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R Tutorial 101: Basic Introduction to R

author: Parash Upreti date: autosize: true

.

Disclaimer: Most of the information may seem basic but I recommend quickly trying the codes and rejuvinating rusty skills.

Overview

This is a very basic R Tutorial and assumes that you have a very little or no previous R knowledge. You can easily follow the slides with R your local installation of the software. Topics here include:

  • Getting Started with R
  • Working on R
  • Workspace
  • Commenting R Code
  • Working Directory
  • Working with data
  • Vectors, list and Matrices
  • R Data Structures
  • Logical Operators and Truth values
  • Programming in R
  • Functions and packages

Getting Started with R

  • R is a statistical and mathematical computational software. It is available free of cost here [https://cran.r-project.org/bin/windows/base/].

  • The popular IDE for R is RStudio which can be downloaded for free as well here [https://www.rstudio.com/products/RStudio/] Choose the desktop version.

  • In RStudio, you will be working on either console or script windows. Inputs can be inserted in both windows but Console is where your results are displayed.

  • RStudio is nicer since you can see two additional windows which can be helpful in many ways.

Lets Start working on R

Do the following. Write type the following lines on your R script and hit control + enter (there are other ways to run code in R) :

1+3*3
3**5  
3^5

As you can see, R has a intreprative command line. It can easily perform basic arithematic operations. Try some other calculations. (Note, you can directly run code in console)

Working on R

Also, you can assign values to a variable we will use "<-" to assign values. (Can also use "=" but we try to avoid.)

x<- 7
x
[1] 7

y<- 3^2
x + y
[1] 16

If you check on the window to your right on the environment tab, aka workspace, you will see x and y as values. You can check what is on your workspace by ls()

Altering the workspace

  • You can alter the workspace by reassigning the value
x<- 1000
x<- x *3
x
[1] 3000
  • You can also remove a particular value by
rm(x)
  • OR
remove(y)

Commenting the code

  • Commenting is the most important part of writing code. Comments can be useful to give instructions or explain what particular line of code does.

  • In R, you can write comments by beginning the line with #

# The following code adds two numbers

3+5    # 3 (first number) comes from x values and 5 (second) from y
[1] 8

The code runs with the command (which is ignored by R)

  • If you need to clear your console you can press cltr + l. It clears the texts from the console but not from the workspace.

Working directory

Working directory is the location on your hard drive thar R is currently accessing. This can be changed anytime. In RStudio, you can see this on the bottom right window under files tab.

#getting current working directory
getwd()
[1] "C:/Users/Machine/Documents/R/R Tutorial"
  • Set working directory by: setwd(#location on your computer)
  • Can also view the working directory by:
dir()
[1] "Data manupulation Presentation.Rmd" 
[2] "Data_manupulation_Presentation.html"
[3] "R Tutorial.Rproj"                   
[4] "R_Tutorial Presentation-figure"     
[5] "R_Tutorial Presentation.html"       
[6] "R_Tutorial Presentation.Rpres"      
[7] "R_Tutorial_Presentation.html"

Working with data

Data in R is in a vector form (for now). Concatinate (c) numbers as entries of vectors:

x<- c(1,3,7,2,9,5)
y<- c(12,9,11, 14,7)

Check sombe basic information about your data

length(x)
[1] 6

length(c(x,y))
[1] 11

Working with data

  • Select particular data point in your vector
x[5]
[1] 9
  • Or remove a particular value
y[-2]
[1] 12 11 14  7
  • Change value or even add extra datapoints
y[2] <- 10
y[6] <- 13 #earlier y only had 6 data points

Merging two vectors

More on this later but just an example of what can be done with vectors

xycol<- cbind(x,y) # x and y have to be same length
xycol
     x  y
[1,] 1 12
[2,] 3 10
[3,] 7 11
[4,] 2 14
[5,] 9  7
[6,] 5 13

xyrow<- rbind (x,y)
xyrow
  [,1] [,2] [,3] [,4] [,5] [,6]
x    1    3    7    2    9    5
y   12   10   11   14    7   13

Create a list

Check to see what list does

list(4,3)
first_list<- list(xcol =x,ycol=y)
first_list

Notice the difference of the

first_list[2]
$ycol
[1] 12 10 11 14  7 13

first_list[[2]]
[1] 12 10 11 14  7 13

Matrices

X= matrix(c(1,2,4,2,2,3,2,1,2,4,2,4,1,3,4), ncol= 5, byrow =T)
X
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    2    4    2    2
[2,]    3    2    1    2    4
[3,]    2    4    1    3    4

By default, matrix is arranged by column. Also, you need to assign the number of columns (or rows). Like vectors, matrix elements can be located or parsed by using:

X[ ,3 ]   #First entry is row and second is column index
[1] 4 1 1

X[2,3]
[1] 1

Table

Table commands tabulates the instances of each datapoint occurances

table(c(1,2,4,2,2,3,2,1,2,4,2,4,1,3,4))

1 2 3 4 
3 6 2 4 

prop.table(table(X)) # converts the counts into probability
X
        1         2         3         4 
0.2000000 0.4000000 0.1333333 0.2666667

R Data Structure

R data comes in following formats:

  • Numeric/Integers: Numeric values including integers and decimals

  • Strings : Characters, (exclude if they are factors)

  • Factors : Levels of classification. Eg. male/female, red/yellow/blue, etc

  • Boolean : True/False (assigns 1 for true and 0 for false). Can add/subtract boolean in R

  • Date/Time : Self explanatory. Try sys.date() and sys.time()

Operation on Matrices

R can perform matrix operations. Try the following on your console

x<-1:3
y<-4:6
z<-7:9
M<-cbind(x,y,z)
N<-matrix(c(10,11,12,50,51,52),3,2,byrow='true')

Verify the following:

dim(N)      #Returns the dimensions of N. 
dim(N)[1]   #Number of rows in N.
dim(N)[2]   #Number of cols in N.

Operation on Matrices

Try the following codes: Comments are provided as hints

M*M         #Elementwise multiplication
M%*%N       #Matrix multiplication
M^2         #Square each element
N+7         #Add 7 to each element of M
M%*%M       #Square M using matrix multiplication
t(M)        #Transpose of A
diag(M)     #Returns the diagonal of M as a vector.
diag(3)     #Creates the 3x3 identity matrix.

Try creating more matrices and try other operations

What does following command do?

M^(-1)

Matrices "solve" command

What does the following "solve" command do?

A<- matrix(c(2,3,4,2), nrow =2)
solve(A)
  • Hint: Try Solve command for a non- square matrix. Try it again for matrix M

More solve:

solve(A)%*%A
B =solve(A,diag(2))  #remember what diag(2) does?
B                    #What is B?
  • More hint: solve is solving something. Matrix multiply A and B to find out. Shouldn't be that hard.

A^(-1) is not same as solve(A)

Logical Operators

These are equivalent to your true false statements.

  • Equal == [careful here, double equal sign]
  • Not equal !=
  • Greater than/equal >/>=
  • Less than/ equal </<=

Run the code and see the results.

3==7  
3!=7
3<7
3<=7
x=1:10
x==7
x<=7
x[x<=7]  #Understand this line of code please

More Logical Operators

  • And [&] -- Or [|] -- Not [!]

Does the word "truth table " ring a bell?

TRUE & FALSE
[1] FALSE

TRUE | FALSE
[1] TRUE

!TRUE
[1] FALSE

Truth Table

P = c(TRUE, TRUE, FALSE, FALSE)
Q = c(TRUE, FALSE, FALSE, TRUE)

cbind(P, Q, "P&Q"= P&Q, "P_or_Q" = P|Q, "Not_P" =!P)
         P     Q   P&Q P_or_Q Not_P
[1,]  TRUE  TRUE  TRUE   TRUE FALSE
[2,]  TRUE FALSE FALSE   TRUE FALSE
[3,] FALSE FALSE FALSE  FALSE  TRUE
[4,] FALSE  TRUE FALSE   TRUE  TRUE

Programming in R

If else statements in R

if(5>1){print('Yes, it is.')}
[1] "Yes, it is."

if(5>1){print('Yes, it is.')}else{print('No, it is not')}
[1] "Yes, it is."

if(5==1){print('Yes, it is.')}else{print('No, it is not')}
[1] "No, it is not"

Using a Function

We have already used many functions available in R.

  • functions are in the form function() such as cbind() or matrix()
  • such functions are available in R base integrated in plain R
  • thre are many statistical functions in R such as mean(), median(), etc. (more on this later)
  • you can create your own function as well. For eg.
operation<- function(x,y){
  sumops= x+y
  difops= x-y
  prodops=x*y
  quotops=x/y
  list(sum =sumops, diff = difops, prod = prodops, quot = quotops)
}
operation(3,22)$sum
[1] 25

Introuction to Statistical Functions

R is equipped with many statistical functions. Other functions will be introduced as we move forward

normal_data =rnorm(1000,100,15)  #sample of size 1000 from a normal distribution with mu=100 and sigma=15
mean(normal_data)   #Computes the sample mean.
[1] 99.82245

sd(normal_data)     #Computes the sample st dev.
[1] 15.12774

sum(normal_data)    #Computes the sum of the elements of x.
[1] 99822.45

R Packages

Since R is an open source software and many users in the community are contributing, it is growing. R packages are developed by a variety of people in the community, academia, industry, and software companies and storing them in CRAN repositories, GitHub or on some websites. R packages could be thought of as functions created by others that are not already availabe in base R.

So they have to be installed in the machine using install.packages("package_name") (only once) and then called in before every section using library(package_name)

Example of R pacakges:

  • ggplot
  • e1071

we will discuss packages and their use in the future

Up Next

  • Using dataset
  • Dataset available in R base
  • Plots