Closed sullivannicole closed 7 years ago
flexdashboard
allows you to construct it like a more traditional R Markdown document, so you don't have to segregate your server and UI code.ggplotly()
makes sense (such as the time chart). For others, maybe not as much. Like the distance tab - hovering over the boxplots reveals numbers associated with the key elements of the boxplot, but I'm not sure I really need the precise values. You can make an interactive Shiny app but still use static visualizations for some aspects. Think carefully when designing an app like this what makes the most sense for each visualization component (i.e. each individual graph) whether to render static or dynamic.download handler
function to allow users to download the raw data for an individual player/team.scale_*_brewer()
to pick the color palettes rather than ggplot2
's default algorithm. This should implement more robust color palettes for distinguishing between discrete categoriestheme()
for each plot. Avoid code duplication! You could store this as a custom theme function at the beginning of server.R
, then reference that in each of the plots.
For this assignment, I created a Shiny app using data from the NBA Shot Logs from 2014-2015. In the future, I definitely want to try out Flexdashboard. Coding for Shiny is way too unwieldy and opaque and making even the slightest changes becomes a struggle when there are more than a couple tabs. I mean, I wrote the code, and I still had difficulties recalling how to layer code or properly insert new code!