Data analytics using R programming

Data Analytics using R
Dr Umang
Associate Professor
Institute of Technology & Science, Ghaziabad
Email: umangsingh@its.edu.in, dr.umang.singh@ieee.org
‘What gets measured, gets managed.’ – Peter Drucker
(c)Dr Umang@ITS

(c)Dr Umang@ITS
Data Analytics
 Process of examining data sets in order
to draw conclusions about the
information they contain, increasingly
with the aid of specialized systems and
software.

(c)Dr Umang@ITS
IDC predicts that by 2021, 25% of large enterprises will have supplemented data scientists with
data ethnographers to provide contextual interpretations of data by using qualitative research
methods that uncover people’s emotions, stories, and perceptions of their world.

(c)Dr Umang@ITS
Hired a Data Scientist
Descriptive Analytics -> Predictive Analytics / Optimization -> Automation
Beautiful Story………………………………..

(c)Dr Umang@ITS
Data Analytics
Data Analytics helps organizations harness their data and use it to
identify new opportunities.
• Data Mining
• Data Management
• Statistical Analysis
• Data Presentation.
Depend on the data being used
and the goal of the analysis.
Primary steps
Extracting data from unstructured data sources.
Involves designing and implementing databases
How the insights are created from data
Allows insights to be shared with stakeholders.
Important tool: Data visualization

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Data Analytics
Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics
What has happened? What could happen in the
future based on previous
trends and patterns?
What should business do?Why did it happen
Identify anomalies
Drill into the analytics
(discovery)
Determine causal
relationships
Observations,
Case studies,
and Surveys
An increase in Twitter followers
after a particular tweet
How should we respond to
those potential future events?
Send marketing campaigns to
customers who are most likely to buy
Based on the combination of AI-
infused software and the domain
expertise of people promise to be
the most effective means for
answering the question:
Why did it happen
Google's self-driving car
https://dataconomy.com/2015/12/ho
w-data-science-is-driving-the-
driverless-car/

(c)Dr Umang@ITS
Data Analytics for Connected Cars
PATTERM MATCHING
Apply to the moment-by-moment driving patterns and
road behavior of all individuals who get behind the wheel
of a particular vehicle. These patterns can be evaluated,
linked to safe or risky behaviors, and combined with other
data sources and vehicle information.
Assess past patterns and driving behavior to make an
informed judgment on the likelihood of various future
possibilities.
Combines intelligence and issues recommendations,
guiding driver behavior to suit the situation based on
past experiences.
https://ims.tech/opinion/connected-car-analytics/

(c)Dr Umang@ITS
Data Analytics using R
 Conceptualized & designed by John Tukey
 It enables data analysts to perform data analysis and visualization.
 Important coin : Exploratory data analysis - it is an approach of data analysis
employed for summarizing and visualizing data set
 It is examination of data of data and relationships among variables through
graphical and numerical methods.
 Important line of defense against bad data
 Business decisions are made unpolished visualization that come out of explotary
data analysis.
 https://www.adma.com.au/resources/how-coca-cola-uses-data-to-supercharge-its-superbrand-status
 https://blog.storagecraft.com/real-data-analytics-success-stories/
 https://analyticsindiamag.com/coca-cola-leans-on-data-analytics-ai-for-deeper-industry-insights/

(c)Dr Umang@ITS http://gfinak.github.io/Talks/RFlowToolsFlowCAP/index.html#1

Working with R
Freely available under the GNU General Public License
$R Command Prompt
R plays a very important role in Data Science
Data Scientists who are
proficient in R make
more than $117,000 (Rs
80,56,093) on an
average per year.
If you want to enter the
field of Data Science and
earn a lucrative salary,
then you must definitely
learn R.
R is used at Facebook for
social network analysis.
It is being used at Twitter
for semantic analysis as
well as visualizations.
R Shiny package
Used for
Data Analytics &
Data Visualization
Example of FLOSS
Free /Libre and Open Source
Software
(c)Dr Umang@ITS

(c)Dr Umang@ITS
Companies using R
https://www.youtube.com/watch?v=Uenf8DbOjz0
Company Application/Contribution
Twitter Monitor user experience
Ford
Analyze social media to support
design decisions for their cars
New York Times Infographics, data journalism
Microsoft
Released Microsoft R Open, an
enhanced R distribution and
Microsoft R server after acquiring
Revolution Analytics in 2015
Human Rights Data
Analysis Group
Measure the impact of war
Google
Created the R style guide for the R
user community inside Google,
Economic forecasting ,
Advertisements effectiveness and
Big Data Statistical Analysis
Microsoft For gaming and Scientific Revenue
Bing Social Search Awareness
Ford Data Driven Decision Making Use cases
NewScientist Data Visualization, Data Analysis for news articles
Facebook
Bigdata Visualization, human behavior with
respect to post and status update
Company Application/Contribution

(c)Dr Umang@ITS
Explotary Data Analysis
 It is examination of data and relationships among variables through graphical
and numerical methods.
 It can lead to insights into the process of building predictive models
 Business decisions are made unpolished visualization that come out of
Explotary Data Analysis.

(c)Dr Umang@ITS
R Studio & R
 RSTUDIO
 Free, open source IDE (integrated
development environment) for R.
 You must install R before you can
install RStudio.
 Interface is organized so that the user
can clearly view graphs, data tables, R
code, and output all at the same time.
 Offers an Import-Wizard-like feature
that allows users to import CSV, Excel,
SAS (*.sas), SPSS (*.sav), and Stata
(*.dta) files into R without having to
write the code to do so.
➢ R
➢ R is a free, open source software program
for statistical analysis.
➢ Free and open source.
➢ Available for Windows, Macintosh, and
Linux.
➢ Publication-quality graphs.
➢ Rivals, SAS and Stata in terms of
availability of advanced statistical
methods and algorithms, through
availability of user-created packages.
➢ Packages for literate statistical
programming - written reports and
analysis code in one document.
Important Websites.
•The R Project for Statistical Computing
•RStudio
•RStudio Cloud

(c)Dr Umang@ITS
Files Associated with R
➢ *.r - An R script.
➢ *.rmd - An R Markdown file. It is saved in format that enables creation of dynamic
presentation, reports and documents.
➢ *.rnw - An R Sweave file.
➢ *.rds - A file containing a [single] R object; can be created using saveRDS(), and loaded
using readRDS().
➢ *.rdata - A file containing one or more R objects or workspaces; can be created
using save(), and loaded using load().
SUMMARY
R Reference Card

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Installations Guidelines

(c)Dr Umang@ITS
Install R
Follow the steps below with respect to the operating system you are using
For Windows :
Download the binary setup file for R from the following link.( R for Windows )
Open the downloaded .exe file and Install R
For Mac :
Download the appropriate version of .pkg file form the following link. ( R for Mac )
Open the downloaded .pkg file and Install R
For Linux :
For complete R System installation in Linux, follow the instructions on the following
link ( Link )
For Ubuntu with Apt-get installed, execute sudo apt-get install r-base in terminal.

(c)Dr Umang@ITS
R Studio
 RStudio is an integrated development environment (IDE) for R.
 It includes a console, syntax-highlighting editor that supports direct code
execution, as well as tools for plotting, history, debugging and workspace
management.
 Important Steps for Installation
 Install R
 Install R-Studio
 Install R-Packages (If needed)

(c)Dr Umang@ITS
Install R Studio
 Run R studio
 Click on the Packages tab in the bottom-right section
and then click on install. The following dialog box will
appear
 In the Install Packages dialog, write the package
name you want to install under the Packages field and
then click install. This will install the package you
searched for or give you a list of matching package
based on your package text.
This completes the installation procedure for R Studio.

(c)Dr Umang@ITS
R Studio on Windows
 To Install RStudio
 Go to www.rstudio.com and click on the "Download RStudio" button.
 Click on "Download RStudio Desktop."
 Click on the version recommended for your system, or the latest Windows version, and
save the executable file. Run the .exe file and follow the installation instructions.

(c)Dr Umang@ITS
Why R??
Many calculations done with vectors
 R is a vector language, so anyone can add functions to a single Vector without
putting in a loop.
 Hence, R is powerful and faster than other languages.
Statistical Language
 R used in biology, genetics as well as in statistics.

(c)Dr Umang@ITS
Variables in R
 Variables are nothing but reserved memory locations to store values. This means that when
you create a variable you reserve some space in memory.
A basic concept in (statistical) programming is called a variable.
A variable allows you to store a value or an object (e.g. a function description) in R.
You can then later use this variable's name to easily access the value or the object that is stored within this
variable.
You can assign a value 4 to a variable my_var with the command
my_var <- 4

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Assignment
Suppose you have a fruit basket with five apples. As a data analyst in training,
you want to store the number of apples in a variable with the name my_apples.
Type the following code in the editor: my_apples <- 5. This will assign the value 5 to my_apples.
Type: my_apples below the second comment. This will print out the value of my_apples.
Click 'Submit Answer', and look at the console: you see that the number 5 is printed. So R now links
the variable my_apples to the value 5.
•Assign to my_oranges the value 6.
•Add the variables my_apples and my_oranges and have R simply print the result.
•Assign the result of adding my_apples and my_oranges to a new variable my_fruit.

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CREATE a Vector c()

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Single Value Vector

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Basic Data Analysis through R/R Studio
Following steps will be performed to achieve our goal:
➢ Downloading/importing data in R
➢ Transforming Data / Running queries on data
➢ Basic data analysis using statistical averages
➢ Plotting data distribution

(c)Dr Umang@ITS
1. Importing Data in R Studio Example-1
sample census data set
install.packages("readxl")
https://readxl.tidyverse.org/

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1. Importing Data in R Studio Example-2
National Morbidity and Mortality Air Pollution Study (NMMAPS)
install.packages("readxl")
https://readxl.tidyverse.org/

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2. Transforming Data (1)
To access a particular column, Ex. age_husband in our case.
acs_or$age_husband

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 To access data as a vector
acs_or[1,3]

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 To run some queries on data, you can use the subset function of R.
 Let's say I want those rows from the dataset in which the age_husband is
greater than age_wife. For this we 'll run the following command in
console
a <- subset(acs_or , age_husband > age_wife)

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 Structure of data set
 str(acs_or)

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Getting Statistical Averages from data (1)
 Following functions can be used to calculate the averages of the dataset
For mean of any column,
run : mean(acs$age_husband)
 Median, run : median(acs$age_husband)
 Quantile , run : quantile(acs$age_wife)
 Variance , run : var(acs$age_wife)
 Standard Deviation , run : sd(acs$age_wife)

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Getting Statistical Averages from data

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4. Plotting Data

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boxplot(acs_or$age_husband~acs_or$internet, col="blue")
title(main="Husband Age Vs Internet Availability",
xlab = "Internet Availability", ylab = "Age of Husband")

(c)Dr Umang@ITS
Linear Regression from beginners
https://www.youtube.com/watch?v=xlJyKDzru1o
Regression is a statistical tool used to
find a relationship between two
variables.
One Variable is the predictor variable
and other is response variable.
In linear regression, these variables are
related through a general equation, i.e.
y=ax+b
To find the linear regression we use a
function lm()
Syntax lm(formula, Data)
Formula is the relationship between x
and y
Data is the set on which formula will be
applied

(c)Dr Umang@ITS
Dplyr
The dplyr is a powerful R-package to manipulate, clean and summarize unstructured
data. In short, it makes data exploration and data manipulation easy and fast in R.
Used for applying filter, selecting specific columns, sorting data, adding or deleting
columns and aggregating data.
dplyr Function Description Equivalent SQL
select() Selecting columns (variables) SELECT
filter() Filter (subset) rows. WHERE
group_by() Group the data GROUP BY
summarise()
Summarise (or aggregate)
data
-
arrange() Sort the data ORDER BY
join() Joining data frames (tables) JOIN
mutate() Creating New Variables COLUMN ALIAS
Important dplyr Functions to remember

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Clustering
There to analyze the data, find hidden pattern and generate useful patterns. This
information could be utilize in business growth.
Clustering Assign data into groups
Applications

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Clustering and Classification
Clustering is unsupervised form of machine learning
Classification is supervised form of machine learning -> goal of
the study is established before hand

(c)Dr Umang@ITS
Data Classification, Clustering or Linear
Regression from following links:
k-NN Classification for beginners
 Using Airquality Dataset
k-means Clustering for beginners
 Using Iris Dataset
Linear Regression for beginners
 Using Iris Dataset

(c)Dr Umang@ITS
Projects
 Personality Project - Using R for psychological researchIntroduction to using R for
psychological research, including introductory and advanced topics (SEM, cluster
analysis, item response theory, etc.)
 Introducing R (UCLA Statistical Consulting Group)Interactive slideshow on how to
get started with R and R packages.
 Introduction to R - Ludwig-Maximilians-Universität MünchenCourse notes and
exercises (in English) with sample datasets from Dr. Martin Hutzenthaler and
Prof. Dirk Metzler's Introduction to R course.
 R for Data Science
 stat545.com: Data wrangling, exploration, and analysis with R

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