‘Species’ column is of factor type and has box representation for its categories.
scatterPlotMatrix(iris, slidersPosition = list( dimCount = 3, # Number of columns to draw xStartingDimIndex = 2, # Index of first drawn column horizontally yStartingDimIndex = 2 # Index of first drawn column vertically ))
Set initial position of sliders, specifying which columns intervals are visible. Here, visible columns starts at second column in x direction, second column in y direction, and three columns are represented.
zAxisDimargument (referenced column is categorical)
scatterPlotMatrix(iris, zAxisDim = "Species")
Each point has a color depending of its ‘Species’ value.
scatterPlotMatrix(iris, zAxisDim = "Species", categoricalCS = "Set1")
Colors used for categories are not the same as previously (supported values: Category10, Accent, Dark2, Paired, Set1).
zAxisDimargument (referenced column is continuous)
scatterPlotMatrix(iris, zAxisDim = "Sepal.Length")
Each point has a color depending of its ‘Sepal.Length’ value.
scatterPlotMatrix(iris, zAxisDim = "Sepal.Length", continuousCS = "YlOrRd")
Colors used for points are not the same as previously (supported values: Blues, RdBu, YlGnBu, YlOrRd, Reds).
Supported values: Empty, Circles, Text, AbsText
scatterPlotMatrix(iris, corrPlotType = "Text")
Correlation plots use simple texts instead of circle tree maps as previously; Value of correlations is used to attribute the color, using a color scale with a domain [-1; 1] and the palette ‘RdBu’.
scatterPlotMatrix(iris, corrPlotType = "AbsText")
Absolute value of correlations is used to attribute the color, using a color scale with a domain [0; 1] and palette ‘Blues’.
Several columns are of numerical type but should be of factor type (for example ‘cyl’).
<- list(NULL, c(4, 6, 8), NULL, NULL, NULL, NULL, NULL, c(0, 1), c(0, 1), 3:5, 1:8) categorical scatterPlotMatrix(mtcars, categorical = categorical, zAxisDim = "cyl")
‘cyl’ and four last columns have a box representation for its categories (use top slider to see the last three columns).
scatterPlotMatrix(iris, zAxisDim = "Species", distribType = 1)
Distribution plots are of type ‘density plot’ (instead of histogram).
scatterPlotMatrix(iris, zAxisDim = "Species", regressionType = 1)
Add linear regression plots.
<- list( cutoffs list( xDim = "Sepal.Length", yDim = "Species", xyCutoffs = list( list(c(4, 8), c(-0.1, 0.1)), list(c(4, 8), c(1.9, 2.1))) ) )scatterPlotMatrix(iris, zAxisDim = "Species", cutoffs = cutoffs)
Traces which are not kept by cutoffs are greyed; only kept traces are used for histograms.
scatterPlotMatrix(iris, zAxisDim = "Species", rotateTitle = TRUE)
Column names are rotated (can be useful for long column names).
<- gsub("\\.", "<br>", colnames(iris)) columnLabels scatterPlotMatrix(iris, zAxisDim = "Species", columnLabels = columnLabels)
Given names are displayed in place of column names found in dataset;
<br> is used to insert line breaks.
scatterPlotMatrix(iris, cssRules = list( ".jitterZone" = "fill: pink", # Set background of plot to pink ".tick text" = c("fill: red", "font-size: 1.8em") # Set text of axes ticks red and greater ))
Apply CSS to the plot. CSS is a simple way to describe how elements on a web page should be displayed (position, colour, size, etc.). You can learn the basics at W3Schools. You can learn how to examine and edit css at MDN Web Docs for Firexox or Chrome devtools for Chrome.
scatterPlotMatrix(iris, plotProperties = list( noCatColor = "DarkCyan", # Color used when categories coloring is not applied point = list( alpha = 0.3, # Opacity value used for points radius = 4 # Radius used to draw points as circles )))
Adjust some properties which can not be set through CSS (mainly size, color and opacity of points). Here, points of plot are customised: two times greater, with opacity reduced from 0.5 to 0.3, and a ‘DarkCyan’ color.
scatterPlotMatrix(iris, controlWidgets = TRUE)
Some widgets are available to control the plot.