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 = 2687
y = 190
vars = c(85,2,14,66,32,74,93,101) # This is a vector
vars[1] #This calls the first value in the vector vars
## [1] 85
vars[8] #This calls the second value in the vector vars
## [1] 101
vars[1:3] #This calls the first through third values in the vector vars
## [1] 85  2 14
vars[4
     ] #This calls the vector 
## [1] 66

Common Arithmetic Operations

Below shows some simple arithmetic operations.

12*230
## [1] 2760
450/15
## [1] 30
22^5
## [1] 5153632
14*22/4
## [1] 77

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: Character                   
#Example:"TRUE",'23.4'

v = "FALSE"                       
class(v)                           
## [1] "character"
#Type: Numeric                
#Example: 12.3,5,42

v = 48.5                  
class(v)                   
## [1] "numeric"
#Type: Logical    
#Example: TRUE,FALSE

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

v = as.factor(c("m", "f", "m"))
class(v)
## [1] "factor"

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

Arithmetic Operations with Data

We can extract values from the dataset to perform calculations.

Piatt= top_il_income$per_capita_income[2]
Piatt-Piatt*Piatt
## [1] -1479056222
Piatt+Piatt/Piatt
## [1] 38460
(Piatt+Piatt)/8
## [1] 9614.75

Basic Statistics

mean(il_income$per_capita_income)
## [1] 25164.14
median(il_income$per_capita_income)*mean(il_income$per_capita_income)
## [1] 624284499
quantile(il_income$per_capita_income)
##       0%      25%      50%      75%     100% 
## 14052.00 22666.00 24808.50 26899.75 38931.00
mode(il_income$per_capita_income)
## [1] "numeric"

summary(il_income)
##       rank              county   per_capita_income   population     
##  Min.   :  1.00   Adams    : 1   Min.   :14052     Min.   :   4135  
##  1st Qu.: 26.25   Alexander: 1   1st Qu.:22666     1st Qu.:  14284  
##  Median : 51.50   Bond     : 1   Median :24809     Median :  26610  
##  Mean   : 51.50   Boone    : 1   Mean   :25164     Mean   : 126078  
##  3rd Qu.: 76.75   Brown    : 1   3rd Qu.:26900     3rd Qu.:  53319  
##  Max.   :102.00   Bureau   : 1   Max.   :38931     Max.   :5238216  
##                   (Other)  :96                                      
##      region     
##  Min.   :1.000  
##  1st Qu.:3.000  
##  Median :4.000  
##  Mean   :3.735  
##  3rd Qu.:5.000  
##  Max.   :5.000  
## 
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.: 36921  
##  Median :12.00   Kendall:1   Median :31430     Median :194782  
##  Mean   :27.10   Lake   :1   Mean   :32919     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(2, 3, 5, 8, 6, 89, 14, 77, 90, 74, 24)
##  [1]  2  3  5  8  6 89 14 77 90 74 24
# vector of logical values.
c(FALSE, TRUE, FALSE, FALSE)
## [1] FALSE  TRUE FALSE FALSE
# vector of character strings.
c("A", "B", "B-", "C", "D", "F", "B+", "C-", "D+", "A-", "D-")
##  [1] "A"  "B"  "B-" "C"  "D"  "F"  "B+" "C-" "D+" "A-" "D-"

Lists

Defining a List

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

scores = c(80, 75, 55, 100, 59, 23, 79, 83, 72)  # vector of numeric values                   
grades = c("B", "C", "D-", "A", "F", "F", "C+", "B", "C-")  # vector of character strings.          

office_hours = c(TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE) # vector of logical values.
student = list(scores,grades,office_hours) # list of vectors
student
## [[1]]
## [1]  80  75  55 100  59  23  79  83  72
## 
## [[2]]
## [1] "B"  "C"  "D-" "A"  "F"  "F"  "C+" "B"  "C-"
## 
## [[3]]
## [1]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE

List Slicing

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

student[2]     
## [[1]]
## [1] "B"  "C"  "D-" "A"  "F"  "F"  "C+" "B"  "C-"
student[3]
## [[1]]
## [1]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
student[1]
## [[1]]
## [1]  80  75  55 100  59  23  79  83  72
# first two components of the list
student[1:3]
## [[1]]
## [1]  80  75  55 100  59  23  79  83  72
## 
## [[2]]
## [1] "B"  "C"  "D-" "A"  "F"  "F"  "C+" "B"  "C-"
## 
## [[3]]
## [1]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
student[2:3]
## [[1]]
## [1] "B"  "C"  "D-" "A"  "F"  "F"  "C+" "B"  "C-"
## 
## [[2]]
## [1]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE

Member Reference

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

student[[3]] # Components of the Scores Vector
## [1]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE

First element of the Scores vector 

student[[1]][2]
## [1] 75

First three elements of the Scores vector 

grades[[3]][1:5]
## [1] "D-" NA   NA   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(outof100 = c(80, 75, 55, 100, 59, 23, 79, 83, 72), lettergrades = c("B", "C", "D-", "A", "F", "F", "C+", "B"), help = c(TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE)) 

student
## $outof100
## [1]  80  75  55 100  59  23  79  83  72
## 
## $lettergrades
## [1] "B"  "C"  "D-" "A"  "F"  "F"  "C+" "B" 
## 
## $help
## [1]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
student$outof100
## [1]  80  75  55 100  59  23  79  83  72
student$lettergrades
## [1] "B"  "C"  "D-" "A"  "F"  "F"  "C+" "B"
student$help
## [1]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE

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 solar system. 

name = c("Pluto", "Earth", "Mars", "Jupiter", "Uranus")
type = c("Planetoid", "Terrestrial","Terrestrial", "Gas giant", "Gas giant")
diameter = c(.003, 1, 0.532, 11.209, 5.989)
rotation = c(.03, 1, 1.03, 0.41, 4)
rings = c(FALSE, FALSE, FALSE, TRUE, TRUE)

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

planets_df = data.frame(name,type,diameter,rotation,rings)
planets_df
##      name        type diameter rotation rings
## 1   Pluto   Planetoid    0.003     0.03 FALSE
## 2   Earth Terrestrial    1.000     1.00 FALSE
## 3    Mars Terrestrial    0.532     1.03 FALSE
## 4 Jupiter   Gas giant   11.209     0.41  TRUE
## 5  Uranus   Gas giant    5.989     4.00  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.