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

By Jeremy Leipzig

How do you utilize R to import, deal with, visualize, and examine real-world information? With this brief, hands-on instructional, you how one can gather on-line facts, therapeutic massage it right into a average shape, and paintings with it utilizing R amenities to have interaction with internet servers, parse HTML and XML, and extra. instead of use canned pattern information, you are going to plot and examine present domestic foreclosures auctions in Philadelphia. This functional mashup workout exhibits you the way to entry spatial facts in different codecs in the community and over the net to provide a map of domestic foreclosure. it really is an exceptional technique to discover how the R surroundings works with R applications and plays statistical research.

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Plots provide a more visually pleasing way to look at this data. packages(latticeExtra) > library(latticeExtra) lattice and latticeExtra are useful packages for data visualization. lattice comes with generic functions to create trellis graphics, and allows extensive customization via usercontrol parameters. In the following examples, we take a closer look at foreclosures grouped by median household income per tract, using tools available in the lattice packages. We can first construct a new variable that groups the median household income into two groups.

075777e+04 (snip) Not all of the columns will return a numeric value, especially if it’s missing. For example, MTFCC00 returns a NA. Its type is considered as a factor, as opposed to a num or int (see output from str() above). rm=TRUE in the sd function removes missing data. rm) : NAs introduced by coercion The warning serves to alert the user that the column is not of num or int type. Of course, the standard deviations of MTFCC00 or FUNCSTAT00 are nonsensical, and therefore uninteresting to calculate.

0000000 cor has a default method using Pearson’s test statistic, calculating the shortest “dis- tance” between each of the pairwise variables. 92. The use="com plete" option suggests that only those variables with no missing values should be used to find correlation. The other option is pairwise. table() is a good way to look at the frequency distribution. > table(ct$FCS) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 91 56 46 43 34 29 20 16 13 9 8 2 6 2 2 3 1 One of the 381 tracts has 16 foreclosures, and 91 tracts have no foreclosures.

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