2020_11_10_phone_history.Rmd

---
title: "Mobile vs Landline Adoption"
date: 2020-11-10
output: html_output
---

# TidyTuesday


```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(tidytuesdayR)
library(scales)
theme_set(theme_light())

```

# Load the weekly Data

Dowload the weekly data and make available in the `tt` object.

```{r Load}

tt <- tt_load("2020-11-10")

mobile <- tt$mobile %>%
  rename(subscriptions = mobile_subs) %>%
  mutate(type = "Mobile")

landline <- tt$landline %>%
  rename(subscriptions = landline_subs) %>%
  mutate(type = "Landline")

phones <- bind_rows(mobile, landline) %>%
  rename(country = entity)

phones
```

```{r}
phones %>%
  filter(country == "Sudan") %>%
  ggplot(aes(year, subscriptions, color = type)) +
  geom_line()

summarize_subscriptions <- . %>%
  filter(!is.na(subscriptions)) %>%
  summarize(avg_subscriptions = mean(subscriptions),
            median_subscriptions = median(subscriptions),
            q25 = quantile(subscriptions, .25),
            q75 = quantile(subscriptions, .75))

phones %>%
  filter(!is.na(subscriptions)) %>%
  group_by(year, continent, type) %>%
  summarize_subscriptions() %>%
  ggplot(aes(year,
             avg_subscriptions,
             color = type)) +
  geom_line() +
  geom_ribbon(aes(ymin = q25, ymax = q75), alpha = .25) +
  facet_wrap(~ continent) +
  labs(x = "Year",
       y = "Median subscriptions per person",
       color = "",
       title = "How do mobile and landline adoption differ between continents?",
       subtitle = "Ribbon shows the 25th-75th percentile range")
```

```{r}
library(WDI)

country_incomes <- WDI(indicator = c(gdp_per_capita = "NY.GDP.PCAP.PP.KD",
                                     pop = "SP.POP.TOTL"),
                       start = 2005, end = 2005, extra = TRUE) %>%
  as_tibble() %>%
  select(code = iso3c, income, gdp_per_capita, pop) %>%
  filter(!is.na(income)) %>%
  mutate(income = fct_relevel(income, "Low income", "Lower middle income", "Upper middle income"))

by_year_income <- phones %>%
  inner_join(country_incomes, by = "code") %>%
  group_by(year, income, type) %>%
  summarize_subscriptions()

by_year_income %>%
  ggplot(aes(year,
             median_subscriptions,
             color = type)) +
  geom_line() +
  geom_ribbon(aes(ymin = q25, ymax = q75), alpha = .25) +
  facet_wrap(~ income) +
  labs(x = "Year",
       y = "Median subscriptions per person",
       color = "",
       title = "How do mobile and landline adoption differ between income categories?",
       subtitle = "Ribbon shows the 25th-75th percentile range")

by_year_income %>%
  mutate(income = fct_rev(income)) %>%
  ggplot(aes(year,
             median_subscriptions,
             color = income)) +
  geom_line() +
  facet_wrap(~ type, ncol = 1) +
  labs(y = "Median subscriptions / 100 people", color = "Income")
```

```{r}
mobile %>%
  inner_join(landline %>% select(code, year, total_pop, gdp_per_cap),
             by = c("code", "year"),
             suffix = c("_mobile", "_landline")) %>%
  ggplot(aes(total_pop_mobile, total_pop_landline)) +
  geom_point() +
  geom_abline(color = "red")
```

What are ways we could summarize stats for each country?

```{r}
country_sizes <- phones %>%
  group_by(country) %>%
  summarize(avg_population = mean(total_pop, na.rm = TRUE)) %>%
  arrange(desc(avg_population))

phones %>%
  semi_join(country_sizes %>% top_n(50, avg_population), by = "country") %>%
  ggplot(aes(year,
             subscriptions,
             color = type,
             group = interaction(type, country))) +
  geom_line() +
  facet_wrap(~ continent) +
  geom_hline(yintercept = 50, lty = 2) +
  labs(color = "")

countries_summarized <- phones %>%
  filter(!is.na(subscriptions)) %>%
  select(-total_pop, gdp_per_cap) %>%
  pivot_wider(names_from = type, values_from = subscriptions) %>%
  group_by(continent, country, code) %>%
  summarize(year_past_50_mobile = na_if(min(year[Mobile >= 50]), Inf),
            peak_landline = max(Landline, na.rm = TRUE),
            peak_mobile = max(Mobile),
            n_mobile = sum(!is.na(Mobile))) %>%
  ungroup() %>%
  inner_join(country_incomes, by = "code") %>%
  filter(n_mobile >= 25) %>%
  arrange(desc(year_past_50_mobile))

countries_summarized %>%
  ggplot(aes(gdp_per_capita,
             year_past_50_mobile,
             color = continent,
             size = pop)) +
  geom_point() +
  scale_x_log10(labels = dollar) +
  labs(x = "GDP per capita (in 2005)",
       y = "Year mobile subscriptions passed 50/100 people")

countries_summarized %>%
  filter(!is.na(gdp_per_capita)) %>%
  ggplot(aes(gdp_per_capita,
             peak_landline,
             color = continent,
             size = pop)) +
  geom_point() +
  scale_x_log10(labels = dollar) +
  labs(x = "GDP per capita (in 2005)",
       y = "Peak landline") +
  facet_wrap(~ continent) +
  theme(legend.position = "none")

countries_summarized %>%
  filter(continent == "Africa") %>%
  arrange(desc(gdp_per_capita)) %>%
  View()
```

```{r}
library(fuzzyjoin)

world_map_mobile <- map_data("world") %>%
  as_tibble() %>%
  regex_left_join(maps::iso3166, c(region = "mapname")) %>%
  left_join(mobile, by = c(a3 = "code"))

library(gganimate)

world_map_mobile %>%
  ggplot(aes(long, lat, group = group, fill = subscriptions)) +
  geom_polygon() +
  transition_manual(year) +
  coord_fixed(1.5) +
  scale_fill_gradient2(low = "blue", high = "red",
                       midpoint = 30) +
  ggthemes::theme_map() +
  labs(fill = "# of subscriptions")
```




```{r}
phones %>%
  arrange(desc(subscriptions)) %>%
  View()
```



# Readme

Take a look at the readme for the weekly data to get insight on the dataset.
This includes a data dictionary, source, and a link to an article on the data.

```{r Readme, eval = interactive()}

tt

```


# Glimpse Data

Take an initial look at the format of the data available.

```{r Glimpse}

tt %>% 
  map(glimpse)

```

# Wrangle

Explore the data and process it into a nice format for plotting! Access each dataset by name by using a dollarsign after the `tt` object and then the name of the data set.

```{r Wrangle}



```


# Visualize

Using your processed dataset, create your unique visualization.

```{r Visualize}


  
```

# Save Image

Save your image for sharing. Be sure to use the `#TidyTuesday` hashtag in your post on twitter! 

```{r}

# This will save your most recent plot
ggsave(
  filename = "My TidyTuesday Plot.png",
  device = "png")

```