Final Assignment BIOL01301

Due as a Pull Request to sjspielman/datascience_final_assignment by Tuesday May 12th at 11:59 pm. Due to university grade deadlines there are absolutely NO exceptions!!

Submit early by Friday 5/8/20 at 11:59 pm for a bonus 10%!!!

This assignment grade is worth two regular assignments

Setting up

1. Fork the repository sjspielman/datascience_final_assignment into your github account, and clone your repository for use
2. Create a directory lastname_firstname, and use git mv to move the files covid_data_load.R and app.R into your directory. Add, commit, push, and you're off to the races!

Part One: Prepare the data in covid_data_load.R

You will read in three files (two from JHU and one from NYT) and ultimately produce two TIDY tibbles for use in the shiny app. Bear in mind: All data here is cumulative (the total number of cases or deaths up to and including that day!).

1. A tibble of the NYT data exactly called nyt_data that should ultimately contain these final columns that use these exact names:

   • date
   • county
   • state
   • fips (this is a location code used by maps, stands for "Federal Information Processing Standard")
   • covid_type (A categorical variable containing either "cases" or "deaths")
   • cumulative_number (The number associated with covid_type)

2. A tibble of the JHU data exactly called jhu_data that should ultimately contain these final columns that use these exact names:

   • province_or_state
   • country_or_region
   • latitude
   • longitude
   • date
   • covid_type (A categorical variable containing either "cases" or "deaths")
   • cumulative_number (The number associated with covid_type)

Notes and hints:

• After you finish making the JHU tibble, you need to re-cast the date column for each to clearly be treated as a date: jhu_data$date <- lubridate::as_date(jhu_data$date). The date column in the NYT data should have been read in properly as a date (since this was a tidy column in the first place). You can also do the re-casting with mutate() if you are comfortable with that approach.
• You should NOT save any data with write_csv()!! Because this script is sourced in the shiny app file app.R, all the variables you create in this script are fully usable within app.R itself. Don't make your life harder than it needs to be.

Part Two: Make a shiny application!

Your shiny app will live in the file app.R - never change the name of this file. Your final application will have two panels (one for NYT data and one for JHU data), each with its own input sidepanel and mainpanel for output - this has already been templated for you!

Each panel should reveal a line plot of its associated data. Components of a line plot should include:

• Time (aka date) along the X-axis and cumulative number on the Y-axis
• Use points within your line plot to emphasize the time points
• Color lines based on covid_type

Alternatively, you may make a bar plot showing counts over time, where you color bars based on covid_type (either dodged or stacked, your call!).

There should be user-input widgets associated with each plot that indicate what should be plotted:

For NYT data, there must be at least SIX widgets: (more widgets might get you some bonus if they make sense and work!)

• Choice for which state to plot
• Option to show counties as facets (using facet_wrap()), OR "ignore" county distinctions and show all data for the state in a single plot
• Option to show Y-axis on linear or log scale
• Theme for the plot (users should have at least FOUR options to choose from). These can either be built-in ggplot themes, or they can be from a different library of your choosing.
• Options for colors to use (this has been templated for you, with defaults - please choose your own defaults!). This is two widgets (one color widget for cases, one color widget for deaths)

For JHU data, there must be at least FIVE widgets: (more widgets might get you some bonus if they make sense and work!)

• Choice for which country/region to plot
• Option to show Y-axis on linear or log scale
• Theme for the plot (users should have at least FOUR options to choose from). These can either be built-in ggplot themes, or they can be from a different library of your choosing.
• Options for colors to use (this has been templated for you, with defaults - please choose your own defaults!). This is two widgets (one color widget for cases, one color widget for deaths)

Notes and hints:

• Remember: Everything on the server side needs to be within an appropriate context!!
  • You should use reactive variables to store the subsetted data (e.g. subsetted to state/region of interest). This reactive variable should then be plotted.
  • Plots should be defined within renderPlot({}) constructs.
• You will need to use if/else constructs for adding the theme to plots!
• 99% of the bugs you will have are because of missing/extra commas in the UI. Welcome to shiny.
• Remember to choose your own app theme!!! See the line in app.R that opens navbarPage()
• Ensure that all plot renderings are fully legible. You may need to alter the size of the plots in plotOutput()!

Resources

1. All the shiny control widgets (inputs!)
2. All the shiny outputs
3. THE DEFINITIVE tutorial

Bonus opportunities

• Submit early by 5/8/20 at 11:59 pm for an automatic extra 10%
• An option to display X-axis from the day with N infections
• Instead of using default shiny widgets, use widgets associated with the library shinyWidgets
• Include a third tabPanel displaying a map of USA (using the NYT data) where states are colored by either cases or deaths. There should be an input option indicating whether cases or deaths should be displayed, and you can use whatever color scheme you want!
• Use the library plotly to make your plots interactive!
• Toss the template out the window and make a Shiny Dashboard with your own beautiful design
• Serve the application with a public-facing URL with a free-tier account on https://www.shinyapps.io/