Task 1: Reflection

“I thoroughly enjoyed this week’s content! Within my organization, there is a desire for more dashboards and interactivity. Currently, we use PowerBI and simple Excel Dashboards. In terms of bad dashboards, I would say the main culprit is a lack of usability and relevancy. If the dashboard is too complicated for the intended audience to use, then its utility is rather limited. Thus, I think dashboards should be designed with the end user in mind. In terms of dashboards not providing insights or relevant data, I’ve encountered that many people download the raw data behind an existing PowerBI dashboard. To me, this signals that the dashboard is not doing its job! If people find it too difficult to slice and filter the data based on available options and would rather work with the raw data, then the dashboard has missed the mark.”

Task 2: Interactive plots

chooseCRANmirror(ind = 1)

library(tidyverse)
install.packages("plotly")
## package 'plotly' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\andre\AppData\Local\Temp\RtmpgRpRZK\downloaded_packages
library(plotly)
library(gapminder)
install.packages("flexdashboard")
## package 'flexdashboard' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\andre\AppData\Local\Temp\RtmpgRpRZK\downloaded_packages
library(flexdashboard)
my_data <- gapminder
gap_format <- my_data %>%
  group_by(year, continent) %>%
  summarise(lifeExp = mean(lifeExp))
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
my_plot <-  ggplot(gap_format, aes(x = year, y = lifeExp, color = continent)) + geom_point(aes(text = paste(continent, year))) +theme_minimal() + labs(x = "Year", y = "Average Life Expectancy", color = "Continent", title = "Average Life Expectancy Trend", subtitle = "Averaged across countries" )
## Warning in geom_point(aes(text = paste(continent, year))): Ignoring unknown
## aesthetics: text
my_plot

ggplotly(my_plot, tooltip = "text" )

Do the following:

  1. Make a plot. Any kind of plot will do (though it might be easiest to work with geom_point()).

  2. Make the plot interactive with ggplotly().

  3. Make sure the hovering tooltip is more informative than the default.

Good luck and have fun!

Task 3:

Install the {flexdashboard} package and create a new R Markdown file in your project by going to File > New File… > R Markdown… > From Template > Flexdashboard.

Using the documentation for {flexdashboard} online, create a basic dashboard that shows a plot (static or interactive) in at least three chart areas. Play with the layout if you’re feeling brave.