R-Package Review 📦

Are the improvements that could be made to the code? 🤚

I think the code is well-written, and gives a concise and easy to understand documentation. However, I have two suggestions based on your code.

First of all, add some arguments to your function. If a function without any argument when executed, it will generate some limitations, such as the function is not reproducible or meaningless. The existing two functions could only use in your shiny application, so there is no need to include an argument within the function. I would suggest adding a new function based on your repeating menu icon in the dashboard.

@export
add_menu <- function(id, text_name, icon_name){
  shinydashboard::menuItem(id, tabName = text_name, icon = icon(icon_name))
}

This function aims to add menu bar in the shiny dashboard. It contains three arguments: sidebar menu which be used for a Shiny input value, the name of a tab that this menu item will activate and the available icons.

In addition, the function with several arguments will be more complicated. You could add an example to tell users how to use this function.

Here is an example:

@examples
add_menu("Confirmed Cases by State", "dashboard", "dashboard")

Are there improvements that could be made to the shiny app inside the package? 💭

This shiny dashboard is very impressive and I really like how interactive it is. This shiny dashboard provided a more structured format instead of the complex shiny format. Some complexity shiny format would be easy for the end user’s eye to stray and become confused by several plots on the same page. It generates a number of informative plots such as the line chart and the bar chart, and I was able to learn a lot about the state of coronavirus in Australia.

I think the improvement that I would recommend would be the aesthetics of the application could have been better. Being able to communicate data stylishly and beautifully is as important as the data analysis itself, in my opinion. Specifically, I think the use of sliderInput and valueBoxes might have made the application pop a bit more.

Furthermore, this shiny dashboard could improve the application by using more complex and varied user input options. For example, you could add checkboxGroupInput where user is able to select multiple states. Or perhaps in the fourth section called ‘Deaths in Victoria’, adding a date range input so the user can obtain more specific information about arbitrary time periods (as opposed to a single day).

Is the documentation sufficient to get started? 🤔

The documentation in the README file is a bit simpler, which only contains how to install packages and the example of the data set. I would recommend that provide a detailed overview of the package, such as display the data set in the form of a simple graph or give some screenshots of this well structured shiny dashboard.

The functions is well documented, however, my recommendation would be to provide additional information in oz_covid_data documentation. Specifically, in interpreting each variable, the format can be more standardized. You could follow the following code to make your documentation more profession.

\describe{
   \item{date}{the date on a particular day}
   \item{Daily Confirmed}{the daily confirmed coronavirus cases in Australia on that given date}
   \item{...}{...}
   }

Documentation

The package includes all the following forms of documentation:

• The Installation instructions: for the package is found in README
• The Vignette(s) demonstrating major functionality that runs successfully locally
• The Function Documentation: for all exported functions in R help
• The Examples for all exported functions in R Help that run successfully locally.

Functionality

• Installation: Installation succeeds as documented.
• Functionality: Any functional claims of the software been confirmed.
• Automated tests: Unit tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.