Tapping the Data Deluge with R Finding and using supplemental data to add context to your analysis by Jeffrey Breen Principal, Think Big AcademyCode & Data on githubhttp://bit.ly/pawdata email: email@example.com blog: http://jeffreybreen.wordpress.com Twitter: @JeffreyBreen 1
Data data everywhere!This may be how you picture the data deluge looks like if you work for the Economist.But those of us who wrangle data for living know that it’s usually not so prosaic or buttoned-down, proper or quaint.
Real data hits us in the face... 3Real data can hit you in the face.Yet we keep coming back for more.
...and then there’s Big Data. 4And I’m not even going to talk about Big Data tonight. (For a change!)
Finding the right data makes all the diﬀerence 5Tonight we’re going to look at a few different places to find those data sets which can make a difference, and a few techniquesto access them so you can incorporate them into your analysis.
The two types of data Data you have Data you don’t have... yet 6Perhaps you’ve heard the joke: There are two kinds of people: People who think there are two kinds of people and peoplewho don’t.I like to think that there are two kinds of data.
The two types of data • Data you have – CSV ﬁles, spreadsheets – ﬁles from other sta>s>cs packages (SPSS, SAS, Stata,...) – databases, data warehouses (SQL, NoSQL, HBase,...) – whatever your boss emailed you on his way to lunch – datasets within R and R packages • Data you don’t have... yet – ﬁle downloads & web scraping – data marketplaces and other APIsCode & Data on github: http://bit.ly/pawdata 7
Reading CSV ﬁles is easy $ head -5 data/mpg-3-13-2012.csv | cut -c 1-60 "Model Yr","Mfr Name","Division","Carline","Verify Mfr Cd"," 2012,"aston martin","Aston Martin Lagonda Ltd","V12 Vantage" 2012,"aston martin","Aston Martin Lagonda Ltd","V8 Vantage", 2012,"aston martin","Aston Martin Lagonda Ltd","V8 Vantage", 2012,"aston martin","Aston Martin Lagonda Ltd","V8 Vantage", data = read.csv(data/mpg-3-13-2012.csv) View(data)see R/01-read.csv-mpg.R 8
But so is reading Excel ﬁles directly library(XLConnect) wb = loadWorkbook("data/mpg.xlsx", create=F) data = readWorksheet(wb, sheet=3-7-2012)see R/02-XLConnect-mpg.R 9
RelaMonal databases library(RMySQL) con = dbConnect(MySQL(), user="root", dbname="test") data = dbGetQuery(con, "select * from airport") dbDisconnect(con) View(data) airport_code airport_name location state_code country_name time_zone_code 1 ATL WILLIAM B. HARTSFIELD ATLANTA,GEORGIA GA USA EST 2 BOS LOGAN INTERNATIONAL BOSTON,MASSACHUSETTS MA USA EST 3 BWI BALTIMORE/WASHINGTON INTERNATIONAL BALTIMORE,MARYLAND MD USA EST 4 DEN STAPLETON INTERNATIONAL DENVER,COLORADO CO USA MST 5 DFW DALLAS/FORT WORTH INTERNATIONAL DALLAS/FT. WORTH,TEXAS TX USA CST 6 OAK METROPOLITAN OAKLAND INTERNATIONAL OAKLAND,CALIFORNIA CA USA PST 7 PHL PHILADELPHIA INTERNATIONAL PHILADELPHIA PA/WILMTON,DE PA USA EST 8 PIT GREATER PITTSBURGH PITTSBURGH,PENNSYLVANIA PA USA EST 9 SFO SAN FRANCISCO INTERNATIONAL SAN FRANCISCO,CALIFORNIA CA USA PSTsee R/04-RMySQL-airport.R 11
weird emails from the boss con = textConnection( # Hi: # # Please invite these paid volunteers to the spontaneous rally at 3PM today: # Name Department "Hourly Rate" email Alice Operations 32 firstname.lastname@example.org Billy Logistics 5 email@example.com Winston Records 20 firstname.lastname@example.org # #Thanks, #Your Boss #! ! ! ! ! ) data = read.table(con, header=T, comment.char=#) close.connection(con) View(data) Name Department Hourly.Rate email 1 Alice Operations 32 email@example.com 2 Billy Logistics 5 firstname.lastname@example.org 3 Winston Records 20 email@example.com R/05-textConnection-email.R 13
> data()Data sets in package ‘datasets’:AirPassengers Monthly Airline Passenger Numbers 1949-1960BJsales Sales Data with Leading IndicatorBJsales.lead (BJsales) Sales Data with Leading IndicatorBOD Biochemical Oxygen DemandCO2 Carbon Dioxide Uptake in Grass PlantsChickWeight Weight versus age of chicks on different dietsDNase Elisa assay of DNaseEuStockMarkets Daily Closing Prices of Major European Stock Indices, 1991-1998Formaldehyde Determination of FormaldehydeHairEyeColor Hair and Eye Color of Statistics StudentsHarman23.cor Harman Example 2.3Harman74.cor Harman Example 7.4Indometh Pharmacokinetics of IndomethacinInsectSprays Effectiveness of Insect SpraysJohnsonJohnson Quarterly Earnings per Johnson & Johnson ShareLakeHuron Level of Lake Huron 1875-1972LifeCycleSavings Intercountry Life-Cycle Savings DataLoblolly Growth of Loblolly pine treesNile Flow of the River NileOrange Growth of Orange TreesOrchardSprays Potency of Orchard SpraysPlantGrowth Results from an Experiment on Plant GrowthPuromycin Reaction Velocity of an Enzymatic ReactionSeatbelts Road Casualties in Great Britain 1969-84Theoph Pharmacokinetics of TheophyllineTitanic Survival of passengers on the TitanicToothGrowth The Effect of Vitamin C on Tooth Growth in Guinea PigsUCBAdmissions Student Admissions at UC BerkeleyUKDriverDeaths Road Casualties in Great Britain 1969-84UKgas UK Quarterly Gas ConsumptionUSAccDeaths Accidental Deaths in the US 1973-1978USArrests Violent Crime Rates by US StateUSJudgeRatings Lawyers Ratings of State Judges in the US Superior CourtUSPersonalExpenditure Personal Expenditure DataVADeaths Death Rates in Virginia (1940)WWWusage Internet Usage per MinuteWorldPhones The Worlds Telephonesability.cov Ability and Intelligence Testsairmiles Passenger Miles on Commercial US Airlines, 1937-1960airquality New York Air Quality Measurements[...]
> library(zipcode)> data(zipcode)> str(zipcode)data.frame: 44336 obs. of 5 variables: $ zip : chr "00210" "00211" "00212" "00213" ... $ city : chr "Portsmouth" "Portsmouth" "Portsmouth" "Portsmouth" ... $ state : chr "NH" "NH" "NH" "NH" ... $ latitude : num 43 43 43 43 43 ... $ longitude: num -71 -71 -71 -71 -71 ...> subset(zipcode, city==Boston & state==MA) zip city state latitude longitude664 02101 Boston MA 42.37057 -71.02696665 02102 Boston MA 42.33895 -70.91963666 02103 Boston MA 42.33895 -70.91963667 02104 Boston MA 42.33895 -70.91963668 02105 Boston MA 42.33895 -70.91963669 02106 Boston MA 42.35432 -71.07345670 02107 Boston MA 42.33895 -70.91963671 02108 Boston MA 42.35790 -71.06408672 02109 Boston MA 42.36148 -71.05417673 02110 Boston MA 42.35653 -71.05365674 02111 Boston MA 42.34984 -71.06101675 02112 Boston MA 42.33895 -70.91963676 02113 Boston MA 42.36503 -71.05636677 02114 Boston MA 42.36179 -71.06774678 02115 Boston MA 42.34308 -71.09268679 02116 Boston MA 42.34962 -71.07372680 02117 Boston MA 42.33895 -70.91963681 02118 Boston MA 42.33872 -71.07276682 02119 Boston MA 42.32451 -71.08455683 02120 Boston MA 42.33210 -71.09651684 02121 Boston MA 42.30745 -71.08127685 02122 Boston MA 42.29630 -71.05454686 02123 Boston MA 42.33895 -70.91963687 02124 Boston MA 42.28713 -71.07156688 02125 Boston MA 42.31685 -71.05811690 02127 Boston MA 42.33499 -71.04562691 02128 Boston MA 42.37830 -71.02550696 02133 Boston MA 42.33895 -70.91963726 02163 Boston MA 42.36795 -71.12056757 02196 Boston MA 42.33895 -70.91963[...]
image credit: http://njarb.com/2012/08/untangle-this-mess-of-wires/Now let’s turn our attention to tapping into the internet for other data sources
The two types of data • Data you have – CSV ﬁles, spreadsheets – ﬁles from other sta>s>cs packages (SPSS, SAS, Stata,...) – databases, data warehouses (SQL, NoSQL, HBase,...) – whatever your boss emailed you on his way to lunch – datasets within R and R packages • Data you don’t have... yet – ﬁle downloads & web scraping – data marketplaces and other APIsCode & Data on github: http://bit.ly/pawdata 17
Many base funcMons take URLs url = http://ichart.finance.yahoo.com/table.csv? s=YHOO&d=8&e=28&f=2012&g=d&a=3&b=12&c=1996& ignore=.csv data = read.csv(url) ggplot(data) + geom_point(aes(x=as.Date(Date), y=Close), size = 1) + scale_y_log10() + theme_bw()see R/06-read.csv-url-yahoo.R 20
image credit: http://groovynoms.com/2011/07/25/beer-of-the-week-2/Now, I don’t mean to oversell this next one, but if you’ve spent as much time as I have finding -- and trying to deal with --interesting data sets on web pages, you might agree that this next function alone is worth the price of admission.
not even HTML tables are safe library(XML) url = http://en.wikipedia.org/wiki/List_of_capitals_in_the_United_States state.capitals.df = readHTMLTable(url, which=2) State Abr. Date of statehood Capital Capital since Land area (miÂ²) Most populous city? 1 Alabama AL 1819 Montgomery 1846 155.4 No 2 Alaska AK 1959 Juneau 1906 2716.7 No 3 Arizona AZ 1912 Phoenix 1889 474.9 Yes 4 Arkansas AR 1836 Little Rock 1821 116.2 Yes 5 California CA 1850 Sacramento 1854 97.2 No 6 Colorado CO 1876 Denver 1867 153.4 Yes 7 Connecticut CT 1788 Hartford 1875 17.3 No 8 Delaware DE 1787 Dover 1777 22.4 No 9 Florida FL 1845 Tallahassee 1824 95.7 No 10 Georgia GA 1788 Atlanta 1868 131.7 Yessee R/08-readHTMLTable.R 24As you’d expect from a package called “XML”, it parses well-formed XML files.But I didn’t expect it would do such a good job with HTML.And I certainly didn’t expect to find a function as handy as readHTMLTable()!
..and couldn’t be easier to access. library(rdatamarket) oil.prod = dmseries("http://data.is/nyFeP9") plot(oil.prod)see R/09-rdatamarket.R 27DataMarket includes its own URL shortner -- like bit.ly but just for their data.Long or short, just give dmseries() the URL, and it will download the data set for you.
Make a withdrawal from the World Bank > library(WDI) > WDIsearch(population, total) indicator name "SP.POP.TOTL" "Population, total" > WDIsearch(fertility .*total) indicator name "SP.DYN.TFRT.IN" "Fertility rate, total (births per woman)" > WDIsearch(life expectancy .*birth.*total) indicator name "SP.DYN.LE00.IN" "Life expectancy at birth, total (years)" > WDIsearch(GDP per capita .*constant) indicator name [1,] "NY.GDP.PCAP.KD" "GDP per capita (constant 2000 US$)" [2,] "NY.GDP.PCAP.KN" "GDP per capita (constant LCU)" > WDIsearch(population, total) indicator name "SP.POP.TOTL" "Population, total"see R/10-WDI.R 28
Swedish Accent Not Included data = WDI(country=c(BR, CN, GB, JP, IN, SE, US), ! ! ! indicator=c(SP.DYN.TFRT.IN, SP.DYN.LE00.IN, SP.POP.TOTL, ! ! ! ! ! ! NY.GDP.PCAP.KD), ! ! ! start=1900, end=2010) library(googleVis) g = gvisMotionChart(data, idvar=country, timevar=year) plot(g)see R/10-WDI.R 29
quantmod: the king of symbols• getSymbols() downloads Mme series data from source speciﬁed by “src” parameter: – yahoo = Yahoo! Finance – google = Google Finance – FRED = St. Louis Fed’s Federal Reserve Economic Data – oanda = OANDA Forex Trading & Exchange Rates – csv – MySQL – RData 30
Hello, FRED55,000 economic +me series • Federal Reserve Bank of Kansas • Thomson Reuters/University of from 45 sources: City Michigan • Federal Reserve Bank of • U.S. Congress: Congressional • AutomaMc Data Processing, Inc. Philadelphia Budget Oﬃce• Banca dItalia • Federal Reserve Bank of St. Louis • U.S. Department of Commerce: • Banco de Mexico Bureau of Economic Analysis • Freddie Mac• Bank of Japan • U.S. Department of Commerce: • Haver AnalyMcs• Bankrate, Inc. Census Bureau • InsMtute for Supply Management• Board of Governors of the • U.S. Department of Energy: Federal Reserve System • InternaMonal Monetary Fund Energy InformaMon • London Bullion Market AdministraMon• BofA Merrill Lynch AssociaMon• BriMsh Bankers AssociaMon • U.S. Department of Housing and • NaMonal AssociaMon of Realtors Urban Development• Central Bank of the Republic of Turkey • NaMonal Bureau of Economic • U.S. Department of Labor: Research Bureau of Labor StaMsMcs• Chicago Board OpMons Exchange • OrganisaMon for Economic Co-‐ • U.S. Department of Labor: • CredAbility Nonproﬁt Credit operaMon and Development Employment and Training Counseling & EducaMon • Reserve Bank of Australia AdministraMon• Deutsche Bundesbank • Standard and Poors • U.S. Department of the Treasury: • Dow Jones & Company Financial Management Service • Swiss NaMonal Bank• Eurostat • U.S. Department of • The White House: Council of • Federal Financial InsMtuMons Economic Advisors TransportaMon: Federal Highway ExaminaMon Council AdministraMon • The White House: Oﬃce of • Federal Housing Finance Agency Management and Budget • Wilshire Associates Incorporated• Federal Reserve Bank of Chicago • World Bank 31