Data in R packages

data()               # list data in the `datasets` package
data(package="vcd")  # in the vcd package

vcdExtra::datasets() gives more detailed info on datasets in a package. knitr::kable() turns this into a nicely formatted table.

vcdExtra::datasets("vcd") |> knitr::kable()
Item class dim Title
Arthritis data.frame 84x5 Arthritis Treatment Data
Baseball data.frame 322x25 Baseball Data
BrokenMarriage data.frame 20x4 Broken Marriage Data
Bundesliga data.frame 14018x7 Ergebnisse der Fussball-Bundesliga
Bundestag2005 table 16x5 Votes in German Bundestag Election 2005
Butterfly table 24 Butterfly Species in Malaya
CoalMiners table 2x2x9 Breathlessness and Wheeze in Coal Miners
DanishWelfare data.frame 180x5 Danish Welfare Study Data
Employment table 2x6x2 Employment Status
Federalist table 7 ‘May’ in Federalist Papers
Hitters data.frame 154x4 Hitters Data
HorseKicks table 5 Death by Horse Kicks
Hospital table 3x3 Hospital data
JobSatisfaction data.frame 8x4 Job Satisfaction Data
JointSports data.frame 40x5 Opinions About Joint Sports
Lifeboats data.frame 18x8 Lifeboats on the Titanic
MSPatients array 4x4x2 Diagnosis of Multiple Sclerosis
NonResponse data.frame 12x4 Non-Response Survey Data
OvaryCancer data.frame 16x5 Ovary Cancer Data
PreSex table 2x2x2x2 Pre-marital Sex and Divorce
Punishment data.frame 36x5 Corporal Punishment Data
RepVict table 8x8 Repeat Victimization Data
Rochdale table 2x2x2x2x2x2x2x2 Rochdale Data
Saxony table 13 Families in Saxony
SexualFun table 4x4 Sex is Fun
SpaceShuttle data.frame 24x6 Space Shuttle O-ring Failures
Suicide data.frame 306x6 Suicide Rates in Germany
Trucks data.frame 24x5 Truck Accidents Data
UKSoccer table 5x5 UK Soccer Scores
VisualAcuity data.frame 32x4 Visual Acuity in Left and Right Eyes
VonBort data.frame 280x4 Von Bortkiewicz Horse Kicks Data
WeldonDice table 11 Weldon’s Dice Data
WomenQueue table 11 Women in Queues

Typically, load data from a package using data()

data(UCBAdmissions)
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" ...
sum(UCBAdmissions)
## [1] 4526
margin.table(UCBAdmissions, 1)
## Admit
## Admitted Rejected 
##     1755     2771
margin.table(UCBAdmissions, 2:3)
##         Dept
## Gender     A   B   C   D   E   F
##   Male   825 560 325 417 191 373
##   Female 108  25 593 375 393 341

Data frames

Create a data frame from vectors

GSS <- data.frame(
  sex =   rep(c("female", "male"), times = 3), 
  party = rep(c("dem", "indep", "rep"), each = 2),
  count = c(279,165,73,47,225,191))
GSS
##      sex party count
## 1 female   dem   279
## 2   male   dem   165
## 3 female indep    73
## 4   male indep    47
## 5 female   rep   225
## 6   male   rep   191

Load a dataset from a package

data(Arthritis, package = "vcd")
str(Arthritis)
## 'data.frame':    84 obs. of  5 variables:
##  $ ID       : int  57 46 77 17 36 23 75 39 33 55 ...
##  $ Treatment: Factor w/ 2 levels "Placebo","Treated": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Sex      : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Age      : int  27 29 30 32 46 58 59 59 63 63 ...
##  $ Improved : Ord.factor w/ 3 levels "None"<"Some"<..: 2 1 1 3 3 3 1 3 1 1 ...
head(Arthritis)      # see the first few lines
##   ID Treatment  Sex Age Improved
## 1 57   Treated Male  27     Some
## 2 46   Treated Male  29     None
## 3 77   Treated Male  30     None
## 4 17   Treated Male  32   Marked
## 5 36   Treated Male  46   Marked
## 6 23   Treated Male  58   Marked

Making tables from data frame variables

table(Arthritis$Improved)
## 
##   None   Some Marked 
##     42     14     28
table(Arthritis$Treatment, Arthritis$Sex)
##          
##           Female Male
##   Placebo     32   11
##   Treated     27   14

a shorthand to avoid repeating the name of the dataset

with(Arthritis, table(Treatment, Sex))
##          Sex
## Treatment Female Male
##   Placebo     32   11
##   Treated     27   14

xtabs() is often easier

art.table <- xtabs(~ Sex + Treatment + Improved, data=Arthritis)
ftable(art.table)    # display as flattened table
##                  Improved None Some Marked
## Sex    Treatment                          
## Female Placebo              19    7      6
##        Treated               6    5     16
## Male   Placebo              10    0      1
##        Treated               7    2      5
summary(art.table)   # chi-square test for mutual independence
## Call: xtabs(formula = ~Sex + Treatment + Improved, data = Arthritis)
## Number of cases in table: 84 
## Number of factors: 3 
## Test for independence of all factors:
##  Chisq = 19.6, df = 7, p-value = 0.006501
##  Chi-squared approximation may be incorrect

Plotting a table: mosaic plot

plot(art.table, shade=TRUE)

Reading data from external files

read a data table from a local file (NB: ‘/’ not ’' for all systems) arthritis <- read.csv(“N:/psy6136/data/arthritis.csv”) arthritis <- read.csv(file.choose())
or, read the same data from a web URL …

arthritis <- read.csv("https://raw.githubusercontent.com/friendly/psy6136/master/data/Arthritis.csv")
str(arthritis)
## 'data.frame':    84 obs. of  5 variables:
##  $ ID       : int  57 46 77 17 36 23 75 39 33 55 ...
##  $ Treatment: chr  "Treated" "Treated" "Treated" "Treated" ...
##  $ Sex      : chr  "Male" "Male" "Male" "Male" ...
##  $ Age      : int  27 29 30 32 46 58 59 59 63 63 ...
##  $ Improved : chr  "Some" "None" "None" "Marked" ...

read.csv doesn’t make categorical variables factors. They are just character variables.

levels(arthritis$Improved)    
## NULL

Make an ordered factor

arthritis$Improved <- ordered(arthritis$Improved,
                              levels=c("None", "Some", "Marked"))

Make a variable discrete: cut() and arithmetic

table(10*floor(arthritis$Age/10)) 
## 
## 20 30 40 50 60 70 
##  4 11 11 29 26  3
table(cut(arthritis$Age, breaks=6))
## 
## (22.9,31.5]   (31.5,40]   (40,48.5]   (48.5,57]   (57,65.5] (65.5,74.1] 
##           8           7          10          19          26          14

assign new variable in data frame

arthritis$AgeGroup <- factor(10*floor(arthritis$Age/10))

simple plots

plot(arthritis$Age)      # index plot

plot(arthritis$AgeGroup) # barplot for a factor

plot(Improved ~ AgeGroup, data=arthritis) # spineplot for two factors

plot(arthritis[,2:5])    # scatterplot matrix; not too useful for factors