(This article was first published on NIR-Quimiometria, and kindly contributed to R-bloggers)
Boxplots are a nice way to compare the three sample sets of the Shoot-out 2012 data files.
There is a category variable (Set) in the data frame with the labels (Cal = Training Set, Test = Test Set and Val = Validation Set).
# IMPORTING THE SAMPLE SETS #
shootout2012.raw<-read.csv("Shootout2012_R.csv",header=TRUE)
# ORGANIZING THE DATA-FRAME #
NIT<-shootout2012.raw[,4:375]
Active<-shootout2012.raw[,3]
Set<-shootout2012.raw[,2]
shootout2012<-data.frame(Set=I(Set),Active=I(Active),
+ NIT=I(NIT))
names(shootout2012)
# "Set" "Active" "NIT"
attach(shootout2012)
boxplot(Active ~ Set,main="Shootout 2012",xlab="Sample Sets",
+ col="grey")
aggregate(Active ~ Set, summary, data=shootout2012)
Set Active.Min. Active.1st Qu. Active.Median Active.Mean
1 Cal 4.740 6.680 8.390 7.550
2 Test 5.120 7.050 7.950 7.386
3 Val 4.610 7.240 8.000 7.520
Active.3rd Qu. Active.Max.
1 8.750 9.790
2 8.142 8.480
3 8.135 8.580Previous posts in this blog about the Shoot-out 2012 data.
See also Label "Shootout 2012)"
Sample Sets" plots (Shootout-2012)
Shootout 2012: Test & Val Sets proyections
Working with Shootout - 2012 in R (001)
Shootout 2012 files
To leave a comment for the author, please follow the link and comment on his blog: NIR-Quimiometria.
R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...


Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).