1. Data Analytics with R and SQL Server
Stéphane Fréchette
Thursday March 19, 2015

2. Who am I?
My name is Stéphane Fréchette
SQL Server MVP | Consultant | Speaker | Data & BI Architect | Big Data
|NoSQL | Data Science. Drums, good food and fine wine.
I have a passion for architecting, designing and building solutions that
matter.
Twitter: @sfrechette
Blog: stephanefrechette.com
Email: stephanefrechette@ukubu.com

3. Topics
• What is R?
• Should I use R?
• Data Structures
• Graphics
• Data Manipulation in R
• Connecting to SQL Server
• Demos
• Resources
• Q&A

4. DISCLAIMER
This is not a course nor a tutorial, but
an introduction, a walkthrough to
inspire you to further explore and
learn more about R and statistical computing

5. “ Analysis of data is a process of inspecting, cleaning,
transforming, and modeling data with the goal of
discovering useful information, suggesting conclusions,
and supporting decision-making. Data analysis has
multiple facets and approaches, encompassing diverse
techniques under a variety of names, in different business,
science, and social science domains.”
- Wikipedia

6. What is R?
• A programming language, environment for statistical computing and graphics
• R has its origins in the S programming language created in the 1970’s
• Best used to manipulate moderately sized datasets, do statistical analysis and
produce data-centric documents and presentations
• These tools are distributed as packages, which any user can download to
customize the R environment
• Cross-platform: runs on Mac, Windows and Unix based systems

7. Should I use R?
Are you
doing
statistics
?
No Yes
No Yes
Where “statistics” can mean machine learning, predictive analytics, data
science, anything that falls under a rather broad umbrella…
But if you have some data that makes sense to represent in a tabular like
structure, and you want to do some cool analytical or statistics stuff with it, R is
definitely a good choice…

8. Downloading and Installing R
http://www.r-project.org/ http://www.rstudio.com/

9. The IDE (RStudio)
1. View Files and Data
2. See Workspace and
History
3. See Files, Plots,
Packages and Help
4. Console
1 2
34

10. Installing Packages
• To use packages in R, one must first install them using the install.packages
function
• Downloads the packages from CRAN and installs it to ready to be use

11. Loading Packages
• To use particular packages in your current R session, one must load it into the
R environment using the library or require functions

12. Common Data Structures in R
To make the best of the R language, one needs a strong understanding of the
basic data types and data structures and how to operate and use them.
R has a wide variety of data types including scalars, vectors (numerical,
character, logical), matrices, data frames, and lists…
To understand computations in R, two slogans are helpful:
• Everything that exists is an object
• Everything that happens is a function call
John Chambers
creator of the S programming language, and core member of the R programming language project.

13. Data Structures - Vectors
The simplest structure is the numeric vector, which is a single entity consisting of an ordered
collection of numbers.

14. Data Structures - Matrices
Matrices are nothing more than 2-dimensional vectors. To define a matrix, use the function
matrix.

15. Data Structures - Data frames
Time series are often ordered in data frames. A data frame is a matrix with names above the
columns. This is nice, because you can call and use one of the columns without knowing in
which position it is.

16. Data Structures - Lists
An R list is an object consisting of an ordered collection of objects known as its components.

17. Data Structures - Date and Time
Sys.time() # returns the current system date time

18. Data Structures - Date and Time
Two main (internal) formats for date-time are: POSIXct and POSIXlt
• POSIXct: A short format of date-time, typically used to store date-time columns in a data-frame
• POSIXlt: A long format of date-time, various other sub-units of time can be extracted from here

19. Data Structures - Others
Other useful and important data type
• NULL: Typically used for initializing variables. (x = NULL) creates a variable x of length zero.
The function is.null() returns TRUE or FALSE and tells whether a variable is NULL or not.
• NA: Used for denoting missing values. (x = NA) creates a variable x with missing values.
The function is.na() returns TRUE or FALSE and tells whether a variable is NA or not.
• NaN: NaN stands for “Not a Number”. Prints a warning message in console. The function
is.nan() lets you check whether the value of a variable is NaN or not.
• Inf: Inf stands for “Infinity”. (x = 10/0 ; y = -3/0) sets value of x to Inf ad y to –Inf. The
function is.finite() lets you check whether the value of a variable is infinity or not.

20. Graphics
One of the main reasons data analysts and data
scientists turn to R is for its strong graphic
capabilities.
Basic Graphs:
• These include density plots (histograms and kernel
density plots), dot plots, bar charts (simple,
stacked, grouped), line charts, pie charts (simple,
annotated, 3D), boxplots (simple, notched, violin
plots, bagplots) and scatter plots (simple, with fit
lines, scatterplot matrices, high density plots, and
3D plots).

21. Graphics
Advances Graphs:
• Graphical parameters describes how to change a
graph's symbols, fonts, colors, and lines. Axes and
text describe how to customize a graph's axes, add
reference lines, text annotations and a legend.
Combining plots describes how to organize
multiple plots into a single graph.
• The lattice package provides a comprehensive
system for visualizing multivariate data, including
the ability to create plots conditioned on one or
more variables. The ggplot2 package offers a
elegant systems for generating univariate and
multivariate graphs based on a grammar of
graphics.

22. Data Manipulation in R
dplyr an R package for fast and easy data manipulation.
Data manipulation often involves common tasks, such as selecting certain variables, filtering
on certain conditions, deriving new variables from existing variables, and so forth. If we
think of these tasks as “verbs”, we can define a grammar of sorts for data manipulation.
In dplyr the main verbs (or functions) are:
• filter: select a subset of the rows of a data frame
• arrange: works similarly to filter, except that instead of filtering or selecting rows, it
reorders them
• select: select columns of a data frame
• mutate: add new columns to a data frame that are functions of existing columns
• summarize: summarize values
• group_by: describe how to break a data frame into groups of rows

23. Demo
[dplyr – manipulating data]

24. Connecting R and SQL Server
The RODBC package provides access to databases (including Microsoft Access
and Microsoft SQL Server) through an ODBC interface
Function Description
odbcConnection(dsn, uid = “”, pwd = “”) Open a connection to an ODBC database
sqlFetch(channel, sqtable) Read a table from an ODBC database into a data frame
sqlQuery(channel, query) Submit a query to an ODBC database and return the
results
sqlSave(channel, mydf, tablename = sqtable, append
= FALSE)
Write or update (append=TRUE) a data frame to a
table in the ODBC database
sqlDrop(channel, sqtable) Remove a table from the ODBC database
close(channel) Close the connection

25. RODBC Example

26. Other interface
The RJDBC package provides access to databases through a JDBC interface.
(requires JDBC driver from Microsoft)

27. Demo
[Let’s analyze - R and SQL Server]

28. Resources
• The R Project for Statistical Computing http://www.r-project.org/
• RStudio http://www.rstudio.com/
• Revolution Analytics http://www.revolutionanalytics.com/
• Shiny http://shiny.rstudio.com/
• {swirl} Learn R, in R http://swirlstats.com/
• R-bloggers http://www.r-bloggers.com/
• Online R resources for Beginners http://bit.ly/1x2q6Gl
• 60+ R resources to improve your data skills http://bit.ly/1BzW4ox
• Stack Overflow - R http://stackoverflow.com/tags/r
• Cerebral Mastication - R Resources http://bit.ly/17YhZj4
• Microsoft JDBC Drivers 4.1 and 4.0 for SQL Server http://bit.ly/1kEgJ7O

29. What Questions Do You Have?

30. Thank You
For attending this session