tabplot (version 0.9-1)

tableplot: Visualization of large statistical datasets.

Description

A tableplot is a visualisation of a (large) statistical dataset with a dozen of variables, both both numeric and categorical. Each column represents a variable and each row bin is an aggregate of a certain number of records. For numeric variables, a bar chart of the mean values is depicted. For categorical variables, a stacked bar chart is depicted of the proportions of categories. Missing values are taken into account. Also supports large ffdf datasets from the ff package. Use tableGUI to customize this function with a GUI.

Usage

tableplot(dat, colNames = names(dat), sortCol = 1, decreasing = TRUE, scales = "auto", palet = c(1, 9, 3, 10), nBins = 100, from=0, to=100)

Arguments

dat

a data.frame or an ffdf object (required)

colNames

character vector containing the names of the columns of dat that are visualized in the tablelplot. If omitted, all columns are visualized. All selected columns should be of class: numeric, integer, factor, or logical.

sortCol

columns that are sorted. sortCol is either a vector of column names of a vector of indices of colNames

decreasing

determines whether the columns are sorted decreasingly (TRUE) of increasingly (FALSE). decreasing can be either a single value that applies to all sorted columns, or a vector of the same length as sortCol.

scales

determines the horizontal axes of the numeric variables, options: "lin", "log", and "auto" for automatic detection. It should be either one value (applied to all numeric variables) or a vector of values of the same length as colNames.

palet

a vector of indices that determines the startings colors of the selected categorical variables. The indices are taken from a fixed palet (see tableplot(data.frame(palet_1_8 = factor(1:8), palet_9_16 = factor(9:16)))). If necessary, pale

nBins

number of row bins

from

percentage from which the data is shown

to

percentage to which the data is shown

Examples

require(ggplot2)
	
	diamondsNA <- diamonds
	# simulate missing data
	is.na(diamondsNA$price) <- diamondsNA$cut == "Ideal"
	tableplot(diamondsNA)

	data(movies)
	tableplot(movies[,c(3:5,17:24)], sortCol="rating", decreasing=FALSE, scales="lin", nBins=100)

irisNA <- iris
# simulate missing data
is.na(irisNA$Sepal.Width) <- sample(1:nrow(iris), 30)
is.na(irisNA$Species) <- sample(1:nrow(iris), 15)

tableplot(irisNA)