Package LearnEDA. R topics documented: February 15, Type Package. Title Functions for Learning Exploratory Data Analysis. Version 1.

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1 Package LearnEDA February 15, 2013 Type Package Title Functions for Learning Exploratory Data Analysis Version 1.2 Date Author Jim Albert Maintainer Jim Albert Depends aplpack, vcd LazyData yes LearnEDA contains a collection of functions helpful in learning the basic tenets of Exploratory Data Analysis. License GPL (>= 2) Repository CRAN Date/Publication :11:51 NeedsCompilation no R topics documented: act.scores baseball.attendance batting.history beatles boston.marathon boston.marathon.wtimes braves.attendance car.measurements church.2way church.tseries

2 2 R topics documented: college.ratings farms fit.gaussian football gestation.periods grandma half.slope.ratio han heaviest.fish hinkley home.prices homeruns homeruns immigrants island.areas lake lval mortality.rates mtrans olympics.run olympics.speed.skating olympics.swim pitching.history plot2way pop.change pop.densities pop.england power.t rent.prices rline salaries slider.compare slider.match slider.power slider.straighten snowfall spread.level.plot studentdata symplot temperatures tukey.24a tukey.24b tukey.26a tukey.26b tukey.26c us.pop Index 38

3 act.scores act.scores ACT scores of states in the US. Average ACT composite scores for all states for the year. act.scores A data frame with 51 observations on the following 5 variables. STATE name of state Abbrev state abbreviation ACT average ACT composite score Per.Grads percentage of students who took the ACT exam Region region of the United States World Almanac and Book of Facts 2008 baseball.attendance Attendance at baseball teams. Average attendance for home and away games for all major league baseball games in the first half of the 2010 season. baseball.attendance

4 4 batting.history A data frame with 30 observations on the following 8 variables. Team name of team League league where the team belongs N.Home number of home games Avg.Home average attendance for home games Pct.Home percentage of park capacity for home games N.Away number of away games Avg.Away average attendance for away games Pct.Away percentage of park capacity for away games web site batting.history Yearly Batting Statistics for Major League Baseball Year to year batting statistics for all years of professional American baseball. batting.history A data frame with 140 observations on the following 27 variables. Year baseball season year Tms number of teams N.Bat number of players used BatAge average age of batters R runs scored per game G number of games played PA number of plate appearances per game AB number of at-bats per game H average number of hits per game X2B average number of doubles per game X3B average number of triples per game

5 beatles 5 HR average number of home runs per game RBI average number of runs-batted-in per game SB average number of stolen bases per game CS average number of caught stealing per game BB average number of base-on-balls per game SO average number of strikeouts per game BA average batting average OBP average on-base percentage SLG average slugging percentage OPS average OBP + SLG TB average total bases per game GDP average grounded into double plays per game HBP average hit by pitch per game SH average sacrifice hits per game SF average sacrifice fliew per game IBB average number of intentional walks per game web site beatles Lengths of songs from Beatles albums. Length of songs from a selection of albums from the popular pop and rock group The Beatles. beatles A data frame with 113 observations on the following 2 variables. time time in seconds of the song album name of Beatles album Collected by the author from the album descriptions

6 6 boston.marathon.wtimes boston.marathon Boston Marathon completion times of women of different ages. Completion times for a sample of women at the 2001 Boston Marathon. boston.marathon A data frame with 108 observations on the following 2 variables. time completion time in minutes age age of the participant website boston.marathon.wtimes Boston marathon winning times Winning time in mens Boston marathon through the years. boston.marathon.wtimes A data frame with 99 observations on the following 2 variables. year year minutes winning time in minutes 2001 ESPN Information Please Sports Almanac

7 braves.attendance 7 braves.attendance Attendance at games of a baseball team Attendance of Atlanta Braves home games during a particular season. brave.at A data frame with 72 observations on the following 2 variables. Game game number Attendance attendance count website car.measurements Car measurements Measurements on 38 automobiles from the model year. car.measurements A data frame with 38 observations on the following 8 variables. Country the nationality of the manufacturer Car the car name MPG the mileage, measured in miles per gallon (MPG) Weight the weight Drive.Ratio the drive ratio Horsepower the horsepower Displacement the displacement of the car (in cubic inches) Cylinders the number of cylinders

8 8 church.tseries Consumer Reports used in the article "Building Regression Models Interactively." by H. V. Henderson and P. F. Velleman (1981), Biometrics, 37, church.2way Church attendance as a two-way table. Two-way table of average worship attendance at a Ohio church by year and month. church.2way A data frame with 12 observations on the following 5 variables. month month y1993 average worship attendance count in 1993 y1994 average worship attendance count in 1994 y1995 average worship attendance count in 1995 y1996 average worship attendance count in 1996 Collected by the author. church.tseries Time series of worship attendance Time series of weekly worship attendance from a church in Ohio, U.S. church.tseries

9 college.ratings 9 A data frame with 209 observations on the following 3 variables. year the year month the month worship count of worship attendance Collected by the author. college.ratings Ratings of National Universities in the U.S. Ratings for National Universities based on a 2001 survey. college.ratings A data frame with 249 observations on the following 12 variables. School name of university Tier tier grouping of the univesity Type private or public institution Reputation measure of academic reputation F.retention average freshmen retention rate a.grad.rate percentage of freshmen who graduated within a six-year period Under.20 percentage of undergraduate classes with fewer than 20 students Over.50 percentage of undergraduate classes with 50 students or more enrolled Full.time percentage of the total number of faculty employed on a full-time basis Top.10 proportion of students enrolled who graduated in the top 10 percent of their high school class Acc.rate ratio of the number of students admitted to the number of applicants Alum.giving average percent of undergraduate alumni of record who donated money to the college or university U.S. News and World Report

10 10 fit.gaussian farms Number of farms in the states of the U.S. The number of farms (in 1000s) for each of the 50 states in the United States in farms A data frame with 50 observations on the following 2 variables. state name of the state count count of the number of farms 2001 New York Times Almanac fit.gaussian Fitting a Gaussian curve to binned data Fits a Gaussian curve with known mean and standard deviation to binned data. fit.gaussian(data,bins,g.mean,g.sd) Arguments data bins g.mean g.sd numeric vector of values of variable a vector giving the breakpoints between histogram cells mean of Gaussian distribution standard deviation of Gaussian distribution Value counts probs expected residual vector of frequencies of the bins vector of fitted probabilities of the bins vector of expected frequencies of the bins vector of simple rootogram residuals

11 football 11 Author(s) Jim Albert Examples # fit Gaussian curve to simulated data from t distribution data=rt(200,df=5) bins=pretty(range(data)) g.mean=0 g.sd=1 fit.gaussian(data,bins,g.mean,g.sd) football American football scores Scores for a group of American football games. football A data frame with 465 observations on the following 2 variables. winner score of winning team loser score of losing team Collected by the author gestation.periods Gestation periods for different animals. Average gestation periods for different animals. gestation.periods

12 12 half.slope.ratio A data frame with 43 observations on the following 2 variables. Animal name of animal Period average gestation period World Almanac and Book of Facts 2001 grandma Grandmas Marathon completion times Completion times in running Grandmas Marathon (Duluth, Minnesota) for women of ages 19 to 34 grandma A data frame with 1208 observations on the following 1 variable. time completion time in seconds web site half.slope.ratio Half slope ratio Computes the half slope ratio for three summary points and power transformations on the two variables. half.slope.ratio(sx,sy,px,py)

13 han 13 Arguments sx sy px py numeric vector of summary values of the x variable numeric vector of summary values of the y variable power of the transformation on the x variable power of the transformation on the y variable Value Value of the half slope ratio on the transformed summary points Author(s) Jim Albert Examples sx=c(10,30,50) sy=c(5,8,20) half.slope.ratio(sx,sy,1,1) half.slope.ratio(sx,sy,-.5,-.5) han Hanning a sequence Performs hanning operation on a sequence of values where the end values are copied on. han(sequence) Arguments sequence numeric vector of values Value vector of smoothed values from hanning operations. Author(s) Jim Albert

14 14 hinkley Examples # illustrates 3RSS and 3RSSH smooths plot(wwwusage) plot(smooth(wwwusage,kind="3rss")) plot(han(smooth(wwwusage,kind="3rss"))) heaviest.fish Fish world record catches Weights in pounds of world record catches of saltwater fish. heaviest.fish A data frame with 25 observations on the following 2 variables. Species name of country Weight region of the world World Almanac and Book of Facts 2008 hinkley Hinkley s quick method Computes Hinkley s simple measure of asymmetry of a batch. hinkley(d) Arguments d numeric vector of values Value Hinkley s measure of asymmetry

15 home.prices 15 Author(s) Jim Albert Examples data(state) raw=state.x77[,"population"] hinkley(raw) logs=log(raw) hinkley(logs) home.prices Home sales prices in the U.S. Average sales prices of houses for 21 cities in the U.S. home.prices A data frame with 21 observations on the following 3 variables. City name of the city y1985 average home price in 1985 y2000 average home price in New York Times Almanac homeruns.2000 Team home run numbers for different seasons. Total number of home runs hit all teams in the major league baseball league for the years 1900,1910,..., homeruns.2000

16 16 immigrants A data frame with 210 observations on the following 2 variables. YEARS season HOMERUNS count of team home runs Sean Lahman s database at homeruns.61 Home run counts in 1961 The number of the number of home runs hit by all major league players in the year homeruns.61 A data frame with 698 observations on the following 1 variables. HR count of home runs Sean Lahman s database at immigrants Immigrant counts to US. Immigrant counts to US from various countries. immigrants

17 island.areas 17 A data frame with 41 observations on the following 4 variables. Country name of country Region region of the world Count.1998 the count of immigrants in 1998 Count.2008 the count of immigrants in New York Times Almanac island.areas Areas of islands from different continents. Total number of home runs hit all teams in the major league baseball league for the years 1900,1910,..., islands.areas A data frame with 59 observations on the following 3 variables. Ocean ocean where the island lies Name name of the island Area area of the island World Almanac and Book of Facts

18 18 lval lake Lake measurements Measurements of lakes in the Vilas and Oneida counties of northern Wisconsin (from Minitab dataset collection). lake A data frame with 71 observations on the following 5 variables. Area area of lake in acres Depth maximum depth of lake in feet PH ph (acidity) measurement Wshed watershed area in square miles Hions concentration of hydrogen ions Minitab data collection lval Letter values Computes the letter values for a batch of numbers. lval(x,na.rm=true) Arguments x na.rm numeric vector of values logical value indicating whether NA values should be stripped before the computation proceeds Value dataframe with components depth, lo, hi, mids, and spreads

19 mortality.rates 19 Author(s) Jim Albert Examples lval(rnorm(100)) mortality.rates Infant mortality rates of countries Infant mortality rates in 2008 for 62 countries (Afghanistan through Guyana). mortality.rates A data frame with 62 observations on the following 2 variables. Country name of country Rate infant mortality rate 2010 New York Times Almanac mtrans Matched transformation Computes a matched power transformation on data that preserves the median. mtrans(d,p) Arguments d p numeric vector of values power of transformation

20 20 olympics.run Value vector of values of matched transformation Author(s) Jim Albert Examples data(state) raw=state.x77[,"population"] matched.roots=mtrans(raw,0.5) matched.logs=mtrans(raw,0) boxplot(data.frame(raw,matched.roots,matched.logs)) olympics.run Olympics running times. Winning time in Olympics mens running race classified by length of race and year. olympics.run A data frame with 9 observations on the following 6 variables. year year of the Olympics X100m winning time in seconds for 100m X200m winning time in seconds for 200m X400m winning time in seconds for 400m X800m winning time in seconds for 800m X1500m winning time in seconds for 1500m World Almanac and Book of Facts 2008

21 olympics.speed.skating 21 olympics.speed.skating Olympics speed skating times. Winning time in Olympics mens speed skating race classified by length of race and year. olympics.speed.skating A data frame with 9 observations on the following 6 variables. YEAR year of the Olympics X500m winning time in seconds for 500m X1000m winning time in seconds for 1000m X1500m winning time in seconds for 1500m X5000m winning time in seconds fro 5000m X10000m winning time in seconds for 10000m World Almanac and Book of Facts 2008 olympics.swim Olympics swimming times. Winning time in Olympics womens swimming race classified by length of race and year. olympics.swim A data frame with 10 observations on the following 5 variables. YEAR year of the Olympics X100m winning time in seconds for 100m X200m winning time in seconds for 200m X400m winning time in seconds for 400m X800m winning time in seconds for 800m

22 22 pitching.history 2001 ESPN Information Please Sports Almanac pitching.history Yearly Pitching Statistics for Major League Baseball Year to year pitching statistics for all years of professional American baseball. batting.history A data frame with 140 observations on the following 31 variables. Year baseball season year Tms number of teams N.Pitch number of pitchers used PitchAge average age of pitchers R runs scored per game per team ERA average earned run average G number of games played GF proportion of games finished CG proportion of complete games SHO proportion of shutouts SV number of saves per game IP innings pitched per game H average number of hits ER average number of earned runs HR average number of home runs BB average number of base-on-balls IBB average number of intentional walks SO average number of strikeouts HBP average number of hit by pitch BK average number of balks WP average number of wild pitchers BF average number of batters faced

23 plot2way 23 WHIP average hits + walks per inning H.9 average number of hits per 9 innings HR.9 average number of home runs per 9 innings BB.9 average number of walks per 9 innings SO.9 average number of strikeouts per 9 innings SO.BB strikeouts to walks ratio E average number of errors Attendance total attendance Attend.G attendance per game web site plot2way Plot of an additive fit Graphs an additive fit of a two-way table. plot2way(row.part,col.part,row.lab=null,col.lab=null) Arguments row.part col.part row.lab col.lab numeric vector of values of row component numeric vector of values of column component character vector of labels of row component character vector of labels of column component Author(s) Jim Albert Examples temps=matrix(data=c(50,30,35,21,38, 73,58,65,57,63, 88,83,89,84,86, 73,62,68,59,66),nrow=5,ncol=4, dimnames=list(c("atlanta","detroit","kansas City", "Minneapolis","Philadelphia"),c("January","April", "July","October"))) fit=medpolish(temps) plot2way(fit$row+fit$overall,fit$col, dimnames(temps)[[1]],dimnames(temps)[[2]])

24 24 pop.densities pop.change Population change for all states in the U.S. Population change for all states in the United States between the years 2000 and pop.change A data frame with 51 observations on the following 5 variables. State name of state Abbrev state abbreviation Pop.2000 population of state in 2000 Pop.2009 population of state in 2009 Pct.change percentage change in population 2010 New York Times Almanac pop.densities Population densities of states for different years. Population density of each state in the United States for the years 1960, 1970, 1980, 1990, 2000, and pop.densities

25 pop.england 25 A data frame with 51 observations on the following 7 variables. State name of state Abbrev state abbreviation y1960 population density of state in 1960 y1970 population density of state in 1960 y1980 population density of state in 1980 y1990 population density of state in 1990 y2000 population density of state in 2000 y2008 population density of state in New York Times Almanac pop.england England population Population of England and Wales over the years. pop.england A data frame with 14 observations on the following 2 variables. YEAR year POPULATION population count Tukey, EDA

26 26 rent.prices power.t Power transformation Computes the standardized power transformation on a vector. A power of 0 is interpreted as the natural log transformation. power.t(x, power) Arguments x power numeric vector of values scalar value of power Value vector of reexpressed values Author(s) Jim Albert Examples power.t(c(3,6,5,4,7),0.5) rent.prices Rent prices in different cities. Rental prices (different types from different cities). rent.prices

27 rline 27 A data frame with 9 observations on the following 6 variables. City city Studio rent of a studio apartment One.Bedroom rent of a one bedroom apartment Two.Bedroom rent of a two bedroom apartment Three.Bedroom rent of a three bedroom apartment Four.Bedroom rent of a four bedroom apartment 2001 New York Times Almanac rline Resistant line Fits Tukey s resistant line of form a + b (x - xc). rline(x,y,iter=1) Arguments x y iter numeric vector of values of explanatory variable numeric vector of values of response variable number of iterations of algorithm Value a b value of intercept a value of slope b xc value of xc half.slope.ratio value of half slope ratio after one iteration residual spoints.x spoints.y Author(s) Jim Albert vector of residuals vector of summary x coordinates vector of summary y coordinates

28 28 slider.compare Examples x=1:10 y=3*x+rnorm(10,0,1) y[1]=20 fit=rline(x,y,iter=5) plot(x,y) curve(fit$a+fit$b*(x-fit$xc),add=true) # contrast with least-squares fit abline(lm(y~x)) salaries Salaries of different professions in different cities salaries of seven different professions in six cities. salaries A data frame with 42 observations on the following 3 variables. Salary salary in hundreds of Swiss francs City name of city Profession name of profession unknown slider.compare Interactive comparison of groups by a power transformation Constructs a boxplot of power-transformed data, where the power of the transformation is controlled by a slider. slider.compare(x,group,...)

29 slider.match 29 Arguments x numeric vector of values group grouping variable... additional graphics parameters passed to the boxplot function Author(s) Jim Albert Examples ## Not run: ## This example cannot be run by examples() but should be work in an interactive R session slider.compare(homeruns.2000$homeruns, homeruns.2000$years) ## End(Not run) slider.match Interactive reexpression by a power transformation using matched reexpressions Constructs boxplots of matched raw and power-transformed data, where the power of the transformation is controlled by a slider. Hinkley s method of asymmetry of the batch is displayed. slider.match(x,...) Arguments x numeric vector of values... additional graphics parameters passed to the hist function Author(s) Jim Albert Examples ## Not run: ## This example cannot be run by examples() but should be work in an interactive R session slider.match(rchisq(100,df=4)) ## End(Not run)

30 30 slider.straighten slider.power Interactive reexpression by a power transformation Constructs a histogram of power-transformed data, where the power of the transformation is controlled by a slider. Hinkley s method of asymmetry of the batch is displayed. slider.power(x,...) Arguments x numeric vector of values... additional graphics parameters passed to the hist function Author(s) Jim Albert Examples ## Not run: ## This example cannot be run by examples() but should be work in an interactive R session slider.power(rchisq(100,df=4)) ## End(Not run) slider.straighten Interactive straightening of plots by power transformations Constructs a scatterplot and residual plot of power-transformed data, where the powers of the x and y transformations are controlled by a slider. The half-slope ratio of the three summary points is displayed. slider.straighten(x, y,...)

31 snowfall 31 Arguments x numeric vector of values along the horizontal axis y numeric vector of values along the vertical axis... additional graphics parameters passed to the plot function Author(s) Jim Albert Examples ## Not run: ## This example cannot be run by examples() but should be work in an interactive R session slider.straighten(car.measurements$displacement, car.measurements$mpg) ## End(Not run) snowfall Snowfall amounts of two cities Seasonal snowfall (in inches) in Buffalo, New York and Cairo, Illinois from through snowfall A data frame with 40 observations on the following 3 variables. Season season where 1918 indicates the season City name of the city Snowfall snowfall in inches Tukey (1977), Exploratory Data Analysis, exhibit 11 of chapter 4

32 32 studentdata spread.level.plot Spread versus level plot Constructs a spread versus level plot and displays a resistant fit. spread.level.plot(response,group) Arguments response group numeric vector of response values vector of values of the grouping variable Author(s) Jim Albert Examples data(state) spread.level.plot(state.x77[,"area"],state.region) studentdata Student dataset Answers to a sheet of questions given to a large number of students in introductory statistics classes studentdata A data frame with 657 observations on the following 11 variables. Student student number Height height in inches Gender gender Shoes number of pairs of shoes owned Number number chosen between 1 and 10

33 symplot 33 Dvds name of movie dvds owned ToSleep time the person went to sleep the previous night (hours past midnight) WakeUp time the person woke up the next morning Haircut cost of last haircut including tip Job number of hours working on a job per week Drink usual drink at suppertime among milk, water, and pop Collected by the author during the Fall 2006 semester. symplot Symmetry plot Constructs a symmetry plot. symplot(d) Arguments d numeric vector of values Author(s) Jim Albert Examples # symmetry plot for normally distributed data symplot(rnorm(100)) # symmetry plot for exponential data symplot(rexp(100))

34 34 tukey.24a temperatures Temperatures for different cities. Two-table of average temperatures of cities for different months. temperatures A data frame with 5 observations on the following 5 variables. City city January average temperature in January April average temperature in April July average temperature in July October average temperature in October website tukey.24a Tukey straightening exercise 24a Data from exercise in Tukey, EDA. tukey.24a A data frame with 16 observations on the following 3 variables. latitude latitude index index temp temp Tukey, EDA

35 tukey.24b 35 tukey.24b Tukey straightening exercise 24b Data from exercise in Tukey, EDA. tukey.24b A data frame with 34 observations on the following 2 variables. diameter diameter stretch stretch Tukey, EDA tukey.26a Tukey straightening exercise 24a Measurement of a certain impurity in DDT; change of scale factor with temperature. tukey.26a A data frame with 14 observations on the following 2 variables. temp temp rate rate Tukey, EDA

36 36 tukey.26c tukey.26b Tukey straightening exercise 24b Demand deposits in post-office savings accounts in Switzerland. tukey.26b A data frame with 29 observations on the following 2 variables. year year deposits deposits Tukey, EDA tukey.26c Tukey straightening exercise 24c Revenue passenger miles on U.S. passenger airlines. tukey.26c A data frame with 24 observations on the following 2 variables. year year miles miles Tukey, EDA

37 us.pop 37 us.pop Population of United States Population of the United States over time. us.pop A data frame with 22 observations on the following 2 variables. YEAR year POP population count in millions Tukey, EDA

38 Index Topic datasets act.scores.06.07, 3 baseball.attendance, 3 batting.history, 4 beatles, 5 boston.marathon, 6 boston.marathon.wtimes, 6 braves.attendance, 7 car.measurements, 7 church.2way, 8 church.tseries, 8 college.ratings, 9 farms, 10 football, 11 gestation.periods, 11 grandma.19.40, 12 heaviest.fish, 14 home.prices, 15 homeruns.2000, 15 homeruns.61, 16 immigrants, 16 island.areas, 17 lake, 18 mortality.rates, 19 olympics.run, 20 olympics.speed.skating, 21 olympics.swim, 21 pitching.history, 22 pop.change, 24 pop.densities, 24 pop.england, 25 rent.prices, 26 salaries, 28 snowfall, 31 studentdata, 32 temperatures, 34 tukey.24a, 34 tukey.24b, 35 tukey.26a, 35 tukey.26b, 36 tukey.26c, 36 us.pop, 37 Topic dplot fit.gaussian, 10 han, 13 hinkley, 14 mtrans, 19 plot2way, 23 rline, 27 spread.level.plot, 32 symplot, 33 Topic manip half.slope.ratio, 12 lval, 18 power.t, 26 slider.compare, 28 slider.match, 29 slider.power, 30 slider.straighten, 30 act.scores.06.07, 3 baseball.attendance, 3 batting.history, 4 beatles, 5 boston.marathon, 6 boston.marathon.wtimes, 6 braves.attendance, 7 car.measurements, 7 church.2way, 8 church.tseries, 8 college.ratings, 9 farms, 10 fit.gaussian, 10 football, 11 gestation.periods, 11 grandma.19.40, 12 38

39 INDEX 39 half.slope.ratio, 12 han, 13 heaviest.fish, 14 hinkley, 14 home.prices, 15 homeruns.2000, 15 homeruns.61, 16 immigrants, 16 island.areas, 17 lake, 18 lval, 18 mortality.rates, 19 mtrans, 19 olympics.run, 20 olympics.speed.skating, 21 olympics.swim, 21 pitching.history, 22 plot2way, 23 pop.change, 24 pop.densities, 24 pop.england, 25 power.t, 26 rent.prices, 26 rline, 27 salaries, 28 slider.compare, 28 slider.match, 29 slider.power, 30 slider.straighten, 30 snowfall, 31 spread.level.plot, 32 studentdata, 32 symplot, 33 temperatures, 34 tukey.24a, 34 tukey.24b, 35 tukey.26a, 35 tukey.26b, 36 tukey.26c, 36 us.pop, 37