The goal of ClickViz is to create interesting visualisations of Markov chains using clickstream data. Clickstream data is collected from online browsing, where clicks provide a sequence of page visits. We can use these plots to discover common behaviours and further insights into the usability of the website.
You can install the released version of ClickViz from Github with:
install_github("ryanjessop/ClickViz")
Examples of the heatmap visualisation of the first order Markov chain transition matrix
library(ClickViz)
plot_transition_heatmap(clickstreams)
plot_transition_heatmap(clickstreams_large)
Examples of the network visualisation of the first order Markov chain transition matrix
labels <- c("Type1", "Type2", "Type2", "Type3")
state <- c("H", "P", "D", "E")
vertex_labels_example <- tibble::tibble(state=state,
vertex_label=labels)
edges <- prepare_network_edges(clickstreams, alpha=0.01)
nodes <- prepare_network_vertices(clickstreams,
vertex_labels=vertex_labels_example)
plot_transition_network(edges,
nodes,
edge_width_factor=5,
edge_arrow_size=0.6,
edge_curve_factor=0.5,
vertex_size_factor=1.2,
legend_x_position=-3.0,
legend_y_position=-0.5)
labels = c("Content", "Purchasing", "Content", "Products", "Artificial",
"Home", "Site Information", "Profile", "Products", "Artificial",
"Products", "Profile", "Purchasing", "Purchasing")
state <- c("A", "B", "C", "D", "E", "H", "I", "L", "P", "R", "S", "U", "X", "Y")
vertex_labels_example <- tibble::tibble(state=state,
vertex_label=labels)
edges <- prepare_network_edges(clickstreams_large, alpha=0.01)
nodes <- prepare_network_vertices(clickstreams_large,
vertex_labels=vertex_labels_example)
plot_transition_network(edges,
nodes,
edge_width_factor=3,
edge_arrow_size=0.6,
edge_curve_factor=0.5,
vertex_size_factor=0.41,
legend_x_position=-2.0,
legend_y_position=-0.5)
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