Data Modeling Design

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

By Jeremy Leipzig

How do you utilize R to import, deal with, visualize, and examine real-world facts? With this brief, hands-on educational, you the best way to gather on-line information, therapeutic massage it right into a average 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 will plot and learn present domestic foreclosures auctions in Philadelphia. This sensible mashup workout exhibits you the way to entry spatial facts in numerous codecs in the neighborhood and over the internet to supply a map of domestic foreclosure. it truly is a good option to discover how the R atmosphere works with R programs and plays statistical research.

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We also have the ability to change the labels and the size of the points and boxes. Figure 2-3. info Correlation Can we find any associations between the attributes of each tract and its foreclosures? Correlation is a basic statistic used frequently by researchers and statisticians. In R, we can create multidimensional correlation graphs using the pairs() scatterplot matrix package. labels=2) The resulting graph is shown in Figure 2-4. In this plot, we observe that total population, total families households, and total housing units are all highly correlated (as would be expected).

A > prompt appears when R is ready for another command. In this book, all commands that a user enters appear in bold after the prompt. 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.

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. We also need to include the foreclosure data. y="PID") Changing the names for each column will facilitate scripting later on. Identifier", "totalPop", "totalHousehold", "familyHousehold", "nonfamilyHousehold", "TravelTime", "TravelTime90+minutes", "totalDisabled", "medianHouseholdIncome", "povertyStatus", "BelowPoverty","OccupiedHousing", "ownedOccupied", "rentOccupied", "FCS") Descriptive Statistics The calculation of mean, median, and standard deviation is performed with mean(), median(), and sd(), respectively.

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