Add a save_slides() function

wlandau · Jul 12, 2019 · b61b42c · b61b42c
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Showing 11 changed files with 85 additions and 34 deletions.
diff --git a/NAMESPACE b/NAMESPACE
@@ -3,6 +3,7 @@
 export(launch_app)
 export(save_app)
 export(save_notebooks)
+export(save_slides)
 export(view_slides)
 importFrom(drake,drake_plan)
 importFrom(drake,make)

diff --git a/R/notebooks.R b/R/notebooks.R
@@ -3,13 +3,13 @@
 #'   and supporting files.
 #' @export
 #' @seealso [view_slides()], [launch_app()]
-#' @param path Character, where to write the files
-#' @param overwrite Logical, whether to over
+#' @param path Character, where to write the files.
+#' @param overwrite Logical, whether to overwrite the files.
 #' @examples
 #' \dontrun{
 #' save_notebooks()
 #' }
-save_notebooks <- function(path = "learndrake", overwrite = FALSE) {
+save_notebooks <- function(path = "learndrake-notebooks", overwrite = FALSE) {
   origin <- system.file("notebooks", package = "learndrake", mustWork = TRUE)
   fs::dir_copy(path = origin, new_path = path, overwrite = overwrite)
   invisible()

diff --git a/R/slides.R b/R/slides.R
@@ -1,7 +1,24 @@
+#' @title Save the introductory slides
+#' @description Save the slides to a folder.
+#' @export
+#' @seealso [view_slides()], [save_notebooks()], [launch_app()]
+#' @param path Character, where to write the files.
+#' @param overwrite Logical, whether to overwrite the files.
+#' @examples
+#' \dontrun{
+#' save_slides()
+#' }
+save_slides <- function(path = "learndrake-slides", overwrite = FALSE) {
+  origin <- system.file("slides", package = "learndrake", mustWork = TRUE)
+  fs::dir_copy(path = origin, new_path = path, overwrite = overwrite)
+  invisible()
+}
+
+
 #' @title View the introductory slides
 #' @description Open the slides in a web browser.
 #' @export
-#' @seealso [save_notebooks()], [launch_app()]
+#' @seealso [save_slides()], [save_notebooks()], [launch_app()]
 #' @param browse Logical, whether to actually view the slides.
 #'   For testing purposes only.
 #' @examples

diff --git a/README.Rmd b/README.Rmd
@@ -4,11 +4,11 @@ output: github_document
 
 [![Travis build status](https://travis-ci.org/wlandau/learndrake.svg?branch=master)](https://travis-ci.org/wlandau/learndrake)
 [![Codecov test coverage](https://codecov.io/gh/wlandau/learndrake/branch/master/graph/badge.svg)](https://codecov.io/gh/wlandau/learndrake?branch=master)
-[![Launch Rstudio Binder](http://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wlandau/learndrake/binder?urlpath=rstudio)
+[![Launch RStudio Binder](http://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wlandau/learndrake/binder?urlpath=rstudio)
 
-# Machine learning workflow management with drake
+# Reproducible workflows at scale with `drake`
 
-Machine learning workflows can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the [`drake`](https://github.com/ropensci/drake) R package. [`drake`](https://github.com/ropensci/drake) resolves the dependency structure of your analysis pipeline, skips tasks that are already up to date, executes the rest with [optional distributed computing](https://ropenscilabs.github.io/drake-manual/hpc.html), and organizes the output so you rarely have to think about data files. This workshop will teach you how to create and maintain machine learning projects with [`drake`](https://github.com/ropensci/drake)-powered automation.
+Ambitious workflows in R, such as machine learning analyses, can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the [`drake`](https://github.com/ropensci/drake) R package. [`drake`](https://github.com/ropensci/drake) resolves the dependency structure of your analysis pipeline, skips tasks that are already up to date, executes the rest with [optional distributed computing](https://ropenscilabs.github.io/drake-manual/hpc.html), and organizes the output so you rarely have to think about data files. This workshop will teach you how to create and maintain machine learning projects with [`drake`](https://github.com/ropensci/drake)-powered automation.
 
 # Installation
 
@@ -23,18 +23,19 @@ keras::install_keras()
 
 # Usage: browser
 
-Just click this badge: [![Launch Rstudio Binder](http://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wlandau/learndrake/binder?urlpath=rstudio). Your browser will open the materials in a free RStudio Server instance. The exercises are in the notebooks (`1-churn/1-churn.Rmd`, `2-setup/2-setup.Rmd`, etc.).
+Just click this badge: [![Launch RStudio Binder](http://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wlandau/learndrake/binder?urlpath=rstudio). Your browser will open the materials in a free RStudio Server instance. The exercises are in the notebooks (`1-churn/1-churn.Rmd`, `2-setup/2-setup.Rmd`, etc.).
 
 # Usage: local
 
-The functions in `learndrake` help navigate and deploy the workshop materials. If you installed the package and dependencies as above, you can take the workshop locally without an internet connection.
+The functions in `learndrake` help navigate and deploy the workshop materials. If you installed the package and dependencies as above, you can take the workshop locally without an internet connection. Start with the introductory slides, then move on to the notebooks. Launch apps along the way as directed.
 
 Function | Purpose
 ---|---
-`view_slides()` | Open the introductory slides in a web browser.
-`save_notebooks()` | Save the tutorials to your computer: R notebooks and supporting files.
 `launch_app()` | Launch a Shiny app that accompanies a tutorial.
 `save_app()` | Save the app files so you can deploy to [shinyapps.io](https://www.shinyapps.io) or [Shiny Server](https://www.rstudio.com/products/shiny/shiny-server).
+`save_notebooks()` | Save the tutorials to your computer: R notebooks and supporting files.
+`save_slides()` | Save the introductory slides to your computer.
+`view_slides()` | Open the introductory slides in a web browser.
 
 # Introductory slides
 

diff --git a/README.md b/README.md
@@ -3,19 +3,20 @@
 status](https://travis-ci.org/wlandau/learndrake.svg?branch=master)](https://travis-ci.org/wlandau/learndrake)
 [![Codecov test
 coverage](https://codecov.io/gh/wlandau/learndrake/branch/master/graph/badge.svg)](https://codecov.io/gh/wlandau/learndrake?branch=master)
-[![Launch Rstudio
+[![Launch RStudio
 Binder](http://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wlandau/learndrake/binder?urlpath=rstudio)
 
-# Machine learning workflow management with drake
-
-Machine learning workflows can be difficult to manage. A single round of
-computation can take several hours to complete, and routine updates to
-the code and data tend to invalidate hard-earned results. You can
-enhance the maintainability, hygiene, speed, scale, and reproducibility
-of such projects with the [`drake`](https://github.com/ropensci/drake) R
-package. [`drake`](https://github.com/ropensci/drake) resolves the
-dependency structure of your analysis pipeline, skips tasks that are
-already up to date, executes the rest with [optional distributed
+# Reproducible workflows at scale with `drake`
+
+Ambitious workflows in R, such as machine learning analyses, can be
+difficult to manage. A single round of computation can take several
+hours to complete, and routine updates to the code and data tend to
+invalidate hard-earned results. You can enhance the maintainability,
+hygiene, speed, scale, and reproducibility of such projects with the
+[`drake`](https://github.com/ropensci/drake) R package.
+[`drake`](https://github.com/ropensci/drake) resolves the dependency
+structure of your analysis pipeline, skips tasks that are already up to
+date, executes the rest with [optional distributed
 computing](https://ropenscilabs.github.io/drake-manual/hpc.html), and
 organizes the output so you rarely have to think about data files. This
 workshop will teach you how to create and maintain machine learning
@@ -38,7 +39,7 @@ keras::install_keras()
 
 # Usage: browser
 
-Just click this badge: [![Launch Rstudio
+Just click this badge: [![Launch RStudio
 Binder](http://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wlandau/learndrake/binder?urlpath=rstudio).
 Your browser will open the materials in a free RStudio Server instance.
 The exercises are in the notebooks (`1-churn/1-churn.Rmd`,
@@ -48,15 +49,18 @@ The exercises are in the notebooks (`1-churn/1-churn.Rmd`,
 
 The functions in `learndrake` help navigate and deploy the workshop
 materials. If you installed the package and dependencies as above, you
-can take the workshop locally without an internet
-connection.
+can take the workshop locally without an internet connection. Start with
+the introductory slides, then move on to the notebooks. Launch apps
+along the way as
+directed.
 
 | Function           | Purpose                                                                                                                                                  |
 | ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `view_slides()`    | Open the introductory slides in a web browser.                                                                                                           |
-| `save_notebooks()` | Save the tutorials to your computer: R notebooks and supporting files.                                                                                   |
 | `launch_app()`     | Launch a Shiny app that accompanies a tutorial.                                                                                                          |
 | `save_app()`       | Save the app files so you can deploy to [shinyapps.io](https://www.shinyapps.io) or [Shiny Server](https://www.rstudio.com/products/shiny/shiny-server). |
+| `save_notebooks()` | Save the tutorials to your computer: R notebooks and supporting files.                                                                                   |
+| `save_slides()`    | Save the introductory slides to your computer.                                                                                                           |
+| `view_slides()`    | Open the introductory slides in a web browser.                                                                                                           |
 
 # Introductory slides
 

diff --git a/inst/WORDLIST b/inst/WORDLIST
@@ -6,10 +6,12 @@ github
 http
 https
 io
+Keras
 learndrakeflow
 learndrakeplans
 Nantz
 reproducibility
+RStudio
 shinyapps
 tensorflow
 wlandau

diff --git a/man/save_app.Rd b/man/save_app.Rd
diff --git a/man/save_notebooks.Rd b/man/save_notebooks.Rd
diff --git a/man/save_slides.Rd b/man/save_slides.Rd
diff --git a/man/view_slides.Rd b/man/view_slides.Rd
diff --git a/tests/testthat/test-materials.R b/tests/testthat/test-materials.R
@@ -2,7 +2,9 @@ context("materials")
 
 test_that("slides", {
   expect_silent(view_slides(browse = FALSE))
-  # view_slides() # nolint
+  path <- tempfile()
+  expect_silent(save_slides(path))
+  expect_true("index.Rmd" %in% list.files(path))
 })
 
 test_that("notebooks", {