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✔ purrr 1.0.2
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library(dslabs)library(gganimate)library(gifski)
data(gapminder)
Filtering the data from 1960 to 2016
gapminder_filtered <- gapminder %>%filter(year >=1960& year <=2016)
Creating the base plot
# Function to create base plotcreate_base_plot <-function() {ggplot(gapminder_filtered, aes(x = gdp, y = life_expectancy, size = population, color = continent)) +geom_point(alpha =0.5, shape =20) +scale_size_continuous(range =c(2, 20), labels = scales::comma) +scale_x_log10(labels = scales::comma) +labs(title ="GDP vs Life Expectancy", x ="GDP", y ="Life Expectancy") +scale_color_brewer(palette ="Pastel1") +theme_dark() +theme(legend.position ="bottom",legend.key =element_blank(), panel.border =element_rect(color ="black", fill =NA, linewidth =1), # Add dark outline around plotplot.title =element_text(size =14, face ="bold", hjust =0.5), # Adjust title fontaxis.text =element_text(size =12, color ="black"), # Adjust axis text font and coloraxis.title =element_text(size =12, color ="black"))}
Create the base plot
p_base <-create_base_plot()
# Print the base plotprint(p_base)
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Creating the animated plot
p_animated <- p_base +transition_time(year) +labs(title ="GDP vs Life Expectancy\nYear: {frame_time}")
# Animate the plotanimate(p_animated, renderer =gifski_renderer())
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Creating the faceted plot
p_faceted <- p_base +facet_wrap(~continent) +transition_time(year) +labs(title ="GDP vs Life Expectancy\nYear: {frame_time}")
# Animate the faceted plotanimate(p_faceted, renderer =gifski_renderer())
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Summary:
In this analysis, I wanted to explore the connection between a country’s GDP and its life expectancy. To do this, I utilized the “gapminder” dataset, a collection of economic and health data spanning numerous countries and decades. Specifically, I zoomed in on the period from 1960 to 2016 to observe recent trends. Upon running the initial code, a static graph appeared, prominently featuring Africa and Asia as focal points. This observation prompts consideration regarding the reasons behind this prominence.
Subsequently, when the animated plot was generated, intriguing patterns emerged. Asia stood out with the largest population, and all continents exhibited a positive trend, with varying degrees of increase. Notably, Africa’s trajectory appeared slightly lower compared to other continents.
Futhermore, when generating the faceted plot, the unique pace of increase for each continent became apparent. Asia notably outpaced other continents in its upward trajectory. The Americas and Europe showed steady increases with some notable peaks. Oceania had the smallest points but also experienced growth. Across all continents, there was a consistent pattern of overall increase, though occasionally interrupted by fluctuations. To sum up, the analysis uncovered a consistent global trend: as GDP increases, so does life expectancy.
