Task 1: Reflection—

The most recent dashboard I have used is the John’s Hopkins covid-19 dashboard. I believe they use Tableau. I refer to it a lot as I used it to guide me in my data management project in the spring. It’s a handy tool and very informative. Personally, the black background is hard for me to navigate and I would prefer a less busy screen. But I am not in the public health field so perhaps their goals are different than mine as a general public viewer. That said, when I first started using their dashboard, I was impressed with the interactive nature and never thought that was something I would learn this summer. So I am really excited that I will have a little experience with it now.

When I read the anecdote about Walmart’s reporting to their board of directors using basic Excel tables, I was surprised. I expected a little more savvy in their internal reporting. But I guess, if one isn’t exposed to something, it’s almost like you don’t know you’re missing it. The change in the nature of their reporting was drastically different. I can imagine how more effective their decision making abilities were once they were spoonfed results rather than culling through tables. And reviewing some of the dashboard examples was interesting as well. Some seemed very fancy but not necessarily informative. Others took me a while to find the information I thought might be important. I tended to prefer the more simple ones for ease of use. We’ll see if that is always the best barometer.

Task 2: Interactive plots

library(tidyverse)  
library(scales)     
library(plotly)     
library(gapminder)
library(htmlwidgets)
gapminder_raw <- gapminder%>%
  filter(year == 2007)%>%
  mutate(fancy_label = paste0(country, "<br>", 
                              round(lifeExp,0), " life exp", "<br>",
                              round(gdpPercap, 0), " GDP PerCap"))

Make a plot

life_wealth <- ggplot(gapminder_raw, aes(x=lifeExp, y=gdpPercap, color=continent))+
  geom_point(aes(text=fancy_label))+
  scale_y_continuous(labels = comma) +
    theme_minimal()+
  labs(title = "Health and Wealth by Country in 2007", caption = "source from Gapminder dataset", x = "Life Expectancy", y = "Logged GDP per Capita")
## Warning: Ignoring unknown aesthetics: text
life_wealth

ggplotly(life_wealth, tooltip="fancy_label")
life_wealth_interactive <- ggplotly(life_wealth)

htmlwidgets::saveWidget(life_wealth_interactive, "fancy_lifeplot.html")