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 = 3874
y = 23874
vars = c(2,4,8,16,32,45,53,64,76,82,106,146) # This is a vector
vars[1] #This calls the first value in the vector vars
## [1] 2
vars[2] #This calls the second value in the vector vars
## [1] 4
vars[1:3] #This calls the first through third values in the vector vars
## [1] 2 4 8
vars #This calls the vector 
##  [1]   2   4   8  16  32  45  53  64  76  82 106 146

Common Arithmetic Operations

Below shows some simple arithmetic operations.

mult = 12*6
div = 128/16
pow = 9^2
pow*mult
## [1] 5832
div/pow
## [1] 0.09876543

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

v = 38                  
class(v)                   
## [1] "numeric"
#Type: Logical    
#Example: TRUE,FALSE

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

DuPage = top_il_income$per_capita_income[1]
Lake = top_il_income$per_capita_income[2]
DuPage-Lake*5
## [1] -153364
DuPage+Lake/78
## [1] 39424.06
(DuPage+Lake)/2
## [1] 38695

Basic Statistics

mean(il_income$per_capita_income)
## [1] 25164.14
median(il_income$per_capita_income)
## [1] 24808.5
quantile(il_income$per_capita_income)
##       0%      25%      50%      75%     100% 
## 14052.00 22666.00 24808.50 26899.75 38931.00

summary(top_il_income$county)
##   DuPage     Kane  Kendall     Lake  McHenry   McLean   Monroe    Piatt 
##        1        1        1        1        1        1        1        1 
## Sangamon     Will 
##        1        1

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, 16, 24)
## [1]  2  3  5  8 16 24
# vector of logical values.
c(TRUE, FALSE, TRUE)
## [1]  TRUE FALSE  TRUE
# vector of character strings.
c("A-", "B+", "B-", "C-", "F")
## [1] "A-" "B+" "B-" "C-" "F"

Lists

Defining a List

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

scores = c(80, 75, 55)  # vector of numeric values                   
grades = c("B", "C", "D-")  # vector of character strings.          

office_hours = c(TRUE, TRUE, FALSE) # vector of logical values.
student = list(scores,grades,office_hours) # list of vectors
student
## [[1]]
## [1] 80 75 55
## 
## [[2]]
## [1] "B"  "C"  "D-"
## 
## [[3]]
## [1]  TRUE  TRUE FALSE

List Slicing

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

student[1]   
## [[1]]
## [1] 80 75 55
student[2]
## [[1]]
## [1] "B"  "C"  "D-"
student[3]
## [[1]]
## [1]  TRUE  TRUE FALSE
# first two components of the list
student[1:2]
## [[1]]
## [1] 80 75 55
## 
## [[2]]
## [1] "B"  "C"  "D-"

Member Reference

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

student[[1]] # Components of the Scores Vector
## [1] 80 75 55

First element of the Scores vector 

student[[1]][1]
## [1] 80

First three elements of the Scores vector 

student[[1]][1:3]
## [1] 80 75 55

Named List Members

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

student = list(scores = c(50, 99, 93), grades = c("A", "B", "F"), office_hours = c(TRUE, TRUE, FALSE))


student
## $scores
## [1] 50 99 93
## 
## $grades
## [1] "A" "B" "F"
## 
## $office_hours
## [1]  TRUE  TRUE FALSE
student$scores
## [1] 50 99 93
student$grades
## [1] "A" "B" "F"
student$office_hours
## [1]  TRUE  TRUE 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 solar system. 

name = c("Mercury", "Venus", "Pluto")
type = c("Terrestrial","Terrestrial", "Gas giant")
diameter = c(1, .2, .3)
rotation = c(.5, 2.5, .6)
rings = c(FALSE, TRUE, FALSE)

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 Mercury Terrestrial      1.0      0.5 FALSE
## 2   Venus Terrestrial      0.2      2.5  TRUE
## 3   Pluto   Gas giant      0.3      0.6 FALSE

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.