Include RMarkdown install in GH Action (#261)

* Include RMarkdown install in GH Action

* Add more packages to installation

* Use library calls and native R pipe

* Set up R caching for speed

* Install libcurl in runner

* Add sessioninfo dependency

* Don't include R in caching

* Check removal of condition in action

* Only install needed packages

.github/workflows/publish.yml

@@ -14,6 +14,18 @@ jobs:
       - name: Check out repository
         uses: actions/checkout@v4
 
+      - name: Cache R packages
+        uses: actions/cache@v3
+        id: cache
+        with:
+          path: ${{ github.workspace }}
+          key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
+          restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-
+
+      - name: Install libcurl
+        run: |
+          sudo apt-get install libcurl4-openssl-dev
+
       - name: Set up Quarto
         uses: quarto-dev/quarto-actions/setup@v2
 
@@ -22,6 +34,16 @@ jobs:
         with:
           r-version: '4.4.0'
 
+      - name: Install R Packages
+        # if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          installed.packages()
+          dependencies <- c("rmarkdown", "dplyr", "ggplot2", "plotly", "sessioninfo")
+          needs_installation <- dependencies[!dependencies %in% installed.packages()]
+          install.packages(needs_installation)
+          library("rmarkdown")
+        shell: Rscript {0}
+
       - name: Render and Publish
         uses: quarto-dev/quarto-actions/publish@v2
         with:

plot_trait_evolution.Rmd

@@ -32,9 +32,9 @@ Participants will then get to **see the skull and beak shape** corresponding to
 First we load the required packages and create some objects to compile data on trait evolution for each species.
 
 ```{r warning=F, message=FALSE}
-require(dplyr)
-require(ggplot2) #3.5.1
-require(plotly) #4.10.4
+library(dplyr)
+library(ggplot2) #3.5.1
+library(plotly) #4.10.4
 set.seed(1)
 
 t <- 0:100  # generate time vector
@@ -58,7 +58,7 @@ for(spp in spp.files){
   
   # generate trait evolution time-series and compile plotting data
   dt <-  rbind(dt, data.frame(t, 
-                              trait = c(0, rnorm(n = length(t) - 1, sd = sqrt(sig2)) %>% cumsum()),
+                              trait = c(0, rnorm(n = length(t) - 1, sd = sqrt(sig2)) |> cumsum()),
                               species = species.name))
   cols <- c(cols, color)
 }