group_by and summarise functions

Use group_by and summarise functions to see a summary of the original data set.

The table below shows the original gapmind tibble.

We would like to see per continent the total population by year. The group_by and summarise functions allow you to get an overview easily by using the pipe operator.

gapminder %>%
  group_by(continent, year) %>%
  summarise(continent.population = sum(as.numeric(pop))) %>% 
  head()

Pipe operation can chain your summarised data into a plot

gapminder %>%
  group_by(continent, year) %>%
  summarise(continent.population = sum(as.numeric(pop))) %>%
  ggplot(aes(x = year, y = continent.population)) +
  geom_area(aes(fill = continent), position = "stack") +
  ggtitle("Gapminder population growth per continent",
          subtitle = paste("from", min(gapminder$year), "to", max(gapminder$year)))

Static plot by applying ggplot

gapminder %>%
  filter(year == max(year), !continent == "Oceania") %>%
  ggplot(aes(x = continent, y = pop)) +
  geom_violin() + coord_flip() +
  geom_jitter(position = position_jitter(width = 0.05, height = 0),
              alpha = 1 / 4) +
  ggtitle(
    "Gapminder country population distribution, split by continent",
    subtitle = paste("for the year", max(gapminder$year))
  )

Interactive plot by applying htmlwidgets

library("highcharter")
gapminder %>%
  group_by(continent, year) %>%
  summarise(continent.population = sum(as.numeric(pop))) %>%
  hchart("area",
         hcaes(x = year, 
               y = continent.population, 
               group = continent)
         ) %>%
  hc_plotOptions(area = list(stacking = "normal")) %>%
  hc_tooltip(shared = TRUE)