Use GitHub data to show how the DORA metrics changed over time as I
developed and used rdev.
git log
Idea: use gert::git_log() tables across all my public
and personal (private) R repositories over time to create an annotated
timeline and visualization of my work, and implementation of the DORA
technical practices.
Import git logs from my repositories:
gitlogs_tz <- tz(gitlogs$time)
Filter logs by repository, adding cutoff dates when active
development ended for timeline visualization. Drop commits past April 30
to remove partial months.
filtered_gitlogs <- gitlogs |>
filter(!(repo == "rstudio-training" & time > ymd_h("2020-12-28 0", tz = gitlogs_tz))) |>
filter(!(repo == "software-resilience" & time > ymd_h("2021-02-22 0", tz = gitlogs_tz))) |>
filter(!(repo == "rtraining" & time > ymd_h("2021-10-08 0", tz = gitlogs_tz))) |>
filter(!(repo == "workshop7" & time > ymd_h("2021-12-07 0", tz = gitlogs_tz))) |>
filter(!(repo == "jbplot" & time > ymd_h("2022-02-07 0", tz = gitlogs_tz))) |>
filter(time < ymd_h("2022-05-01 0", tz = gitlogs_tz)) |>
mutate(repo = factor(repo, levels = c(
"rstudio-training", "software-resilience", "rtraining", "rdev", "workshop7", "jbplot",
"siracon2022"
)))
Plot monthly commits by repository.
filtered_gitlogs |>
mutate(time = floor_date(time, unit = "month")) |>
group_by(time, repo) |>
summarize(commits = n(), .groups = "drop") |>
ggplot(aes(x = time, y = commits, color = repo)) +
geom_point() +
geom_line() +
labs(title = "Monthly commits by repository") +
labs(x = "", y = "", color = "repository") +
theme_quo()
ggsave("rendered/monthly-commits-repo.png", width = 16 * 0.6, height = 9 * 0.6, bg = "white")
High resolution
plot
Key events
Plot key events on a timevis() timeline. Full page version.
key_events <- read_csv("data/key-events.csv", col_types = cols(
id = col_integer(),
start = col_date(format = ""),
end = col_date(format = ""),
content = col_character(),
group = col_integer(),
group_content = col_character(),
intro = col_logical(),
milestone = col_logical()
))
dora_groups <- key_events |>
select(id = group, content = group_content) |>
unique() |>
arrange(id)
key_events |>
render_timevis(groups = dora_groups, file = "rendered/key-events.html", showZoom = TRUE)
2020-09-08: Starting out, rstudio-training, renv
- Version Control
- Stored project files, notebook (Rmd and html) in private git
repository
- Use renv to store package dependencies in source control
- Trunk-based Development
- Direct commits to master (not recommended)
- Shifting Left on Security
- Start development with
renv::update()
2020-09-11: Published “Working
with R”
2020-09-30: (Aside) First bug discovered, https://github.com/rstudio/renv/issues/547 !
2020-10-06: setup-r script
- Version Control
- Automate setup of local R development environment
2020-12-02: Adoption of styler and lintr
- Code Maintainability
- Consistent formatting (styler)
- Consistent code (lintr)
- Continuous Testing
- Static code analysis (lintr)
2020-12-27: Migration to rtraining package
- Continuous Integration
- Continuous Testing
- Version Control
2020-12-29: build-site script
- Deployment Automation
- build-site: shell script to publish notebooks using
rmarkdown::render_site()
- MVP for publishing notebooks using GitHub Pages
2020-12-30: First release: rtraining 0.0.1
2020-12-30: GitHub Actions
- Continuous Integration, Continuous Testing
2020-12-30: lint_all()
- Continuous Integration, Continuous Testing
- lint all files locally
- first testthat tests
- roxygen2 documentation
2020-12-30: style_all()
- Continuous Integration, Code Maintainability
- run styler on all files locally
2020-12-31: Switch GitHub Actions to lint_all()
- Continuous Integration, Continuous Testing
- match GitHub and local CI checks
2021-01-01: ci(), check_renv()
- Continuous Integration, Continuous Testing
- run all CI checks locally
- eliminate toil
- match GitHub and local CI checks
2021-01-01: Migration to rdev package
- Code Maintainability
- Moved most functions to new rdev package
- Consistent tools across projects
2021-01-02: Multi-platform R CMD check
- Continuous Integration, Continuous Testing
- ensure package works on Windows and macOS
2021-01-03: First version of build_analysis_site()
- Deployment Automation
- Automatically build GitHub Pages site with functions, notebooks
- Still a shell script
- Beginning of standard deployment and release pattern:
- bump version
- write code
- update NEWS.md
- “GitHub Release”
- build_site
2021-01-09: Analysis Package Layout
- Code Maintainability
- Consistent package layout across projects
- Supported future automation for creating packages
2021-01-12: Native R version of
build_analysis_site()
2021-01-16: Migrated build_analysis_site() from
rtraining to rdev
- Code Maintainability
- Cross-platform support
- Moves all automation to R Console
- Deployment Automation
- Automated builds across all projects
2021-09-29: Formal R Analysis Package Layout, Documented release
process
- Code Maintainability
- Consistent package layout across projects
- Supported future automation for creating packages
- Deployment Automation
- Supported future automation for creating releases
2021-12-04: Documented package creation process
- Code Maintainability
- Consistent package layout across projects
- Supported future automation for creating packages
2021-12-23: theme_quo(): a personalized theme to
visually identify my ggplots.
2022-01-01: Automate package configuration with
use_analysis_package()
- Code Maintainability
- Consistent package layout across projects
2022-01-10: Create package automation (rdev 0.7.0)
create_github_repo(): Create new GitHub repository
following rdev conventions in the active user’s account and create a
basic package
use_rdev_package(): Add rdev templates and settings
within the active package. Normally invoked when first setting up a
package.
Added build_rdev_site(), a wrapper for
pkgdown::build_site() optimized for rdev workflow that
updates README.md and performs a clean build using
pkgdown
Added ‘Analysis Notebook’ R markdown template for RStudio (File
> New File > Rmarkdown > From Template)
Migrated ggplot2 themes/styles (theme_quo(),
viridis_quo()) to new package,
jabenninghoff/jbplot
Code Maintainability
- Cross-platform support
- Moves all automation to R Console
Deployment Automation
- Automated builds across all projects
2022-01-10: Automate notebook listings in README
library(rdev)
library(fs)
library(dplyr)
library(purrr)
notebooks <- dir_ls("analysis", glob = "*.Rmd") |>
map_dfr(rmd_metadata) |>
mutate(bullet = paste0("- [", title, "](", url, ") (", date, "): ", description)) |>
pull(bullet)
writeLines(notebooks)
2022-01-17: Release automation (rdev 0.8.0)
2022-01-19: More workflow automation
- Added
new_branch(): Create a new feature branch, and
(optionally) bump the version in DESCRIPTION
2022-01-21 - 2022-02-06: adding test coverage
- Continuous Testing
- Biggest challenge yet
- Significantly improved code quality
- “Unit” testing
- Just test
- Test program flow
- Don’t test other people’s code
- Mock external functions
- Fix bugs by writing a test
- Code coverage, and code coverage metrics
- Test Driven Development
- Tests Give You Confidence (to Refactor)
(Show plot of increasing code coverage from codecov.io)
2022-01-24: write_eval() is a really bad idea:
write_eval <- function(string) {
if (!is.character(string)) stop("not a character vector")
if (string == "") stop("nothing to evaluate")
writeLines(string)
eval(parse(text = string))
}
2022-01-30: Manual test script for new package setup
- Continuous Testing
- Manual tests evolve into partially or fully automated tests
2022-02-02: Added local_temppkg() test helper
function
- Continuous Testing
- Test helpers - testing test helpers helps!
2022-02-06: rdev 1.0.0 !
- Release automation: Stage and create GitHub releases, including
GitHub pages
- Continuous Integration: Local continuous integration checks and
dependency management
- Package Setup: Package setup tasks, typically performed once
2022-02-06 - Today: Continuous Improvement
- Improve CI workflow to catch mistakes
- Spell checks
- Branch protection automation
- Options
- Dependency management
- Product health
Releases
Get releases from GitHub using
siracon2022::gh_releases():
if (!exists("releases")) {
repos <- c("rtraining", "rdev", "workshop7", "jbplot", "siracon2022")
repos <- setNames(repos, repos)
releases <- map_dfr(repos, gh_releases, "jabenninghoff", .id = "repo") |>
arrange(time)
}
Filter releases past April 30 to remove partial months.
filtered_releases <- releases |>
mutate(time = with_tz(time, tzone = gitlogs_tz)) |>
filter(time < ymd_h("2022-05-01 0", tz = gitlogs_tz))
Plot releases over time: total GitHub releases per period (for all
repositories) to show changes in release frequency. The dotted line
marks the implementation of release automation.
monthly_releases <- filtered_releases |>
mutate(time = floor_date(time, unit = "month")) |>
group_by(time) |>
summarize(releases = n(), .groups = "drop") |>
add_row(time = ymd("2020-11-01"), releases = 0) |>
add_row(time = ymd("2020-10-01"), releases = 0) |>
add_row(time = ymd("2020-09-01"), releases = 0) |>
arrange(time)
monthly_releases |>
ggplot(aes(x = time, y = releases)) +
geom_point() +
geom_line() +
geom_vline(xintercept = ymd_h("2020-12-01 0", tz = gitlogs_tz), linetype = "dotted") +
geom_vline(xintercept = ymd_h("2022-01-01 0", tz = gitlogs_tz), linetype = "dotted") +
coord_cartesian(ylim = c(0, NA)) +
labs(title = "Monthly GitHub releases") +
labs(x = "", y = "") +
theme_quo()
ggsave("rendered/monthly-releases.png", width = 16 * 0.6, height = 9 * 0.6, bg = "white")
High resolution plot
However, the number of releases per month might just represent how
much work is being done, and looks similar to the plot of all commits by
month:
gitlogs |>
filter(time < ymd_h("2022-05-01 0", tz = gitlogs_tz)) |>
mutate(time = floor_date(time, unit = "month")) |>
group_by(time) |>
summarize(commits = n(), .groups = "drop") |>
arrange(time) |>
ggplot(aes(x = time, y = commits)) +
geom_point() +
geom_line() +
geom_vline(xintercept = ymd_h("2020-12-01 0", tz = gitlogs_tz), linetype = "dotted") +
geom_vline(xintercept = ymd_h("2022-01-01 0", tz = gitlogs_tz), linetype = "dotted") +
coord_cartesian(ylim = c(0, NA)) +
labs(title = "Monthly git commits") +
labs(x = "", y = "") +
theme_quo()
ggsave("rendered/monthly-commits.png", width = 16 * 0.6, height = 9 * 0.6, bg = "white")
High resolution plot
Also plot releases per commit, which will fall between 0 and 1. The
dotted lines mark adoption of GitHub and implementation of release
automation.
gitlogs |>
filter(time < ymd_h("2022-05-01 0", tz = gitlogs_tz)) |>
mutate(time = floor_date(time, unit = "month")) |>
group_by(time) |>
summarize(commits = n()) |>
full_join(monthly_releases, by = "time") |>
replace_na(list(commits = 0, releases = 0)) |>
mutate(rpc = releases / commits) |>
ggplot(aes(x = time, y = rpc)) +
geom_point() +
geom_line() +
geom_vline(xintercept = ymd_h("2020-12-01 0", tz = gitlogs_tz), linetype = "dotted") +
geom_vline(xintercept = ymd_h("2022-01-01 0", tz = gitlogs_tz), linetype = "dotted") +
labs(title = "Monthly GitHub releases per commit") +
labs(x = "", y = "") +
theme_quo()
ggsave("rendered/releases-per-commit.png", width = 16 * 0.6, height = 9 * 0.6, bg = "white")
High resolution
plot
