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.
factor
type)slidersPosition
argumentzAxisDim
argument (referenced column is categorical)categoricalCS
argumentzAxisDim
argument (referenced column is continuous)continuousCS
argumentcorrPlotType
argumentfactor
type)categorical
argumentdistribType
argumentregressionType
argumentcutoffs
argumentrotateTitle
argumentcolumnLabels
argumentcssRules
argumentplotProperties
argumentcontrolWidgets
argument