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week_03/case_study_03.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))
+  )