This visualization includes bins of 5000 years which caused the histogram to be extremely wide scope. This demonstrates that the majority of the data is condensed at much lower donation amounts, however, the outliers are incredibly far. The few donations that are so much money will cause the distribution to be incredibly skewed. The median of the data would be immensely smaller than the mean of the data. These outliers will result in the data having an extremely positive skew making it quite far from normally distributed.
I chose this binning to investigate the data from a birds-eye view. Massive donations result in the data being difficult to analyze as the mean is not highly representative of the data overall. Within our lab, we showed the data with smaller bin sizes to show the high amount of variability within the data.
# this is the same histogram as above but with different sized bins that makes it more difficult to see each individual bin but easier to see the total overview of the data.
hist(totalDonations, breaks = c(5000))
