Parallel chart with the MASS library in R
To analyze and visualize high-dimensional data, one can use Parallel Coordinates. A background is drawn consisting of n parallel lines, often vertical and evenly spaced, to display a set of points in an n-dimensional space. A point in n-dimensional space is represented by a polyline with vertices on parallel axes; the ith coordinate of the point corresponds to the position of the vertex on the ith axis.
Parallel chart with the MASS library in R Programming Language
This representation is similar to time series visualization, except that it is used with data that does not have a natural order because the axes do not correlate to points in time. As a result, several axis layouts may be of interest.
Parallel Coordinates with MASS Library
The parcoord() function in the MASS package creates a parallel coordinates chart automatically. A data frame with solely numeric variables can be used as the input dataset. Each variable will be utilized to construct one of the chart’s vertical axes.
R
# Libraries library (MASS) # default data in R data <- iris head (data) # plotting the graphs parcoord (iris[, c (1:4)] , # choosing first 4 parameters # selecting the color palette based on the plot col = colors ()[ as.numeric (iris$Species)*8] ) |
Output:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa
Customizing the Color Palette
Basically, there are not any built-in methods or attributes in this package for color customization. We will use colorRampPalette() methods color range between two colors specified points
R
# Libraries library (MASS) # choosing the graph color library (RColorBrewer) # default data in R data <- iris head (data) # define a color palette palette <- brewer.pal (5, "Set1" ) # plotting the graphs parcoord (iris[, c (1:4)] , # choosing first 4 parameters # selecting the color palette based on the plot col = palette[ as.numeric (iris$Species)] ) |
Output:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa
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