README.md

GithubMetrics

It's now recomended to use this package instead: https://github.com/r-world-devs/GitStats

The aim of this package is to provide a wrapper on gh to quickly get you key Github repo information you need.The code here is used within Roche to quickly let me pull answer simple questions like:

• How many studies have more than 1 data scientist (and roughly what’s the commit split)
• What are the common languages being used (proxied through file type distribution within repos)
• Pull commit metadata to enrich other study info held in other systems

Installation

You can install the released version of GithubMetrics from CRAN with:

install.packages("GithubMetrics")

Setup

library(GithubMetrics)
library(tidyverse)
library(glue)

organisation <- "openpharma"

Repos in an org

Pull all the repos present within an org (that I can see).

repos_raw <- gh_repos_get(
  org = organisation
  )

repos_clean <- gh_repos_clean(repos_raw)

glimpse(repos_clean) 
#> Rows: 14
#> Columns: 7
#> $ name           <chr> "BBS-causality-training", "GithubMetrics", "facetsr", …
#> $ full_name      <chr> "openpharma/BBS-causality-training", "openpharma/Githu…
#> $ size           <int> 27, 118, 2163, 5435, 87, 939, 1817, 79487, 329, 0, 482…
#> $ updated_at     <chr> "2021-01-29T18:01:35Z", "2021-02-03T07:07:43Z", "2020-…
#> $ default_branch <chr> "main", "master", "master", "master", "master", "maste…
#> $ language       <chr> "R", "R", "R", "Unsure", "Python", "R", "C", "R", "R",…
#> $ MB             <dbl> 0.0, 0.1, 2.1, 5.3, 0.1, 0.9, 1.8, 77.6, 0.3, 0.0, 0.5…

Realistically, research code is likely to be on Github Enterprise, so the .api_url and .token parameters can be passed through to gh(). Commented code below shows how you can use an on-premise Github server.

# repos_raw <- gh_repos_get(
#   org = organisation,
#   .api_url = "https://github.roche.com/api/v3",
#   .token = Sys.getenv("GITHUB_PAT_ROCHE")
#   )

Commits

Get every commit for all the repos in this organisation.

repo_all_commits <- gh_commits_get(
  repos_clean %>% filter(size > 0) %>% pull(full_name), 
  days_back = 365*10
)

glimpse(repo_all_commits)
#> Rows: 1,762
#> Columns: 5
#> $ full_name      <chr> "openpharma/BBS-causality-training", "openpharma/BBS-c…
#> $ author         <chr> "heinzmann537", "heinzmann537", "heinzmann537", "epiji…
#> $ datetime       <chr> "2021-01-29T18:00:10Z", "2021-01-29T12:55:54Z", "2021-…
#> $ sha            <chr> "5ac98df2a99db3b50abae114e37c00e433903094", "059569252…
#> $ commit_message <chr> "Update variable naming ADALM", "Small change", "First…

People

Pull all the people that have committed in r.

contributors <- repo_all_commits %>%
  group_by(author) %>%
  summarise(
    commits = n()
  ) %>%
  filter(!author %in% c(".gitconfig missing email","actions-user"))
  
contributors <- contributors %>%
  left_join(
    gh_user_get(contributors$author),
    by = c("author"="username")
  )

contributors %>%
  arrange(-commits) %>%
  mutate(
    last_active = Sys.Date() - last_active,
    contributor = glue('<img src="{avatar}" alt="" height="30"> {author}'),
    blog = case_when(
      blog == "" ~ "",
      TRUE ~ as.character(glue('<a href="{blog}">link</a>'))
      )
    ) %>%
  select(contributor,commits,name,last_active,company,location,blog) %>%
  knitr::kable(
    
  )

contributor	commits	name	last_active	company	location	blog
evanmiller	936	Evan Miller	17 days	NA	Chicago, IL	link
SHAESEN2	127	Steven Haesendonckx	20 days	NA	NA
diego-s	122	Diego S	255 days	NA	NA
bailliem	109	Mark Baillie	0 days	NA	Basel, CH	link
epijim	89	James Black	5 days	Roche	Basel, Switzerland	link
jaredhobbs	70	Jared Hobbs	89 days	YearEnd, Inc.	Salt Lake City, UT	link
kalimu	42	Kamil Wais	9 days	7N / Roche	Rzeszów	link
Jonnie-Bevan	28	NA	63 days	NA	NA
cschaerfe	21	Charlotta	118 days	NA	NA
davidanthoff	12	David Anthoff	1 days	University of California, Berkeley	Berkeley, CA	link
jar1karp	12	Jari Karppinen	154 days	NA	NA	link
mikmart	12	Mikko Marttila	2 days	NA	NA	link
reikoch	8	NA	6 days	NA	NA
afeld	6	Aidan Feldman	0 days	@GSA and personal projects	Brooklyn, NY	link
erblast	6	Björn Oettinghaus	22 days	NA	Switzerland	link
lionel-	6	Lionel Henry	70 days	@rstudio	NA
bpfoley	5	Brian Foley	94 days	NA	Seattle, Washington
rebecca-albrecht	4	NA	5 days	NA	NA
dazim	3	Tim Treis	23 days	NA	Heidelberg, Germany
heinzmann537	3	NA	5 days	NA	NA
kentm4	3	Matt Kent	2 days	Genesis Research	NA
PaulJordan57	3	NA	19 days	NA	NA
galachad	2	Adam Foryś	20 days	@Roche	Warsaw, Poland	link
gerph	2	Charles Ferguson	8 days	NA	NA
hadley	2	Hadley Wickham	0 days	@rstudio	Houston, TX	link
kawap	2	NA	289 days	Roche / 7N	NA
kleschenko	2	Kostya Leschenko	5 days	@datarobot	Lviv, Ukraine
kshedden	2	Kerby Shedden	1 days	NA	NA
kurt-vd	2	Kurt Van Dijck	63 days	NA	NA
mrocklin	2	Matthew Rocklin	2 days	@coiled	San Juan Capistrano, CA	link
thomas-neitmann	2	Thomas Neitmann	1 days	Roche	Basel, Switzerland	link
waddella	2	Adrian Waddell	27 days	NA	NA	link
ararslan	1	Alex Arslan	0 days	Beacon Biosignals	Seattle, WA
ginberg	1	NA	14 days	NA	Remote	link
ivarref	1	Ivar Refsdal	13 days	NA	Bergen, Norway
jonathon-love	1	Jonathon Love	1 days	NA	NA	link
Karissa	1	NA	363 days	NA	NA
thanos-siadimas	1	NA	1 days	NA	NA

Files

Pull a specific file using gh_file_get().

desc_formatted <- gh_file_get(
  repo = "GithubMetrics",
  org = "OpenPharma",
  file = "DESCRIPTION"
) %>%
  # format the description
  desc::desc(text = .)

# Print it
desc_formatted$get(c("Package","Title","Version")) %>%
  tibble::enframe() %>%
  knitr::kable()

name	value
Package	GithubMetrics
Title	Quickly get key metrics on Github repositaries
Version	0.1.0

Get all of the files present in the last commit of all the repos using gh_repo_files_get().

repo_files <- gh_repo_files_get(
  repo_commits = repo_all_commits,
  only_last_commit = TRUE
)
#> Pulling files in latest commit from 13 repos

glimpse(repo_files)
#> Rows: 1,311
#> Columns: 6
#> $ repo       <chr> "openpharma/visR-docs", "openpharma/visR-docs", "openpharm…
#> $ file       <chr> "readme.md", "docs", "docs/404.html", "docs/code_of_conduc…
#> $ sha_repo   <chr> "5b35fdbc39b87a154c9426e363c8f5a2c83d66b0", "5b35fdbc39b87…
#> $ sha_commit <chr> "642856728e165746076a17c6522b9264f693f37d", "642856728e165…
#> $ extension  <chr> "md", "docs", "html", "html", "html", "html", "png", "png"…
#> $ lang       <chr> "Markdown", NA, "HTML", "HTML", "HTML", "HTML", NA, NA, NA…

repo_files %>%
  group_by(repo) %>%
  summarise(
    Files = n(),
    `R files` = sum(lang %in% "R"),
    `Python files` = sum(lang %in% c("Python","Jupyter Notebook"))
  ) %>% knitr::kable(
    caption = "Types of files in the organisation"
  )

repo	Files	R files	Python files
openpharma/BBS-causality-training	4	2	0
openpharma/CTP	100	30	0
openpharma/facetsr	63	13	0
openpharma/GithubMetrics	43	22	0
openpharma/openpharma.github.io	76	1	0
openpharma/pypharma_nlp	131	0	49
openpharma/RDO	105	11	0
openpharma/ReadStat	207	0	0
openpharma/sas7bdat	8	0	2
openpharma/simaerep	145	32	0
openpharma/syntrial	67	24	0
openpharma/visR	177	81	0
openpharma/visR-docs	185	0	0

Types of files in the organisation

results <- gh_repo_search(
  code = "tidyverse",
  organisation = organisation
)

glimpse(results)
#> Rows: 12
#> Columns: 7
#> $ full_name <chr> "openpharma/GithubMetrics", "openpharma/GithubMetrics", "op…
#> $ name      <chr> "GithubMetrics", "GithubMetrics", "GithubMetrics", "GithubM…
#> $ file_name <chr> "README.md", "README.Rmd", "DESCRIPTION", "test-gh_repos_XX…
#> $ path      <chr> "README.md", "README.Rmd", "DESCRIPTION", "tests/testthat/t…
#> $ url       <chr> "https://github.com/openpharma/GithubMetrics/blob/fa7764869…
#> $ score     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
#> $ lang      <chr> "Markdown", "R", NA, "R", "Markdown", "R", "Markdown", "R",…

helper_gh_repo_search <- function(x, org = "openpharma"){
  
  ## Slow it down! as search has 30 calls a minute rate limit.
  ## If you prem the search rate limit is higher, so usually not needed
  if(interactive()){message("Wait 5 seconds")}
  Sys.sleep(5)
  ## End slow down
  
  
   results <- gh_repo_search(
      code = x,
      organisation = org
    ) 
   
  if(is.na(results)) {
    results <- return()
  }
  results %>% 
    mutate(Package = x, Organisation = org) %>%
    group_by(Organisation,Package) %>%
    summarise(
      Repos = n_distinct(full_name), .groups = "drop"
    )
}

packages <- c(
  "tidyverse","pkgdown","dplyr","data.table"
  )

package_use <- bind_rows(
  packages %>%
    map_df(
      helper_gh_repo_search, org = "PHCAnalytics"
    ),
  packages %>%
    map_df(
      helper_gh_repo_search, org = "openpharma"
    ),
  packages %>%
    map_df(
      helper_gh_repo_search, org = "AstraZeneca"
    ),
  packages %>%
    map_df(
      helper_gh_repo_search, org = "Roche"
    ),
  packages %>%
    map_df(
      helper_gh_repo_search, org = "Genentech"
    ),
  packages %>%
    map_df(
      helper_gh_repo_search, org = "Novartis"
    )
)
#> pkgdown does not appear in PHCAnalytics.
#> query = 'pkgdown in:file  user:PHCAnalytics'
#> tidyverse does not appear in AstraZeneca.
#> query = 'tidyverse in:file  user:AstraZeneca'
#> pkgdown does not appear in AstraZeneca.
#> query = 'pkgdown in:file  user:AstraZeneca'
#> data.table does not appear in AstraZeneca.
#> query = 'data.table in:file  user:AstraZeneca'


package_use %>%
  pivot_wider(names_from = "Package", values_from = "Repos") %>%
  mutate(Total = rowSums(.[,-1], na.rm = TRUE)) %>%
  arrange(-Total) %>%
  knitr::kable(
    caption = "Package use detected within repositaries in Pharma orgs"
  )

Organisation	tidyverse	dplyr	data.table	pkgdown	Total
Novartis	4	10	12	6	32
openpharma	4	6	2	6	18
Roche	3	2	3	3	11
Genentech	3	3	3	2	11
PHCAnalytics	2	4	4	NA	10
AstraZeneca	NA	1	NA	NA	1

Package use detected within repositaries in Pharma orgs