# here we lay out our calculations

# load gapminder data
library(gapminder)

# want unique countries
gc <- gapminder$country |> unique()

# check to see if a chosen country is in gc
'Togo' %in% gc # single quotes are good
"Merica" %in% gc # doubles too


print('now we are done')


library(ggrepel)

hi_pop_countries <- filter(gapminder, pop > 50000000)

ggplot(hi_pop_countries, aes(x = year, y = gdpPercap)) +
  geom_point() +
  geom_text_repel(aes(label = country))


ggplot(hi_pop_countries, aes(y = lifeExp, x = reorder(country, lifeExp))) +
  geom_boxplot()


TCU <- filter(gapminder, country %in% c("Taiwan", "China", "United States"))

TCU |>
  ggplot(
    aes(x = lifeExp)
  ) +
  geom_histogram(bins = 2) # binwidth here controls how


# explore storms data
#
recommended
to
use

-glimpse()
-names()
-head()
-summary()

# how many different storms are listed?

# perhaps filter by status ?
# this would allow you to look at just hurricanes