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Why is R used in Analytics so Much?

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on 14 November 2016

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Transcript of Why is R used in Analytics so Much?

Why is R used in Analytics so Much?
Joe Connelly & Zorela Georgescu

What is R?
A language program for statistical computing and graphics
Free & Open Sourced
Developed in New Zealand by professors Robert Gentleman and Ross Ihaka
Modeled after, coded and implemented similarly to S
Allows experts to interpret, interact with, and visualize data quickly and easily
Capabilities of R
Why R is important
Data handling and storage facilities
Rapid prototyping
A suite of operators for calculations on arrays, in particular matrices
Graphical facilities for high-quality and reproducible data analysis and display
When should R be used in Marketing?
Shortcomings of R
How Facebook uses R
Other Industries that use R
How AirBnB uses R
One of the most cutting-edge statistical environments used today
Nearly every major organization uses R
Minimal programming knowledge is needed
Over 5,000 packages to be used for a variety of situations
Data can only be stored in physical memory
Troublesome for small systems or large data sets
Lacks data security
Shouldn't be used in web-based browsers
Price Elasticity calculations
Sales Driver analysis
Advanced statistical computing and design problems
Modeling, graphing, test and analysis
Market analysis
Polling analysis
Supply Chain
Predict shipping trends
Sports Management
Statistical prediction & Analysis
How are Companies Affected?
Increased productivity
More accurate sales forecasts
Improved inventory management
Crucial customer behavior analysis
Price elasticity
Sales driver analytics
Understanding how users interact with the service by visualizing data

"Generally, we use R to move fast when we get a new data set. With R, we don’t need to develop custom tools or write a bunch of code. Instead, we can just go about cleaning and exploring the data." -
Solomon Messing, data scientist at Facebook
Societal Trends
Times series analysis
Famously data-driven company experiencing rapid growth
64% of data scientists use R as their primary data analysis
As data science teams grow, however, contributors may develop their own tools to solve similar problems, leading to 3 challenges: 1.) duplication of work within the team, 2.) lack of transparency about how tools are written, 3.) difficulty sharing developments with other contributors
R packages addressed these challenges
Packages are the basic units of reproducible R code. They can include functions, documentation, data, tests, add-ins, vignettes, and R markdown templates.

How are Consumers Affected?
Needs and preferences are more efficiently met
Less product stock-outs
More useful recommendations
Expectations can be more accurately predicted
Package includes more than 60 functions, has several active developers, and is actively used by members of Engineering, Data Science, Analytics, and User Experience teams.
Most used functions in Rbnb allow move of aggregated or filtered data from a Hadoop or SQL environment into R, where visualization and in-memory analysis happen more naturally
allows for consistent internal branding
MC Questions:
1. What industry does NOT use R to enhance their business?
a) Marketing
b) Media
c) Finance
d) Supply Chain
e) All the above industries use R
2. What can marketers use R for?
a) Sales forecasting
b) Customer recommendations
c) Price sensitivity analysis
d) All of the above
3. Roughly how many different packages of R are currently available?
a) 500
b) 2,500
c) 5,000
d) 10,000
4. What is a major shortcoming of R?
a) Predictive Analysis
b) Its security features
c) It is Open Sourced
d) Bugs take a long time to be resolved
5. True or False: An extensive programming background is required to use R
a) True
b) False

ggplot 2 package
Full transcript