tl;dr

I made an interactive graph network of the podcast host relationships on Relay FM using R. You can interact with it in a separate window and find out below how it was made.

I refreshed the data and style of the visualisation on 02 Jan 2020.

Podcast networks

Podcasting is becoming big business. Music-streaming giant Spotify just acquired the podcast network Gimlet for a reported $200 million.

Other networks include The Incomparable, 5by5 and Radiotopia. Such networks can boost revenue and listener numbers and provide access to expertise, management and resources.

Relay FM is a network that focuses largely on tech content¹. It was started by Myke Hurley and Stephen Hackett in 2014 and you can find a list of shows and personnel on their site. Many of Relay FM’s hosts have hosted more than one podcast within the network. What do the relationships between them look like?

This post is about preparing and visualising this ‘Relayverse’ using the R packages tidygraph for network data handling and ggraph for network visualisation (both by Thomas Lin Pedersen), along with the visNetwork package (from Benoit Thieurmel and Titouan Robert of Datastorm, wrapping Almende BV’s vis.js library) for interactive visualisation.

Scroll to the bottom to find the interactive tool if you aren’t interested in the code.

Packages

I’m using two suites of ‘tidy’ packages in this post: one set for data collection and manipulation, and one set for graph network building, analysis and visualisation.

# Data collection and manipulation
library(dplyr)  # data manipulation
library(rvest)  # for scraping webpages
library(stringr)  # string manipulation
library(tidyr)  # tidying dataframes
library(purrr)  # applying functions over data

# Graph networks
library(tidygraph)  # set up graph network
library(ggraph)  # visualise static graphs
library(cowplot)  # for plot arrangement
library(visNetwork)  # wrapper for javascript interactive viz

Harvest data

We can use the rvest package by Hadley Wickham to scrape podcast details from the Wikipedia page for Relay FM. There are two separate tables: one for current and one for retired (discontinued) shows.

One of the target Wikipedia tables of current Relay FM shows

read_html() gets the HTML for the selected page; html_node() identifies which element needs to be scraped²; and html_table() interprets the HTML information as a data frame. I’ve removed the show ‘B-Sides’ because it’s clips from other shows.

# Get the HTML for the selected page
relay_wiki <- read_html("https://en.wikipedia.org/wiki/Relay_FM")

# Get the table with current shows
current <- relay_wiki %>%
  html_node(xpath = '//*[@id="mw-content-text"]/div/table[2]') %>%
  html_table() %>%
  filter(
    !Podcast %in% c("Members Only", 'Paid "Members Only" Shows', "B-Sides")
  ) %>%
  mutate(Status = "Current")  # label rows as current shows

# Get the table with retired shows
retired <- relay_wiki %>%
  html_node(xpath = '//*[@id="mw-content-text"]/div/table[3]') %>%
  html_table() %>%
  select(-`Number of episodes`) %>%
  mutate(Status = "Retired")  # label rows as retired shows

# Combine the tables into one dataframe
shows <- bind_rows(current, retired)

# Look at a few random hosts/podcasts from the table
select(shows, Podcast, Hosts) %>%
  sample_n(5) %>% knitr::kable()

Unfortunately the host names are in the form ‘First LastFirst Last’ so we need a regular expression to split the string where a lowercase letter meets an uppercase letter³. This leaves us with a list column that we can unnest() to get one row per podcast-host combination.

# Clean the host names
shows_clean <- shows %>%
  mutate(
    Hosts = str_remove_all(Hosts, "formerly hosted by.*$"),  # remove text and former hosts
    Hosts = str_remove_all(Hosts, " \\(originally\\)"),  # remove '(originally)' text
    Hosts = str_remove_all(Hosts, "\\[[:digit:]\\]"),  # remove Wikipedia references
    Hosts = str_split(Hosts, "(?<=[a-z])(?=[A-Z])") # split where lowercase meets uppercase
  ) %>%
  filter(Hosts != "Maddy Myers") %>%  # hack to remove a former host
  unnest() %>% 
  select(Podcast, Hosts, Status)

## Warning: `cols` is now required.
## Please use `cols = c(Hosts)`

# Print a random sample of 10
sample_n(shows_clean, 10) %>% knitr::kable()

Notice there were a couple of cleaning steps there to remove the text ‘formerly hosted by’ and the names of the former hosts. There’s one instance where this protocol isn’t followed in the table and the host’s name is followed by ‘(originally)’. I removed the text with regex, but then just removed the host’s name manually with a filter() to avoid some complex regex.

Our data frame is now tidy and ready for Tidygraph, but before moving on, we can do things like look at the host with the most active shows.

shows_clean %>% 
  filter(Status == "Current") %>% 
  count(Hosts) %>%
  filter(n > 2) %>% 
  arrange(desc(n)) %>% 
  knitr::kable()

Or out of interest, the shows that have had the most hosts.

shows_clean %>% 
  count(Podcast) %>%
  arrange(desc(n)) %>% 
  slice(1:6) %>% 
  knitr::kable()

Tidygraph

The tidygraph package was created by Thomas Lin Pedersen. It is:

an entry into the tidyverse that provides a tidy framework for all things relational (networks/graphs, trees, etc)

To use the package, we need every host combination⁴ for each podcast, which can be achieved with the combn() function. This gives us a pair of points (‘nodes’ in graph-speak) that can be connected by a line (an ‘edge’) to indicate their relationship.

But a show with one host doesn’t have a pair of points, so I’m going to duplicate these rows first. If we don’t do this, we can’t plot these shows because they won’t have a connection between two nodes.

# Isolate the shows with one host 
solo_vec <- shows_clean %>%
  count(Podcast) %>%
  filter(n == 1) %>%
  pull(Podcast)

# Filter the show-host dataframe by solo-hosted podcasts
solo_df <- filter(shows_clean, Podcast %in% solo_vec)

Now we can bind these rows to the data and then get our host combinations. Big shout out to William Chase for a solution to getting all combinations of elements within some parent element (e.g. hosts within podcasts).

# Prepare host combinations per show
relay_combos <- shows_clean %>%
  bind_rows(solo_df) %>%  # to duplicate the shows with solo hosts
  group_by(Podcast) %>%  # operate within each podcast
  split(.$Podcast) %>%  # split on podcast
  map(., 2) %>%  # gets vector of hosts per podcast list element
  map(~combn(.x, m = 2)) %>%   # all pair combiantions
  map(~t(.x)) %>%  # transpose the matrix
  map(as_tibble) %>%  # convert to a tibble dataframe
  bind_rows(.id = "Podcast") %>%  # list-element name to column
  select(V1, V2, Podcast)

sample_n(relay_combos, 10) %>% knitr::kable()  # random sample of 10

We can turn this data frame of host-pair combinations into a tidygraph object with the as_tbl_graph() function. This class of object contains two data frames (the nodes and the edges) and some metadata.

We can also use functions from the tidygraph package to help calculate various network statistics. For example, the centrality_degree() function tells us the nodes with the most connections. We can add this as a column in our node data and use this later to do things like resize nodes depending on their centrality.

You can manipulate the nodes and edges data frames in this tidygraph object by using the activate() function to switch between them. Currently the node data are active (it says ‘active’ above the nodes data frame), so our application of centrality_degree() to the network object will affect the node data specifically.

relay_graph <- as_tbl_graph(relay_combos, directed = FALSE)%>% 
  mutate(connections = centrality_degree()) %>%  # number of connections
  arrange(desc(connections))  # order by number of shows

print(relay_graph)  

## # A tbl_graph: 38 nodes and 64 edges
## #
## # An undirected multigraph with 8 components
## #
## # Node Data: 38 x 2 (active)
##   name               connections
##   <chr>                    <dbl>
## 1 Myke Hurley                 21
## 2 Stephen M. Hackett          11
## 3 Brianna Wu                   9
## 4 Mikah Sargent                9
## 5 Federico Viticci             8
## 6 Georgia Dow                  7
## # … with 32 more rows
## #
## # Edge Data: 64 x 3
##    from    to Podcast   
##   <int> <int> <chr>     
## 1    10    10 Almanac   
## 2     1    30 Analog(ue)
## 3    11    31 Automators
## # … with 61 more rows

The edges and nodes are now in the same object. You can see we have about 38 nodes and 64 edges and that the network is undirected (nodes don’t ‘point’ to each other). We also have eight ‘components’, which are the isolated groups of nodes that connect to each other, but not to other groups.

Visualisation

g1 <- ggraph(
  graph = relay_graph,
  layout = "linear",
  circular = TRUE
) +
  geom_node_point(aes(size = connections)) +
  geom_edge_arc() +
  theme_void() +
  theme(legend.position = "none")

g2 <- ggraph(
  graph = relay_graph,
  layout = "linear"
) +
  geom_node_point(aes(size = connections)) +
  geom_edge_arc() +
  theme_void() +
  theme(legend.position = "none")

plot_grid(g1, g2, labels = c("A", "B"))

ggraph(graph = relay_graph, layout = "nicely") + 
  geom_node_point(aes(size = connections)) +
  geom_edge_fan() +
  theme_void() +
  theme(legend.position = "none")

# Dataframe of unique nodes with an ID value
nodes <- shows_clean %>%  # take our podcast-host dataset
  distinct(Hosts) %>%  # get column of unique hosts
  arrange(Hosts) %>%  # alphabetical order
  mutate(id = as.character(row_number()), label = Hosts) %>% 
  select(-Hosts) #%>%  # provides pop up value on viz

# Dataframe of 'origin' and 'destination' nodes for edge drawing
edges <- relay_combos %>% 
  left_join(nodes, by = c("V1" = "label")) %>% 
  left_join(nodes, by = c("V2" = "label")) %>%
  rename(from = id.x, to = id.y, title = Podcast)

visNetwork(  # add main features
  nodes, edges,  # add node and edge data
  main = list(  # set main title and style it
    text = "The Relayverse",
    style = "font-family:Lekton, monospace;font-weight:bold;font-size:30px;text-align:center;"
  ),
  submain = list(  # set subtitle and style it
    text = "Click hosts (nodes) to see co-host relationships (edges)",
    style = "font-family:Lekton, monospace;font-weight:regular;font-size:20px;text-align:center;"
  ),
  footer = list(  # add a footer and style it
    text = paste0("Source: Wikipedia (", format(Sys.Date(), "%Y/%m/%d"), ")"),
    style = "font-family:Lekton, monospace;font-weight:regular;font-size:15px;text-align:right;"
  )
) %>% 
  visNodes(  # node styling
    shape = "icon",  # node is an icon specified below 
    icon = list(code = "f130", size = 75, color = "#000000"),  # microphone icon
    font = list(face = "Lekton", size = 20)
  ) %>% 
  visEdges(  # edge styling
    color = list(color = "#447d9b", highlight = "#c83c3c", opacity = 0.5),
    width = 3, selectionWidth = 5  # selected edge is thicker
  ) %>% 
  visOptions(  # general graph options
    highlightNearest = TRUE,  # on-hover highlight nearest nodes
    nodesIdSelection = TRUE  # select node from dropdown
  ) %>%
  visPhysics(  # set physics 'engine' and options
    solver = "forceAtlas2Based", 
    forceAtlas2Based = list(gravitationalConstant = -50)
  ) %>%
  visLayout(randomSeed = 1337) %>%  # reproduce the same network each time
  visInteraction(navigationButtons = TRUE) %>%  # add naviagation buttons
  addFontAwesome()  # makes sure that FontAwesome dependency is in place

What now?

This was really just an introduction to tidygraph, ggraph and visNetwork. These packages make network analysis a little more consistent and can provide some interesting stats and visuals very quickly. A next step might be to produce a ‘network of podcast networks’, since Relay FM hosts appear in shows on other networks as well.

## [1] "Last updated 2020-01-02"

## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
## 
## Locale: en_GB.UTF-8 / en_GB.UTF-8 / en_GB.UTF-8 / C / en_GB.UTF-8 / en_GB.UTF-8
## 
## Package version:
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