About

R is a language and environment for statistical computing and graphics.R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, .) and graphical techniques, and is highly extensible.

This notebook is a tutorial on how to use R.

Basics Operations

First we will begin with a few basic operations.

Variable assignment

A variable allows you to store values or an object (e.g. a function).

x = 3000
y = 25
vars = c(3,5,12,16,20,55) # This is a vector
vars[3] #This calls the first value in the vector vars
## [1] 12
vars[5] #This calls the second value in the vector vars
## [1] 20
vars[2:5] #This calls the first through third values in the vector vars
## [1]  5 12 16 20
vars #This calls the vector 
## [1]  3  5 12 16 20 55

Common Arithmetic Operations

Below shows some simple arithmetic operations.

126*16
## [1] 2016
3542/6
## [1] 590.3333
99^6
## [1] 941480149401

Basic Data Types

R works with numerous data types. Some of the most basic types are: numeric,integers, logical (Boolean-TRUE/FALSE) and characters (string-"TEXT").

#Type: numeric                   
#Example:"12",'23.4'

v = 12                      
class(v)                           
## [1] "numeric"
#Type: Numeric                
#Example: 12.3,5

v = "false"                  
class(v)                   
## [1] "character"
#Type: Logical    
#Example: TRUE,FALSE

v = FALSE
class(v)
## [1] "logical"
#Type: Factor
#Example: m f m f m

v = (c("m", "f", "m"))
class(v)
## [1] "character"

Setting the Working Directory

Before starting to work with R, we need to set the working directory.

Importing Data and Variable Assignment

il_income = read.csv("data/il_income.csv")
top_il_income = read.csv("data/top_il_income.csv")

Arithmetic Operations with Data

We can extract values from the dataset to perform calculations.

Monroe = top_il_income$per_capita_income[1]
Lake = top_il_income$per_capita_income[2]
Monroe-Lake
## [1] 472
Monroe+Lake
## [1] 77390
(Monroe+Lake)/3
## [1] 25796.67

Basic Statistics

mean(il_income$population)
## [1] 126078.4
median(il_income$population)
## [1] 26610
quantile(il_income$population)
##         0%        25%        50%        75%       100% 
##    4135.00   14284.25   26610.00   53319.00 5238216.00

summary(top_il_income)
##       rank           county  per_capita_income   population    
##  Min.   : 2.00   DuPage :1   Min.   :30594     Min.   :  7032  
##  1st Qu.: 4.25   Kane   :1   1st Qu.:30744     1st Qu.: 36920  
##  Median :12.00   Kendall:1   Median :31430     Median :194782  
##  Mean   :27.10   Lake   :1   Mean   :32918     Mean   :334866  
##  3rd Qu.:41.00   McHenry:1   3rd Qu.:33103     3rd Qu.:648159  
##  Max.   :90.00   McLean :1   Max.   :38931     Max.   :933736  
##                  (Other):4                                     
##      region   
##  Min.   :2.0  
##  1st Qu.:2.0  
##  Median :3.0  
##  Mean   :3.2  
##  3rd Qu.:4.0  
##  Max.   :5.0  
##

Vectors

Defining a Vector

A sequence of data elements of the same basic type is defined as a vector.

# vector of numeric values
c(3, 6 , 12, 27)
## [1]  3  6 12 27
# vector of logical values.
c("false")
## [1] "false"
# vector of character strings.
c("A+", "A" , "B", "B-", "C" )
## [1] "A+" "A"  "B"  "B-" "C"

Lists

Defining a List

Lists, as opposed to vectors, can hold components of different types. 

scores = c(60, 70, 30)  # vector of numeric values                   
grades = c("D-", "C-", "F")  # vector of character strings.          

office_hours = c(FALSE, FALSE, FALSE) # vector of logical values.
student = list(scores,grades,office_hours) # list of vectors
student
## [[1]]
## [1] 60 70 30
## 
## [[2]]
## [1] "D-" "C-" "F" 
## 
## [[3]]
## [1] FALSE FALSE FALSE

List Slicing

We can retrieve components of the list with the single square bracket [] operator.

student[3]     
## [[1]]
## [1] FALSE FALSE FALSE
student[1]
## [[1]]
## [1] 60 70 30
student[2]
## [[1]]
## [1] "D-" "C-" "F"
# last two components of the list
student[2:3]
## [[1]]
## [1] "D-" "C-" "F" 
## 
## [[2]]
## [1] FALSE FALSE FALSE

Member Reference

Using the double square bracket [[]] operator we can reference a member of the list directly. 

scores[[3]] # Components of the Scores Vector
## [1] 30

second element of the Scores vector 

student[[2]][1]
## [1] "D-"

last two elements of the Scores vector 

grades[[2]][2:3]
## [1] NA NA

Named List Members

It’s possible to assign names to list members and reference them by names instead of by numeric indexes. 

student = list(grades = c("D-", "C-", "F"), scores = c(60, 70, 30), office_hours = c(FALSE, FALSE, FALSE)) 

student
## $grades
## [1] "D-" "C-" "F" 
## 
## $scores
## [1] 60 70 30
## 
## $office_hours
## [1] FALSE FALSE FALSE
student$grades
## [1] "D-" "C-" "F"
student$scores
## [1] 60 70 30
student$office_hours
## [1] FALSE FALSE FALSE

Data Frames

Defining a Data Frame

When we need to store data in table form, we use data frames, which are created by combining lists of vectors of equal length. The variables of a data set are the columns and the observations are the rows.

The str() function helps us to display the internal structure of any R data structure or object to make sure that it’s correct.

str(top_il_income)
## 'data.frame':    10 obs. of  5 variables:
##  $ rank             : int  2 3 32 44 67 16 4 8 5 90
##  $ county           : Factor w/ 10 levels "DuPage","Kane",..: 1 4 5 7 8 3 10 6 2 9
##  $ per_capita_income: int  38931 38459 33118 33059 31750 31110 30791 30728 30645 30594
##  $ population       : int  933736 703910 46045 33879 16387 123355 687263 266209 530847 7032
##  $ region           : int  2 2 4 5 4 2 2 5 2 4

Creating a Data Frame

Snapshot of the color preference. 

name = c("Purple", "Gold", "Black")
type = c("Color","Metal", "Color")
preference = c(33, 20 , 47 )
color = c(TRUE, TRUE, TRUE)

Now, by combining the vectors of equal size, we can create a data frame object.

planets_df = data.frame(name,type,type,preference,color)
planets_df
##     name  type type.1 preference color
## 1 Purple Color  Color         33  TRUE
## 2   Gold Metal  Metal         20  TRUE
## 3  Black Color  Color         47  TRUE

Exercises & Resources

Exercises

  • Datacamp - Learn Data Science from your browser:

  • R-tutor - An R intro to stats that explains basic R concepts:

Data Sources

  • “SELECTED ECONOMIC CHARACTERISTICS 2006-2010 American Community Survey 5-Year Estimates” - U.S. Census Bureau. Retrieved 2016-09-09.