Section 1.1

Bargraph vs Barchart

Use the bargraph command if the value of a categorical variable is given for every observation.

bargraph(~feed,data=chickwts)

Use the barchart command to create a bar graph for a data set where the frequencies of a catagorical variable are given.

gps<-read.file("/home/emesekennedy/Data/Ch1/gps.txt")
## Reading data with read.table()
barchart(MarketShare~Company,data=gps)

barchart(Company~MarketShare,data=gps)

Time Plots

tree1<-subset(Orange, Tree==1)
xyplot(circumference~age,data=tree1)

xyplot(circumference~age,data=tree1,type="l")

xyplot(circumference~age,data=tree1, type="b")

Section 1.2

Mean and Median

time24<-read.file("/home/emesekennedy/Data/Ch1/timetostart24.txt")
## Reading data with read.table()
histogram(~TimeToStart,data=time24,type="count")

mean(~TimeToStart,data=time24)
## [1] 37.375
median(~TimeToStart,data=time24)
## [1] 36.5

Same data set with an outlier

time25<-read.file("/home/emesekennedy/Data/Ch1/timetostart25.txt")
## Reading data with read.table()
histogram(~TimeToStart,data=time25,type="count")

mean(~TimeToStart,data=time25)
## [1] 63.64
median(~TimeToStart,data=time25)
## [1] 40

Create data set with grades

grades<-c(80, 73, 95,98,100,77,72,60,82)
histogram(grades,type="count",width=10)

mean(grades)
## [1] 81.88889
median(grades)
## [1] 80

Five Number Summary and Boxplot

favstats(grades)
##  min Q1 median Q3 max     mean       sd n missing
##   60 73     80 95 100 81.88889 13.42986 9       0

Save the result as a variable named stats

stats<-favstats(grades)

Calculate the interquartile range

IQR<-stats[1,4]-stats[1,2]

Find cutoff values for suspected outliers

stats[1,4]+1.5*IQR
## [1] 128
stats[1,2]-1.5*IQR
## [1] 40

Create a boxplot

bwplot(grades)

Five number summary and boxplot for the time24 data:

favstats(~TimeToStart, data=time24)
##  min Q1 median    Q3 max   mean       sd  n missing
##    4 23   36.5 46.25  77 37.375 18.57491 24       0
bwplot(~TimeToStart,data=time24)

Five number summary and boxplot for the time25 data (with the outlier):

favstats(~TimeToStart, data=time25)
##  min Q1 median Q3 max  mean       sd  n missing
##    4 23     40 47 694 63.64 132.5779 25       0
bwplot(~TimeToStart,data=time25)

Note: R Studio recognized the outlier and created a modified boxplot