Historical Phone Usage

The data this week comes from OurWorldInData.org.

Hannah Ritchie (2017) - “Technology Adoption”. Published online at OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/technology-adoption’ [Online Resource]

Pew research also has a nice article about the adoption of mobile phones by country.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2020-11-10')
tuesdata <- tidytuesdayR::tt_load(2020, week = 46)

mobile <- tuesdata$mobile

# Or read in the data manually

mobile <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-11-10/mobile.csv')
landline <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-11-10/landline.csv')

Data Dictionary

mobile.csv

variable	class	description
entity	character	Country
code	character	Country code
year	double	Year
total_pop	double	Gapminder total population
gdp_per_cap	double	GDP per capita, PPP (constant 2011 international $)
mobile_subs	double	Fixed mobile subscriptions (per 100 people)
continent	character	Continent

landline.csv

variable	class	description
entity	character	Country
code	character	Country code
year	double	Year
total_pop	double	Gapminder total population
gdp_per_cap	double	GDP per capita, PPP (constant 2011 international $)
landline_subs	double	Fixed telephone subscriptions (per 100 people)
continent	character	Continent

Cleaning Script

library(tidyverse)
library(countrycode)
library(janitor)

raw_mobile <- read_csv("2020/2020-11-10/mobile-phone-subscriptions-vs-gdp-per-capita.csv")

raw_landline <- read_csv("2020/2020-11-10/fixed-landline-telephone-subscriptions-vs-gdp-per-capita.csv")

mobile_df <- raw_mobile %>% 
  janitor::clean_names() %>% 
  rename(
    total_pop = 4,
    "gdp_per_cap" = 6,
    "mobile_subs" = 7
  ) %>% 
  filter(year >= 1990) %>% 
  select(-continent) %>% 
  
  mutate(continent = countrycode::countrycode(
    entity,
    origin = "country.name",
    destination = "continent"
  )) %>% 
  filter(!is.na(continent))

landline_df <- raw_landline %>% 
  janitor::clean_names() %>% 
  rename(
    total_pop = 4,
    "gdp_per_cap" = 6,
    "landline_subs" = 7
  ) %>% 
  filter(year >= 1990) %>% 
  select(-continent) %>% 
  mutate(continent = countrycode::countrycode(
    entity,
    origin = "country.name",
    destination = "continent"
  )) %>% 
  filter(!is.na(continent))

mobile_df %>% 
  write_csv("2020/2020-11-10/mobile.csv")

landline_df %>% 
  write_csv("2020/2020-11-10/landline.csv")