Enable automatic pkgdown builds

.Rbuildignore

@@ -6,7 +6,9 @@
 ^pkgdown$
 ^.lintr$
 .vscode$
+^\.github$
 terraform/
 DEVELOPER.md
 ^README\.Rmd$
 ^CODE_OF_CONDUCT\.md$
+

.github/.gitignore

@@ -0,0 +1 @@
+*.html

.github/workflows/pkgdown.yaml

@@ -0,0 +1,50 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown.yaml
+
+permissions: read-all
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      GARGLE_ENCRYPTION_KEY: ${{ secrets.GARGLE_ENCRYPTION_KEY }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = TRUE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/github-pages-deploy-action@v4.5.0
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

DESCRIPTION

@@ -40,4 +40,6 @@ Suggests:
     testthat (>= 3.0.0),
     zoo
 VignetteBuilder: quarto
+URL: https://nih-cfde.github.io/programets/
+BugReports: https://github.com/nih-cfde/programets/issues
 Config/testthat/edition: 3

_pkgdown.yml

@@ -1,6 +1,7 @@
-url: ~
+url: https://seandavi.github.io/programets/
 template:
   bootstrap: 5
   bslib:
     bootswatch: materia
     pkgdown-nav-height: 100px
+

vignettes/googleanalytics.qmd

@@ -21,19 +21,21 @@ The package provides functions to authenticate with your Google account, retriev
 
 # Getting started with `googleAnalyticsR`
 
-
 ```{r}
-## setup
+#| label: setup
 library(googleAnalyticsR)
 library(programets)
 ```
 
-```{r eval=FALSE}
-## authenticate
+```{r}
+#| eval: false
+#| label: authenticate
 ga_auth(email = "seandavi@gmail.com")
 ```
 
-```{r echo=FALSE}
+```{r}
+#| echo: false
+#| label: service_account
   json_file <- gargle::secret_decrypt_json(
     path = system.file(
       "secret",
@@ -49,25 +51,21 @@ ga_auth(email = "seandavi@gmail.com")
 ```
 
 ```{r}
-## get your accounts
+#| label: get-accounts
 account_list <- ga_account_list("ga4")
 
-## account_list will have a column called "viewId"
-account_list$viewId
+## account_list will have a column called "propertyId"
+account_list$propertyId
 
 ## View account_list and pick the one that you want to use
 ## In this case, we will use Bioconductor
 ga_id <- 388188354
-
 ```
 
 The resulting `res` object will contain the data for the specified date range, metrics, and dimensions. You can view the first few rows of the data using the `head()` function. 
 
-
-
-
 ```{r}
-library(googleAnalyticsR)
+#| label: get-data
 library(lubridate)
 
 start_date <- Sys.Date() - 365
@@ -85,28 +83,27 @@ daily_user_country_data <- ga_data(
 
 ```{r}
 #| label: moving-average
-
 library(ggplot2)
 library(zoo)
 library(dplyr)
 
 # Group by user type and country, then calculate rolling average
-moving_avg_data <- daily_user_country_data %>%
-    arrange(date) %>%
-    group_by(newVsReturning, country) %>%
+moving_avg_data <- daily_user_country_data |>
+    arrange(date) |>
+    group_by(newVsReturning, country) |>
     mutate(
         activeUsers_7day_avg = rollmean(activeUsers, k = 7, fill = NA, align = "right"),
         sessions_7day_avg = rollmean(sessions, k = 7, fill = NA, align = "right")
-    ) %>%
+    ) |>
     ungroup()
 
 # Let's see the results
 head(moving_avg_data)
 
 # Plot the moving average for active users
-moving_avg_data %>%
-    group_by(date, newVsReturning) %>%
-    summarise(activeUsers_7day_avg = sum(activeUsers_7day_avg, na.rm = TRUE)) %>%
+moving_avg_data |>
+    group_by(date, newVsReturning) |>
+    summarise(activeUsers_7day_avg = sum(activeUsers_7day_avg, na.rm = TRUE)) |>
     ggplot(aes(x = date, y = activeUsers_7day_avg, color = newVsReturning)) +
     geom_line() +
     labs(