What else can I do with an R package?

What else can I do with an R package?

Store data and files

What else can I do with an R package?

Store data and files

Create R Markdown and other templates

What else can I do with an R package?

Store data and files

Create R Markdown and other templates

Launch and store Shiny apps

Data in packages

data in ggplot2

## # A tibble: 11 x 2
##    data         description                                 
##    <chr>        <chr>                                       
##  1 diamonds     Prices of over 50,000 round cut diamonds    
##  2 economics    US economic time series                     
##  3 economics_l… US economic time series                     
##  4 faithfuld    2d density estimate of Old Faithful data    
##  5 luv_colours  'colors()' in Luv space                     
##  6 midwest      Midwest demographics                        
##  7 mpg          Fuel economy data from 1999 to 2008 for 38 …
##  8 msleep       An updated and expanded version of the mamm…
##  9 presidential Terms of 11 presidents from Eisenhower to O…
## 10 seals        Vector field of seal movements              
## 11 txhousing    Housing sales in TX

Data packages

data in babynames

## # A tibble: 4 x 2
##   data       description
##   <chr>      <chr>      
## 1 applicants Applicants.
## 2 babynames  Baby names.
## 3 births     Births     
## 4 lifetables Lifetables

Data packages

data in claremontrun

## # A tibble: 7 x 2
##   data              description                             
##   <chr>             <chr>                                   
## 1 character_visual… Counts of character speech, thought, na…
## 2 characters        Descriptions of character actions in th…
## 3 comic_bechdel     Whether or not an issue of another comi…
## 4 covers            Data on covers of issues of Uncanny X-M…
## 5 issue_collaborat… Data about other collaborators on each …
## 6 locations         Locations of issues in the Claremont run
## 7 xmen_bechdel      Whether or not an issue of Uncanny X-Me…

Data dictionary

data_dictionary <- 
  tibble::tibble(
    database = "daily_active_users",
    variable = c("users", "sector"),
    description = c(
      "Active reactor-using households", 
      "Midgar Sector #"
    ),
  )

use_data_raw("data_dictionary"): data_dictionary.R

## code to prepare `data_dictionary` dataset goes here
usethis::use_data(data_dictionary, overwrite = TRUE)

use_data_raw("data_dictionary"): data_dictionary.R

## code to prepare `data_dictionary` dataset goes here
data_dictionary <-
  tibble::tibble(
    database = "daily_active_users",
    variable = c("users", "sector"),
    description = c(
      "Active reactor-using households",
      "Midgar Sector #"
    ),
  )
usethis::use_data(data_dictionary, overwrite = TRUE)

use_data_raw("data_dictionary"): data_dictionary.R

## code to prepare `data_dictionary` dataset goes here
data_dictionary <- 
  tibble::tibble(
    database = "daily_active_users",
    variable = c("users", "sector"),
    description = c(
      "Active reactor-using households", 
      "Midgar Sector #"
    ),
  )
usethis::use_data(data_dictionary, overwrite = TRUE)

library(shinRa)
data_dictionary

## # A tibble: 2 x 3
##   database           variable description                   
##   <chr>              <chr>    <chr>                         
## 1 daily_active_users users    Active reactor-using househol…
## 2 daily_active_users sector   Midgar Sector #

Your Turn 1

Let's create a data dictionary for avalanche and add the results to a vignette. First, run use_data_raw(). Call the data "data_dictionary". In the data processing file, put this code before the use_data() line. Then, source the script. This will run use_data() for you.

Open R/tables.R. Add a function called gt_data_dictionary() that wraps data_dictionary in gt::gt(). You can use gt_donations() as a starting point for the new function.

Re-document and re-build the package.

Open vignettes/data-dictionary.Rmd. On line 24, run gt_data_dictionary() and knit the vignette.

Your Turn 1

use_data_raw("data_dictionary")
source("data-raw/data_dictionary.R")

#' Create gt table for the data dictionary
#'
#' @return a `gt` table
#' @export
gt_data_dictionary <- function() {
  gt::gt(data_dictionary)
}

R Markdown Templates

xaringan template

xaringan template: template.yaml

name: Ninja Presentation
description: >
  Create a presentation using the xaringan package based 
  on remark.js.
create_dir: false

xaringan template: skeleton.Rmd

xaringan Chinese template: template.yaml

name: Ninja Presentation (Simplified Chinese)
description: >
  Create a presentation using the xaringan package based 
  on remark.js.
  create_dir: true

xaringan Chinese template

## inst/rmarkdown/templates/xaringan_zh-CN/skeleton/
## ├── skeleton.Rmd
## └── zh-CN.css

xaringan Chinese template

Your Turn 2

Create a new R Markdown template called "avalanche-report" using use_rmarkdown_template(). For the template_description argument, set it to: "A weekly report of AVALANCHE activities."

Inside the exercises/ folder, there is a file called report.Rmd. Copy and paste its contents into inst/rmarkdown/templates/avalanche-report/skeleton/skeleton.Rmd, which you just created.

Re-build the package (build() or Cmd/Ctrl + Shift + B).

Create a new R Markdown document using File > New File > R Markdown.... Then, find your template under the "From Template" tab. Open it.

Your Turn 2

use_rmarkdown_template(
  "template_description", 
  "A weekly report of AVALANCHE activities."
)

On inst/

We can put any arbitrary file in inst/.

On inst/

We can put any arbitrary file in inst/.

Be careful not to name the folders the same as any of the top-level package folders, e.g. R/ or data/.

On inst/

We can put any arbitrary file in inst/.

Be careful not to name the folders the same as any of the top-level package folders, e.g. R/ or data/.

Access content with system.file() or fs::path_package()

system.file()

system.file(
  file.path("rmarkdown", "templates", "report.Rmd"),
  package = "shinRa", 
  mustWork = TRUE
)

system.file()

system.file(
  file.path("rmarkdown", "templates", "report.Rmd"), 
  package = "shinRa", 
  mustWork = TRUE
)

## [1] "path/to/shinRa/rmarkdown/templates/report.Rmd"

Other templates

Other templates

Analysis workflows or projects

Other templates

Analysis workflows or projects

More detailed R Markdown templates

Other templates

Analysis workflows or projects

More detailed R Markdown templates

YAML and other configuration files

Templates with usethis

use_cran_comments()

Templates with usethis

use_cran_comments()

use_cran_comments <- function(open = interactive()) {
  check_is_package("use_cran_comments()")
    use_template(
      "cran-comments.md", 
      data = list(rversion = glue("{version$major}.{version$minor}")), 
      ignore = TRUE, 
      open = open
    )
}

Templates with usethis

use_cran_comments()

use_cran_comments <- function(open = interactive()) {
  check_is_package("use_cran_comments()")
    use_template(
      "cran-comments.md",
      data = list(rversion = glue("{version$major}.{version$minor}")), 
      ignore = TRUE, 
      open = open
    )
}

use_template() and inst/templates

## # A tibble: 56 x 2
##    path                type 
##    <chr>               <fct>
##  1 CODE_OF_CONDUCT.md  file 
##  2 Jenkinsfile         file 
##  3 Makefile            file 
##  4 NAMESPACE           file 
##  5 NEWS.md             file 
##  6 addins.dcf          file 
##  7 appveyor.yml        file 
##  8 article.Rmd         file 
##  9 azure-pipelines.yml file 
## 10 circleci-config.yml file 
## # … with 46 more rows

cran-comments.md

# ## Test environments
# * local R installation, R {{{ rversion }}}
# * ubuntu 16.04 (on travis-ci), R {{{ rversion }}}
# * win-builder (devel)
# 
# ## R CMD check results
# 
# 0 errors | 0 warnings | 1 note
# 
# * This is a new release.

whisker

whisker::whisker.render(
"## Test environments
  * local R installation, R {{{ rversion }}}
  * ubuntu 16.04 (on travis-ci), R {{{ rversion }}}
 * win-builder (devel)
 ## R CMD check results
 0 errors | 0 warnings | 1 note
 * This is a new release.",
data = list(rversion = glue::glue("{version$major}.{version$minor}"))
)

whisker

## ## Test environments
##  * local R installation, R 3.6.1 
##  * ubuntu 16.04 (on travis-ci), R 3.6.1 
##  * win-builder (devel)
## 
##  ## R CMD check results
## 
##  0 errors | 0 warnings | 1 note
## 
##  * This is a new release.

Creating a template: inst/templates/config.yml

library(fs)
dir_create(path("inst", "templates"))
file_create(path("inst", "templates", "config.yml"))

In config.yml:

name: {{{name}}}
  affiliation: Shinra, Inc.
description: {{{description}}}

Wrapping use_template()

use_yaml_template <- function(path, name, description) {
  usethis::use_template(
    "config.yml",
    save_as = file.path(path, "config.yml"),
    package = "shinRa",
    data = list(name = name, description = description)
  )
}

use_yaml_template(
  ".", 
  "Malcolm Barrett", 
  "A project to analyze reactor data"
)

## ✔ Writing '/var/folders/03/9x7925g54mncswxx06wpkxl0000...

name: Malcolm Barrett
  affiliation: Shinra, Inc.
description: A project to analyze reactor data

Your Turn 3

Let's create a usethis-style function to help set up analysis projects. In the folder exercises/templates/, there are three files: "packages.R", "analysis.R", and "report.Rmd". Open them up and take a look. Note that report.Rmd has some whisker variables in the YAML!

Copy the files to inst/templates by running this code in the console: fs::dir_copy("exercises/templates/", "inst/templates")

Open R/create_analysis.R. create_analysis() is going to help us set up the project directory, but we need to complete it. In lines 22, 23, and 25, add the template names: "packages.R", "analysis.R", and "report.Rmd".

Remember that "report.Rmd" has data to whisk. We need to tell it what to pass to the final file. The data argument takes a named list. For this argument, write:list(author = author, title = title)`.

Below create_analysis() is a helper function, usethis::use_template(), to create files from templates. Change the package argument to "avalanchr".

Re-build your package and run create_analysis() in the console. If something went wrong, just delete the folder avalanche_analysis and try again.

Your Turn 3

create_analysis <- function(path = ".", folder = "avalanche_analysis", author = "Author", title = "Untitled Analysis") {
  analysis_path <- fs::path(path, folder)
  if (fs::dir_exists(analysis_path)) fs::dir_delete(analysis_path)
  usethis::ui_done("Writing {usethis::ui_path(folder)}")
  fs::dir_create(analysis_path)
  use_avalanche_template("packages.R", folder = folder)
  use_avalanche_template("analysis.R", folder = folder)
  use_avalanche_template(
    "report.Rmd",
    folder = folder,
    data = list(author = author, title = title)
  )
  invisible(analysis_path)
}

Your Turn 3

use_avalanche_template <- function(template, folder, data = list()) {
  usethis::use_template(
    template = template,
    save_as = fs::path(folder, template),
    data = data,
    package = "avalanchr"
  )
}

create_analysis()

Shiny apps

--

Add shiny app to inst/shinyapps/ (or something similar)

Shiny apps

Add shiny app to inst/shinyapps/ (or something similar)

Then, use an R function like this to launch it:

--

launch_app <- function(app) {
  app_dir <- system.file(
    "shinyapps", 
    app, 
    package = "shinRa", 
    mustWork = TRUE
  )
  shiny::runApp(app_dir)
}

Shiny apps

Add shiny app to inst/shinyapps/ (or something similar)

Then, use an R function like this to launch it:

launch_app <- function(app) {
  app_dir <- system.file(
    "shinyapps",
    app,
    package = "shinRa", 
    mustWork = TRUE
  )
  shiny::runApp(app_dir)
}

Shiny apps

Add shiny app to inst/shinyapps/ (or something similar)

Then, use an R function like this to launch it:

launch_app <- function(app) {
  app_dir <- system.file(
    "shinyapps", 
    app, 
    package = "shinRa", 
    mustWork = TRUE
  )
  shiny::runApp(app_dir)
}

--

See "Supplementing your R package with a Shiny app" for more.

Your Turn 4

There is an app in the examples/ folder called plot_app/. It has two Shiny files: ui.R and server.R.

Run use_package("shiny")

Create a folder in inst/ called shinyapps

Create an R file with use_r() called "launch_app".

Write a function to launch the app

Add a roxygen skeleton and add a title and describe any parameters you used (if you used any)

Re-build your package and launch the app with your function

Your Turn 4

#' Launch Reactor Data Shiny App
#'
#' @return a shiny app
#' @export
launch_app <- function() {
  app_dir <- system.file(
    "shinyapps",
    "shiny_reactor_report",
    package = "avalanchr",
    mustWork = TRUE
  )
  shiny::runApp(app_dir)
}

launch_app()

With inst/, the world is yours

With inst/, the world is yours

Source files with inst/scripts and sys.source()

With inst/, the world is yours

Source files with inst/scripts and sys.source()

Add citations to inst/CITATION

With inst/, the world is yours

Source files with inst/scripts and sys.source()

Add citations to inst/CITATION

Much more. See R Packages, ed 2

Ignoring files at the top-level

--

use_build_ignore(): don't include when building R package

Ignoring files at the top-level

use_build_ignore(): don't include when building R package

use_git_ignore(): don't commit (credentials, certain rendered files, etc)

Ignoring files at the top-level

use_build_ignore(): don't include when building R package

use_git_ignore(): don't commit (credentials, certain rendered files, etc)

Try git_vaccinate()

RStudio Add-ins

Small shiny apps using miniui and RStudio.

Create one with use_addin()

See "Shiny Gadgets" and "Designing Gadget UI"