Getting Familiar with Categorical Data in R
Kejia You
2020-04-02
Homework #1 is worth 100 points and each question is worth 6.5 points each.
Submission Instructions: save the .HTML file as ‘Familiar_ Categorical_Data_Assignmentyourlastname.HTML’ and upload the HTML file to the assignment entitled ‘Getting Familiar with Categorical Data in R’ on Canvas on or before Thursday April 02, 2020 by 11:59p.m. EST. No late assignments are accepted.
- #2.1 p.p. 60-61
Run the code chunk below.
library(vcd)## Warning: package 'vcd' was built under R version 3.6.3## Loading required package: gridlibrary(grid)
library(gnm)## Warning: package 'gnm' was built under R version 3.6.3library(vcdExtra)## Warning: package 'vcdExtra' was built under R version 3.6.3ds <- datasets(package = c("vcd", "vcdExtra"))
str(ds, vec.len=2)## 'data.frame': 76 obs. of 5 variables:
## $ Package: chr "vcd" "vcd" ...
## $ Item : chr "Arthritis" "Baseball" ...
## $ class : chr "data.frame" "data.frame" ...
## $ dim : chr "84x5" "322x25" ...
## $ Title : chr "Arthritis Treatment Data" "Baseball Data" ...## 'data.frame': 76 obs. of 5 variables:
## $ Package: chr "vcd" "vcd" ...
## $ Item : chr "Arthritis" "Baseball" ...
## $ class : chr "data.frame" "data.frame" ...
## $ dim : chr "84x5" "322x25" ...
## $ Title : chr "Arthritis Treatment Data" "Baseball Data" ...
head(ds)## Package Item class dim
## 1 vcd Arthritis data.frame 84x5
## 2 vcd Baseball data.frame 322x25
## 3 vcd BrokenMarriage data.frame 20x4
## 4 vcd Bundesliga data.frame 14018x7
## 5 vcd Bundestag2005 table 16x5
## 6 vcd Butterfly table 24
## Title
## 1 Arthritis Treatment Data
## 2 Baseball Data
## 3 Broken Marriage Data
## 4 Ergebnisse der Fussball-Bundesliga
## 5 Votes in German Bundestag Election 2005
## 6 Butterfly Species in Malaya## Package Item class dim
## 1 vcd Arthritis data.frame 84x5
## 2 vcd Baseball data.frame 322x25
## 3 vcd BrokenMarriage data.frame 20x4
## 4 vcd Bundesliga data.frame 14018x7
## 5 vcd Bundestag2005 table 16x5
## 6 vcd Butterfly table 24
## Title
## 1 Arthritis Treatment Data
## 2 Baseball Data
## 3 Broken Marriage Data
## 4 Ergebnisse der Fussball-Bundesliga
## 5 Votes in German Bundestag Election 2005
## 6 Butterfly Species in Malaya
head(UCBAdmissions)## [1] 512 313 89 19 353 207## [1] 512 313 89 19 353 207
str(UCBAdmissions)## 'table' num [1:2, 1:2, 1:6] 512 313 89 19 353 207 17 8 120 205 ...
## - attr(*, "dimnames")=List of 3
## ..$ Admit : chr [1:2] "Admitted" "Rejected"
## ..$ Gender: chr [1:2] "Male" "Female"
## ..$ Dept : chr [1:6] "A" "B" "C" "D" ...## 'table' num [1:2, 1:2, 1:6] 512 313 89 19 353 207 17 8 120 205 ...
## - attr(*, "dimnames")=List of 3
## ..$ Admit : chr [1:2] "Admitted" "Rejected"
## ..$ Gender: chr [1:2] "Male" "Female"
## ..$ Dept : chr [1:6] "A" "B" "C" "D" ...- How many data sets are there altogether? How many are there in each package? There are 33 datasets in vcd and 43 datasets in vcdExtra.
nrow(ds)## [1] 76## [1] 76
dsvcd <- datasets(package = "vcd")
nrow(dsvcd)## [1] 33## [1] 33
dsextra <- datasets(package = "vcdExtra")
nrow(dsextra)## [1] 43## [1] 43- Make a tabular display of the frequencies by Package and class.
ds## Package Item class dim
## 1 vcd Arthritis data.frame 84x5
## 2 vcd Baseball data.frame 322x25
## 3 vcd BrokenMarriage data.frame 20x4
## 4 vcd Bundesliga data.frame 14018x7
## 5 vcd Bundestag2005 table 16x5
## 6 vcd Butterfly table 24
## 7 vcd CoalMiners table 2x2x9
## 8 vcd DanishWelfare data.frame 180x5
## 9 vcd Employment table 2x6x2
## 10 vcd Federalist table 7
## 11 vcd Hitters data.frame 154x4
## 12 vcd HorseKicks table 5
## 13 vcd Hospital table 3x3
## 14 vcd JobSatisfaction data.frame 8x4
## 15 vcd JointSports data.frame 40x5
## 16 vcd Lifeboats data.frame 18x8
## 17 vcd MSPatients array 4x4x2
## 18 vcd NonResponse data.frame 12x4
## 19 vcd OvaryCancer data.frame 16x5
## 20 vcd PreSex table 2x2x2x2
## 21 vcd Punishment data.frame 36x5
## 22 vcd RepVict table 8x8
## 23 vcd Rochdale table 2x2x2x2x2x2x2x2
## 24 vcd Saxony table 13
## 25 vcd SexualFun table 4x4
## 26 vcd SpaceShuttle data.frame 24x6
## 27 vcd Suicide data.frame 306x6
## 28 vcd Trucks data.frame 24x5
## 29 vcd UKSoccer table 5x5
## 30 vcd VisualAcuity data.frame 32x4
## 31 vcd VonBort data.frame 280x4
## 32 vcd WeldonDice table 11
## 33 vcd WomenQueue table 11
## 34 vcdExtra Abortion table 2x2x2
## 35 vcdExtra Accident data.frame 80x5
## 36 vcdExtra AirCrash data.frame 439x5
## 37 vcdExtra Alligator data.frame 80x5
## 38 vcdExtra Bartlett table 2x2x2
## 39 vcdExtra Burt data.frame 36x5
## 40 vcdExtra Caesar table 3x2x2x2
## 41 vcdExtra Cancer table 2x2x2
## 42 vcdExtra Cormorants data.frame 343x8
## 43 vcdExtra CyclingDeaths data.frame 208x2
## 44 vcdExtra DaytonSurvey data.frame 32x6
## 45 vcdExtra Depends table 15
## 46 vcdExtra Detergent table 2x2x2x3
## 47 vcdExtra Donner data.frame 90x5
## 48 vcdExtra Draft1970 data.frame 366x3
## 49 vcdExtra Draft1970table table 12x3
## 50 vcdExtra Dyke table 2x2x2x2x2
## 51 vcdExtra Fungicide array 2x2x2x2
## 52 vcdExtra GSS data.frame 6x3
## 53 vcdExtra Geissler data.frame 90x4
## 54 vcdExtra Gilby table 6x4
## 55 vcdExtra Glass data.frame 25x3
## 56 vcdExtra HairEyePlace array 4x5x2
## 57 vcdExtra Hauser79 data.frame 25x3
## 58 vcdExtra Heart table 2x2x3
## 59 vcdExtra Heckman table 2x2x2x2x2
## 60 vcdExtra HospVisits table 3x3
## 61 vcdExtra Hoyt table 4x3x7x2
## 62 vcdExtra ICU data.frame 200x22
## 63 vcdExtra JobSat table 4x4
## 64 vcdExtra Mammograms matrix 4x4
## 65 vcdExtra Mental data.frame 24x3
## 66 vcdExtra Mice data.frame 30x4
## 67 vcdExtra Mobility table 5x5
## 68 vcdExtra PhdPubs data.frame 915x6
## 69 vcdExtra ShakeWords data.frame 100x2
## 70 vcdExtra TV array 5x11x3
## 71 vcdExtra Titanicp data.frame 1309x6
## 72 vcdExtra Toxaemia data.frame 60x5
## 73 vcdExtra Vietnam data.frame 40x4
## 74 vcdExtra Vote1980 data.frame 28x4
## 75 vcdExtra WorkerSat data.frame 8x4
## 76 vcdExtra Yamaguchi87 data.frame 75x4
## Title
## 1 Arthritis Treatment Data
## 2 Baseball Data
## 3 Broken Marriage Data
## 4 Ergebnisse der Fussball-Bundesliga
## 5 Votes in German Bundestag Election 2005
## 6 Butterfly Species in Malaya
## 7 Breathlessness and Wheeze in Coal Miners
## 8 Danish Welfare Study Data
## 9 Employment Status
## 10 'May' in Federalist Papers
## 11 Hitters Data
## 12 Death by Horse Kicks
## 13 Hospital data
## 14 Job Satisfaction Data
## 15 Opinions About Joint Sports
## 16 Lifeboats on the Titanic
## 17 Diagnosis of Multiple Sclerosis
## 18 Non-Response Survey Data
## 19 Ovary Cancer Data
## 20 Pre-marital Sex and Divorce
## 21 Corporal Punishment Data
## 22 Repeat Victimization Data
## 23 Rochdale Data
## 24 Families in Saxony
## 25 Sex is Fun
## 26 Space Shuttle O-ring Failures
## 27 Suicide Rates in Germany
## 28 Truck Accidents Data
## 29 UK Soccer Scores
## 30 Visual Acuity in Left and Right Eyes
## 31 Von Bortkiewicz Horse Kicks Data
## 32 Weldon's Dice Data
## 33 Women in Queues
## 34 Abortion Opinion Data
## 35 Traffic Accident Victims in France in 1958
## 36 Air Crash Data
## 37 Alligator Food Choice
## 38 Bartlett data on plum root cuttings
## 39 Burt (1950) Data on Hair, Eyes, Head and Stature
## 40 Risk Factors for Infection in Caesarian Births
## 41 Survival of Breast Cancer Patients
## 42 Advertising Behavior by Males Cormorants
## 43 London Cycling Deaths
## 44 Dayton Student Survey on Substance Use
## 45 Dependencies of R Packages
## 46 Detergent preference data
## 47 Survival in the Donner Party
## 48 USA 1970 Draft Lottery Data
## 49 USA 1970 Draft Lottery Table
## 50 Sources of Knowledge of Cancer
## 51 Carcinogenic Effects of a Fungicide
## 52 General Social Survey- Sex and Party affiliation
## 53 Geissler's Data on the Human Sex Ratio
## 54 Clothing and Intelligence Rating of Children
## 55 British Social Mobility from Glass(1954)
## 56 Hair Color and Eye Color in Caithness and Aberdeen
## 57 Hauser (1979) Data on Social Mobility
## 58 Sex, Occupation and Heart Disease
## 59 Labour Force Participation of Married Women 1967-1971
## 60 Hospital Visits Data
## 61 Minnesota High School Graduates
## 62 ICU data set
## 63 Cross-classification of job satisfaction by income
## 64 Mammogram Ratings
## 65 Mental impariment and parents SES
## 66 Mice Depletion Data
## 67 Social Mobility data
## 68 Publications of PhD Candidates
## 69 Shakespeare's Word Type Frequencies
## 70 TV Viewing Data
## 71 Passengers on the Titanic
## 72 Toxaemia Symptoms in Pregnancy
## 73 Student Opinion about the Vietnam War
## 74 Race and Politics in the 1980 Presidential Vote
## 75 Worker Satisfaction Data
## 76 Occupational Mobility in Three Countries## Package Item class dim
## 1 vcd Arthritis data.frame 84x5
## 2 vcd Baseball data.frame 322x25
## 3 vcd BrokenMarriage data.frame 20x4
## 4 vcd Bundesliga data.frame 14018x7
## 5 vcd Bundestag2005 table 16x5
## 6 vcd Butterfly table 24
## 7 vcd CoalMiners table 2x2x9
## 8 vcd DanishWelfare data.frame 180x5
## 9 vcd Employment table 2x6x2
## 10 vcd Federalist table 7
## 11 vcd Hitters data.frame 154x4
## 12 vcd HorseKicks table 5
## 13 vcd Hospital table 3x3
## 14 vcd JobSatisfaction data.frame 8x4
## 15 vcd JointSports data.frame 40x5
## 16 vcd Lifeboats data.frame 18x8
## 17 vcd MSPatients array 4x4x2
## 18 vcd NonResponse data.frame 12x4
## 19 vcd OvaryCancer data.frame 16x5
## 20 vcd PreSex table 2x2x2x2
## 21 vcd Punishment data.frame 36x5
## 22 vcd RepVict table 8x8
## 23 vcd Rochdale table 2x2x2x2x2x2x2x2
## 24 vcd Saxony table 13
## 25 vcd SexualFun table 4x4
## 26 vcd SpaceShuttle data.frame 24x6
## 27 vcd Suicide data.frame 306x6
## 28 vcd Trucks data.frame 24x5
## 29 vcd UKSoccer table 5x5
## 30 vcd VisualAcuity data.frame 32x4
## 31 vcd VonBort data.frame 280x4
## 32 vcd WeldonDice table 11
## 33 vcd WomenQueue table 11
## 34 vcdExtra Abortion table 2x2x2
## 35 vcdExtra Accident data.frame 80x5
## 36 vcdExtra AirCrash data.frame 439x5
## 37 vcdExtra Alligator data.frame 80x5
## 38 vcdExtra Bartlett table 2x2x2
## 39 vcdExtra Burt data.frame 36x5
## 40 vcdExtra Caesar table 3x2x2x2
## 41 vcdExtra Cancer table 2x2x2
## 42 vcdExtra Cormorants data.frame 343x8
## 43 vcdExtra CyclingDeaths data.frame 208x2
## 44 vcdExtra DaytonSurvey data.frame 32x6
## 45 vcdExtra Depends table 15
## 46 vcdExtra Detergent table 2x2x2x3
## 47 vcdExtra Donner data.frame 90x5
## 48 vcdExtra Draft1970 data.frame 366x3
## 49 vcdExtra Draft1970table table 12x3
## 50 vcdExtra Dyke table 2x2x2x2x2
## 51 vcdExtra Fungicide array 2x2x2x2
## 52 vcdExtra GSS data.frame 6x3
## 53 vcdExtra Geissler data.frame 90x4
## 54 vcdExtra Gilby table 6x4
## 55 vcdExtra Glass data.frame 25x3
## 56 vcdExtra HairEyePlace array 4x5x2
## 57 vcdExtra Hauser79 data.frame 25x3
## 58 vcdExtra Heart table 2x2x3
## 59 vcdExtra Heckman table 2x2x2x2x2
## 60 vcdExtra HospVisits table 3x3
## 61 vcdExtra Hoyt table 4x3x7x2
## 62 vcdExtra ICU data.frame 200x22
## 63 vcdExtra JobSat table 4x4
## 64 vcdExtra Mammograms matrix 4x4
## 65 vcdExtra Mental data.frame 24x3
## 66 vcdExtra Mice data.frame 30x4
## 67 vcdExtra Mobility table 5x5
## 68 vcdExtra PhdPubs data.frame 915x6
## 69 vcdExtra ShakeWords data.frame 100x2
## 70 vcdExtra TV array 5x11x3
## 71 vcdExtra Titanicp data.frame 1309x6
## 72 vcdExtra Toxaemia data.frame 60x5
## 73 vcdExtra Vietnam data.frame 40x4
## 74 vcdExtra Vote1980 data.frame 28x4
## 75 vcdExtra WorkerSat data.frame 8x4
## 76 vcdExtra Yamaguchi87 data.frame 75x4
## Title
## 1 Arthritis Treatment Data
## 2 Baseball Data
## 3 Broken Marriage Data
## 4 Ergebnisse der Fussball-Bundesliga
## 5 Votes in German Bundestag Election 2005
## 6 Butterfly Species in Malaya
## 7 Breathlessness and Wheeze in Coal Miners
## 8 Danish Welfare Study Data
## 9 Employment Status
## 10 'May' in Federalist Papers
## 11 Hitters Data
## 12 Death by Horse Kicks
## 13 Hospital data
## 14 Job Satisfaction Data
## 15 Opinions About Joint Sports
## 16 Lifeboats on the Titanic
## 17 Diagnosis of Multiple Sclerosis
## 18 Non-Response Survey Data
## 19 Ovary Cancer Data
## 20 Pre-marital Sex and Divorce
## 21 Corporal Punishment Data
## 22 Repeat Victimization Data
## 23 Rochdale Data
## 24 Families in Saxony
## 25 Sex is Fun
## 26 Space Shuttle O-ring Failures
## 27 Suicide Rates in Germany
## 28 Truck Accidents Data
## 29 UK Soccer Scores
## 30 Visual Acuity in Left and Right Eyes
## 31 Von Bortkiewicz Horse Kicks Data
## 32 Weldon's Dice Data
## 33 Women in Queues
## 34 Abortion Opinion Data
## 35 Traffic Accident Victims in France in 1958
## 36 Air Crash Data
## 37 Alligator Food Choice
## 38 Bartlett data on plum root cuttings
## 39 Burt (1950) Data on Hair, Eyes, Head and Stature
## 40 Risk Factors for Infection in Caesarian Births
## 41 Survival of Breast Cancer Patients
## 42 Advertising Behavior by Males Cormorants
## 43 London Cycling Deaths
## 44 Dayton Student Survey on Substance Use
## 45 Dependencies of R Packages
## 46 Detergent preference data
## 47 Survival in the Donner Party
## 48 USA 1970 Draft Lottery Data
## 49 USA 1970 Draft Lottery Table
## 50 Sources of Knowledge of Cancer
## 51 Carcinogenic Effects of a Fungicide
## 52 General Social Survey- Sex and Party affiliation
## 53 Geissler's Data on the Human Sex Ratio
## 54 Clothing and Intelligence Rating of Children
## 55 British Social Mobility from Glass(1954)
## 56 Hair Color and Eye Color in Caithness and Aberdeen
## 57 Hauser (1979) Data on Social Mobility
## 58 Sex, Occupation and Heart Disease
## 59 Labour Force Participation of Married Women 1967-1971
## 60 Hospital Visits Data
## 61 Minnesota High School Graduates
## 62 ICU data set
## 63 Cross-classification of job satisfaction by income
## 64 Mammogram Ratings
## 65 Mental impariment and parents SES
## 66 Mice Depletion Data
## 67 Social Mobility data
## 68 Publications of PhD Candidates
## 69 Shakespeare's Word Type Frequencies
## 70 TV Viewing Data
## 71 Passengers on the Titanic
## 72 Toxaemia Symptoms in Pregnancy
## 73 Student Opinion about the Vietnam War
## 74 Race and Politics in the 1980 Presidential Vote
## 75 Worker Satisfaction Data
## 76 Occupational Mobility in Three Countries
table(ds$Package, ds$class)##
## array data.frame matrix table
## vcd 1 17 0 15
## vcdExtra 3 24 1 15## array data.frame matrix table
## vcd 1 17 0 15
## vcdExtra 3 24 1 15- Choose one or two data sets from this list, and examine their help files (e.g., help(Arthritis) or ?Arthritis). You can use, e.g., example(Arthritis) to run the R code for a given example.
?Arthritis## starting httpd help server ... done## starting httpd help server ... done
example("Arthritis")##
## Arthrt> data("Arthritis")
##
## Arthrt> art <- xtabs(~ Treatment + Improved, data = Arthritis, subset = Sex == "Female")
##
## Arthrt> art
## Improved
## Treatment None Some Marked
## Placebo 19 7 6
## Treated 6 5 16
##
## Arthrt> mosaic(art, gp = shading_Friendly)
##
## Arthrt> mosaic(art, gp = shading_max)
## Arthrt> data("Arthritis")
##
## Arthrt> art <- xtabs(~ Treatment + Improved, data = Arthritis, subset = Sex == "Female")
##
## Arthrt> art
## Improved
## Treatment None Some Marked
## Placebo 19 7 6
## Treated 6 5 16
##
## Arthrt> mosaic(art, gp = shading_Friendly)- p. 61 #2.3
- Find the total number of cases contained in this table.
sum(UCBAdmissions)## [1] 4526## [1] 4526- For each department, find the total number of applicants.
margin.table(UCBAdmissions,3)## Dept
## A B C D E F
## 933 585 918 792 584 714## Dept
## A B C D E F
## 933 585 918 792 584 714- For each department, find the overall proportion of applicants who were admitted.
UCBA_df<-as.data.frame(UCBAdmissions)
Total<-xtabs(Freq~Dept+Admit,data = UCBA_df)
prop.table(Total,1)## Admit
## Dept Admitted Rejected
## A 0.64415863 0.35584137
## B 0.63247863 0.36752137
## C 0.35076253 0.64923747
## D 0.33964646 0.66035354
## E 0.25171233 0.74828767
## F 0.06442577 0.93557423## Admit
## Dept Admitted Rejected
## A 0.64415863 0.35584137
## B 0.63247863 0.36752137
## C 0.35076253 0.64923747
## D 0.33964646 0.66035354
## E 0.25171233 0.74828767
## F 0.06442577 0.93557423- Construct a tabular display of department (rows) and gender (columns), showing the proportion of applicants in each cell who were admitted relative to the total applicants in that cell.
Total2<-xtabs(Freq~Dept+Gender+Admit,data = UCBA_df)
prop.table(Total2,1)## , , Admit = Admitted
##
## Gender
## Dept Male Female
## A 0.54876742 0.09539121
## B 0.60341880 0.02905983
## C 0.13071895 0.22004357
## D 0.17424242 0.16540404
## E 0.09075342 0.16095890
## F 0.03081232 0.03361345
##
## , , Admit = Rejected
##
## Gender
## Dept Male Female
## A 0.33547696 0.02036442
## B 0.35384615 0.01367521
## C 0.22331155 0.42592593
## D 0.35227273 0.30808081
## E 0.23630137 0.51198630
## F 0.49159664 0.44397759## , , Admit = Admitted
##
## Gender
## Dept Male Female
## A 0.54876742 0.09539121
## B 0.60341880 0.02905983
## C 0.13071895 0.22004357
## D 0.17424242 0.16540404
## E 0.09075342 0.16095890
## F 0.03081232 0.03361345
##
## , , Admit = Rejected
##
## Gender
## Dept Male Female
## A 0.33547696 0.02036442
## B 0.35384615 0.01367521
## C 0.22331155 0.42592593
## D 0.35227273 0.30808081
## E 0.23630137 0.51198630
## F 0.49159664 0.44397759- p. 61 #2.4 a, c, e
- Find the total number of cases represented in this table.
str(DanishWelfare)## 'data.frame': 180 obs. of 5 variables:
## $ Freq : num 1 4 1 8 6 14 8 41 100 175 ...
## $ Alcohol: Factor w/ 3 levels "<1","1-2",">2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Income : Factor w/ 4 levels "0-50","50-100",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Status : Factor w/ 3 levels "Widow","Married",..: 1 1 1 1 1 2 2 2 2 2 ...
## $ Urban : Factor w/ 5 levels "Copenhagen","SubCopenhagen",..: 1 2 3 4 5 1 2 3 4 5 ...## 'data.frame': 180 obs. of 5 variables:
## $ Freq : num 1 4 1 8 6 14 8 41 100 175 ...
## $ Alcohol: Factor w/ 3 levels "<1","1-2",">2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Income : Factor w/ 4 levels "0-50","50-100",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Status : Factor w/ 3 levels "Widow","Married",..: 1 1 1 1 1 2 2 2 2 2 ...
## $ Urban : Factor w/ 5 levels "Copenhagen","SubCopenhagen",..: 1 2 3 4 5 1 2 3 4 5 ...
sum(DanishWelfare$Freq)## [1] 5144## [1] 5144- Convert this data frame to table form, DanishWelfare.tab, a 4-way array containing the frequencies with appropriate variable names and level names.
DanishWelfare.tab<-structable(Freq~Income+Status+Urban+Alcohol,data = DanishWelfare)
DanishWelfare.tab## Status Widow Married Unmarried
## Alcohol <1 1-2 >2 <1 1-2 >2 <1 1-2 >2
## Income Urban
## 0-50 Copenhagen 1 3 2 14 15 1 6 2 3
## SubCopenhagen 4 0 0 8 7 2 1 3 0
## LargeCity 1 1 2 41 15 2 2 9 1
## City 8 4 1 100 25 7 6 9 5
## Country 6 2 0 175 48 7 9 7 1
## 50-100 Copenhagen 8 1 3 42 39 14 7 12 2
## SubCopenhagen 2 1 0 51 59 21 5 3 0
## LargeCity 7 3 2 62 68 14 9 11 3
## City 14 8 1 234 172 38 20 20 12
## Country 5 4 3 255 143 35 27 23 13
## 100-150 Copenhagen 2 5 2 21 32 20 3 6 0
## SubCopenhagen 3 4 1 30 68 31 2 10 2
## LargeCity 1 1 1 23 43 10 1 5 3
## City 5 9 1 87 128 36 12 21 9
## Country 2 4 0 77 86 21 4 15 7
## >150 Copenhagen 42 26 21 24 43 23 33 36 38
## SubCopenhagen 29 34 13 30 76 47 24 23 20
## LargeCity 17 14 5 50 70 21 15 48 13
## City 95 48 20 167 198 53 64 89 39
## Country 46 24 8 232 136 36 68 64 26## Status Widow Married Unmarried
## Alcohol <1 1-2 >2 <1 1-2 >2 <1 1-2 >2
## Income Urban
## 0-50 Copenhagen 1 3 2 14 15 1 6 2 3
## SubCopenhagen 4 0 0 8 7 2 1 3 0
## LargeCity 1 1 2 41 15 2 2 9 1
## City 8 4 1 100 25 7 6 9 5
## Country 6 2 0 175 48 7 9 7 1
## 50-100 Copenhagen 8 1 3 42 39 14 7 12 2
## SubCopenhagen 2 1 0 51 59 21 5 3 0
## LargeCity 7 3 2 62 68 14 9 11 3
## City 14 8 1 234 172 38 20 20 12
## Country 5 4 3 255 143 35 27 23 13
## 100-150 Copenhagen 2 5 2 21 32 20 3 6 0
## SubCopenhagen 3 4 1 30 68 31 2 10 2
## LargeCity 1 1 1 23 43 10 1 5 3
## City 5 9 1 87 128 36 12 21 9
## Country 2 4 0 77 86 21 4 15 7
## >150 Copenhagen 42 26 21 24 43 23 33 36 38
## SubCopenhagen 29 34 13 30 76 47 24 23 20
## LargeCity 17 14 5 50 70 21 15 48 13
## City 95 48 20 167 198 53 64 89 39
## Country 46 24 8 232 136 36 68 64 26- Use structable () or ftable () to produce a pleasing flattened display of the frequencies in the 4-way table. Choose the variables used as row and column variables to make it easier to compare levels of Alcohol across the other factors.
ftable(Alcohol~Income+Status+Urban,data = DanishWelfare.tab)## Alcohol <1 1-2 >2
## Income Status Urban
## 0-50 Widow Copenhagen 1 3 2
## SubCopenhagen 4 0 0
## LargeCity 1 1 2
## City 8 4 1
## Country 6 2 0
## Married Copenhagen 14 15 1
## SubCopenhagen 8 7 2
## LargeCity 41 15 2
## City 100 25 7
## Country 175 48 7
## Unmarried Copenhagen 6 2 3
## SubCopenhagen 1 3 0
## LargeCity 2 9 1
## City 6 9 5
## Country 9 7 1
## 50-100 Widow Copenhagen 8 1 3
## SubCopenhagen 2 1 0
## LargeCity 7 3 2
## City 14 8 1
## Country 5 4 3
## Married Copenhagen 42 39 14
## SubCopenhagen 51 59 21
## LargeCity 62 68 14
## City 234 172 38
## Country 255 143 35
## Unmarried Copenhagen 7 12 2
## SubCopenhagen 5 3 0
## LargeCity 9 11 3
## City 20 20 12
## Country 27 23 13
## 100-150 Widow Copenhagen 2 5 2
## SubCopenhagen 3 4 1
## LargeCity 1 1 1
## City 5 9 1
## Country 2 4 0
## Married Copenhagen 21 32 20
## SubCopenhagen 30 68 31
## LargeCity 23 43 10
## City 87 128 36
## Country 77 86 21
## Unmarried Copenhagen 3 6 0
## SubCopenhagen 2 10 2
## LargeCity 1 5 3
## City 12 21 9
## Country 4 15 7
## >150 Widow Copenhagen 42 26 21
## SubCopenhagen 29 34 13
## LargeCity 17 14 5
## City 95 48 20
## Country 46 24 8
## Married Copenhagen 24 43 23
## SubCopenhagen 30 76 47
## LargeCity 50 70 21
## City 167 198 53
## Country 232 136 36
## Unmarried Copenhagen 33 36 38
## SubCopenhagen 24 23 20
## LargeCity 15 48 13
## City 64 89 39
## Country 68 64 26## Alcohol <1 1-2 >2
## Income Status Urban
## 0-50 Widow Copenhagen 1 3 2
## SubCopenhagen 4 0 0
## LargeCity 1 1 2
## City 8 4 1
## Country 6 2 0
## Married Copenhagen 14 15 1
## SubCopenhagen 8 7 2
## LargeCity 41 15 2
## City 100 25 7
## Country 175 48 7
## Unmarried Copenhagen 6 2 3
## SubCopenhagen 1 3 0
## LargeCity 2 9 1
## City 6 9 5
## Country 9 7 1
## 50-100 Widow Copenhagen 8 1 3
## SubCopenhagen 2 1 0
## LargeCity 7 3 2
## City 14 8 1
## Country 5 4 3
## Married Copenhagen 42 39 14
## SubCopenhagen 51 59 21
## LargeCity 62 68 14
## City 234 172 38
## Country 255 143 35
## Unmarried Copenhagen 7 12 2
## SubCopenhagen 5 3 0
## LargeCity 9 11 3
## City 20 20 12
## Country 27 23 13
## 100-150 Widow Copenhagen 2 5 2
## SubCopenhagen 3 4 1
## LargeCity 1 1 1
## City 5 9 1
## Country 2 4 0
## Married Copenhagen 21 32 20
## SubCopenhagen 30 68 31
## LargeCity 23 43 10
## City 87 128 36
## Country 77 86 21
## Unmarried Copenhagen 3 6 0
## SubCopenhagen 2 10 2
## LargeCity 1 5 3
## City 12 21 9
## Country 4 15 7
## >150 Widow Copenhagen 42 26 21
## SubCopenhagen 29 34 13
## LargeCity 17 14 5
## City 95 48 20
## Country 46 24 8
## Married Copenhagen 24 43 23
## SubCopenhagen 30 76 47
## LargeCity 50 70 21
## City 167 198 53
## Country 232 136 36
## Unmarried Copenhagen 33 36 38
## SubCopenhagen 24 23 20
## LargeCity 15 48 13
## City 64 89 39
## Country 68 64 26- p. 62 #2.5 a, b, c
#code from text
data("UKSoccer", package = "vcd")
ftable(UKSoccer)## Away 0 1 2 3 4
## Home
## 0 27 29 10 8 2
## 1 59 53 14 12 4
## 2 28 32 14 12 4
## 3 19 14 7 4 1
## 4 7 8 10 2 0## Away 0 1 2 3 4
## Home
## 0 27 29 10 8 2
## 1 59 53 14 12 4
## 2 28 32 14 12 4
## 3 19 14 7 4 1
## 4 7 8 10 2 0
str(UKSoccer)## 'table' num [1:5, 1:5] 27 59 28 19 7 29 53 32 14 8 ...
## - attr(*, "dimnames")=List of 2
## ..$ Home: chr [1:5] "0" "1" "2" "3" ...
## ..$ Away: chr [1:5] "0" "1" "2" "3" ...## 'table' num [1:5, 1:5] 27 59 28 19 7 29 53 32 14 8 ...
## - attr(*, "dimnames")=List of 2
## ..$ Home: chr [1:5] "0" "1" "2" "3" ...
## ..$ Away: chr [1:5] "0" "1" "2" "3" ...- Verify that the total number of games represented in this table is 380.
margin.table(UKSoccer)## [1] 380## [1] 380- Find the marginal total of the number of goals scored by each of the home and away teams.
addmargins(UKSoccer)## Away
## Home 0 1 2 3 4 Sum
## 0 27 29 10 8 2 76
## 1 59 53 14 12 4 142
## 2 28 32 14 12 4 90
## 3 19 14 7 4 1 45
## 4 7 8 10 2 0 27
## Sum 140 136 55 38 11 380## Away
## Home 0 1 2 3 4 Sum
## 0 27 29 10 8 2 76
## 1 59 53 14 12 4 142
## 2 28 32 14 12 4 90
## 3 19 14 7 4 1 45
## 4 7 8 10 2 0 27
## Sum 140 136 55 38 11 380- Express each of the marginal totals as proportions.
addmargins(prop.table(UKSoccer))## Away
## Home 0 1 2 3 4 Sum
## 0 0.071052632 0.076315789 0.026315789 0.021052632 0.005263158 0.200000000
## 1 0.155263158 0.139473684 0.036842105 0.031578947 0.010526316 0.373684211
## 2 0.073684211 0.084210526 0.036842105 0.031578947 0.010526316 0.236842105
## 3 0.050000000 0.036842105 0.018421053 0.010526316 0.002631579 0.118421053
## 4 0.018421053 0.021052632 0.026315789 0.005263158 0.000000000 0.071052632
## Sum 0.368421053 0.357894737 0.144736842 0.100000000 0.028947368 1.000000000## Away
## Home 0 1 2 3 4
## 0 0.071052632 0.076315789 0.026315789 0.021052632 0.005263158
## 1 0.155263158 0.139473684 0.036842105 0.031578947 0.010526316
## 2 0.073684211 0.084210526 0.036842105 0.031578947 0.010526316
## 3 0.050000000 0.036842105 0.018421053 0.010526316 0.002631579
## 4 0.018421053 0.021052632 0.026315789 0.005263158 0.000000000
## Sum 0.368421053 0.357894737 0.144736842 0.100000000 0.028947368
## Away
## Home Sum
## 0 0.200000000
## 1 0.373684211
## 2 0.236842105
## 3 0.118421053
## 4 0.071052632
## Sum 1.000000000- Run the code below and notice there is a data frame entitled SpaceShuttle. Using the R help, read about the details of this data frame. That is, familiarize yourself with the context and understand the meaning of the different rows.
library(vcd)
library(vcdExtra)
ds <- datasets(package = c("vcd", "vcdExtra"))
str(ds)## 'data.frame': 76 obs. of 5 variables:
## $ Package: chr "vcd" "vcd" "vcd" "vcd" ...
## $ Item : chr "Arthritis" "Baseball" "BrokenMarriage" "Bundesliga" ...
## $ class : chr "data.frame" "data.frame" "data.frame" "data.frame" ...
## $ dim : chr "84x5" "322x25" "20x4" "14018x7" ...
## $ Title : chr "Arthritis Treatment Data" "Baseball Data" "Broken Marriage Data" "Ergebnisse der Fussball-Bundesliga" ...## 'data.frame': 76 obs. of 5 variables:
## $ Package: chr "vcd" "vcd" "vcd" "vcd" ...
## $ Item : chr "Arthritis" "Baseball" "BrokenMarriage" "Bundesliga" ...
## $ class : chr "data.frame" "data.frame" "data.frame" "data.frame" ...
## $ dim : chr "84x5" "322x25" "20x4" "14018x7" ...
## $ Title : chr "Arthritis Treatment Data" "Baseball Data" "Broken Marriage Data" "Ergebnisse der Fussball-Bundesliga" ...
head(ds)## Package Item class dim
## 1 vcd Arthritis data.frame 84x5
## 2 vcd Baseball data.frame 322x25
## 3 vcd BrokenMarriage data.frame 20x4
## 4 vcd Bundesliga data.frame 14018x7
## 5 vcd Bundestag2005 table 16x5
## 6 vcd Butterfly table 24
## Title
## 1 Arthritis Treatment Data
## 2 Baseball Data
## 3 Broken Marriage Data
## 4 Ergebnisse der Fussball-Bundesliga
## 5 Votes in German Bundestag Election 2005
## 6 Butterfly Species in Malaya- Using the structable() function, create a “flat” table that has the Damage Index on the columns and whether the O-ring failed and how many failures on the rows.
Space_table<-structable(Damage~Fail+nFailures,data = SpaceShuttle)
Space_table## Damage 0 2 4 11
## Fail nFailures
## no 0 15 0 1 0
## 1 0 0 0 0
## 2 0 0 0 0
## yes 0 0 0 0 0
## 1 0 1 4 0
## 2 0 0 1 1## Damage 0 2 4 11
## Fail nFailures
## no 0 15 0 1 0
## 1 0 0 0 0
## 2 0 0 0 0
## yes 0 0 0 0 0
## 1 0 1 4 0
## 2 0 0 1 1- Construct the same formatted table that you did in part a, but now use the xtabs() and ftable() functions.
space_table2<-xtabs(~Fail+nFailures+Damage,data = SpaceShuttle)
ftable(space_table2)## Damage 0 2 4 11
## Fail nFailures
## no 0 15 0 1 0
## 1 0 0 0 0
## 2 0 0 0 0
## yes 0 0 0 0 0
## 1 0 1 4 0
## 2 0 0 1 1## Damage 0 2 4 11
## Fail nFailures
## no 0 15 0 1 0
## 1 0 0 0 0
## 2 0 0 0 0
## yes 0 0 0 0 0
## 1 0 1 4 0
## 2 0 0 1 1