Data Mashups in R.: A Case Study in Real-World Data Analysis by Jeremy Leipzig

By Jeremy Leipzig

How do you employ R to import, deal with, visualize, and study real-world info? With this brief, hands-on instructional, you how to acquire on-line info, therapeutic massage it right into a moderate shape, and paintings with it utilizing R amenities to engage with internet servers, parse HTML and XML, and extra. instead of use canned pattern info, you are going to plot and learn present domestic foreclosures auctions in Philadelphia. This functional mashup workout indicates you the way to entry spatial facts in different codecs in the neighborhood and over the internet to provide a map of domestic foreclosure. it really is a very good approach to discover how the R atmosphere works with R programs and plays statistical research.

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These make it somewhat easier to browse available packages and documentation. In Unix, the R executable will generally install into /usr/bin/R and uses the X Window System (X11) for graphs. The commands in this book work for all R platforms. Quick and Dirty Essentials of R Upon starting R, you will see a prompt describing the version of R you are accessing, a disclaimer about R as a free software, and some functions regarding license, contributors, and demos of R. R uses an interactive shell—each line is interpreted after you hit return.

Built-in functions and simple mathematical calculations are the basics of R language. By typing 1+1 and hitting Enter, you’ll observe the following: > 1+1 [1] 2 > myAnswer<-sqrt(81) > myAnswer [1] 9 Just like a calculator, you can also take logs with log(), find the sin of angles with sin(), and take absolute values of any real number with abs(). R allows you to store your results in a variable by using the <- operator. To view the value of a variable, simply type its name. info variables. You can also create a vector (a collection of elements) using variables of the same type (int, num, etc): > x<-c(0,1,2,3) # R treats everything behind the pound sign as comments x [1] 0 1 2 3 > x[1] #access to the first element of the vector [1] 0 To view the internal help page for a unfamiliar function, type the keyword with ?.

The columns we need are in different tables. CensusTable1 contains the tracts, Census Table2 has all the interesting survey variables, while FCs and polyData have foreclosure and shape information. The str() and merge() function can be quite useful in this case. frame': 381 obs. of 9 variables: $ PID : int 1 2 3 4 5 6 7 8 9 10 ... : 1 112 223 316 327 ... $ FIPSSTCO: Factor w/ 1 level "42101": 1 1 1 1 1 1 1 1 1 1 ... : 1 2 3 4 5 6 7 ... : 1 2 3 4 5 6 7 8 9 10 ... info Now we have a connection between the tracts and our census data.

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