ENTRIES TAGGED "R"

A Hands-on Introduction to R

OSCON 2013 Speaker Series

R is an open-source statistical computing environment similar to SAS and SPSS that allows for the analysis of data using various techniques like sub-setting, manipulation, visualization and modeling. There are versions that run on Windows, Mac OS X, Linux, and other Unix-compatible operating systems.

To follow along with the examples below, download and install R from your local CRAN mirror found at r-project.org. You’ll also want to place the example CSV into your Documents folder (Windows) or home directory (Mac/Linux).

After installation, open the R application. The R Console will pop-up automatically. This is where R code is processed. To begin writing code, open an editor window (File -> New Script on Windows or File -> New Document on a Mac) and type the following code into your editor:

Place your cursor anywhere on the “1+1” code line, then hit Control-R (in Windows) or Command-Return (in Mac). You’ll notice that your “1+1” code is automatically executed in the R Console. This is the easiest way to run code in R. You can also run R code by typing the code directly into your R Console, but using the editor is much easier.

If you want to refresh your R Console, click anywhere inside of it and hit Control-L (in Windows) or Command-Option-L (in Mac).

Now let’s create a Vector, the simplest possible data structure in R. A Vector is similar to a column of data inside a spreadsheet. We use the combine function to do so:

To view the contents of raysVector, just run the line of code above. After running the code shown above, double-click on raysVector (in the editor) and then run the code that is automatically highlighted after double-clicking. You will now see the contents of raysVector in your R Console.

The object we just created is now stored in memory and we can see this by running the following code:

R is an interpreted language with support for procedural and object-oriented programming. Here we use the mean statistical function to calculate the statistical mean of raysVector:

Getting help on the mean function is easy using:

We can create a simple plot of raysVector using:

Importing CSV files is simple too:

We can subset the CSV data in many different ways. Here are two different methods that do the same thing:

There are many ways to transform your data in R. Here’s a method that doubles everyone’s age:

The apply function allows us to apply a standard or custom function without loops. Here we apply the mean function column-wise to the first 3 rows of the dataset in order to analyze the age and height columns of the dataset. We will also ignore missing values during the calculation:

Here we build a linear regression model that predicts a person’s weight based on their age and height:

We can plot our residuals like this:

We can install the Predictive Model Markup Language (PMML) package to quickly deploy our predictive model in a Business Intelligence system without custom SQL: Read more…

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Scaling People, Process, and Technology with Python

OSCON 2013 Speaker Series

NOTE: If you are interested in attending OSCON to check out Dave’s talk or the many other cool sessions, click over to the OSCON website where you can use the discount code OS13PROG to get 20% off your registration fee.

Since 2009, I’ve been leading the optimization team at AppNexus, a real-time advertising exchange. On this exchange, advertisers participate in real-time auctions to bid on individual ad impressions. The highest bid wins the auction, and that advertiser gets to show an ad. This allows advertisers to carefully target where they advertise—maximizing the effectiveness of their advertising budget—and lets websites maximize their ad revenue.

We do these auctions often (~50 billion a day) and fast (<100 milliseconds). Not surprisingly, this creates a lot of technical challenges. One of those challenges is how to automatically maximize the value advertisers get for their marketing budgets—systematically driving consumer engagement through ad placements on particular websites, times of day, etc.—and we call this process “optimization.” The volume of data is large, and the algorithms and strategies aren’t trivial.

In order to win clients and build our business to the scale we have today, it was crucial that we build a world-class optimization system. But when I started, we didn’t have a scalable tech stack to process the terabytes of data flowing through our systems every day, and we didn't have the team to do any of the required data modeling.

People

So, we needed to hire great people fast. However, there aren’t many veterans in the advertising optimization space, and because of that, we couldn’t afford to narrow our search to only experts in Java or R or Matlab. In order to give us the largest talent pool possible to recruit from, we had to choose a tech stack that is both powerful and accessible to people with diverse experience and backgrounds. So we chose Python.

Python is easy to learn. We found that people coding in R, Matlab, Java, PHP, and even those who have never programmed before could quickly learn and get up to speed with Python. This opened us up to hiring a tremendous pool of talent who we could train in Python once they joined AppNexus. To top it off, there’s a great community for hiring engineers and the PyData community is full of programmers who specialize in modeling and automation.

Additionally, Python has great libraries for data modeling. It offers great analytical tools for analysts and quants and when combined, Pandas, IPython, and Matplotlib give you a lot of the functionality of Matlab or R. This made it easy to hire and onboard our quants and analysts who were familiar with those technologies. Even better, analysts and quants can share their analysis through the browser with IPython.

Process

Now that we had all of these wonderful employees, we needed a way to cut down the time to get them ramped up and pushing code to production.

First, we wanted to get our analysts and quants looking at and modeling data as soon as possible. We didn’t want them worrying about writing database connector code, or figuring out how to turn a cursor into a data frame. To tackle this, we built a project called Link.

Imagine you have a MySQL database. You don’t want to hardcode all of your connection information because you want to have a different config for different users, or for different environments. Link allows you to define your “environment” in a JSON config file, and then reference it in code as if it is a Python object.

Now, with only three lines of code you have a database connection and a data frame straight from your mysql database. This same methodology works for Vertica, Netezza, Postgres, Sqlite, etc. New “wrappers” can be added to accommodate new technologies, allowing team members to focus on modeling the data, not how to connect to all these weird data sources.

By having the flexibility to easily connect to new data sources and APIs, our quants were able to adapt to the evolving architectures around us, and stay focused on modeling data and creating algorithms.

Second, we wanted to minimize the amount of work it took to take an algorithm from research/prototype phase to full production scale. Luckily, with everyone working in Python, our quants, analysts, and engineers are using the same language and data processing libraries. There was no need to re-implement an R script in Java to get it out across the platform.
Read more…

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R as a Programming Language

Moving beyond traditional tools makes data analysis faster and more powerful

Garrett Grolemund is an O’Reilly author and teaches classes on data analysis for R Studios.

We sat down to discuss why data scientists, statisticians, and programmers alike can use the R language to make data analysis easier and more powerful.

Key points from the full video (below) interview include:

  • R is a free, open-source language that has its roots in S-PLUS [Discussed at the 0:27 mark]
  • What does it mean for R to be a programming language versus just a data analysis tool? [Discussed at the 1:00 mark]
  • R comes with many useful data analysis methods already implemented, so you don’t have to start from scratch. [Discussed at the 4:23 mark]
  • R is a mix of functional and object-oriented programming that is optimal for handling data structures that data analysts expect (e.g. vectors) [Discussed at the 6:08 mark]
  • A discussion of using R in conjunction with other languages like Python, along with packages that help with this [Discussed at the 7:30 mark]
  • Getting started using R isn’t really any harder than using a calculator [Discussed at the 9:28 mark]

You can view the entire interview in the following video.

Related:

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Making Government Transparent Using R

Danese Cooper thinks it will be an important tool in Open Gov

With Open Source now considered an accepted part of the software industry, some people are starting to wonder if we can’t bring the same degree of openness and innovation into government. Danese Cooper, who is actively involved in the open source community through her work with the Open Source Initiative and Apache, as well as working as an R wonk for Revolution Computing, would love to see the government become more open. Part of that openness is being able to access and interpret the mass of data that the government collects, something Cooper thinks R would be a great tool for. She’ll be talking about R and Open Government at O’Reilly’s Open Source Conference, OSCON.

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