Beginner's guide to R: Get your data into R
In part 2 of our hands-on guide to the hot data-analysis environment, we provide some tips on how to import data in various formats, both local and on the Web.
Computerworld - Once you've installed and configured R to your liking, it's time to start using it to work with data. Yes, you can type your data directly into R's interactive console. But for any kind of serious work, you're a lot more likely to already have data in a file somewhere, either locally or on the Web. Here are several ways to get data into R for further work.
[This story is part of Computerworld's "Beginner's guide to R." To read from the beginning, check out the introduction; there are links on that page to the other pieces in the series.]
If you just want to play with some test data to see how they load and what basic functions you can run, the default installation of R comes with several data sets. Type:
into the R console and you'll get a listing of pre-loaded data sets. Not all of them are useful (body temperature series of two beavers?), but these do give you a chance to try analysis and plotting commands. And some online tutorials use these sample sets.
One of the less esoteric data sets is mtcars, data about various automobile models that come from Motor Trends. (I'm not sure from what year the data are from, but given that there are entries for the Valiant and Duster 360, I'm guessing they're not very recent; still, it's a bit more compelling than whether beavers have fevers.)
You'll get a printout of the entire data set if you type the name of the data set into the console, like so:
There are better ways of examining a data set, which I'll get into later in this series. Also, R does have a print() function for printing with more options, but R beginners rarely seem to use it.
Existing local data
R has a function dedicated to reading comma-separated files. To import a local CSV file named filename.txt and store the data into one R variable named mydata, the syntax would be:
mydata <- read.csv("filename.txt")
(Aside: What's that <- where you expect to see an equals sign? It's the R assignment operator. I said R syntax was a bit quirky. More on this in the section on R syntax quirks.)
And if you're wondering what kind of object is created with this command, mydata is an extremely handy data type called a data frame -- basically a table of data. A data frame is organized with rows and columns, similar to a spreadsheet or database table.
The read.csv function assumes that your file has a header row, so row 1 is the name of each column. If that's not the case, you can add header=FALSE to the command:
mydata <- read.csv("filename.txt", header=FALSE)
In this case, R will read the first line as data, not column headers (and assigns default column header names you can change later).
If your data use another character to separate the fields, not a comma, R also has the more general read.table function. So if your separator is a tab, for instance, this would work:
mydata <- read.table("filename.txt", sep="\t", header=TRUE)
The command above also indicates there's a header row in the file with header=TRUE.
If, say, your separator is a character such as | you would change the separator part of the command to sep="|"
- 15 Non-Certified IT Skills Growing in Demand
- How 19 Tech Titans Target Healthcare
- Twitter Suffering From Growing Pains (and Facebook Comparisons)
- Agile Comes to Data Integration
- Slideshow: 7 security mistakes people make with their mobile device
- iOS vs. Android: Which is more secure?
- 11 sure signs you've been hacked
- The value of smarter oil and gas fields With global energy requirements continuing to rise, the exploration, development and production of new oil and gas resources are shifting to increasingly challenging...
- Smarter Environmental Analytics Solutions: Offshore Oil and Gas Installations Example This IBM Redbooks® Solution Guide describes a solution for implementing smarter environmental monitoring and analytics for oil and gas industries. The solution implements...
- Piecing Together the Business Intelligence Puzzle Business intelligence (BI) technology collects and analyzes company data, delivering relevant information to corporate decision-makers in an effort to produce favorable outcomes.
- Harness IT -- An Introduction to Business Intelligence Solutions Learn the key selection criteria required to provide your organization with the capability to address structured data, unstructured data and mobile demands so...
- Live Webcast Increasing the Value of Your Reports and Dashboards Learn how incorporating other analytical capabilities such as predictive modeling and visualization can increase the value of your reports and dashboards by providing...
- The Software-Defined Data Center: Is your ADC ready? Data center transformation is accelerating beyond virtualization to next-generation cloud architectures and software-defined data centers, bringing new challenges for application performance, scalability and...
- Application Acceleration: Optimize the End-User Experience Watch this on-demand webcast and learn how you can optimize your web content, accelerate performance across any device and browser combination, and offload... All Business Intelligence/Analytics White Papers | Webcasts