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These are beta version because the help manuals are not written yet.  Their functions are already matured to the releasse stage.  I will put them to CRAN as soon as finishing help files, and that will be after a few weeks.  Once submitted to CRAN, things will go slower.  So reporting bugs or giving me any suggestions are especially welcomed before I submit them.  Of course I will welcome your responses after the submission, too.

Installation procedure is:

install.packages('/downloaded/folder/boxplotdbl_1.0.0.tar.gz',
repos=NULL,type='source')
library(boxplotdbl)

repos=NULL,type='source')
library(diaplt)

Instructions are available in the source code located in R folder.  Or following urls:

## boxplotdbl

The boxplotdou function in boxplotdbl package is for,

• Correlation chart of two set (x and y) of data.
• Using Quartiles with boxplot style.
• Visualize the effect of factor.

To start quickly, I use here the built-in iris data frame.  This is a paired data, but the boxplotdou is designed for two independent data frames combined by common factors.

Fig. 1. Sepal.Length v.s. Petal.Length

boxplotdou(iris[c(5,1)],iris[c(5,3)])

Fig. 2. Sepal.Width v.s. Petal.Width

boxplotdou(iris[c(5,2)],iris[c(5,4)])

## diaplt

The beadsplot function in diaplt package is for,

• Visualize one-factor data frame.
• Beads plot consists of diamonds of each factor of each data series.
• A diamond indicates average and range.
• Look over a data frame with many numeric columns and a factor column.

I developed this chart to look over a soil chemical component data between several sites.

Fig. 3. minimums, centers and maximums of iris data (raw values)

beadsplot(iris[1:4],iris[5],scale.range=NULL,scale.mean=NULL)

Fig. 4. minimums, centers and maximums of iris data (scaled values)

beadsplot(iris[1:4],iris[5])