Add gh-actions to automate rendering readme, update readme, update si…

…mdata script

.github/workflows/render-readme.yaml

@@ -0,0 +1,32 @@
+on:
+  push:
+    paths:
+      - 'README.Rmd'
+      - '*.yaml'
+
+name: Render README
+
+jobs:
+  render:
+    name: Render README
+    runs-on: macOS-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GENERAL_PAT }}
+    steps:
+      - uses: actions/checkout@v2
+      - uses: r-lib/actions/setup-r@v1
+      - uses: r-lib/actions/setup-pandoc@v1
+      - name: Install rmarkdown, remotes, and the local package
+        run: |
+          install.packages("remotes")
+          remotes::install_local(".")
+          remotes::install_cran("rmarkdown")
+        shell: Rscript {0}
+      - name: Render README
+        run: Rscript -e 'rmarkdown::render("README.Rmd")'
+      - name: Commit results
+        run: |
+          git config --local user.email "actions@github.com"
+          git config --local user.name "GitHub Actions"
+          git commit README.md -m 'Re-build README.Rmd' || echo "No changes to commit"
+          git push origin || echo "No changes to commit"

README.Rmd

@@ -18,7 +18,7 @@ knitr::opts_chunk$set(
 <!-- badges: start -->
 <!-- badges: end -->
 
-The goal of islet is to help establish secure data pipelines for data cleaning and analytics, designed for teams analyzing patient outcome data. With Islet, you can easily clean and process sensitive patient data, while generating reports that track data irregularities and issues.
+Islet contains personal R functions. I'm also using this to learn how to create R packages.
 
 ## Installation
 
@@ -53,12 +53,4 @@ islet::make_banner(str_to_banner = 'analyze column from dataframe',
 
 ```
 
-You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this. You could also use GitHub Actions to re-render `README.Rmd` every time you push. An example workflow can be found here: <https://github.com/r-lib/actions/tree/v1/examples>.
-
-You can also embed plots, for example:
-
-```{r pressure, echo = FALSE}
-plot(pressure)
-```
-
-In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.
+You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this. You could also use GitHub Actions to re-render `README.Rmd` every time you push. An example workflow can be found here: <https://github.com/r-lib/actions/tree/v1/examples>.

README.md

@@ -6,11 +6,7 @@
 <!-- badges: start -->
 <!-- badges: end -->
 
-The goal of islet is to help establish secure data pipelines for data
-cleaning and analytics, designed for teams analyzing patient outcome
-data. With Islet, you can easily clean and process sensitive patient
-data, while generating reports that track data irregularities and
-issues.
+Personal R functions.
 
 ## Installation
 
@@ -56,9 +52,5 @@ use GitHub Actions to re-render `README.Rmd` every time you push. An
 example workflow can be found here:
 <https://github.com/r-lib/actions/tree/v1/examples>.
 
-You can also embed plots, for example:
-
-<img src="man/figures/README-pressure-1.png" width="100%" />
-
 In that case, don’t forget to commit and push the resulting figure
 files, so they display on GitHub and CRAN.

data-raw/trial.R

@@ -19,25 +19,25 @@ trial <-
     response = runif(n) < response_prob,
     ttdeath_true =
       exp(1 + 0.2 * response +
-            -0.1 * as.numeric(stage) +
-            -0.1 * as.numeric(grade) +
-            rnorm(n, sd = 0.5)) * 12,
+        -0.1 * as.numeric(stage) +
+        -0.1 * as.numeric(grade) +
+        rnorm(n, sd = 0.5)) * 12,
     death = ifelse(ttdeath_true <= 24, 1L, 0L),
     ttdeath = pmin(ttdeath_true, 24) |> round(digits = 2),
-    
+
     # New columns added below:
     visit_date = sample(seq.Date(as.Date("2020-01-01"), as.Date("2022-01-01"), by = "days"), n, replace = TRUE), # Date
     follow_up_date = sample(seq.Date(as.Date("2022-01-02"), as.Date("2024-01-01"), by = "days"), n, replace = TRUE), # Date
-    
+
     has_side_effects = sample(c(TRUE, FALSE), n, replace = TRUE), # Logical
     enrolled_in_study = sample(c(TRUE, FALSE), n, replace = TRUE), # Logical
-    
+
     bmi = round(rnorm(n, mean = 25, sd = 4), 1), # Numeric
     systolic_bp = round(rnorm(n, mean = 120, sd = 15)), # Numeric
-    
+
     patient_id = paste0("ID-", sprintf("%03d", seq(1, n))), # Character
     hospital = sample(c("Hospital A", "Hospital B", "Hospital C"), n, replace = TRUE), # Character
-    
+
     insurance = sample(c("Private", "Medicaid", "Medicare"), n, replace = TRUE) |> factor(), # Factor
     smoking_status = sample(c("Non-smoker", "Former smoker", "Current smoker"), n, replace = TRUE) |> factor() # Factor
   ) |>
@@ -62,4 +62,4 @@ attr(trial$hospital, "label") <- "Hospital"
 attr(trial$insurance, "label") <- "Insurance Type"
 attr(trial$smoking_status, "label") <- "Smoking Status"
 
-trial |> readr::write_csv(file = here::here('data/trial.csv'))
+trial |> readr::write_csv(file = here::here("data/trial.csv"))