

1.10.3 Combining Decision making with loops.

# First, create a variable
age <- 8

while (age < 21) {
  print(age)
  if (age < 12) {
    print("You are a children!")
  } else {
    print("You are a teenager!")
  }
  age <- age + 1
}

## [1] 8
## [1] "You are a children!"
## [1] 9
## [1] "You are a children!"
## [1] 10
## [1] "You are a children!"
## [1] 11
## [1] "You are a children!"
## [1] 12
## [1] "You are a teenager!"
## [1] 13
## [1] "You are a teenager!"
## [1] 14
## [1] "You are a teenager!"
## [1] 15
## [1] "You are a teenager!"
## [1] 16
## [1] "You are a teenager!"
## [1] 17
## [1] "You are a teenager!"
## [1] 18
## [1] "You are a teenager!"
## [1] 19
## [1] "You are a teenager!"
## [1] 20
## [1] "You are a teenager!"