Two new data packages

worldcities

worldcities is a repacking of a dataset provided by SimpleMaps (https://simplemaps.com/data). It contains a dataset of 40,000 cities and associated metadata such as coordinates, country, and population.

You can install it from my github account

remotes::install_github("condwanaland/worldcities")

As with any package, we can load it with library(). At the same time we’ll also load some extra packages for using later

library(worldcities)
library(dplyr)
library(ggplot2)
library(rnaturalearth)
library(rnaturalearthdata)
library(plotly)

You can then access its data by calling data().

data("worldcities")
head(worldcities)

##       city     lat      lng     country iso2 iso3  admin_name capital
## 1    Tokyo 35.6897 139.6922       Japan   JP  JPN       Tokyo primary
## 2  Jakarta -6.2146 106.8451   Indonesia   ID  IDN     Jakarta primary
## 3    Delhi 28.6600  77.2300       India   IN  IND       Delhi   admin
## 4   Mumbai 18.9667  72.8333       India   IN  IND Maharashtra   admin
## 5   Manila 14.6000 120.9833 Philippines   PH  PHL      Manila primary
## 6 Shanghai 31.1667 121.4667       China   CN  CHN    Shanghai   admin
##   population         id
## 1   37977000 1392685764
## 2   34540000 1360771077
## 3   29617000 1356872604
## 4   23355000 1356226629
## 5   23088000 1608618140
## 6   22120000 1156073548

Lets plot the ten most populous cities.

# This grabs a map of the world from the `rnaturaldata` packages
world <- ne_countries(scale = "medium", returnclass = "sf")

top <- worldcities %>% 
  slice_max(population, n = 10)
  
ggplot(data = world) +
  geom_sf() +
  geom_point(data = top,
             aes(x = lng, y = lat, colour = city), size = 2) +
  coord_sf()

phonewar

The second data package I’ve released recently is titled phonewar.

phonewar provides a dataset containing people’s iPhone vs Android preferences based on a survey done by tech magazine ‘The Hustle’ (https://thehustle.co/home/). The survey asked people to fill out:

1. Do they use Android or iPhone
2. Their reason for their choice
3. Whether they have considered switching
4. Whether they have less respect for people using the other type of phone
5. Which city they come from

I’ve enriched this data by cleaning up the ‘city’ responses and adding in extra information about the city (i.e., country and population). For those wondering, yes, it uses worldcities to get this extra data. Cleaning up the city names was a challenging and interesting problem which I’ll write about in a future blog post.

phonewar can also be installed from my github

remotes::install_github("condwanaland/phonewar")

Then you can load the phonedata dataset

library(phonewar)
data("phonedata")
head(phonedata)

## # A tibble: 6 × 11
## # Groups:   key [6]
##     key phone_type reason  considered_swit… less_respect_fo… original_city city 
##   <int> <chr>      <chr>   <chr>            <chr>            <chr>         <chr>
## 1     1 Android    Cost    No               No               spokane       spok…
## 2     2 iPhone     Ecosys… Yes              No               marbella spa… marb…
## 3     3 iPhone     Ease o… Yes              No               cork          cork 
## 4     4 Android    I star… No               No               ottawa        otta…
## 5     5 iPhone     Too ma… No               No               manchester, … manc…
## 6     6 iPhone     Ease o… No               No               bangkok       bang…
## # … with 4 more variables: lat <dbl>, lng <dbl>, country <chr>,
## #   population <dbl>

Lets create a plot of their summary statistics

library(viridis)
library(patchwork)
phone_sum <- phonedata %>% 
  group_by(phone_type) %>% 
  summarise(n = n()) %>% 
  mutate(pct = n/sum(n))

g1 <- ggplot(phone_sum, aes(y = phone_type, x = pct, fill = phone_type)) +
  geom_col() +
  labs(caption = "A: Ownership percentage")

cons_switch <- phonedata %>% 
  select(phone_type, considered_switching) %>% 
  group_by(phone_type, considered_switching) %>% 
  summarise(n = n()) %>% 
  group_by(phone_type) %>% 
  mutate(pct = prop.table(n) * 100) %>% 
  filter(considered_switching == "Yes")


g2 <- ggplot(cons_switch, aes(y = phone_type, x = pct, fill = phone_type)) +
  geom_col() +
  labs(caption = "B: % of owners who have considered \n switching to the other phone type")


respect <- phonedata %>% 
  select(phone_type, less_respect_for_other) %>% 
  group_by(phone_type, less_respect_for_other) %>% 
  summarise(n = n()) %>% 
  group_by(phone_type) %>% 
  mutate(pct = prop.table(n) * 100) %>% 
  filter(less_respect_for_other == "Yes")

g3 <- ggplot(respect, aes(y = phone_type, x = pct, fill = phone_type)) +
  geom_col() +
  labs(caption = "C: % of owners who have less respect \n for people with the other phone type") +
  theme(axis.title.y=element_blank())

g1 / (g2 | g3) +
  plot_layout(guides = "collect") +
  plot_annotation(tag_levels = "A") &
  geom_text(aes(label = paste0(round(pct, 3), "%"), hjust = 1.2), color = "grey") &
  scale_fill_viridis(discrete = TRUE, option = "turbo") &
  theme(
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank()
        )

So we can see there is a slight preference for iPhones, and that iPhone owners are more likely to consider switching. iPhone owners are also a fair bit more likely to have less respect for Android owners.

Do bear in mind the source of this data. It comes from a tech magazine and therefore you could assume its reader base has active opinions on this sort of thing. It probably doesn’t reflect the ‘average’ user of a phone.

I hope you have some fun with these new packages - I certainly had fun making them!