From f17653428ff45992fb41a339ac7c12d883b32fd8 Mon Sep 17 00:00:00 2001 From: Qiushi Yan Date: Mon, 27 Jun 2022 05:38:58 -0500 Subject: [PATCH] automate with gh actions (#76) --- 07-project.Rmd | 163 ++++++++++++++++++++++++++++++++- examples/rmd-ci-functions.Rmd | 15 +++ examples/rmd-ci.Rmd | 53 +++++++++++ examples/rmd-ci.png | Bin 0 -> 172679 bytes images/rmd-ci-github-pages.png | Bin 0 -> 768573 bytes 5 files changed, 230 insertions(+), 1 deletion(-) create mode 100644 examples/rmd-ci-functions.Rmd create mode 100644 examples/rmd-ci.Rmd create mode 100644 examples/rmd-ci.png create mode 100644 images/rmd-ci-github-pages.png diff --git a/07-project.Rmd b/07-project.Rmd index 314958f..28a639b 100644 --- a/07-project.Rmd +++ b/07-project.Rmd @@ -526,4 +526,165 @@ Markdown 文档的初始版本,并通过 `upload_rmd()` 函数将其上传到 **workflowr** 的主要作者 John Blischak 也整理了一个与 R 项目工作流相关的 R 包和指南的非详尽列表,可见 GitHub 仓库: https://github.com/jdblischak/r-project-workflows。 -### 使用 GitHub Actions 实现自动化部署{#github-actions} +### 使用 GitHub Actions 实现自动化部署 {#github-actions} + +使用 R Markdown 输出特定的文件格式后,一个自然的问题是如何与他人共享结果,一些使用场景包括在公司内部发布数据分析报告,发表 blogdown 博客,更新 bookdown 电子书籍等。最直接的做法是在本地执行编译,随后分享输出文件。例如执行在控制台 `rmarkdown::render()` 函数后,上传更新后的 HTML 文件到网络服务器上,或者在 Github 中上传 Markdown 文件。本地手动编译不仅需要重复的人力劳动,如果输出结果依赖于特定的系统环境,也难以保证结果的可重复性。为了使部署过程更高效可靠,包括 Github Actions, Gitlab Pipeline 等在内的持续集成 (continuous integration) 工具被广泛应用在 R Markdown 工作流的自动化部署中。本节以 Github 平台的 Github Actions 为例讲解 R Markdown 的自动化部署方法。 + +尽管 Github Actions 持续集成工具的用途非常广泛,具体在 R Markdown 的语境内,读者可以把它想象为一系列执行 R Markdown 编译的指令,可以是终端的 shell 命令,也可以调用 R 语言或任意工具的命令行接口。用户可以自行定义这些指令的触发条件,例如每周一上午十点运行一次,或每次 Github 仓库有新的代码提交时运行。当这些条件被触发时,Github 会创建一个专属的虚拟环境运行定义好的代码,其中便可以包括用于发布 R Markdown 输出文档的命令。 + +新建任意项目目录,在其中创建 `index.Rmd` 文件,包含如下内容: + +````{r, echo = FALSE} +import_example("rmd-ci.Rmd") +```` + +本地编译结果如下 (需要安装 **prettydoc** [@R-prettydoc] 包): + +```{r, echo = FALSE} +import_example_result("examples/rmd-ci.Rmd") +``` + + +本地验证代码运行无误后,开始设置自动化部署。首先在 Github 上新建对应的仓库,在本地目录下 `git init` 初始化 git 并 `git remote add origin ` 添加该仓库。希望实现的效果为,每次更新 main 分支后,Github Actions 自动编译 `index.Rmd` 并更新至仓库对应的 Github Pages 网页端。 + +Github Actions 使用 yaml 文件定义命令,在根目录下新建 `.github/workflows/deploy.yml` 文件。 + +此时文档结构为: + +```markdown +├── .github +│   └── workflows +│   └── deploy.yml +└── index.Rmd +``` + +其中,`.github/workflows/` 是固定的前缀路径,Github 在此路径下搜索 yaml 文件,每个文件称为一个 workflow,不同的 workflow 通常代表自动化部署的不同任务,例如有的负责获取数据,有的负责更新网页。`deploy.yml` 是本案例使用的唯一 workflow 文件,名称可以自定义,其中内容为: + +```yaml +on: + push: + branches: main + +name: Render + +jobs: + render: + name: Render index.Rmd + runs-on: macOS-latest + steps: + - uses: actions/checkout@v2 + + - uses: r-lib/actions/setup-r@v2 + + - uses: r-lib/actions/setup-pandoc@v1 + + - name: Install rmarkdown + run: Rscript -e 'install.packages(c("rmarkdown", "prettydoc"))' + + - name: Render index.Rmd + run: Rscript -e 'rmarkdown::render("index.Rmd")' + + - name: Commit results + run: | + git add index.html + git commit -m 'Re-build index.Rmd' + git push origin +``` + +`on` 定义了该 workflow 的触发条件,这里为 main 分支收到新提交 (push) 时触发。它的语法通常包括动作与分支,例如 "main 和 release 分支收到 pull request 时触发" 可以表示为 + +```markdown +on: + pull_request: + # 可包含多个触发分支 + branches: + - main + - releases +``` + +。`on` 还支持 cron 语法,例如 + +```markdown +# 每天 5:30 and 17:30 UTC 触发 workflow +on: + schedule: + - cron: '30 5,17 * * *' +``` + +`name` 代表 Github 网页端显示的 workflow 名称。 + +`jobs` 是 workflow 中的核心内容,代表需要执行的一系列指令,可以分为不同的子任务,该文件中包含一个 `render` 子任务,指定运行环境为 `macOS-latest`,其他可选环境包括 windows,linux 等不同版本的机型。`render` 中的每一项代表一组独立的指令。由于每次 workflow 触发时均运行在全新的环境中,必须重新安装项目所需的依赖项,前三个 `uses` 指令是 Github 社区提供的模版,分别在环境中克隆所需的仓库,安装 R 和安装 pandoc。 + +```markdown +# 预定义模版搜索 https://github.com/marketplace?type=actions +- uses: actions/checkout@v2 +- uses: r-lib/actions/setup-r@v2 +- uses: r-lib/actions/setup-pandoc@v1 +``` + + +随后,两个自定义的终端命令为 + +```markdown +- name: Install rmarkdown + run: Rscript -e 'install.packages(c("rmarkdown", "prettydoc"))' + +- name: Render index.Rmd + run: Rscript -e 'rmarkdown::render("index.Rmd")' +``` + +`name` 定义该步骤的 UI 名称, `run` 代表该步骤执行的 shell 命令,这两步安装了 rmarkdown 和 prettydoc 包,并编译 `index.Rmd`。`Rscript` 是 R 提供的命令行接口,另外一种写法是: + +```markdown +- name: Install rmarkdown + shell: Rscript {0} + run: | + install.packages(c("rmarkdown", "prettydoc")) +``` + +最后,workflow 需要把虚拟环境中生成的输出文件同步到主仓库中。这样,每次 main 分支收到更新,Github Actions 便会重新编译 `index.Rmd` 文档,同步输出文件 `index.html` 至仓库,实现自动化编译。 + +```markdown +# 同步输出文件至仓库 +- name: Commit results + run: | + git add index.html + git commit -m 'Re-build index.Rmd' + git push origin +``` + +案例的最后一步是启动 Github Pages 服务,该服务将自动识别仓库内的 `index.html` 文件,基于个人 Github 账号生成公开的网页地址。启动方法为点击仓库的 `settings -> pages` 并选择 Source 为 main 分支下的 root 目录。 + +```{r, echo = FALSE, fig.cap = "为仓库启用 Github Pages,生成地址见 https://qiushiyan.github.io/rmd-ci/"} +knitr::include_graphics("images/rmd-ci-github-pages.png") +``` + +真实生产环境中,应使主文档 `index.Rmd` 尽可能简洁抽象,可以运用 \@ref(child-document) 节学习的子文档知识,将业务逻辑封装为函数放入子文档 `functions.Rmd` 中,`functions.Rmd` 的内容为: + +```{r, echo = FALSE} +import_example("rmd-ci-functions.Rmd") +``` + + +随后在主文档 `index.Rmd` 中引用子文档: + +````{verbatim, lang="markdown"} +```{r, child = "template.Rmd", include = FALSE} +``` + + +```{r} +sales_dat <- fetch_sales_data() +knitr::kable(head(sales_dat, 20)) +``` + + +```{r} +top_10_regions <- top_n_regions(sales_dat, 10) + +barplot(sales ~ region, data = top_10_regions) +``` +```` + +读者可以在 [Github Actions 文档](https://github.com/features/actions) 学习更多语法知识,此外 [rlib/actions](https://github.com/r-lib/actions) 仓库汇集了诸多 R 社区为各项自动化任务定制的 workflow 文件,大部分情况下可以直接复制使用,或仅需要修改少量配置。 + diff --git a/examples/rmd-ci-functions.Rmd b/examples/rmd-ci-functions.Rmd new file mode 100644 index 0000000..83a81da --- /dev/null +++ b/examples/rmd-ci-functions.Rmd @@ -0,0 +1,15 @@ +```{r, include=FALSE} +fetch_sales_data <- function(date = Sys.Date()) { + day <- as.integer(format(date, "%d")) + sales_dat <- data.frame( + region = rep(LETTERS, each = 10), + sales = rpois(26 * 10, day) + ) + sales_dat +} + +top_n_regions <- function(sales_dat, n) { + sales_sum <- aggregate(sales ~ region, data = sales_dat, sum) + head(sales_sum[order(-sales_sum$sales), ], n) +} +``` diff --git a/examples/rmd-ci.Rmd b/examples/rmd-ci.Rmd new file mode 100644 index 0000000..0ecbe5d --- /dev/null +++ b/examples/rmd-ci.Rmd @@ -0,0 +1,53 @@ +--- +title: "用 Github Actions 实现自动化部署" +author: "张三" +date: "`r Sys.Date()`" +output: + prettydoc::html_pretty: + theme: leonids + highlight: github +--- + +## 数据概览 + +`r Sys.Date()` 日各地区销售情况 + +```{r} +day <- as.integer(format(Sys.Date(), "%d")) +sales_dat <- data.frame( + region = rep(LETTERS, each = 10), + sales = rpois(26 * 10, day) +) + +knitr::kable(head(sales_dat, 20)) +``` + +## 描述性分析 + +本日销售量最多对前 10 个地区为: + +```{r} +sales_sum <- aggregate(sales ~ region, data = sales_dat, sum) + +top_10_regions <- head(sales_sum[order(-sales_sum$sales), ], 10) + +barplot(sales ~ region, data = top_10_regions) +``` + + +## 线性模型 + +用简单线性模型探究地区对销售量对影响,公式为: + +$$ +销售量 = \beta_o + \beta_1地区A + \beta_1地区B + \cdots + \beta_1地区Z +$$ + +```{r} +mod <- lm(sales ~ region, data = sales_dat) + +region_coefs <- mod$coefficients[-1] +max_idx <- which.max(region_coefs) +``` + +所有 `r length(unique(sales_dat$region))` 个地区中,回归系数绝对值最大的是 `r LETTERS[max_idx]`,为 `r region_coefs[max_idx]` diff --git a/examples/rmd-ci.png b/examples/rmd-ci.png new file mode 100644 index 0000000000000000000000000000000000000000..9de43ffafbf2a314a57f526dcaf73459cbc682b2 GIT binary patch literal 172679 zcmeFZg;$i}+b)WUf{2QMl1d5)C@GA9qQubMp;FQ@F!UfIpi0 zENW0m^KrcQo=rpT`hBb7l=nd?&^<{bsDmwElvF`$u5#gttb=^bj~ksqRI({reOccb zTE6ZT3}98FN*XB8n+enO{)xvun_alB&F4j|h0F7e^ToWoiCYO{+NM)5^m0sC*aHr* zpa#|Ccaz|g;E(8imn#GWGyGS%!FM&1GdBnbzKq-gPe8Cudesf)gyS8-Q>V@v&IMNrl2(S=co zr445uR${T|QYK6Pn9S!X#z;jYIY*iJEpz(3CVo}HuCXy}3%od3uNo+r+=nAz6JNZt zNng(x#=R`L5ud()Ba8`RRM2(INNPcbizrMx!w=LO#SioNgk)oV6whSXN1y8!X=j*+ zS0{2bIu9 z&MIZGTQwwsi93;Hrk-aa4Sw=GV$~_tAlvT!c(#GABT?7k_=ev3!)F#4kr 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