The goal of trackpack is to track my package ‘lazytrade’

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

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:

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:

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

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)

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

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()

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

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!

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