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Warming up data-wrangling

Let's warm up your tidyverse data wrangling skills a bit and look at the number of cases reported by year for the American region ('AMR').

To do this, we will first filter the dataset to our desired region, then make a simple scatter plot of the year by cases.

In addition, set the opacity of the points to 50% (0.5) so we can get a sense of data overlap.

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Visualization Best Practices in R

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Instructions

  • Filter who_disease so we just only keep data from the 'AMR' region.
  • Modify the aesthetics in the data to map the year to the x-axis, and cases to the y-axis.
  • Lower the opacity (alpha) of the points to 0.5 to get a sense of overlap in geom_point()

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# filter data to AMR region. 
amr_region <- who_disease %>%
    ___(___)

# map x to year and y to cases. 
ggplot(amr_region, aes(___)) + 
	# lower alpha to 0.5 to see overlap.   
  	geom_point(___)
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