matrix.md

why is this interesing?

[RPlot14.pdf]

This diagram visualizes top 100 contributors to rails/rails collaborating on other open-source projects on github. Each cell represents two projects that shared some contributors; darker cells indicate that more rails/rails people collaborated in both projects.

This data was pulled from GitHub Archive stored in Google BigQuery. It contains a total number of over 227 * 10^6 events from 2011 till the present day. BigQuery enables querying this huge dataset with a SQL-like language and reasonable performance. We would start by finding all GitHub users (or actors) that were involved with the rails/rails repository and counting their activities.

select 
  all.actor, 
  datediff(all.last_at, all.first_at) as length, 
  all.cnt as all, all.cnt_meaningful as meaningful, 
  datediff(all.last_at, all.first_at)/all.cnt as freq,
  all.cnt_contributions > 0 as is_contributor,
  all.cnt_contributions as contributions
from 
  (SELECT 
    actor_attributes_login as actor, 
    count(*) as cnt,
    sum(if(type = 'PushEvent' || type = 'CreateEvent' || type = 'PullRequestEvent' || type = 'GollumEvent'
     || type = 'IssueCommentEvent' || type = 'IssuesEvent' || type = 'PullRequestReviewCommentEvent' 
     || type = 'CommitCommentEvent' || type = 'MemberEvent' || type = 'ForkApplyEvent', 1, 0)) as cnt_meaningful,
    sum(if(type = 'PushEvent' || type = 'CreateEvent' || type = 'PullRequestEvent' || type = 'GollumEvent', 1, 0)) as cnt_contributions,
    min(created_at) as first_at, 
    max(created_at) as last_at 
   from
    (SELECT *
     FROM [githubarchive:github.timeline] 
     where repository_url = 'https://github.com/rails/rails')      
   group by actor) all 
where all.cnt > 1 and all.cnt_meaningful >= 1
order by contributions desc
limit 100

For our analysis we used only the top 100 contributors (selected by counting their pull requests and push events). BigQuery does not support the CREATE VIEW syntax common in other SQL implementations, but offers a similar functionality via the GUI. This view was saved as [githubdata.rails_actors].

Then we looked at other project that those users contributed to. In order to exclude personal projects, only the repositories with at least two contributors were selected.

-- CREATE VIEW [githubdata.rails_neighbors];
select 'https://github.com/rails/rails' as repository1, t.repository_url as repository2, a.actor as actor, count(*) as cnt
from [githubarchive:github.timeline] t join [githubdata.rails_actors] a on t.actor = a.actor
where t.type = 'PushEvent' || t.type = 'CreateEvent' || t.type = 'PullRequestEvent' || t.type = 'GollumEvent'
and not t.repository_url contains 'https://github.com/rails/rails'
group by repository1, repository2, actor

-- CREATE VIEW [githubdata.rails_true_neighbors]
select ne.repository1, ne.repository2, ne.actor, ne.cnt, count(*) as contributor_cnt from
( 
  SELECT n.repository2, t.actor as actor_login
  FROM [githubarchive:github.timeline] t join 
    (select repository2 from [githubdata.rails_neighbors] group by repository2) n 
  on t.repository_url = n.repository2
  where t.type = 'PushEvent' or t.type = 'PullRequestEvent'
  group by n.repository2, actor_login
) pushers
join [githubdata.rails_neighbors] ne on ne.repository2 = pushers.repository2
group by ne.repository1, ne.repository2, ne.actor, ne.cnt
having contributor_cnt > 1

Finally, we looked at the interactions between those projects. For each pair, we counted how many rails/rails contributors also contributed to both of them.

select repository1, repository2, count(*) as actors_cnt from (
select actors.repository_url as repository1, ti.repository_url as repository2, ti.actor as actor, count(*) as cnt
from [githubarchive:github.timeline] ti
join each
  (
    select t.actor, t.repository_url
    from [githubarchive:github.timeline] t 
    join [githubdata.rails_true_neighbors] n on n.repository2 = t.repository_url
    where t.actor in (select actor from [githubdata.rails_actors_contributors])
    group by t.actor, t.repository_url
  ) actors
on ti.actor = actors.actor
where ti.repository_url in (select repository2 from [githubdata.rails_true_neighbors])
and ti.repository_url <> actors.repository_url
and ti.repository_url < actors.repository_url
and not ti.repository_url contains 'https://github.com/rails/rails'
and (ti.type = 'PushEvent' || ti.type = 'CreateEvent' || ti.type = 'PullRequestEvent' || ti.type = 'GollumEvent')
group each by repository1, repository2, actor) x
group by repository1, repository2;

The collaboration graph, represented as an weighted edge list, was then saved to csv files. We used R for further analysis and creating the diagram - picking only the 100 most active repositories, converting the graph representation into an adjacency matrix and creating it's graphic representation.

library(igraph)
library(ggplot2)

#cleanup
rails <- read.csv("data/rails_true_neighbors.csv")
rails$repository1 <- substring(rails$repository1, 20)
rails$repository2 <- substring(rails$repository2, 20)
cntrb <- cbind(rails$repository2, rails$contributor_cnt)
rails <- rails[, c("repository1", "repository2", "actors_cnt")]
rbind(cntrb, c("rails/rails", 2049)) -> cntrb
cntrb <- as.data.frame(cntrb)
cntrb$V2 <- as.numeric(as.character(cntrb$V2))
rownames(cntrb) <- cntrb$V1
 
rails_n <- read.csv("data/rails_network2.csv")
rails_n$repository1 <- substring(rails_n$repository1, 20)
rails_n$repository2 <- substring(rails_n$repository2, 20)
rails_n <- rbind(rails, rails_n)

#create a weighted adjacency matrix representation and pick the most popular repos
GG <- graph.data.frame(rails_n, directed = F)
GG <- set.edge.attribute(GG, "alpha", index=E(GG), value = rails_n$actors_cnt/max(rails_n$actors_cnt))
GG <- induced.subgraph(GG, which(match(V(GG)$name, cntrb$V1[order(cntrb$V2, decreasing = T)][1:100]) > 0))
GG_wgh <- cntrb[V(GG)$name, "V2"]

get.adjacency(GG, attr="alpha") -> mat
dat <- expand.grid(y=seq(nrow(mat)), x=seq(ncol(mat)))
dat <- data.frame(dat, value=as.vector(mat))

#let's see!
ggplot(data=dat, aes(x=x, y=y)) + geom_point(aes(alpha=ifelse(value < 1e-5, 0, value)), size=5, shape=15) -> p1
p1 + scale_x_continuous(breaks=1:100, labels=V(GG)$name, expand = c(0.01, 0.01)) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) -> p1
p1 + scale_y_continuous(breaks=1:100, labels=V(GG)$name, expand = c(0.01, 0.01)) -> p1
p1 + theme(axis.text.y = element_text(hjust = 1), panel.background = element_rect(fill="transparent"), panel.grid.major=element_line(color="#d9d9d9")) -> p1
p1 + labs(x="", y="") + theme(legend.position = "none") + scale_alpha_continuous(range = c(0, 1), guide = "none", trans="sqrt")