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Case_study3.R
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Case_study3.R
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# Case Study 3
# Eleanor M. Byrne
# sources/references
# https://cran.r-project.org/web/packages/gapminder/readme/README.html
# https://www.staringatr.com/1-data-exploration-and-manipulation/manipulating-data/3_filter/
# Step 1a. Load the library
# intsall if needed to
# install.packages("gapminder")
# load
library(ggplot2)
library(gapminder)
library(dplyr)
# Step 1b. Remove “Kuwait” from the gapminder
# use filter
gapminder_filtered <- filter(gapminder, country != "Kuwait")
# removed
# Step 2. Use ggplot() and the theme_bw()
# use the new Data variable that was created
gdp_exp <- ggplot(gapminder_filtered,aes(x = lifeExp, y = gdpPercap,
color = continent, size=pop/100000)) +
geom_point() +
facet_wrap(~year, nrow = 1) +
scale_y_continuous(trans = "sqrt") +
theme_bw() +
labs(
x = "Life Expectancy",
y = "GDP per Capita",
size = "Population (100k)",
color = "Continent"
)
plot(gdp_exp)
# Step 3a. Group by function for continent and year
# Group by continent and year and summarize the data
gapminder_continent <- gapminder_filtered %>%
group_by(continent, year) %>%
summarize(
gdpPercapweighted = weighted.mean(x = gdpPercap, w = pop),
pop = sum(as.numeric(pop))
)