Introduction to R and RStudio

Learning Outcomes

What you will learn in this session:

How to install R and RStudio
What is the windows layout of RStudio
How to setup RStudio
How to create a project (folder) in RStudio
How to use major functionalities of RStudio
How to extend R’s functionality with R-packages
Which packages you should install for this book
Data types and data objects in R
How very big and very small numbers can be displayed

Install and Setup R and RStudio

A typical setup to work with R consists of two components:

the R Console which executes R code and
an integrated development environment (IDE) such as RStudio.

You can download R here: Download R

You can download RStudio here: Download RStudio

Detailed installation guides are provided in the Book and the Online Resources sections of this chapter in book.

RStudio — Integrated Development Environment (IDE) 🤓

RStudio Window

First steps to setup R can be found in this video: Click here

Recommended RStudio Settings

1.Do not Restore .RData into workspace at startup:
Tools -> GlobalOptions.

Work with R Projects: This assigns a directory on your hard drive to your R analysis: File -> New Project

First steps to setup R can be found in this video: Click here

R Packages

R Packages extend R’s functionality. They have to be installed only once:

Tools -> Install Packages ...

After installation they need to be loaded in every new R script with library().

Packages frequently used in this course (please install soon):

tidyverse: supports easy data processing .
rio: allows loading various data resources with one import() command from the user’s hard drive or the Internet.
janitor: provides functionality to clean data and rename variable names to avoid spaces and special characters.
tidymodels: streamlines data engineering and machine learning tasks.
kableExtra: supports rendering tables in HTML.
shiny: needed together with the learnr package for the interactive exercises in the book.
learnr package: together with the shiny package for the interactive exercises in the book.

Example: the `rio` and the `tidyverse` Package

Assuming the rio packages is already installed.

Example: How to install the tidyverse package: Click here

Video about the rio package: Click here

library(rio);library(tidyverse)
DataHousing =
  import("https://ai.lange-analytics.com/data/HousingData.csv") |> 
  select(Price=price, Sqft=sqft_living, Bedrooms=bedrooms,Waterfront=waterfront)
print(DataHousing[1:3,])

   Price Sqft Bedrooms Waterfront
1 221900 1180        3         no
2 538000 2570        3         no
3 180000  770        2         no

import() would not work if the rio package were not loaded.

select() would not work if the tidyverse package were not loaded.

Data Types & Data Objects

Data Types: What can R store?
- numerical num
- character chr
- factor
- logic
Data Objects: What are the containers R uses to store data?
- single entry single variable
- list of entries vectors
- table dataframe and tibble
- advanced objects. E.g., for plot, models, prediction results

Data Types 🤓

Numerical Data Type (num): Numerical values (e.g., 1, 523, 3.45) are used for calculation. In contrast, ZIP-Codes are not numerical data type.

Character Data Type (chr): Storing sequence of characters, numbers, and/or symbols to form a word or even a sentence is called a character data type (e.g. first or last names, street addresses, or Zip-codes)

Factor Data Type (factor): A factor is an R data type that stores categorical data in an effective way. factor data types are also required by many classification models in R.

Logic Data Type(logic): A data type that stores the logic states TRUE and FALSE is called a logic object (sometimes called Boolean)

Numerical Data Type (num): Numerical values are used for calculations (therefore ZIP-Codes are not numerical). Numerical data can be discrete (integer) or continuous (double).

A=as.integer(2)
B=as.integer(3)
str(A) # str() returns structure of a variable

 int 2

C=1.23
str(C) # str() returns structure of a variable

 num 1.23

print(A*C)

[1] 2.46

A^B

[1] 8

A/B # Returns num type

[1] 0.6666667

Character Data Type (chr):

Note that what is called a character in R is often called a string in other programming languages.

character data types must be surrounded by quotes:

MyText="Hello world!"
print(MyText)

[1] "Hello world!"

Character variables can be concatenated with the cat() command:

FirstName="Carsten"
LastName="Lange"
cat(FirstName, LastName) # R adds a space automatically

Carsten Lange

A factor is an R data type that stores categorical data in an effective way. Categorical data are character type data covering a few categories such as hair color (blonde, braun, red, black). They can be coded with numbers (e.g., from 1-5 for hair color) and thus use less memory. Another example is sex (male, female).

Code

People=tribble(~Name,~Sex,
                  "John", "male",
                  "Jane", "female",
                  "Mia", "female",
                  "Brid", "female",
                  "Greg", "male")
print(People)

# A tibble: 5 × 2
  Name  Sex   
  <chr> <chr> 
1 John  male  
2 Jane  female
3 Mia   female
4 Brid  female
5 Greg  male

Sex is a charcter variable in the dataset People

str(People$Sex)

 chr [1:5] "male" "female" "female" "female" "male"

Transforming the variable to a factor and looking at its structure (str()) again:

str(as.factor(People$Sex))

 Factor w/ 2 levels "female","male": 2 1 1 1 2

Logic variables: Store TRUE and FALSE. They can be combined with and/or. Internally True is stored as and False is stored as

IsConcertGood=FALSE 
IsCompanyGood=TRUE
cat("Is the concert good?", IsConcertGood, "Is the company good?", IsCompanyGood)

Is the concert good? FALSE Is the company good? TRUE

IsEveningAmazing=IsConcertGood & IsCompanyGood
cat("Is the evening amazing?", IsEveningAmazing)

Is the evening amazing? FALSE

IsEveningGood=IsConcertGood | IsCompanyGood # | stands for "or"
cat("Is the evening good?", IsEveningGood)

Is the evening good? TRUE

IsConcertGood+IsCompanyGood+17

[1] 18

Truth Table for AND and OR
R object A	R object B	A and B	A or B
A	B	A&B	A\|B
TRUE	TRUE	TRUE	TRUE
TRUE	FALSE	FALSE	TRUE
FALSE	TRUE	FALSE	TRUE
FALSE	FALSE	FALSE	FALSE

Data Types & Data Objects

Data Types: What can R store?

Data Objects: What are the containers R uses to store data?

Data Objects

Single Value Object
Vector Object
Data Frame (Tibble) Object
List Object (not covered in this course)
Advanced Object such as plots, models, recipes

Single Value Object

Object just stores a single value:

A=123.768
B=3
C="Hello World"
IsLifeGood=TRUE

Vector-Objects

A vector object stores a list of values (numerical, character, factor, or logic)

Example: Weather during the last three days in Stattown:

VecTemp=c(70, 68, 55)
VecWindSpeed=c("low","low","high")
VecIsSunny=c(TRUE,TRUE,FALSE)

Vector objects can be used as arguments for an R command to calculate:

Code

MeanForecTemp=mean(VecTemp)
cat("The average forecasted temperature is", MeanForecTemp)

The average forecasted temperature is 64.33333

Code

ForecDays=length(VecTemp)
cat("The forecast is for", ForecDays, "days.")

The forecast is for 3 days.

Data Frames (tibbles)

A data frame is similar to an Excel table (note not all columns of the Titanic data frame are shown).

Survived	Pclass	Sex	Age	FareInPounds
0	3	male	22	7.2500
1	1	female	38	71.2833
1	3	female	26	7.9250
1	1	female	35	53.1000
0	3	male	35	8.0500
0	3	male	27	8.4583
0	1	male	54	51.8625
0	3	male	2	21.0750
1	3	female	27	11.1333
1	2	female	14	30.0708
1	3	female	4	16.7000
1	1	female	58	26.5500

A data frame consist of vectors making up the columns. These are the variables for the data analysis (remember: observations are in the rows, variables are in the columns).

DataTitanic=import("https://ai.lange-analytics.com/data/Titanic.csv")
str(DataTitanic)

'data.frame':   887 obs. of  8 variables:
 $ Survived             : int  0 1 1 1 0 0 0 0 1 1 ...
 $ Pclass               : int  3 1 3 1 3 3 1 3 3 2 ...
 $ Name                 : chr  "Mr. Owen Harris Braund" "Mrs. John Bradley Cumings" "Miss. Laina Heikkinen" "Mrs. Jacques Heath Futrelle" ...
 $ Sex                  : chr  "male" "female" "female" "female" ...
 $ Age                  : num  22 38 26 35 35 27 54 2 27 14 ...
 $ SiblingsSpousesAboard: int  1 1 0 1 0 0 0 3 0 1 ...
 $ ParentsChildrenAboard: int  0 0 0 0 0 0 0 1 2 0 ...
 $ FareInPounds         : num  7.25 71.28 7.92 53.1 8.05 ...

Extracting the Vectors and Perform Calculations (numerical Vectors)

VecFareInPounds=DataTitanic$FareInPounds
AvgFare=mean(VecFareInPounds)
cat("The average fare of Titanic passengers was:", AvgFare, "British Pounds")

The average fare of Titanic passengers was: 32.30542 British Pounds

Extracting the Vectors and Perform Calculations (logical Vectors)

DataTitanic$Survived=as.logical(DataTitanic$Survived)
str(DataTitanic)

'data.frame':   887 obs. of  8 variables:
 $ Survived             : logi  FALSE TRUE TRUE TRUE FALSE FALSE ...
 $ Pclass               : int  3 1 3 1 3 3 1 3 3 2 ...
 $ Name                 : chr  "Mr. Owen Harris Braund" "Mrs. John Bradley Cumings" "Miss. Laina Heikkinen" "Mrs. Jacques Heath Futrelle" ...
 $ Sex                  : chr  "male" "female" "female" "female" ...
 $ Age                  : num  22 38 26 35 35 27 54 2 27 14 ...
 $ SiblingsSpousesAboard: int  1 1 0 1 0 0 0 3 0 1 ...
 $ ParentsChildrenAboard: int  0 0 0 0 0 0 0 1 2 0 ...
 $ FareInPounds         : num  7.25 71.28 7.92 53.1 8.05 ...

SurvRate=mean(DataTitanic$Survived)
cat("The average survival rate of Titanic passengers was:", SurvRate)

The average survival rate of Titanic passengers was: 0.3855693

Data Frames vs. Tibbles 🤓

A tibble is a more advanced sub-type of a data frame. If needed, a regular data frame can be coerced into a tibble with the as_tibble() command.

A few of the differences between data frames and tibbles:

A data frame outputs all its rows and columns by default. A tibble outputs only the first 10 rows and the variables that fit on the screen but provides information about omitted variables and rows.
A data frame can have row names, while a tibble cannot.
In R version <4.1 a data frame converts all character values to factor type. This conversion was often confusing and annoying. In contrast, a tibble only coerces character values into factor on demand. Since R version 4.1 regular data frames behave the same as tibbles.

Summary Data Types and Objects

How are Very Big Numbers Presented

The GDP for 2021 in the US was $ 22,996,086,000,000 (rounded to millions)

Let us see what R does:

GDPUS=22996086000000
print(GDPUS)

[1] 2.299609e+13

The probability of getting struck by lightning in the US is about on any randomly chosen day.

In the U.S., a person has a 1:10,000-lifetime risk of being struck by lightning. Assuming a life span of 75 years and 365.25 days per year, the probability per day is:

### What R Does

Let us see what R does:

ProbStruck=0.000000000365
cat("Probabilty to get stuck by a lighning on an avg. day:", ProbStruck)

Probabilty to get stuck by a lighning on an avg. day: 3.65e-10