Chapter 24 Some extras

24.1 Interactive graphs

One step between static graphs and reactive graphs (shiny) are interactive graphs. We’ll see two packages that we can use to transform any ggplot-graph into an interactive graph: plotly and ggiraph.

24.1.1 plotly

With the plotly-package you can turn any ggplot-object into an interactive plotly-object with the function ggplotly. See some ace example here. Let’s try it ourselves.

install.packages("plotly")
library(plotly)

my_plot <- ggplot(gapminder, aes(x = year, y = lifeExp)) +
  geom_line(aes(group = country))

ggplotly(my_plot)

Let’s try another:

my_plot2 <- ggplot(gapminder, aes(x = pop)) +
  geom_histogram(bins = 25) +
  scale_x_continuous(trans = "log10")

ggplotly(my_plot2)

Ok let’s step it up a bit, but now we deviate from the ggplot-style:

gapminder %>%
  plot_ly(
    x = ~gdpPercap, 
    y = ~lifeExp, 
    size = ~pop, 
    color = ~continent, 
    frame = ~year, 
    text = ~country, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  ) %>%
  layout(
    xaxis = list(
      type = "log"
    )
  )

It’s even reactive (sort of).

24.1.2 ggiraph

ggiraph is a different way of making your graph interactive. It’s not as straightforward as plotly, but it allows more flexibility. Also, it works with shiny such that users can select points in a graph to further work with. See here for more information.

my_plot3 <- ggplot(filter(gapminder, country == "Netherlands")) +
    geom_point_interactive(aes(x = year, y = lifeExp, 
                               tooltip = lifeExp, data_id = year)) 

girafe(ggobj = my_plot3)

24.2 Animated graphs

There is another step between interactive graphs and static graphs, and those are animated graphs. In essence, movies made of several static graphs that allows exploring another dimension. The gganimate package is fantastic. See here

install.packages("gganimate")
library(gganimate)

Let’s look at the relationship between GDP and life expectancy for the different continents:

my_plot4 <- ggplot(filter(gapminder, year == 2007), 
                   aes(x = gdpPercap, y = lifeExp, colour = continent)) +
  geom_point(size = 4, alpha = 0.7) +
  scale_colour_brewer(palette = "Set2") +
  scale_x_continuous(trans = "log10") +
  guides(colour = "none")
my_plot4

my_plot4 + 
  transition_states(continent,
                    transition_length = 2,
                    state_length = 1) + 
  ggtitle('Now showing {closest_state}')

Let’s do another one, examining the earlier relationship over time.

my_plot5 <- ggplot(gapminder, 
                   aes(x = gdpPercap, y = lifeExp, colour = continent)) +
  geom_point(alpha = 0.7) +
  scale_colour_brewer(palette = "Set2") +
  scale_x_continuous(trans = "log10") +
  facet_wrap(~ continent, nrow = 1) +
  guides(colour = "none")
my_plot5

Now let’s see this across time. For this we’ll use some gganimate specific code

my_plot5 +
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

Let’s try something a bit more funky (from here)

library(viridis)
ggplot(gapminder, aes(x = gdpPercap, y=lifeExp, 
                           size = pop, colour = country)) +
  geom_point(alpha = 0.7) +
  scale_color_viridis_d() +
  scale_size(range = c(2, 12)) +
  scale_x_continuous(trans = "log10") +
  guides(colour = "none", size = "none") +
  theme_minimal() +
  labs(x = "GDP per capita", y = "Life expectancy") + 
  transition_time(year) +
  labs(title = "Year: {frame_time}") +
  shadow_wake(wake_length = 0.1, alpha = FALSE)

24.3 Maps

Map-data comes in all shapes and forms and can be difficult to work with. Here just some example where we make use of a package maps that includes amongst others, a world-map.

install.packages("maps")
library(maps)

This package includes a worldmap (or a dataframe with latitude and longitude coordinates for the borders of a country!):

world_map <- map_data("world")
head(world_map)

##        long      lat group order region subregion
## 1 -69.89912 12.45200     1     1  Aruba      <NA>
## 2 -69.89571 12.42300     1     2  Aruba      <NA>
## 3 -69.94219 12.43853     1     3  Aruba      <NA>
## 4 -70.00415 12.50049     1     4  Aruba      <NA>
## 5 -70.06612 12.54697     1     5  Aruba      <NA>
## 6 -70.05088 12.59707     1     6  Aruba      <NA>

ggplot(world_map, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "lightgray", colour = "white")

Works best with theme_void

ggplot(world_map, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "lightgray", colour = "white") +
  theme_void()

Let’s focus on the Netherlands:

NL <- world_map %>% filter(region == "Netherlands")

ggplot(NL, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "orange", colour = "white") +
  annotate("label", x = 6.567958, y = 53.219197, label = "grunn") +
  theme_void()

Something more fancy (from here):

# Some EU Countries
part_of_EU <- c(
  "Portugal", "Spain", "France", "Switzerland", "Germany",
  "Austria", "Belgium", "UK", "Netherlands", "Luxembourg",
  "Denmark", "Poland", "Italy", 
  "Croatia", "Slovenia", "Hungary", "Slovakia",
  "Czech Republic"
)

# Retrieve the map data
EU <- world_map %>% filter(region %in% part_of_EU)

ggplot(EU, aes(x = long, y = lat)) +
  geom_polygon(aes(group = group, fill = region)) +
  #geom_text(aes(label = region), data = region.lab.data,  size = 3, hjust = 0.5)+
  scale_fill_viridis_d() +
  theme_void() +
  guides(fill = "none")

Different coordinate systems:

install.packages("mapproj")
library(mapproj)

ggplot(EU, aes(x = long, y = lat)) +
  geom_polygon(aes(group = group, fill = region)) +
  scale_fill_viridis_d() +
  theme_void() +
  guides(fill = "none") +
  coord_map(projection = "mercator")

ggplot(EU, aes(x = long, y = lat)) +
  geom_polygon(aes(group = group, fill = region)) +
  scale_fill_viridis_d() +
  theme_void() +
  guides(fill = "none") +
  coord_map(projection = "orthographic") +
  theme(panel.grid.major = element_line(colour = "lightgrey"))

24.3.1 Using OpenStreetMap and stamen

ggplot2 and Google Maps used to play nicely, but Google changed its services. It’s better to use OpenStreetMap.

# install.packages("OpenStreetMap")
library(OpenStreetMap)

We first have to find longitude and latitude coordinate for the map that we want to use. We can do that through:

upper_left  <- c(53.224149, 6.547523)
lower_right <- c(53.211815, 6.587957)

gron  <- openmap(upper_left, lower_right, type = 'osm')
# You need to project the map to a (particular) coordinate system
gron2 <- openproj(gron)

# Location of bloemstraat
bloemstraat <- data.frame(
  x = 6.567995529739899,
  y = 53.22364048304991,
  text = "you are here!"
)

Now let’s plot:

OpenStreetMap::autoplot.OpenStreetMap(gron2) + 
  geom_point(data = bloemstraat, 
             aes(x = x, y = y), size = 5, alpha = 0.5, colour = "purple") +
  geom_label(data = bloemstraat, 
             aes(x = x, y = y, label = text), nudge_y = -0.001)

24.3.2 geofacet

A nifty little package that deals with maps is geofacet (see here). Let’s see it in action.

install.packages("geofacet")
library(geofacet)

ggplot(eu_gdp, aes(year, gdp_pc)) +
  geom_line(color = "steelblue") +
  facet_geo(~ name, grid = "eu_grid1", scales = "free_y") +
  scale_x_continuous(labels = function(x) paste0("'", substr(x, 3, 4))) +
  ylab("GDP Per Capita in Relation to EU Index (100)") +
  theme_bw()

24.4 Networks

Networks are gaining in popularity. We need to put a bit more effort in to get a network graph. We will require these packages:

# install.packages("tidygraph")
# install.packages("ggraph")
library(tidygraph)
library(ggraph)

df_edges <- data.frame(
  from = c(
    "Gert",
    "Gert",
    "Gert",
    "Gert",
    "Gert",
    "Ben",
    "Anne"
  ),
  to = c(
    "Anne",
    "Ben",
    "Winy",
    "Vera",
    "Laura",
    "Winy",
    "Vera"
  )
)
df_nodes <- data.frame(
  name = c("Gert", "Anne", "Ben", "Winy", "Vera", "Laura"),
  relation = c(
    "Ego", "Partner", "Family", "Family",
    "Friend", "Friend"
  )
)

df_network <- tbl_graph(
  nodes = df_nodes,
  edges = df_edges,
  directed = FALSE
)
df_network

## # A tbl_graph: 6 nodes and 7 edges
## #
## # An undirected simple graph with 1 component
## #
## # A tibble: 6 × 2
##   name  relation
##   <chr> <chr>   
## 1 Gert  Ego     
## 2 Anne  Partner 
## 3 Ben   Family  
## 4 Winy  Family  
## 5 Vera  Friend  
## 6 Laura Friend  
## #
## # A tibble: 7 × 2
##    from    to
##   <int> <int>
## 1     1     2
## 2     1     3
## 3     1     4
## # ℹ 4 more rows

Let’s visualise!

ggraph(df_network, layout = "kk") +
  geom_node_point() +
  geom_edge_link()

## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Let’s improve

ggraph(df_network, layout = "kk") +
  geom_edge_link() +
  geom_node_point(aes(colour = relation), size = 13) +
  geom_node_text(aes(label = name), colour = "white") +
  theme_void() +
  scale_color_brewer(palette = "Set2")

Let’s see another example (from here):

# Create graph of highschool friendships
graph <- as_tbl_graph(highschool) %>% 
    mutate(Popularity = centrality_degree(mode = 'in'))

# plot using ggraph
ggraph(graph, layout = 'kk') + 
    geom_edge_fan(aes(alpha = stat(index)), show.legend = FALSE) + 
    geom_node_point(aes(size = Popularity)) + 
    facet_edges(~year) + 
    theme_graph(foreground = 'steelblue', fg_text_colour = 'white')

## Warning: `stat(index)` was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(index)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

You can do amazing things with this package. Check out here, here, and here.

You can do all sorts of stuff, like creating your family tree: