plotsummary {aplpack}  R Documentation 
plotsummary
shows some important characteristics of the variables of a data set.
For each variable a plot is computed consisting of a barplot, an ecdf,
a density trace and a boxplot.
plotsummary(data, trim = 0, types = c("stripes", "ecdf", "density", "boxplot"), y.sizes = 4:1, design = "chessboard", main, mycols = "RB")
data 
Data set for computing a graphical summary. 
trim 

types 
vector of types of representation of the data set. The elements of the vector will induce small plots which are stacked in vertical order. The first letter of the types is sufficient for defining a type. 
y.sizes 
defines the relative sizes of the small plots. The values are divided by their sum to get percentages. 
design 
if 
main 
defines a title for the graphics. 
mycols 
allows to define some colors for the showing the regions separated by the quartils. 
plotsummary
can be use for a quick and dirty inspection
of a data matrix or a list of variables.
Without further specification some representation of each of the
variables is built and stacked into a plot.
The sizes of the types of representation can be set as well as the
layout design of the graphics device. It is helpful to trim the data
before processing because outliers will often hide
the interesting characteristics.
Peter Wolf, pwolf@wiwi.unibielefeld.de
## Should be DIRECTLY executable !!  ## ==> Define data, use random, ##\tor do help(data=index) for the standard data sets. plotsummary(cars) plotsummary(cars, types=c("ecdf", "density", "boxplot"), y.sizes = c(1,1,1), design ="stripes") plotsummary(c(list(rivers=rivers, co2=co2), cars), y.sizes=c(10,3,3,1), mycols=3) plotsummary(cars, design="chessboard") # find all matrices in your R ds.of.R < function(type="vector"){ dat < ls(pos=grep("datasets",search())) dat.type < unlist(lapply(dat,function(x) { num < mode(x<eval(parse(text=x))) num < ifelse(is.array(x),"array",num) num < ifelse(is.list(x),"list",num) num < ifelse(is.matrix(x),"matrix",num) num < ifelse(is.data.frame(x),"matrix",num) num < ifelse(num=="numeric","vector",num) num })) return(dat[dat.type==type]) } namelist < ds.of.R("matrix") # inspect the matrices one after the other for(i in seq(along=namelist)){ print(i); print(namelist[i]) xy < get(namelist[i]) # plotsummary(xy,y.sizes=4:1,trim=.05,main=namelist[i]) # Sys.sleep(1) }