In vladdsm/trackpack: Shiny App to track Downloads of my own Package

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

trackpack

The goal of trackpack is to track my package 'lazytrade'

Goal of this Repository

This repository was created using golem framework for developing Shiny App. Simple shiny app was developed as an R package.

Installation

This repository will not be released on CRAN. If it would, then it could be installed with:

install.packages("trackpack")

However it is possible to:

Pull and launch ready to use Docker Container with Shiny App trackpack

1. Install/launch Docker Desktop
2. Run in the Terminal: docker run -it --rm -p 80:80 vladdsm/docker-trackpack
3. Launch Shiny App using http://0.0.0.0

Or, it is possible to fork or clone this repository and then:

Build R package to launch App locally

1. Open the trackpack.Rproj file
2. Load and build the package
3. Launch application by running the code trackpack::run_app()

Build a Dockerfile and build Docker image locally

1. Open script '/dev/03_deploy.R'
2. Run code: golem::add_dockerfile() to build Dockerfile
3. Build docker image in the terminal
4. cd '/usr/trackpack/'
5. docker build -t vladdsm/docker-trackpack . [or use different image name]
6. Launch docker container with Shiny App (see above)

Launch Docker container using docker-compose

Example of docker-compose.yml file is available in the repository compose-example

1. Clone code from the repository into the local folder
2. Edit script Run_Shiny_Trackpack and replace local folder path. Well, also replace image name in case local image is available (see above how to build docker image locally)
3. Launch script Run_Shiny_Trackpack (this will start container)
4. Test ShinyApp in the browser http://0.0.0.0
5. Stop container by launching Stop_Shiny_Trackpack or by executing docker-compose down in the Terminal

App 'business logic'

This is a basic core code that is used to solve a common problem:

## business logic code
library(ggplot2)
library(cranlogs)
stats <- cran_downloads("lazytrade", from = Sys.Date() - 100, to = Sys.Date() - 1) 
#sum(stats$count)


ggplot(stats, aes(date, count)) + 
  geom_col() + 
  labs(
    title = sprintf(
      "{stats} downloads to %s", 
      Sys.Date() - 1
      ), 
    caption = "data from {cranlogs}"
  ) + 
  theme_minimal()

Continous Integration

Golem framework also containing 'helper' functions allowing to setup:

• Version control in the repository
• Use travis ci to test the package
• Setup autobuilds on Dockerhub

This is creating possibility of 'sustainable' development. e.g. if new feature is needed, then:

1. Create branch dev_feature1
2. Implement changes, test locally
3. Make a pull request
4. Wait until the Travis test will be ok
5. Join changes into Master Branch

Users may get a new version as soon as Docker autobuild will build updated image

Technical Comment

You'll still need to render README.Rmd regularly, to keep README.md up-to-date.

In that case, don't forget to commit and push the resulting figure files, so they display on GitHub!

Source

thinkr blog golem vignette

vladdsm/trackpack documentation built on May 9, 2020, 1:54 p.m.