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factor type)‘Species’ column is of factor type and has box representation for its categories.
slidersPosition argumentscatterPlotMatrix(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.
zAxisDim argument (referenced column is
categorical)Each point has a color depending of its ‘Species’ value.
categoricalCS argumentColors used for categories are not the same as previously (supported
values: Category10, Accent,
Dark2, Paired, Set1).
zAxisDim argument (referenced column is
continuous)Each point has a color depending of its Sepal.Length
value.
continuousCS argumentColors used for points are not the same as previously (supported
values: Blues, RdBu, YlGnBu,
YlOrRd, Reds).
corrPlotType argumentSupported values: Empty, Circles,
Text, AbsText
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.
Absolute value of correlations is used to attribute the color, using a color scale with a domain [0; 1] and palette ‘Blues’.
factor type)Several columns are of numerical type but should be of factor type
(for example cyl).
categorical argumentcategorical <- list(cyl = c(4, 6, 8), vs = c(0, 1), am = c(0, 1), gear = 3:5, carb = 1:8)
scatterPlotMatrix(mtcars, categorical = categorical, zAxisDim = "cyl")cyl and four last columns have a box representation for
categories (use top slider to see the last three columns).
distribType argumentDistribution plots are of type ‘density plot’ (instead of histogram).
regressionType argumentAdd linear regression plots.
cutoffs argumentcutoffs <- list(
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.
rotateTitle argumentColumn names are rotated (can be useful for long column names).
columnLabels argumentcolumnLabels <- gsub("\\.", "<br>", colnames(iris))
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.
cssRules argumentscatterPlotMatrix(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 Firefox or Chrome devtools for Chrome.
plotProperties argumentscatterPlotMatrix(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.
controlWidgets argumentWidgets are added above the graph, allowing to modify some of its attributes.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.