1 Gapminder example 1

library(gapminder)
library(ggplot2)
library(dplyr)
gm4 <- gapminder %>% 
  filter(country %in% c("Canada", "United States", "Australia", "United Kingdom")) %>%
  mutate( country = droplevels(country))
  

g1 <- ggplot(gm4, aes(x=year, y=gdpPercap, colour=country)) + 
  geom_line() +
  theme_bw() + 
  labs(x="Year", y="GDP/capita", colour="Country") 
g1

2 Gapminder example 2

g2 <- ggplot(gm4, aes(x=year, y=gdpPercap)) + 
  geom_line() +
  facet_wrap(~country, nrow=1) + 
  theme_bw() + 
  labs(x="Year", y="GDP/capita", colour="Country")
g2

3 ggplotly 1

library(plotly)
gp1 <- ggplotly(g1, height=375, width=750)
gp1

4 plotly

library(plotly)
p <- plot_ly(gm4, x= ~year, y = ~gdpPercap, color=~country, 
        width=750, height=275) %>% 
      add_lines() %>% 
      layout(xaxis=list(title="Year"),  
         yaxis=list(title="GDP/capita")) 
p

5 Encoding More Information

p2 <- plot_ly(gm4, height=350, width=600) %>% 
        add_markers(x= ~year, y = ~gdpPercap, color=~country,
                   size=~log(pop)) %>% #<<
      layout(xaxis=list(title="Year"),  
         yaxis=list(title="GDP/capita")) 
p2

6 changing the hover text.

gm4 <- gm4 %>% mutate(text = paste0("Country = ", country, #<<
                             "\nYear = ", year, #<<
                             "\nGDP/capita = ", sprintf("$%.2f", gdpPercap), #<<
                             "\nPopulation = ", sprintf("%.2fM", pop/1000000), sep="")) #<<

p2a <- plot_ly(gm4, height=350, width=600) %>% 
        add_markers(x= ~year, y = ~gdpPercap, color=~country,
                   size=~log(pop), text=~text, hoverinfo="text") %>% #<<
      layout(xaxis=list(title="Year"),  
                      yaxis=list(title="GDP/capita")) 
p2a

7 3D scatterplots

plot_ly(gm4, width=700, height=500, showlegend=FALSE) %>%
      add_markers(x = ~log(pop), z = ~lifeExp, 
              y = ~gdpPercap, color=~country, showlegend=TRUE) %>% 
      layout(autosize=FALSE,  
             scene = list(
              xaxis=list(title="log(Population)"),  
              yaxis=list(title="GDP/capita"), 
              zaxis=list(title="Life Expectancy")
             ))

8 maps

load("counties.rda")
library(sf)
library(stringr)

ohio <- counties %>% 
  filter(state %in% c("Ohio")) %>% 
  mutate(text = paste(NAMELSAD, "\n", cases, " cases", sep=""))

ohio_map <- plot_ly(ohio, split = ~ text,  color = ~log(cases),  alpha = 1, 
        hoverinfo="text", hoveron="fill",  showlegend=FALSE)
ohio_map