---
title: "Data visualizations: Maps using geographic coordinates"
author: "Rubén F. Bustillo"
output:
flexdashboard::flex_dashboard:
source_code: embed
orientation: columns
theme: journal
vertical_layout: fill
---
```{r setup, include=FALSE}
## Packages / libraries to be loaded:
library(flexdashboard)
library(tidyverse)
library(stringr)
library(viridis)
library(mapdata)
library(ggrepel)
```
[www.rquer.netlify.com](https://rquer.netlify.com/)
Row {.tabset .tabset-fade}
-------------------------------------------------------------
### Base Map
```{r, fig.height=6, fig.width= 12}
# WORLD MAP with {mapdata}
mapa_mundo <- map_data("world")
# PLOT BASE MAP:
mapa_mundo %>%
ggplot() +
geom_polygon(aes( x= long, y = lat, group = group),
fill = "black",
color = "white",
size = 0.1) +
coord_fixed (ratio = 1.3)
```
### Map locations I
```{r, fig.height=6, fig.width= 12}
# DATA SET: NUCLEAR EXPLOSIONS. I got this dataset from the Tidytuesday project from 2019-08-20.
# https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-08-20
# Info about the dataset also in Wikipedia: https://en.wikipedia.org/wiki/List_of_nuclear_weapons_tests
# CSV document:
nuclear_explosions <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-08-20/nuclear_explosions.csv")
# PLOT: Each site can be represented using its latitude and longitude coordinates.
# For info about how to do animated maps see: https://rquer.netlify.com/mapa_coordenadas/
mapa_mundo %>%
ggplot() +
geom_polygon(aes( x= long, y = lat, group = group),
fill = "#2a2a2a",
size = 0.1) +
geom_point(data = nuclear_explosions,
aes(x=longitude,
y = latitude,
size = magnitude_surface + 0.5,
color = yield_upper),
alpha = 0.5,
show.legend = T) +
scale_color_gradient(low="orange", high = "magenta4",
labels = scales::comma) +
scale_size_continuous(range = c(0.5,5)) +
coord_fixed (ratio = 1.3) +
labs (title = "Nuclear Weapons Testing Sites",
subtitle = "From 1945 to 1998",
caption = "Ms: Surface wave magnitude of explosion; Explosion yield: Explosion yield upper estimate in kilotons of TNT. Info: https://seismo.berkeley.edu/~rallen/research/nuke/yield.html \nSource: Stockholm International Peace Research Institute",
size = "Ms",
color = "Explosion yield") +
theme_void() +
theme(
axis.line = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
plot.background = element_rect(fill = "lightcyan", color = "lemonchiffon" ),
panel.background = element_rect(fill = "lightcyan", 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 = 0, color = "darkgrey"),
plot.margin = unit(c(0.5,2,0.5,1), "cm"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 6))
```
### Map locations II
```{r, fig.height=6, fig.width= 12}
# We just change the elements to be represented in the map:
nuclear_explosions <- nuclear_explosions%>%
mutate(country = case_when(
country == "CHINA" ~ "China",
country == "FRANCE" ~ "France",
country == "INDIA" ~ "India",
country == "PAKIST" ~ "Pakistan",
country == "UK" ~ "UK",
country == "USA" ~ "USA",
TRUE ~ "USSR"))
mapa_mundo %>%
ggplot() +
geom_polygon(aes( x= long, y = lat, group = group),
fill = "#2a2a2a",
color = "black",
size = 0.1) +
geom_point(data = nuclear_explosions,
aes(x=longitude,
y = latitude,
size = magnitude_surface + 0.5,
color = country),
alpha = 0.6,
show.legend = T) +
scale_colour_manual(values = c("lightpink1","orange", "brown", "darkgrey", "palegreen4", "blue", "orangered3")) +
scale_size_continuous(range = c(1,6)) +
coord_fixed (ratio = 1.3) +
labs (title = "Nuclear Weapons Testing Sites",
subtitle = "From 1945 to 1998",
caption = "Ms: Surface wave magnitude of explosion; Country: Country deploying the nuclear device. Info: https://seismo.berkeley.edu/~rallen/research/nuke/yield.html \nSource: Stockholm International Peace Research Institute",
size = "Ms",
color = "Country") +
theme_void() +
theme(
axis.line = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
plot.background = element_rect(fill = "lightcyan", color = "lemonchiffon" ),
panel.background = element_rect(fill = "lightcyan", 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 = 0, color = "darkgrey"),
plot.margin = unit(c(0.5,2,0.5,1), "cm"),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8))
```
### Change coordinates `xlim & ylim`
```{r, fig.height=6, fig.width= 12}
# With xlim and ylim we can select an area of the map.
mapa_mundo %>%
ggplot() +
geom_polygon(aes( x= long, y = lat, group = group),
fill = "#2a2a2a",
color = "black",
size = 0.1) +
geom_point(data = nuclear_explosions,
aes(x=longitude,
y = latitude,
size = magnitude_surface + 0.5,
color = country),
alpha = 0.5,
show.legend = T) +
scale_colour_manual(values = c("lightpink1","orange", "brown", "darkgrey", "palegreen4", "blue", "orangered3")) +
scale_size_continuous(range = c(1,8)) +
labs (title = "Nuclear Weapons Testing Sites",
subtitle = "From 1945 to 1998",
caption = "Ms: Surface wave magnitude of explosion; Country: Country deploying the nuclear device. Info: https://seismo.berkeley.edu/~rallen/research/nuke/yield.html \nSource: Stockholm International Peace Research Institute",
size = "Ms",
color = "Country") +
theme_void() +
theme(
axis.line = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
plot.background = element_rect(fill = "lightcyan", color = "lemonchiffon" ),
panel.background = element_rect(fill = "lightcyan", 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 = 0, color= "darkgrey"),
plot.margin = unit(c(0.5,2,0.5,1), "cm")) +
coord_fixed(xlim= c(-12,180),
ylim= c(25,80),
ratio= 1.3)
```
### Using `borders("world")`
```{r, fig.height=6, fig.width= 12}
# We can also plot a world map Using `borders("word")
nuclear_explosions %>%
filter(country == "USA") %>%
ggplot(aes(longitude, latitude,
color = type)) +
borders("world") +
labs (title = "Nuclear Weapons Testing Sites \nCountry deploying the nuclear device: USA",
subtitle = "From 1945 to 1998",
caption = "Type: Method of deployment. ATMOSPH (Atmospheric); UG (underground); BALLOON (Balloon drop); AIRDROP (Airplane deployed); ROCKET (Rocket deployed); \nTOWER (deplyed at top of constructed tower); WATERSURFACE (on surface of body of water); BARGE (on barge boat); SURFACE (on surface or in shallow crater); \nUW (Underwater); SHAFT (Vertical Shaft underground); TUNNEL/GALLERY (Horizontal tunnel). Info: https://seismo.berkeley.edu/~rallen/research/nuke/yield.html \nSource: Stockholm International Peace Research Institute \n ",
color = "Type") +
geom_point(size = 4,
alpha = 0.6) +
theme_void() +
theme(
plot.background = element_rect(fill = "beige", color = NA ),
panel.background = element_rect(fill = "beige", 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 = 7.5, hjust = 0, color = "grey20"),
legend.position = c(1.1, 0.4),
legend.title = element_text(size = 8, hjust = 0.5),
legend.text = element_text(size = 6)) +
scale_color_viridis(discrete = T) +
coord_fixed(ratio= 1.3)
```
### Using `borders("world", regions = "USA")`
```{r, fig.height=6, fig.width= 12}
# The dataset comes from: https://simplemaps.com/data/us-cities
# In this ocasion the document is loaded from my PC
library(readxl)
uscities <- read_excel("C:/Users/Usuario/Desktop/r_que_r/r_que_r/content/datasets/uscities.xlsx")
# We use `borders("world", regions = "USA")` to get a regional map of USA awhere we plot the cities from the excel document.
uscities %>%
arrange(desc(ranking)) %>%
ggplot(aes(lng, lat,
color = factor(ranking))) +
borders("world", regions = "USA") +
labs (title = "United States Cities",
subtitle = "",
caption = " Each point corresponds to one US city / town from all 50 states, DC, Puerto Rico and US Virgin Islands. \n Ranking: An integer from 1-3 that captures the importance of a city (1 is most important, 3 least important). \n Source: https://simplemaps.com/data/us-cities (Data updated as of September 2019) \n ",
color = "Ranking") +
geom_point(size = 0.2, alpha = 0.7) +
theme_void() +
theme( plot.background = element_rect(fill = "linen", color = NA ),
panel.background = element_rect(fill = "linen", 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 = 0, color = "grey20"),
legend.title = element_text(size = 10),
legend.text = element_text(size = 10),
legend.position = c(1.05, 0.4)) +
scale_color_manual(values = c("red", "orange", "olivedrab3")) +
coord_fixed(xlim= c(-170,-65),
ylim= c(20,70),
ratio= 1.3)
```
### USA cities
```{r, fig.height=6, fig.width= 12}
# We can easily write some cities in our map.
# In this example we create a data frame with the longitude and latitude of the 10 largest US cities
cities <- c("New York", "Los Angeles", "Chicago", "Houston", "Philadelphia", "Phoenix", "San Antonio", "San Diego", "Dallas", "San José")
coords <- data.frame( long = c(-74.0059413, -118.2436849, -87.6297982, -95.3698028, -75.1652215, -112.0740373, -98.4936282, -117.1610838, -96.7969879, -121.8863286),
lat= c(40.7127837, 34.0522342, 41.8781136, 29.7604267, 39.9525839, 33.4483771, 29.4241219, 32.715738, 32.7766642, 37.3382082),
stringsAsFactors = F)
coords$cities <- cities
#PLOT:
mapa_mundo %>%
ggplot() +
geom_polygon(aes( x= long, y = lat, group = group),
fill = "#FFD300",
size = 0.1,
color = "grey70") +
coord_fixed (ratio = 1.3) +
theme_void() +
theme(
plot.background = element_rect(fill = "lightblue", color = NA ),
panel.background = element_rect(fill = "lightblue", color = NA)) +
coord_fixed(xlim= c(-170,-40),
ylim= c(10,80),
ratio= 1.3)+
geom_point(data=coords, aes(long, lat),
color= "red", size=1) +
geom_text_repel(data = coords,
aes(long, lat,
label =cities),
angle = 10,
size = 2.5,
color = "grey30",
fontface = "bold") +
labs ( title = "10 largest US cities by population",
caption = "Source: https://public.opendatasoft.com \n ") +
theme(plot.title = element_text(size = 16, hjust = 0.5))
```
### USA map with `map_data("state")`
```{r}
# We can also get a regional map of USA from {mapdata}.
states <- map_data("state")
ggplot(data = states) +
geom_polygon(aes(x = long, y = lat,
fill = region,
group = group),
color = "white",
alpha = 0.6) +
coord_fixed(1.3) +
guides(fill=FALSE) +
theme_void() +
geom_point(data=coords, aes(long, lat),
color= "red", size=1) +
geom_text_repel(data = coords,
aes(long, lat,
label =cities),
size = 2.5,
color = "grey30",
fontface = "bold") +
labs ( title = "10 largest US cities by population",
caption = "Source: https://public.opendatasoft.com \n ") +
theme(plot.title = element_text(size = 14, hjust = 0.5),
plot.caption = element_text(size = 8, hjust = 1))
```
### Map with {raster} (level=2)
```{r}
# We can also get a US mapa using the {raster} package
library(raster)
# Data for the map (level = 2)
USA_2 <- getData("GADM", country= "USA", level=2)
# PLOT:
ggplot() +
geom_polygon(data= USA_2,
aes( x= long, y=lat, group= group),
color= "white",
fill = "plum2",
size = 0.2,
alpha = 0.5) +
geom_point(data=coords, aes(long, lat),
color= "red", size=1) +
geom_text_repel(data = coords,
aes(long, lat,
label =cities),
size = 2,
color = "grey30",
fontface = "bold") +
theme_void() +
coord_fixed(xlim= c(-170,-55),
ylim= c(15,75),
ratio= 1.3) +
labs ( title = "10 largest US cities by population",
caption = "Source: https://public.opendatasoft.com ") +
theme(plot.title = element_text(size = 14, hjust = 0.5))
```
### Map with {raster} (level=1)
```{r}
# Data for the map (level = 1)
USA_1 <- getData("GADM", country= "USA", level=1)
# Names:
USA_df<- broom::tidy(USA_1, region = "NAME_1")
# lapply(USA_df, class)
state_names <- aggregate(cbind(long, lat) ~ id, data = USA_df, FUN = mean)
state_names <- state_names %>%
filter(id != "District of Columbia")
# If you are not happy with the position of the labels on the map you can change de lat and long coordinates.
# In this ocassion I will do it manuallly, so I get used to the position of the US states, but you do not need to do it this way.
# You can use another dataset for it with the long and lat coordinates like this one: https://www.latlong.net/category/states-236-14.html
state_names$lat[1] <- 32.3182
state_names$long[1] <- -85.95222
state_names$lat[2] <- 64.16
state_names$long[3] <- -112.21770
state_names$lat[4] <- 34.91510
state_names$long[4] <- -92.00
state_names$lat[5] <- 37.81605
state_names$lat[6] <- 39.57848
state_names$long[6] <- -106.40
state_names$long[7] <- -72.699997
state_names$lat[7] <- 41.599998
state_names$lat[8] <- 39.20
state_names$long[9] <- -81.30778
state_names$lat[9] <- 28.05528
state_names$lat[10] <- 32.10184
state_names$long[10] <- -82.20
state_names$lat[12] <- 44.00000
state_names$lat[13] <- 39.56886
state_names$long[14] <- -86.126976
state_names$lat[14] <- 40.273502
state_names$long[15] <- -92.60341
state_names$long[16] <- -99.5
state_names$lat[16] <- 38.78386
state_names$long[17] <- -83.97675
state_names$long[18] <- -90.94280
state_names$lat[18] <- 30.36833
state_names$lat[19] <- 46.00
state_names$lat[21] <- 42.50
state_names$long[22] <- -84.12698
state_names$lat[22] <- 43.70
state_names$long[23] <- -94.50
state_names$long[24] <- -90.000000
state_names$lat[24] <- 33.000000
state_names$lat[25] <- 37.81605
state_names$long[25] <- -92.60341
state_names$long[26] <- -108.18085
state_names$lat[26] <- 47.63724
state_names$long[27] <- -99.5
state_names$lat[28] <- 39.57848
state_names$long[28] <- -116.00
state_names$lat[29] <- 43.5
state_names$lat[30] <- 40.00
state_names$long[30] <- -73.00
state_names$lat[31] <- 34.39087
state_names$long[31] <- -106.00
state_names$long[32] <- -76.00
state_names$lat[32] <- 43.09639
state_names$lat[33] <- 35.80655
state_names$long[34] <- -99.5
state_names$lat[34] <- 47.77724
state_names$lat[35] <- 40.56886
state_names$long[35] <- -83.00
state_names$lat[36] <- 36.00000
state_names$lat[37] <- 44.00
state_names$long[37]<- -120.62861
state_names$long[38] <- -77.08343
state_names$long[39] <- -68.5
state_names$lat[39] <- 41.7
state_names$lat[40] <- 34.00
state_names$long[41] <- -99.5
state_names$lat[41] <- 45.1
state_names$lat[42] <- 36.00
state_names$long[42] <- -86.8
state_names$long[43] <- -99.5
state_names$lat[43] <- 32.00184
state_names$lat[44] <- 39.57848
state_names$long[46] <- -78.00
state_names$long[47] <- -119.52861
state_names$long[49] <- -90.00
state_names$long[50] <- -107.290283
# PLOT:
ggplot() +
geom_polygon(data = USA_1,
aes(x = long, y = lat, group = group),
colour = "white",
fill = "tan1",
alpha = 0.6)+
geom_text(data = state_names,
aes(x= long, y=lat, label =id),
size = 1, color = "gray20") +
theme_void() +
coord_fixed(xlim= c(-170,-55),
ylim= c(15,75),
ratio= 1.3) +
labs ( title = "US. States",
caption = "") +
theme(plot.title = element_text(size = 16, hjust = 0.5))
```
