Merge pull request #6 from gofflab/jupyter

Jupyter build

.github/workflows/render.yml

@@ -13,13 +13,43 @@ on:
     branches: [ "main" ]
   pull_request:
 
+defaults:
+  run:
+    shell: bash -l {0}
 
 jobs:
-  build:
+  jupyter-build:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-latest]
+        os: [ubuntu-latest] #, macos-latest]
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: mamba-org/provision-with-micromamba@main
+        with:
+          cache-downloads: true
+          cache-env: true
+          environment-name: qmn
+          environment-file: environment.yml
+
+      - name: Execute and export
+        run: |
+          jupytext --to notebook modules/**/*.py
+          jupyter nbconvert --NbConvertApp.recursive_glob=True --output-dir=modules/jupyter --execute --to html modules/**/*.ipynb
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v3
+        with:
+          path: modules/jupyter/
+
+  Rmd-build:
+    needs: jupyter-build
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest] #, macos-latest]
 
     steps:
       - uses: actions/checkout@v3
@@ -33,8 +63,21 @@ jobs:
       - name: Install R dependencies
         uses: r-lib/actions/setup-r-dependencies@v2
 
-      - name: Render
-        run: Rscript render.R
+      - name: Cache Reference
+        uses: actions/cache@v3
+        with:
+          path: modules/module_3/data/reference
+          key: constant
+
+      # - uses: mxschmitt/action-tmate@v3
+
+#       - name: Render
+#         run: Rscript render.R
+
+      - name: Download jupyter artifacts
+        uses: actions/download-artifact@v3
+        with:
+          path: modules/jupyter/
 
       - name: Generate index
         if: matrix.os == 'ubuntu-latest' && github.ref == 'refs/heads/main'

.gitignore

@@ -46,3 +46,4 @@ vignettes/*.pdf
 *.pdf
 
 **/data/**
+*.ipynb

DESCRIPTION

@@ -2,5 +2,12 @@ Package: quant_mol_neuro_2022
 Title: Package
 Version: 0.1.0
 Imports:
+    here,
     rmarkdown,
-    tidyverse
+    tidyverse,
+    ggridges,
+    scRNAseq,
+    scater,
+    scran,
+    uwot,
+    BiocManager,

environment.yml

@@ -0,0 +1,12 @@
+name: qmn
+
+channels:
+  - conda-forge
+
+dependencies:
+  - python=3.10
+  - jupyterlab
+  - jupytext
+  - scanpy
+  - seaborn
+  - pandas

modules/module_2/notebooks/mod2_ans.Rmd

@@ -1,24 +1,14 @@
 ## Part 1
 ```{r}
 library(here)
-tuesdata <- tidytuesdayR::tt_load("2020-01-21")
-spotify_songs <- tuesdata$spotify_songs
-
 # tests if a directory named "data" exists locally
 if (!dir.exists(here::here("data"))) {
     dir.create(here::here("data"))
 }
 
 # saves data only once (not each time you knit a R Markdown)
-if (!file.exists(here::here("data", "spotify_songs.RDS"))) {
-    url_csv <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-21/spotify_songs.csv"
-    spotify_songs <- readr::read_csv(url_csv)
-
-    # save the file to RDS objects
-    saveRDS(spotify_songs, file = here("data", "spotify_songs.RDS"))
-}
-
-spotify_songs <- readRDS(here("data", "spotify_songs.RDS"))
+url_csv <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-21/spotify_songs.csv"
+spotify_songs <- readr::read_csv(url_csv)
 df <- dplyr::as_tibble(spotify_songs)
 ```
 
@@ -146,4 +136,4 @@ colLabels(sce) <- scran::clusterCells(sce,
 # Visualization.
 sce <- scater::runUMAP(sce, dimred = "PCA")
 scater::plotUMAP(sce, colour_by = "label")
-```
+```

modules/module_2/notebooks/mod2_ans.py → modules/module_2/notebooks/mod2_ans_py.py

@@ -10,10 +10,11 @@
 
 here = Path(".")
 data = here / "data"
+data.mkdir(exist_ok=True, parents=True)
 #%% Part 1
 
 # This is a dataframe (like tibble).
-spotify_songs = pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-21/spotify_songs.csv")
+spotify_songs = pd.read_csv(data / "spotify_songs.csv")
 spotify_songs.to_csv(data / 'spotify_songs.csv', index=False)
 
 spotify_songs