Most basic Sankey Diagram

A Sankey diagram represents flows, i.e. weigthed connections going from one node to another. Input data can be stored in 2 different formats:

This post describes how to build a basic Sankey diagram from these 2 types of input.

From connection data frame

A connection data frame lists all the connections one by one in a data frame.

Usually you have a source and a target column. You can add a third column that gives further information for each connection, like the value of the flow.

This is the format you need to use the networkD3 library. Let’s build a connection data frame and represent it as a Sankey diagram:

# Library library(networkD3) library(dplyr) # A connection data frame is a list of flows with intensity for each flow links <- data.frame( source=c("group_A","group_A", "group_B", "group_C", "group_C", "group_E"), target=c("group_C","group_D", "group_E", "group_F", "group_G", "group_H"), value=c(2,3, 2, 3, 1, 3) ) # From these flows we need to create a node data frame: it lists every entities involved in the flow nodes <- data.frame( name=c(as.character(links$source), as.character(links$target)) %>% unique() ) # With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it. links$IDsource <- match(links$source, nodes$name)-1 links$IDtarget <- match(links$target, nodes$name)-1 # Make the Network p <- sankeyNetwork(Links = links, Nodes = nodes, Source = "IDsource", Target = "IDtarget", Value = "value", NodeID = "name", sinksRight=FALSE) p # save the widget # library(htmlwidgets) # saveWidget(p, file=paste0( getwd(), "/HtmlWidget/sankeyBasic1.html"))

From incidence matrix

An incidence matrix is square or rectangle.

Row and column names are node names. The item in row x and column y represents the flow between x and y. In the Sankey diagram we represent all flows that are over 0.

Since the networkD3 library expects a connection data frame, we will fist convert the dataset, and then re-use the code from above.

# Library library(networkD3) library(dplyr) # Create an incidence matrix. Usually the flow goes from the row names to the column names. # Remember that our connection are directed since we are working with a flow. set.seed(1) data <- matrix(sample( seq(0,40), 49, replace=T ), 7, 7) data[data < 35] <- 0 colnames(data) = rownames(data) = c("group_A", "group_B", "group_C", "group_D", "group_E", "group_F", "group_G") # Transform it to connection data frame with tidyr from the tidyverse: links <- data %>% as.data.frame() %>% rownames_to_column(var="source") %>% gather(key="target", value="value", -1) %>% filter(value != 0) # From these flows we need to create a node data frame: it lists every entities involved in the flow nodes <- data.frame( name=c(as.character(links$source), as.character(links$target)) %>% unique() ) # With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it. links$IDsource <- match(links$source, nodes$name)-1 links$IDtarget <- match(links$target, nodes$name)-1 # Make the Network p <- sankeyNetwork(Links = links, Nodes = nodes, Source = "IDsource", Target = "IDtarget", Value = "value", NodeID = "name", sinksRight=FALSE) p # save the widget # library(htmlwidgets) # saveWidget(p, file=paste0( getwd(), "/HtmlWidget/sankeyBasic2.html"))

From connection data frame

From incidence matrix

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