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02 Basic R + Statistics

Agenda

  • Review Basic Operation
  • Data Types & Data Structures
  • Statistics

0. Prerequisites

  1. Open R Studio Desktop
  2. Setting working directory (See in 01-Intro-R)
  3. Create Script file name 02_63130500xxx.R and save in your working directory
  4. Open progress form Class 02 and checked on Study Check-In and 0 prerequisite

1. Review Basic Operation

# create variable
x <- 1
y <- 2
x+y
print(x + y)
z <- x+y
z

2. Data Types

There are 3 types:

  • Numeric: Any number with or without a decimal point. Example - 12.3, 5, 999
  • Logical: Two possible values — either TRUE or FALSE
  • Character: Any grouping of characters on your keyboard (letters, numbers, spaces, symbols, etc.) or text.Example - "Hello World"
# Numeric ex 1, 1.0
varA <- 100
class(varA)

# Logical: TRUE, FALSE
varB <- TRUE
class(varB)
class(1==2)

# Character
varC <- "Hello, My name is Safe"
class(varC)

Data Structure

Main data structure that we will learn in this course are Vector, List, Data Frame

3. Vector

Vector is the simple data structure which is a single entity consisting of an ordered collection of values. We use c() function which means to concatenate the elements into a vector.

3.1.1. Create Vector

# Character Vectors
c("Ant","Bird","Cat")

# Logical Vectors
c(TRUE,FALSE,TRUE)

# Numeric Vectors
c(100,245,305,411,555)
  • Use class() to see class or type of an object
  • Use length() to get the length of vectors
  • Moreover you can get elements at position x by using variable[x] which position is starting with 1

3.1.2. Operation with Vector

# Create Variable name
v1 <- c(1,2,3,4,5)
v2 <- c(6,7,8,9,10)

# Replicate
v3 <- rep(c(1,2,3),5)

# Creating integer sequences
v4 <- c(1:100)

You can use + - * / with vector

v1+v2

4. Lists

An ordered collection of objects (components). A list allows you to gather a variety of (possibly unrelated) objects under one name.

# Initial
name <- c("Antony","Boky","Caty")
age <-c(10,25,30)
club <-c("Sec A","Sec B","Sec A")
retired <- c(T,F,T)

# Create list
myList <- list(name,age,club,retired)
# Or assign name
myList <- list(stdName = name,
               stdAge = age,
               stdClub = club,
               retired = retired)

5. Data Frame

Data frames are tabular data objects. The first column can be numeric while the second column can be character and third column can be logical. It is a list of vectors of equal length.

Data Frames are created using the data.frame() function.

continent <- c("Africa","Asia","Europe","North America","Oceania","South America","Antarctica")
countries <- c(54,48,51,23,14,12,0)
world <- data.frame(continent,countries)
View(world)

6. Statistics

x <- c(1:10)

mean(x)
sum(x)

# Summaries
summary(x)

Summary Notes

# Help Function
?
help()

# Useful Functions
length(object) # number of elements or components
str(object)    # structure of an object
class(object)  # class or type of an object

Study More... https://www.statmethods.net/input/datatypes.html

Assignment 1

Create script file HW01_63130500xxx.R and do exercise in this file and answer by using comment. Example

# Example 0
x <- 1
y <- 2
print(x+y) #3

After finished send in LEB2

Exercise 1

Finding the average, sum, median, sd, variance of 10.4, 5.6, 3.1, 6.4, 21.7

Exercise 2

2.1. Create data structure in variable named marvel_movies and explain why you using this data structure ?

# List of Marvel movies (Order by Marvel Phase released)
names <- c("Iron Man","The Incredible Hulk","Iron Man 2","Thor","Captain America: The First Avenger",
"The Avengers","Iron Man 3","Thor: The Dark World","Captain America: The Winter Soldier",
"Guardians of the Galaxy","Avengers: Age of Ultron","Ant-Man","Captain America: Civil War",
"Doctor Strange","Guardians of the Galaxy 2","Spider-Man: Homecoming","Thor: Ragnarok","Black Panther",
"Avengers: Infinity War","Ant-Man and the Wasp","Captain Marvel","Avengers: Endgame",
"Spider-Man: Far From Home","WandaVision","Falcon and the Winter Soldier","Loki","Black Widow")

# List of released year of Marvel movies
years <- c(2008,2008,2010,2011,2011,2012,2013,2013,2014,2014,2015,2015,2016,2016,
2017,2017,2017,2017,2018,2018,2019,2019,2019,2021,2021,2021,2021)

# Or using Function
years <- c(2008,2008,2010,2011,2011,2012,rep(2013:2016,each=2),
rep(2017,4),rep(2018,2),rep(2019,3),rep(2021,4))

2.2 Finding the information:

  • The numbers of movies
  • Finding the 19th movies name
  • Which year is most released movies (In this question can using observation data, not necessary to used command to find answer)