Libraries / packages

There are plenty of packages available in R designed to create interactive or animated plots, graphs and/or maps. This post is not intended to carry out an exhaustive analysis of all of them, but to present some examples of interactive and animated maps using some tools available in R. I hope that it may be of interest to someone. In a way this post can be considered as a continuation of two dashboards previously made, where it was shown how to make static choropleth maps (link to dashboard) and how to create static maps using geographic coordinates (link to post). At this point I will show a few maps, using {echarts4r}, {ggiraph} and {plotly} packages, but I might add more examples using different packages in the future.


library(tidyverse)
library(gapminder)
library(echarts4r)
library(gganimate)
library(ggiraph)
library(widgetframe)
library(ggthemes)
library(plotly)
library(viridis)
library(DT)

Datasets

In this post we are going to use a well known dataset called gapminder / gapminder_unfiltered included in the package of the same name. If you have read previous post of this blog you might be familiarized with it as I have used the gapminder dataset a few times in other examples, even on the previously mentioned dashboards. The dataframe contains the following information for a list of countries (187 in the gapminder_unfiltered version):

country: Country names.
continent: Continent of each country.
year: The data range covers the period 1952-2007 (in 5-year increments).
lifeExp: Life expectancy of each country in years.
pop: Population.
gdpPercap: GDP per capita in inflation-adjusted dollars.

We will also use the meterorite landings dataset from the Tidytuesday project (11-06-2019). As the name indicates, this datasets is about meteorites and the information comes from the Meteoritical Society of NASA. Although the dataset includes more information, we will mainly be interested in the following variables:

name: Meteorite name
mass: Mass in grams
year: Year found
lat: Latitude
long: Longitude
fall: fall or found meteorite

gapminder dataset:


# country codes in gapminder::country_codes

gapminder_codes <- gapminder::country_codes

# countries with info in gapminder::gapminder_unfiltered

gapminder <-gapminder::gapminder_unfiltered

# We join both datasets with inner_join to get a dataset with the info by country, continent and country-code

gapminder <- gapminder %>%
  inner_join(gapminder_codes, by= "country") %>%
  mutate(code = iso_alpha)

# A map of the world (Antarctica removed)

world <- map_data("world") %>%
  filter(region != "Antarctica")


gapminder_data <- gapminder %>%
  inner_join(maps::iso3166 %>%
               select(a3, mapname), by= c(code = "a3")) %>%
  mutate(mapname = str_remove(mapname, "\\(.*"))


datatable(gapminder_data)

Show entries

Search:

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num	code	mapname
1	Afghanistan	Asia	1952	28.801	8425333	779.4453145	AFG	4	AFG	Afghanistan
2	Afghanistan	Asia	1957	30.332	9240934	820.8530296	AFG	4	AFG	Afghanistan
3	Afghanistan	Asia	1962	31.997	10267083	853.10071	AFG	4	AFG	Afghanistan
4	Afghanistan	Asia	1967	34.02	11537966	836.1971382	AFG	4	AFG	Afghanistan
5	Afghanistan	Asia	1972	36.088	13079460	739.9811058	AFG	4	AFG	Afghanistan
6	Afghanistan	Asia	1977	38.438	14880372	786.11336	AFG	4	AFG	Afghanistan
7	Afghanistan	Asia	1982	39.854	12881816	978.0114388	AFG	4	AFG	Afghanistan
8	Afghanistan	Asia	1987	40.822	13867957	852.3959448	AFG	4	AFG	Afghanistan
9	Afghanistan	Asia	1992	41.674	16317921	649.3413952	AFG	4	AFG	Afghanistan
10	Afghanistan	Asia	1997	41.763	22227415	635.341351	AFG	4	AFG	Afghanistan

Showing 1 to 10 of 3,584 entries

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Meteorites dataset


meteoritos <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-06-11/meteorites.csv")

datatable(meteoritos)

Show entries

Search:

	name	id	name_type	class	mass	fall	year	lat	long	geolocation
1	Aachen	1	Valid	L5	21	Fell	1880	50.775	6.08333	(50.775, 6.08333)
2	Aarhus	2	Valid	H6	720	Fell	1951	56.18333	10.23333	(56.18333, 10.23333)
3	Abee	6	Valid	EH4	107000	Fell	1952	54.21667	-113	(54.21667, -113.0)
4	Acapulco	10	Valid	Acapulcoite	1914	Fell	1976	16.88333	-99.9	(16.88333, -99.9)
5	Achiras	370	Valid	L6	780	Fell	1902	-33.16667	-64.95	(-33.16667, -64.95)
6	Adhi Kot	379	Valid	EH4	4239	Fell	1919	32.1	71.8	(32.1, 71.8)
7	Adzhi-Bogdo (stone)	390	Valid	LL3-6	910	Fell	1949	44.83333	95.16667	(44.83333, 95.16667)
8	Agen	392	Valid	H5	30000	Fell	1814	44.21667	0.61667	(44.21667, 0.61667)
9	Aguada	398	Valid	L6	1620	Fell	1930	-31.6	-65.23333	(-31.6, -65.23333)
10	Aguila Blanca	417	Valid	L	1440	Fell	1920	-30.86667	-64.55	(-30.86667, -64.55)

Showing 1 to 10 of 45,716 entries

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1. Interactive choropleth maps with {ggiraph}

The dashboard presented in this post showed different ways of representing the life expectancy of a list of countries on a static choropleth map using the gapminder_unfiltered dataset. If we want a tooltip to appear when we hover the pointer over a country we can use the {ggiraph} and the {widgetframe} packages. We will use the geom_polygon_interactive() function from the {ggiraph} package and the framewidget() function from the {widgetframe} package as follows:



lifeExp_map <- gapminder_data%>%
  filter(year == 2007) %>%
  right_join(world, by= c(mapname = "region")) %>%
  ggplot() +
  geom_polygon_interactive(color = "white", size = 0.01, 
                           aes(long, lat, group= group, fill= lifeExp,
                               tooltip = sprintf("%s<br/>%s", country, lifeExp))) +
  theme_void() +
  scale_fill_viridis(option = "B") +
  labs(title="Life Expectancy",
       subtitle = "Year: 2007",
       caption = "Source: gapminder.org",
       fill = "Years")  +
  theme(
    axis.line = element_blank(),
    axis.text = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    plot.background = element_rect(fill = "snow", color = NA),
    panel.background = element_rect(fill= "snow", color = NA),
    plot.title = element_text(size = 16, hjust = 0.5),
    plot.subtitle = element_text(size = 12, hjust = 0.5),
    plot.caption = element_text(size = 8, hjust = 1),
    legend.title = element_text(color = "grey40", size = 8),
    legend.text = element_text(color = "grey40", size = 7, hjust = 0),
    legend.position = c(0.05, 0.25),
    plot.margin = unit(c(0.5,2,0.5,1), "cm")) +
  coord_fixed (ratio = 1.3)

widgetframe::frameWidget(ggiraph(code=print(lifeExp_map)))

2. Interactive choropleth maps with {plotly}

On the other hand, we can also create an interactive map with the package {plotly}. As in the previous example, {plotly} allows us to add a tooltip to our map, but it also allows to perform additional actions, such as zooming in and out, lasso or box selecting o downloading the map as a png file.




gapminder_lifeExp_07 <- gapminder_data %>%
  filter(year == 2007) %>%
  select(mapname, code, lifeExp)

lifeExp_07<- plot_geo(gapminder_lifeExp_07)

lifeExp_07<- lifeExp_07 %>% add_trace(
  z = ~lifeExp, color = ~lifeExp, colors = 'Oranges',
  text = ~mapname, locations = ~code)

lifeExp_07 <- lifeExp_07 %>% colorbar(title = 'Years')
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

lifeExp_07 <- lifeExp_07 %>% layout(
  title = 'Life Expectancy in 2007<br>Source:<a href="https://www.gapminder.org"> gapminder.org</a>',geo = lifeExp_07)

lifeExp_07

3. Interactive maps showing locations with {plotly}

In a previous example we used {plotly} to create an interactive choropleth map. Similarly, we can use this package to plot another map indicating locations using a list of geographical coordinates. The procedure will be similar to the choropleth example, but in this ocassion we will have to indicate the longtitud and latitute coordinates in the plot_geo() function. Note that in the tooltip we have added information about the name of the location where the meteorite fell, its mass (in grams) and the year (year found).


meteoritos_fell <- meteoritos %>%
  filter(fall == "Fell")

meteoritos_map <- list(
  #scope = 'usa',
  projection = list(type = 'Mercator'),
  showland = TRUE,
  landcolor = toRGB("grey80")
)


meteo_map <- plot_geo(meteoritos_fell, lat = ~lat, lon = ~long)
meteo_map <- meteo_map %>% add_markers(
  text = ~paste(paste("Name:", name), 
                paste("Year:", year), 
                paste("Mass:", mass), sep = "<br />"),
  color = ~mass, symbol = I("circle-dot"), size = I(8), hoverinfo = "text"
)
meteo_map <- meteo_map %>% colorbar(title = "Mass")
## Warning: Ignoring 10 observations
meteo_map <- meteo_map %>% layout(
  title = 'Meteorite Landings<br />(Meteorite falls)', geo = meteoritos_map
)

meteo_map

Interactive Maps with {echarts4r}

As explained in a previous post, we can also represent the evolution of a variable on a map (i.e. life expectancy or another variable of interest) with the package {echarts4r}. That map would allows us to visualize the evolution of each country and instantly detect those countries that are not included in the {gapminder} package dataframe. In fact, as we can observe in the next example, there is no information for many countries in most of the years included in the dataset. Notice that {echarts4r} also allows adding a tooltip with the function e_tooltip().


datos_gapminder_map <- gapminder %>%
  mutate(Name = recode_factor(country,
                              `Congo, Dem. Rep.`= "Dem. Rep. Congo",
                              `Congo, Rep.`= "Congo",
                              `Cote d'Ivoire`= "Côte d'Ivoire",
                              `Central African Republic`= "Central African Rep.",
                              `Yemen, Rep.`= "Yemen",
                              `Korea, Rep.`= "Korea",
                              `Korea, Dem. Rep.`= "Dem. Rep. Korea",
                              `Czech Republic`= "Czech Rep.",
                              `Slovak Republic`= "Slovakia",
                              `Dominican Republic`= "Dominican Rep.",
                              `Equatorial Guinea`= "Eq. Guinea"))


datos_gapminder_map %>%
  group_by(year) %>%
  e_chart(Name, timeline = TRUE) %>%
  e_map(lifeExp) %>%
  e_visual_map(min= 30, max= 90,
               type = 'piecewise') %>%
  e_title("Life expectancy by country and year", left = "center") %>%
  e_tooltip(
    trigger = "item",
    formatter = e_tooltip_choro_formatter())

3D map with the function e_map_3d():


datos_gapminder_map %>%
  group_by(year) %>%
  e_chart(Name, timeline = TRUE) %>%
  e_map_3d(lifeExp) %>%
  e_visual_map(min= 30, max= 90,
               type = 'piecewise') %>%
  e_title("Life expectancy by country and year", left = "center") %>%
  e_tooltip(
    trigger = "item",
    formatter = e_tooltip_choro_formatter())