co2 <- co2_data%>%
filter(!is.na(iso_code), !str_starts(iso_code, "OWID"), year == 2022) %>%
mutate(gdp_per_person = gdp / population) %>%
drop_na(gdp_per_person, co2_per_capita, energy_per_capita)
#Prepares data keeping only 2022 and makes a GDP per person column
fit <- lm(log10(co2_per_capita) ~ log10(gdp_per_person), data = df)
summary(fit)
#This is how we find the regression line, summary(fit) gives us the intercept, slope, and R^2
ggplot(df, aes(gdp_per_person, co2_per_capita)) +
geom_point() +
geom_smooth(method = "lm") +
scale_x_log10() + scale_y_log10()