Technique example: data loader, R to ZIP (#1423)

* r to ZIP technique example

* save

* clearer column names in prediction table

* only load individual files from ZIP, not entire archive in client

* Update examples/loader-r-to-zip/src/data/penguin-mlr.zip.R

Co-authored-by: Philippe Rivière <[email protected]>

* Update examples/loader-r-to-zip/src/index.md

Co-authored-by: Philippe Rivière <[email protected]>

* Add to readme list

---------

Co-authored-by: Allison Horst <[email protected]>
Co-authored-by: Allison Horst <[email protected]>
Co-authored-by: Philippe Rivière <[email protected]>

Author: 4 people

examples/README.md

@@ -71,6 +71,7 @@
 - [`loader-r-to-csv`](https://observablehq.observablehq.cloud/framework-example-loader-r-to-csv/) - Generate CSV from R
 - [`loader-r-to-jpeg`](https://observablehq.observablehq.cloud/framework-example-loader-r-to-jpeg/) - Generate JPEG from R
 - [`loader-r-to-json`](https://observablehq.observablehq.cloud/framework-example-loader-r-to-json/) - Generate JSON from R
+- [`loader-r-to-zip`](https://observablehq.observablehq.cloud/framework-example-loader-r-to-zip/) - Generate ZIP from R
 - [`loader-rust-to-json`](https://observablehq.observablehq.cloud/framework-example-loader-rust-to-json/) - Generate JSON from Rust
 - [`loader-snowflake`](https://observablehq.observablehq.cloud/framework-example-loader-snowflake/) - Load data from Snowflake
 - [`netcdf-contours`](https://observablehq.observablehq.cloud/framework-example-netcdf-contours/) - Convert NetCDF to GeoJSON with `netcdfjs` and `d3-geo-voronoi`

examples/loader-r-to-zip/.gitignore

@@ -0,0 +1,6 @@
+.DS_Store
+/dist/
+node_modules/
+yarn-error.log
+.Rhistory
+.RData

examples/loader-r-to-zip/README.md

@@ -0,0 +1,9 @@
+[Framework examples →](../)
+
+# R data loader to generate a ZIP archive
+
+View live: <https://observablehq.observablehq.cloud/framework-example-loader-r-to-zip/>
+
+This Observable Framework example demonstrates how to write an R data loader that accesses penguin body size measurements, performs multiple linear regression, then outputs model estimates and predictions as two separate CSV files in a ZIP archive.
+
+The data loader lives in [`src/data/penguin-mlr.zip.R`](./src/data/penguin-mlr.zip.R).

examples/loader-r-to-zip/observablehq.config.js

@@ -0,0 +1,3 @@
+export default {
+  root: "src"
+};

examples/loader-r-to-zip/package.json

@@ -0,0 +1,20 @@
+{
+  "type": "module",
+  "private": true,
+  "scripts": {
+    "clean": "rimraf src/.observablehq/cache",
+    "build": "rimraf dist && observable build",
+    "dev": "observable preview",
+    "deploy": "observable deploy",
+    "observable": "observable"
+  },
+  "dependencies": {
+    "@observablehq/framework": "^1.7.0"
+  },
+  "devDependencies": {
+    "rimraf": "^5.0.5"
+  },
+  "engines": {
+    "node": ">=18"
+  }
+}

examples/loader-r-to-zip/src/.gitignore

@@ -0,0 +1 @@
+/.observablehq/cache/

examples/loader-r-to-zip/src/data/penguin-mlr.zip.R

@@ -0,0 +1,27 @@
+# Attach required packages (must be installed)
+library(readr)
+library(tidyr)
+library(dplyr)
+library(broom)
+
+# Data access, wrangling and analysis
+penguins <- read_csv("src/data/penguins.csv") |>
+    drop_na(body_mass_g, species, sex, flipper_length_mm, culmen_depth_mm)
+
+penguins_mlr <- lm(body_mass_g ~ species + sex + flipper_length_mm + culmen_depth_mm, data = penguins)
+
+mlr_est <- tidy(penguins_mlr)
+
+mlr_fit <- penguins |>
+    mutate(
+        predicted_mass = penguins_mlr$fitted.values,
+        residual = penguins_mlr$residuals
+    )
+
+# Write the data frames as CSVs to a temporary directory
+setwd(tempdir())
+write_csv(mlr_est, "estimates.csv")
+write_csv(mlr_fit, "predictions.csv")
+
+# Zip the contents of the temporary directory
+system("zip - -r .")