MA213 Basic Statistics and Probability

Lab 1: Introduction to R

In this lab session, we will learn basic syntax of R.

Learning Objectives

Use R for Data Management and Exploration: Utilize R to load, pre-process, and explore data through visualization and summarization techniques.

Install R and RStudio.

You can download and install R from

https://www.r-project.org/

And you can download and install RStudio from

https://posit.co/download/rstudio-desktop/

We assume that you have already installed R and Rstudio (You need to install first R and then install Rstudio because Rstudio is an IDE for R).

Introduction of R

R is Programming language and environment statistical computing, data manipulation and graphical display.
R is free and open source.
Rstudio is also free and open source IDE (Integrated Development Environment) for R.

Brief walk through about RStudio interface

RStudio overview

window 1 : script window (text editor)

window 2 : console window (this is where you type your R code and run)

window 3 : plots, files, packages, help… window

window 4 : environment window

Set up `working directory`

You can go to Session → set working directory → choose directory and then choose your working directory.

or
You can go to Files tap → ☸︎ →‘copy folder path to Clipboard’

then paste into setwd() function

You can go to Files tap → ☸︎ →‘Set As Working Directory’

How to run code?

Directly type your command in the console window.
You can run your R chunk by pressing run button ▶ in the R chunk in your R markdown file.
You can write your commands on your R script file and highlight the commands and press run

Run shortcut : `ctrl` + `enter` (windows) or `cmd` + `enter` (mac).

R is basically a big calculator (some mathematics) : basic math operations to matrix operations

Basic math operations

+: add

- : substract

* : multiply

/ : divide

^ or **: exponent

For example

$(\frac{12}{24})^2$

(12/24)^(2)

## [1] 0.25

#or
(12/24)**(2)

## [1] 0.25

Assign values to a variable

Let’s say if you want to save 10 to variable A. You can do the following command.

A <- 10
A <<- 10
10 -> A
10 ->> A
A = 10

# A <- 10
# A = 10 
# commonly used ones

And you can check what value the variable `A` has by calling it or using `print()` function

## [1] 10

print(A)

## [1] 10

Assigning character type information to a variable

SchoolName <- "Boston University"
SchoolName

## [1] "Boston University"

Assign values to a vector

You can assign multiple values to a vector.

GPA <- c(3.2, 3.7, 3.9, 2.3, 2.7)
FirstName <- c("Tom", "Sarah", "Nick", "Amy", "John")
GPA

## [1] 3.2 3.7 3.9 2.3 2.7

FirstName

## [1] "Tom"   "Sarah" "Nick"  "Amy"   "John"

There are some useful functions for a vector

seq(from=a, to=b, by=c) : this generates a sequence vector from value a to b by c increment.
rep(x, times = n) : this replicate element x for n times.
length(x) : this will give you the length of vector x

Examples

my_vector = seq(1, 10, 2)
my_vector

## [1] 1 3 5 7 9

another_vector = 1:10
another_vector

##  [1]  1  2  3  4  5  6  7  8  9 10

vector3 = rep("BU", 25)
vector3

##  [1] "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU"
## [16] "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU" "BU"

Accessing vector elements

Each element in a vector is associated with index number. The index in R starts from 1 as the first item.

For example,

#
SomeNames <- c("Alpha", "Bravo", "Charlie")

Index of Vector Elements

For example

You can access the third element of SomeNames by SomeNames[3] .

You can also access, for example, the second and the third elements by SomeNames[2:3] .

#
SomeNames[3]

## [1] "Charlie"

SomeNames[2:3]

## [1] "Bravo"   "Charlie"

Numeric vector can be subsetted using condition

                         x[condition]

the conditional statements (condition) you can use could be

x == 20    # x equals to 20
x < 100
x <= 100
x > 100
x >= 100
x != 100   # x not equal to 100

For example

Numbers <- c(20, 30, 50, 70, 120, 200)
Numbers[Numbers<100]

## [1] 20 30 50 70

Subsetting vector using condition

Importing and Subsetting Data

You can import Data by using read.table() function.

my_data <- read.csv("lab1/data/mtcars.csv")
# to be able to import this file, this file needs to be in your working directory

You can access subset data

Specific element by my_data[row, column]
- | for example, the element in the third row and the fourth column my_data[3,4]
Specific row by my_data[row,]
- | for example, all the element of the third row my_data[3,]

Also,

Specific column by my_data[,column]
- | for example, all the element of the fifth column my_data[,5]
Access the column by using $ sign such as my_data$column_name
- | for example, hp column from my_data is my_data$hp
Conditional statement works similar as vectors (row wise and column wise)
- | for example, my_data[my_data$hp<100,]

Multiple conditions can be combined

You can combine multiple logical conditions using & (AND), | (OR), and ! (NOT) operators inside the row index to filter rows. For example

Rows where hp is less than 100 AND cyl equals 4:

my_data[(my_data$hp < 100) & (my_data$cyl == 4), ]

##                 X  mpg cyl  disp hp drat    wt  qsec vs am gear carb
## 3      Datsun 710 22.8   4 108.0 93 3.85 2.320 18.61  1  1    4    1
## 8       Merc 240D 24.4   4 146.7 62 3.69 3.190 20.00  1  0    4    2
## 9        Merc 230 22.8   4 140.8 95 3.92 3.150 22.90  1  0    4    2
## 18       Fiat 128 32.4   4  78.7 66 4.08 2.200 19.47  1  1    4    1
## 19    Honda Civic 30.4   4  75.7 52 4.93 1.615 18.52  1  1    4    2
## 20 Toyota Corolla 33.9   4  71.1 65 4.22 1.835 19.90  1  1    4    1
## 21  Toyota Corona 21.5   4 120.1 97 3.70 2.465 20.01  1  0    3    1
## 26      Fiat X1-9 27.3   4  79.0 66 4.08 1.935 18.90  1  1    4    1
## 27  Porsche 914-2 26.0   4 120.3 91 4.43 2.140 16.70  0  1    5    2

Rows where hp is less than 100 OR cyl equals 4:

my_data[my_data$hp < 100 | my_data$cyl == 4, ]

##                 X  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 3      Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## 8       Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## 9        Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## 18       Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## 19    Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## 20 Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## 21  Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## 26      Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## 27  Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## 28   Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## 32     Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Rows where hp is NOT less than 100:

my_data[! (my_data$hp < 100), ]

##                      X  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 1            Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## 2        Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## 4       Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## 5    Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## 6              Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## 7           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## 10            Merc 280 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## 11           Merc 280C 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## 12          Merc 450SE 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## 13          Merc 450SL 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## 14         Merc 450SLC 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## 15  Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## 16 Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## 17   Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## 22    Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## 23         AMC Javelin 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## 24          Camaro Z28 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## 25    Pontiac Firebird 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## 28        Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## 29      Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## 30        Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## 31       Maserati Bora 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## 32          Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

If an expression or condition is True or False, programming language gives two answers `TRUE` or `FALSE` . It is sometimes can be shown to `1` or `0`.

3==3 # True

## [1] TRUE

10 == 3 # False

## [1] FALSE

my_data$hp < 100 # if the horse power of the car is less than 100 it gives 1, 0 otherwise.

##  [1] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
## [25] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE

my_data[my_data$hp < 100,] # this is how we are only printing the cars data with horse power < 100.

##                 X  mpg cyl  disp hp drat    wt  qsec vs am gear carb
## 3      Datsun 710 22.8   4 108.0 93 3.85 2.320 18.61  1  1    4    1
## 8       Merc 240D 24.4   4 146.7 62 3.69 3.190 20.00  1  0    4    2
## 9        Merc 230 22.8   4 140.8 95 3.92 3.150 22.90  1  0    4    2
## 18       Fiat 128 32.4   4  78.7 66 4.08 2.200 19.47  1  1    4    1
## 19    Honda Civic 30.4   4  75.7 52 4.93 1.615 18.52  1  1    4    2
## 20 Toyota Corolla 33.9   4  71.1 65 4.22 1.835 19.90  1  1    4    1
## 21  Toyota Corona 21.5   4 120.1 97 3.70 2.465 20.01  1  0    3    1
## 26      Fiat X1-9 27.3   4  79.0 66 4.08 1.935 18.90  1  1    4    1
## 27  Porsche 914-2 26.0   4 120.3 91 4.43 2.140 16.70  0  1    5    2

MA213 Basic Statistics and Probability - Lab1 Guide

Lab 1: Introduction to R

Learning Objectives

Install R and RStudio.

Introduction of R

Brief walk through about RStudio interface

Set up working directory

How to run code?

R is basically a big calculator (some mathematics) : basic math operations to matrix operations

For example

Assign values to a variable

And you can check what value the variable A has by calling it or using print() function

Assign values to a vector

Examples

Accessing vector elements

For example

Numeric vector can be subsetted using condition

For example

Importing and Subsetting Data

You can access subset data

Also,

Multiple conditions can be combined

If an expression or condition is True or False, programming language gives two answers TRUE or FALSE . It is sometimes can be shown to 1 or 0.

Set up `working directory`

And you can check what value the variable `A` has by calling it or using `print()` function

If an expression or condition is True or False, programming language gives two answers `TRUE` or `FALSE` . It is sometimes can be shown to `1` or `0`.