This document gives a few suggestions to analyse a dataset composed by a list of GPS coordinates
It considers the geographic position of about 200k tweets containing the hashtags #surf, #windsurf or #kitesurf.
This dataset has been built harvesting twitter every day during about 300 days. It is fully available in this Github repository. Basically it looks like the table beside.

# Libraries
library(tidyverse)
library(viridis)
library(hrbrthemes)
library(kableExtra)
options(knitr.table.format = "html")
library(mapdata)

# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/17_ListGPSCoordinates.csv", sep=",", header=T)

# show data
data %>% select(homelat, homelon) %>% head(3) %>% kable() %>%
  kable_styling(bootstrap_options = "striped", full_width = F)

homelat	homelon
18.28548	-70.33012
39.10312	-84.51202
19.41095	-99.27186

Showing a few dots

If your dataset is composed by a few data point only, you can just display them on a map. If you have specific information to display concerning these positions, use an interactive: more information are available when you click data points.

A dot density map

A dot density map is used when the sample size of your dataset is high.

# Get the world polygon
world <- map_data("world")

p <- data %>%
  #head(1000) %>%
  ggplot( aes(x=homelon, y=homelat)) +
    geom_polygon(data = world, aes(x=long, y = lat, group = group), fill="grey", alpha=0.1) +
    geom_point(size=0.8, color="#69b3a2", alpha=0.5) +
    coord_equal() +
    theme_void() +
    theme(
        panel.spacing=unit(c(0,0,0,0), "null"),
        plot.margin=grid::unit(c(0,0,0,0), "cm"),
    ) +
    ggplot2::annotate("text", x = -150, y = -45, hjust = 0, size = 11, label = paste("Where surfers live."), color = "Black") +
    ggplot2::annotate("text", x = -150, y = -51, hjust = 0, size = 8, label = paste("data-to-viz.com | 200,000 #surf tweets recovered"), color = "black", alpha = 0.5) +
    xlim(-180,180) +
    ylim(-60,80) +
    scale_x_continuous(expand = c(0.006, 0.006)) +
    coord_equal()

ggsave(p, file="IMG/Surfer_position.png", width = 36, height = 15.22, units = "in", dpi = 90)

Dotmaps give a good idea about where samples are distributed. However, once dots start to overlap, it gets impossible to distinguish how many of them are displayed on a certain zone. That’s where binning becomes an interesting option.

Hexbin

To create a hexbin map, the territory is divided in many hexagons and the number of sample per hexagon is counted and represented by a color.

data %>%
  filter(homecontinent=='Europe') %>%
  ggplot( aes(x=homelon, y=homelat)) +
    geom_hex(bins=59) +
    ggplot2::annotate("text", x = -27, y = 72, label="Where people tweet about #Surf", colour = "black", size=5, alpha=1, hjust=0) +
    ggplot2::annotate("segment", x = -27, xend = 10, y = 70, yend = 70, colour = "black", size=0.2, alpha=1) +
    theme_void() +
    xlim(-30, 70) +
    ylim(24, 72) +
    scale_fill_viridis(
      trans = "log",
      breaks = c(1,7,54,403,3000),
      name="Tweet # recorded in 8 months",
      guide = guide_legend( keyheight = unit(2.5, units = "mm"), keywidth=unit(10, units = "mm"), label.position = "bottom", title.position = 'top', nrow=1)
    )  +
    ggtitle( "" ) +
    theme(
      legend.position = c(0.8, 0.09),
      legend.title=element_text(color="black", size=8),
      text = element_text(color = "#22211d"),
      plot.background = element_rect(fill = "#f5f5f2", color = NA),
      panel.background = element_rect(fill = "#f5f5f2", color = NA),
      legend.background = element_rect(fill = "#f5f5f2", color = NA),
      plot.title = element_text(size= 13, hjust=0.1, color = "#4e4d47", margin = margin(b = -0.1, t = 0.4, l = 2, unit = "cm")),
    )

Note that this is very close from an 2d histogram map. Basically, it splits the space into a set of squares instead of hexagons, and uses the same process:

# Make the hexbin map with the geom_hex function
ggplot(data, aes(x=homelon, y=homelat)) +
    geom_polygon(data = world, aes(x=long, y = lat, group = group), fill="grey", alpha=0.3) +
    geom_bin2d(bins=100) +
    ggplot2::annotate("text", x = 175, y = 80, label="Where people tweet about #Surf", colour = "black", size=4, alpha=1, hjust=1) +
    ggplot2::annotate("segment", x = 100, xend = 175, y = 73, yend = 73, colour = "black", size=0.2, alpha=1) +
    theme_void() +
    ylim(-70, 80) +
    scale_fill_viridis(
      trans = "log",
      breaks = c(1,7,54,403,3000),
      name="Tweet # recorded in 8 months",
      guide = guide_legend( keyheight = unit(2.5, units = "mm"), keywidth=unit(10, units = "mm"), label.position = "bottom", title.position = 'top', nrow=1)
    )  +
    ggtitle( "" ) +
    theme(
      legend.position = c(0.8, 0.09),
      legend.title=element_text(color="black", size=8),
      text = element_text(color = "#22211d"),
      plot.background = element_rect(fill = "#f5f5f2", color = NA),
      panel.background = element_rect(fill = "#f5f5f2", color = NA),
      legend.background = element_rect(fill = "#f5f5f2", color = NA),
      plot.title = element_text(size= 13, hjust=0.1, color = "#4e4d47", margin = margin(b = -0.1, t = 0.4, l = 2, unit = "cm")),
    )

Choropleth

It is also possible to divide your territory (the world here) in regions other than square or hexagones. This will result in a chloropleth map. Of course, you need the information of the exact shape of your regions.

Going further

You can learn more about each type of graphic presented in this story in the dedicated sections. Click the icon below:

Showing a few dots

A dot density map

Hexbin

Choropleth

Going further

Dataviz decision tree