Lecture 3_R Basics

Copyright 2014, Simplilearn, All rights reserved.


Lesson 3

Basic Analytic Techniques Using R


Get a basic introduction to R

Understand exploration of data

Explore data using R

Visualize data using R

Understand diagnostic analytics

Implement diagnostic analytics using R

After completing this course, you will be able to:

Objective Slide


Introduction to R

Programming language for graphics and statistical computations

Available freely under the GNU public license

Used in data mining and statistical analysis

Included time series analysis, linear and non linear modeling among others

Very active community and package contributions

Very little programming language knowledge necessary

Can be downloaded from http://www.r-project.org/

R Studio - optional

http://www.r-project.org/http://www.r-project.org/http://www.r-project.org/


Packages -

install.packages('package_name')

library(package_name)

Loading data

data(dataset_name)

read and write functions

getwd() and setwd(dir)

read and write functions use full path name

Example : read.csv(C:/Rtutorials/Sampledata.csv).

Assignment operator :


Basic functions for data exploration in R

Data stored as data frames

Data frames tabular representation of data with rows and columns

Every row denotes a particular case

Sample data frame (iris data set)

Data exploration using R

Sepal length Sepal width Petal length Petal width Species

7.9 3.8 6.4 2 I. virginica

7.7 3 6.1 2.3 I. virginica

5.6 2.5 3.9 1.1 I. versicolor

5.6 2.8 4.9 2 I. virginica

5.5 4.2 1.4 0.2 I. setosa

5.5 3.5 1.3 0.2 I. setosa

7.1 3 5.9 2.1 I. virginica

7 3.2 4.7 1.4 I. versicolor


Iris dataset built in data frame

View data set

iris

View first few rows of the data set

head(iris, n)

View last few rows of the data set

tail(iris, n)

Viewing data frame


View the dimensions of the data set

dim(iris)

View the number of columns

ncol(iris)

View the number of rows

nrow(iris)

Dimensions of data frame


View column names/headers

names(iris)

View all attributes

attributes(iris)

Attributes of Data frame


iris$Petal.Length

iris* , Petal.Length+

View column data


View particular rows

iris[10:15, ]

View particular rows of a single column

iris*10:15, Petal.Length+

View row data


summary(data_frame)

summary(iris)

Output : Mean, Median, Minimum, Maximum, 1st and 3rd quartile

table(dataframe$columnname)

Example : table(iris$Species)

Summarizing data in R


min(column_name)

max(column_name)

range(column_name)

mean(column_name)

median(column_name)

IQR(column_name)

sd(column_name)

var(column_name)

Individual summaries in R


aggregate() - function for column wise aggregation

Numerical summaries for a subset of data

aggregate(formula, data, function)

Subset summary


plot() - generic function for plotting in R

plot(iris)

Data Visualization in R


Plot Sepal Length against Species

Attributes of plot function : main, xlab, ylab

plot(iris$Sepal.Length, iris$Species,

main = "Iris Data",

xlab = "Sepal Length,

ylab = "Species")

Data Visualization in R


Pie charts to visualize the numerical proportion of different classes through sectors of the circle

pie(table(iris$Species),

main = "Iris Data by Species")

Table of data by species -

Pie Charts

Setosa Virginica Versicolor

50 50 50


USPersonalExpenditure

barplot(USPersonalExpenditure,

main = "US Personal Expenditure by Year",

xlab = "Year",

ylab = "Expenditures")

Bar plots

1940 1945 1950 1955 1960

Food and Tobacco 22.2 44.5 59.6 73.2 86.8

Household Operation 10.5 15.5 29 36.5 46.2

Medical and Health 3.53 5.76 9.71 14 21.1

Personal Care 1.04 1.98 2.45 3.4 5.4

Private Education 0.341 0.974 1.8 2.6 3.64


boxplot(Sepal.Length ~ Species,

data = iris,

main = "Iris Data Set",

xlab = "Species type",

ylab = "Sepal Length")

Box Plot


Histograms to depict frequency distribution

islands dataset

hist(iris$Sepal.Length,

main = "Iris data",

xlab = "Sepal Length",

ylab = "Frequency")

Histogram

Sepal length

Freq

uen

cy


Class of statistical relationships between variables

Default of cor.test() Pearsons correlation

cor.test(column1, column2)

Correlation


aov() generic method to implement Analysis of Variance

Analysis of variance


Implement and print the result of chi-squared test for goodness of fit

margin.table(HairEyeColor, 1)

chisq.test(variable) or chisq.test(variable, probabilities)

Chi-squared test


Pairwise t-tests

data(anorexia, package = MASS)

attributes Treatment, pre-weight, post weight

T-test


T test

Independent t-tests


A basic introduction to R

Data exploration using R

Data visualizations in R

Pie Charts Bar plots

Box plots

Histogram

Diagnostic analytics using R

Chi Squared test

T tests

Analysis of Variance

Summary

Here is a quick recap of what we have learned in this lesson


Quiz


QUIZ

a.

b.

c.

d.

Linear modeling

Non linear modeling

Developing web applications

Time series analysis

1 Which of the following is not a functionality of R?


QUIZ

a.

b.

c.

d.

Answer: d.

Explanation: R is a statistical analysis and data mining tool.

Linear modeling

Non linear modeling

Developing web applications

Time series analysis

1 Which of the following is not a functionality of R?


QUIZ

a.

b.

c.

d.

Which of the following is the function to display the first 5 rows of data?

top(data,5)

first(data,5)

2

first(5, data)

head(data,5)


QUIZ

a.

b.

c.

d.

Answer: b.

Explanation: head() function is used to display the first few rows of data

Which of the following is the function to display the first 5 rows of data?

top(data,5)

first(data,5)

2

first(5, data)

head(data,5)


QUIZ

a.

b.

c.

d.

What will be the result of the following command class(iris)

integer

matrix

data.frame

vector

3


QUIZ

a.

b.

c.

d.

Answer: d.

Explanation: Iris is a sample data set that is stored as a data frame

What will be the result of the following command class(iris)

integer

matrix

data.frame

vector

3


QUIZ

a.

b.

c.

d.

Aggregate no columns

Aggregate the first column

Aggregate the last column

Aggregate all columns

4 What is the significance of the dot symbol before tilda in aggregate function?


QUIZ

a.

b.

c.

d.

Answer: a.

Explanation: The dot symbol in the formula is used to specify aggregation on all columns

Aggregate no columns

Aggregate the first column

Aggregate the last column

Aggregate all columns

4 What is the significance of the dot symbol before tilda in aggregate function?


QUIZ

a.

b.

c.

d.

40

30

45

55

5 Create a histogram of the islands dataset. What is the highest frequency of the dataset?


QUIZ

a.

b.

c.

d.

Answer: b.

Explanation: Plot the histogram using hist(islands). The highest frequency from the graph is 40.

40

30

45

55

5 Create a histogram of the islands dataset. What is the highest frequency of the dataset?


QUIZ

a.

b.

c.

d.

t.test(preheight, postheight)

t.test(preheight, postheight, paired = TRUE)

chisq.test(preheight, postheight)

aov(preheight~postheight)

6

The heights of a sample population is recorded before and after a height increasing drug. Which of the following commands would be used to BEST check if there is an effect of the drug on height of a person?


QUIZ

a.

b.

c.

d.

Answer: c.

Explanation: The variables are paired, and hence paired t tests need to be used to best learn the relation.

t.test(preheight, postheight)

t.test(preheight, postheight, paired = TRUE)

chisq.test(preheight, postheight)

aov(preheight~postheight)

6

The heights of a sample population is recorded before and after a height increasing drug. Which of the following commands would be used to BEST check if there is an effect of the drug on height of a person?


Thank You


Lecture 3_R Basics

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