Here at Sharp Sight, we make a lot of maps.

There are a few reasons for this.

First, good maps are typically ‘information dense.’ You can get a lot of information at a glance from a good map. They are good visualization tools for finding and communicating insights.

Second, it’s extremely easy to get data that you can use to make a map. From a variety of sources, you’ll find data about cities, states, counties, and countries. If you know how to retrieve this data and wrangle it into shape, it will be easy to find data that you can use to make a map.

Finally, map making is just good practice. To create a map like the one we’re about to make, you’ll typically need to use a variety of data wrangling and data visualization tools. Maps make for excellent practice for intermediate data scientists who have already mastered some of the basics.

With that in mind, this week we’ll make a map of “world cities.” This set of cities has been identified by the Globalization and World Cities (GaWC) Research Network as being highly connected and influential in the world economy.

We’re going to initially create a very basic map, but we’ll also create a small multiple version of the map (broken out by GaWC ranking).

Let’s get started.

First, we’ll load the packages that we’ll need.

#==============
# LOAD PACKAGES
#==============

library(tidyverse)
library(ggmap)
library(forcats)

Next, we’ll input the cities by hard coding them as data frames. To be clear, there is more than one way to do this (e.g., we could scrape the data), but there isn’t that much data here, so doing this manually is acceptable.

#===================
# INPUT ALPHA CITIES
#===================

df_alpha_plus_plus <- tibble(city = c('London','New York'))
df_alpha_plus <- tibble(city = c('Singapore', 'Hong Kong', 'Paris', 'Beijing'
                                 ,'Tokyo', 'Dubai', 'Shanghai'))
df_alpha <- tibble(city = c('Sydney', 'São Paulo', 'Milan', 'Chicago'
                            ,'Mexico City', 'Mumbai', 'Moscow', 'Frankfurt'
                            ,'Madrid', 'Warsaw', 'Johannesburg', 'Toronto'
                            ,'Seoul', 'Istanbul', 'Kuala Lumpur', 'Jakarta'
                            ,'Amsterdam', 'Brussels', 'Los Angeles'))
df_alpha_minus <- tibble(city = c('Dublin', 'Melbourne', 'Washington', 'New Delhi'
                                  ,'Bangkok', 'Zurich', 'Vienna', 'Taipei'
                                  ,'Buenos Aires', 'Stockholm', 'San Francisco'
                                  ,'Guangzhou', 'Manila', 'Bogotá', 'Miami', 'Luxembourg'
                                  ,'Riyadh', 'Santiago', 'Barcelona', 'Tel Aviv', 'Lisbon'))

Now, we’ll create a new variable called < inline_code>rating. This will contain the global city rating.

Notice that this is a very straightforward use of < inline_code>dplyr::mutate(), one of the tidyverse functions you should definitely master.

#=======================
# ADD GLOBAL CITY RATING
#=======================

df_alpha_plus_plus <- df_alpha_plus_plus %>% mutate(rating = 'alpha++')
df_alpha_plus <- df_alpha_plus %>% mutate(rating = 'alpha+')
df_alpha <- df_alpha %>% mutate(rating = 'alpha')
df_alpha_minus <- df_alpha_minus %>% mutate(rating = 'alpha-')

Next, we’ll combine the different data frames into one using < inline_code>rbind().

#======================================
# COMBINE DATAFRAMES INTO ONE DATAFRAME
#======================================

alpha_cities <- rbind(df_alpha_plus_plus
      ,df_alpha_plus
      ,df_alpha
      ,df_alpha_minus
      )

Now that the data are combined into a single data frame, we’ll get the longitude and latitude using < inline_code>geocode().

#========
# GEOCODE
#========

latlong <- geocode(alpha_cities$city)



#========
# INSPECT
#========

alpha_cities
latlong

Once we have the longitude and latitude data, we need to combine it with the original data in the < inline_code>alpha_cities data frame. To do this, we will use < inline_code>cbind().

#============================
# BIND LAT/LONG TO CITY NAMES
#============================

alpha_cities <- cbind(alpha_cities, latlong) %>% rename(long = lon)

alpha_cities

#names(alpha_cities)

Now we have the data that we need, but we’ll need to clean things up a little.

In the visualization we’ll make, we will need to use the faceting technique from < inline_code>ggplot2. When we do this, we’ll facet on the < inline_code>rating variable, but we will need the levels of that variable to be ordered properly (otherwise the facets will be out of order).

To reorder the factor levels of < inline_code>rating, we will use < inline_code>fct_relevel().

#================================================
# REORDER LEVELS OF GLOBAL CITY RATING 
# - the global city ratings should be ordered
#   i.e., alpha++, then alpha+ ....
# - to do this, we'll use forecats::fct_relevel()
#================================================

alpha_cities <- mutate(alpha_cities, rating = fct_relevel(rating, 'alpha++','alpha+','alpha','alpha-'))

levels(alpha_cities$rating)

Because we will be building a map, we’ll need to retrive a map of the world. We can get a world map by using < inline_code>map_data(“world”).

#==============
# GET WORLD MAP
#==============

map_world <- map_data("world")

Ok. We basically have everything we need. Now we will make a simple first draft.

#================
# FIRST DRAFT MAP
#================

ggplot() +
  geom_polygon(data = map_world, aes(x = long, y = lat, group = group)) +
  geom_point(data = alpha_cities, aes(x = long, y = lat), color = 'red')

… and now we’ll use the faceting technique to break out our plot using the < inline_code>rating variable.

#==========================
# CREATE SMALL MULTIPLE MAP
#==========================

ggplot() +
  geom_polygon(data = map_world, aes(x = long, y = lat, group = group)) +
  geom_point(data = alpha_cities, aes(x = long, y = lat), color = 'red') +
  #facet_grid(. ~ rating)
  #facet_grid(rating ~ .)
  facet_wrap(~ rating)

Once again, this is a good example of an intermediate-level project that you could do to practice your data wrangling and data visualization skills.

Having said that, before you attempt to do something like this yourself, I highly recommend that you first master the individual tools that we used here (i.e., the tools from < inline_code>ggplot2, < inline_code>dplyr, and the tidyverse).

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