R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

# Wrangling the Gapminder dataset for the year 2007
gapminder_2007 <- gapminder %>%
  filter(year == 2007) %>%
  select(country, continent, lifeExp, gdpPercap, pop) %>%
  mutate(
    gdp_million = gdpPercap * pop / 1e6
  )

# Display a snapshot of the wrangled data
head(gapminder_2007)
## # A tibble: 6 × 6
##   country     continent lifeExp gdpPercap      pop gdp_million
##   <fct>       <fct>       <dbl>     <dbl>    <int>       <dbl>
## 1 Afghanistan Asia         43.8      975. 31889923      31079.
## 2 Albania     Europe       76.4     5937.  3600523      21376.
## 3 Algeria     Africa       72.3     6223. 33333216     207445.
## 4 Angola      Africa       42.7     4797. 12420476      59584.
## 5 Argentina   Americas     75.3    12779. 40301927     515034.
## 6 Australia   Oceania      81.2    34435. 20434176     703658.
# Generate summary statistics for each continent
summary_table <- gapminder_2007 %>%
  group_by(continent) %>%
  summarise(
    avg_lifeExp = mean(lifeExp),
    total_population = sum(pop),
    total_gdp_million = sum(gdp_million)
  ) %>%
  arrange(desc(total_population))

# Display the table
kable(summary_table, caption = "Summary Statistics by Continent")
Summary Statistics by Continent
continent avg_lifeExp total_population total_gdp_million
Asia 70.72848 3811953827 20707950.0
Africa 54.80604 929539692 2380485.7
Americas 73.60812 898871184 19418085.7
Europe 77.64860 586098529 14795499.3
Oceania 80.71950 24549947 807314.1

Including Plots

You can also embed plots, for example:

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point(size = 3, alpha = 0.7) +
  scale_x_log10() +
  labs(
    title = "Life Expectancy vs GDP per Capita (2007)",
    x = "GDP per Capita (log scale)",
    y = "Life Expectancy"
  ) +
  theme_minimal()

# Generate random sample coordinates for the map
gapminder_2007_map <- gapminder_2007 %>%
  mutate(
    long = runif(n(), min = -180, max = 180),
    lat = runif(n(), min = -90, max = 90)
  )

# Create the map
leaflet(gapminder_2007_map) %>%
  addTiles() %>%
  addCircleMarkers(
    ~long, ~lat,
    color = ~ifelse(lifeExp > 70, "green", "red"),
    radius = ~sqrt(pop) / 10000,
    popup = ~paste0(
      "<b>Country:</b> ", country, "<br>",
      "<b>Life Expectancy:</b> ", lifeExp, "<br>",
      "<b>GDP:</b> $", round(gdpPercap, 2)
    )
  )

Summary

1. Countries with higher GDP per capita generally exhibit higher life expectancy, as seen in the scatter plot.

2.Africa has the lowest average life expectancy and GDP per capita, while Europe has the highest.

3.The interactive map highlights disparities in life expectancy, with countries in Africa and parts of Asia showing lower values.

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.