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Fun with Sankey diagrams

I’m currently working on the final project for my data visualization course. The dataset that I’ve chosen to work with can be downloaded here – it’s a compendium Reddit resubmissions over a period of several years (ie, images that were submitted to more than one and/or to multiple subreddits). I waffled for a long time trying to decide what the best way to visualize the flow of images through various subreddits would be, but just in the nick of time, I stumbled across Christopher Gandrud’s new d3Network package for R, and that was enough cause for me to settle on a Sankey diagram. If you’ve never heard of Reddit, the illustrious CPG Grey will enlighten you.

The task of massaging columnar data consisting of an image ID, subreddit name, and timestamp for each submission into a more networky format suitable for this type of visualization was interesting enough that I thought it might be a good post. If nothing else, Christopher’s awesome package deserves some love.

Python is my go-to language for data munging of this calibre, so we will use a Pandas -> NetworkX -> R -> D3 worflow. Without further ado, lets load the Python modules we will need and take a look at the data.

csvclean data/redditSubmissions.csv
import pandas as pd
import networkx as nx
from networkx.readwrite import json_graph
from itertools import islice

d = pd.read_csv("data/redditSubmissions_out.csv")

Note that I first called csvclean, from the csvkit suite of command line utilities. The bang (!) symbol calls the command line from IPython. csvclean fixes a couple of formatting errors in the original dataset that interfere with R/Panda’s parsing functions (something to do with quotes or commas in the “title” field, I believe). The repaired CSV is saved with a _out prepended to the filename. Nothing fancy is required for the read_csv call in our case.

Now for the hard/interesting part. How do we map the flow of each image submission through the various subreddits?

  • First we sort by image and (crucially) timetamp on line 4.
  • On line 5, I simply extract the 3 columns that we care about.
  • Now we drop resubmissions of each image to the same subreddit with drop_duplicates on line 6, which only keeps each image’s first submission to a particular subreddit (why we sorted first).
  • The last thing we need Pandas for is to group by image ID (line 7).

On line 7, we pull the list of subreddits (now unique and nicely ordered) for each image. The nested list comprehension is necessary only because calling .subreddit on the groupby object g returns a tuple by default, and we’d rather have a list of lists.

g = (d.sort(['#image_id', 'unixtime'])
     .ix[:,['#image_id', 'unixtime', 'subreddit']]
     .drop_duplicates(cols = ['#image_id', 'subreddit'])
flow = [[el for el in x[1]] for x in list(g.subreddit)]

Now we need a function window that rolls along each of the lists in flow and connects every subsequent pair of subreddits that a particular image was submitted to. We’ll do this with the help of the wonderful `itertools` module, creating two dimensional tuples that encode the “from” subreddit and the “to” subreddit, respectively. In lines 14 and 15, we apply the function and flatten the result to a single list.

In order to truly capture the “flow,” however, we need to distinguish between the “gifs” subreddit node where images are popping up for the first time and the “gifs” subreddit node when the image has already appeared in another subreddit (say, “pics”). The `enumerate` in line 14 does this by tacking on the ordinality to the name of the node, admittedly very hacky, but we have a lot of tuples floating around already.

# Roll along the list of subreddits each image has been submitted to,
# creating an edge tuple for each subsequent pair
def window(seq, n=2):
    '''Returns a sliding window (of width n) over data from the iterable
       s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ...'''
    it = iter(seq)
    result = tuple(islice(it, n))
    if len(result) == n:
        yield result
    for elem in it:
        result = result[1:] + (elem,)
        yield result

sankey = [list(window([str(i) + x for i, x in enumerate(sub)])) for sub in flow]
sankey = [item for sublist in sankey for item in sublist] # flatten

At last we have a list of edges that on some level describes the flow that we are trying to get at. Now we can just iterate through them and use `NetworkX` to create the graph and weight the edges appropriately. In lines 10–13, I prune back the tiny edges that clutter up the diagram, and then the nodes that are no longer associated with any edges. Last but not least, we export the structure to a JSON in line 16.

# Create network structure
S = nx.DiGraph()
for edge in sankey:
    if S.has_edge(*edge):
        S[edge[0]][edge[1]]['weight'] +=1
        S.add_edge(*edge, weight = 1)

# Trim edges
S.remove_edges_from([x for x in S.edges(data=True) if x[2]['weight'] < 50])
flagged = [x for x, el in S.out_degree().items() if (x[0] != '3') & (el == 0)]
S.remove_edges_from([x for x in S.edges(data=True) if x[1] in flagged])
S.remove_nodes_from([x for x, n in if n == 0])

# Export
json_graph.dump(S, open('data/sankey.json', 'w'))

Time for R!

## install.packages('devtools')
## devtools::install_github("christophergandrud/d3Network")

This is finally the point at which Christopher Gandrud’s package simplifies everything for us. We simply read in the nodes and linkes (edges) from the JSON file (they get converted to two dataframes). Note that we have to strip the janky ordinality numbers that we tacked onto the node names (line 3). Now that different nodes have the same names, the package will even make sure that each subreddit node has the same color every time it appears!

The call to d3Sankey points to the the nodes dataframe, the links dataframe, the name of the sources/targets in the links dataframe, the name of the column that holds the link weights, and then some display configuration stuff.

nodes <- JSONtoDF(file = paste0('sankey.json'), array = 'nodes')
nodes$id <- substring(nodes$id, 2)
links <- JSONtoDF(file = paste0('sankey.json'), array = 'links')
d3Sankey(Nodes = nodes, Links = links, Source = 'source',
         Target = 'target', Value = 'weight', NodeID = 'id',
         width = 600, height = 500, fontsize = 12,
         standAlone = TRUE, iframe = TRUE, file = 'sankey.html')

We can render the results in an iframe below. Hover over edges for some nice brushing or click and drag the nodes to untangle a relationship you’re interested in.

This type of “tiered” Sankey diagram is a little unconventional, but so far its the best way I can come up with to visualize the interesting phenomenon of submission flow through Reddit. Leave a comment if this gives you any interesting ideas, I’d love to hear them!