README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  knitr.kable.NA = ''
)
```

# GithubMetrics

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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](https://CRAN.R-project.org) with:

``` r
install.packages("GithubMetrics")
```


## Setup

```{r setup, warning=FALSE, message=FALSE}
library(GithubMetrics)
library(tidyverse)
library(glue)

organisation <- "openpharma"
```

## Repos in an org

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

```{r}
repos_raw <- gh_repos_get(
  org = organisation
  )

repos_clean <- gh_repos_clean(repos_raw)

glimpse(repos_clean) 
```

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.

```{r, eval = FALSE}
# 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.

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

glimpse(repo_all_commits)
```

## People

Pull all the people that have committed in `r `.

```{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(
    
  )
```

## Files

Pull a specific file using `gh_file_get()`.

```{r}
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()
```

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

```{r}
repo_files <- gh_repo_files_get(
  repo_commits = repo_all_commits,
  only_last_commit = TRUE
)

glimpse(repo_files)

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"
  )
```

```{r}
results <- gh_repo_search(
  code = "tidyverse",
  organisation = organisation
)

glimpse(results)
```

```{r, warning=FALSE}
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"
    )
)


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"
  )
```