Say we are interested in comparing gun homicide rates across regions of the US. We see this plot below.

library(dslabs)
data("murders")
murders %>% mutate(rate = total/population*100000) %>%
group_by(region) %>%
summarize(avg = mean(rate)) %>%
mutate(region = factor(region)) %>%
ggplot(aes(region, avg)) +
geom_bar(stat="identity") +
ylab("Murder Rate Average")

1. We decided to move to a state in the western region. What is the main problem with this interpretation? Store your answer in q1.

1. The categories are ordered alphabetically.
2. The graph does not show standarad errors.
3. It does not show all the data. We do not see the variability within a region and it’s possible that the safest states are not in the West.
4. The Northeast has the lowest average.

2. Make a boxplot of the murder rates defined as

data("murders")
murders %>% mutate(rate = total/population*100000)

by region, showing all the points and ordering the regions by their median rate. Store the resulting ggplot object in p.