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2020_11_10_tidy_tuesday.Rmd
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---
title: 'Tidy Tuesday 11/10'
author: "Lisa Lendway"
output:
html_document:
df_print: paged
code_download: true
theme: cerulean
---
```{r setup, include=FALSE}
# You may want to comment this out at first so you see important messages and warnings
knitr::opts_chunk$set(echo = TRUE, error=TRUE, message=FALSE, warning=FALSE)
```
```{r libraries}
library(tidyverse) # for graphing and data cleaning
library(lubridate) # for date manipulation
library(ggthemes) # for even more plotting themes
library(ggtext) # for coloring text in labels
library(naniar) # for exploring missing values
library(gghighlight) # for highlighting certain values
library(maps) # for map data
library(ggthemes) # for themes, including theme_map()
library(gganimate) # for animation
theme_set(theme_minimal()) # My favorite ggplot() theme :)
```
```{r}
# Read in the data for the week
mobile <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-10/mobile.csv')
landline <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-10/landline.csv')
```
This week's data is all about phones!
<center>
{width=200} {width=200}
</center>
Read more about it, including definitions of variables, [here](https://github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-11-10).
### Code I worked on during the 30 minute screencast
```{r}
landline %>%
add_n_miss() %>%
arrange(desc(n_miss_all))
```
```{r}
landline %>%
drop_na(landline_subs) %>%
group_by(entity) %>%
fill(total_pop, gdp_per_cap, .direction = "up") %>%
ggplot(aes(x = year,
y = landline_subs,
group = entity,
color = continent)) +
geom_line() +
facet_wrap(vars(continent)) +
gghighlight(max(landline_subs>100)) +
theme(legend.position = "none")
```
# animated graph over time
Added some details after 30 minute video.
```{r, eval=FALSE}
world_map <- map_data("world")
landline %>%
drop_na(landline_subs) %>%
group_by(entity) %>%
fill(total_pop, gdp_per_cap, .direction = "up") %>%
ggplot() +
geom_map(map = world_map,
aes(map_id = entity,
fill = landline_subs,
group = year)) +
#This assures the map looks decently nice:
expand_limits(x = world_map$long, y = world_map$lat) +
theme_map() +
theme(legend.background = element_blank(),
plot.background = element_rect("gray70")) +
transition_states(year) +
labs(title = "Landlines subscriptions per 100 people",
subtitle = "Year: {closest_state}",
caption = "Data from Ourworldindata.org, viz by @lisalendway")
anim_save("landline_over_time.gif", path = "images/")
```
In the previous plot, there are missing countries. This code investigates which countries are missing.
```{r}
world_map %>%
distinct(region) %>%
anti_join(landline,
by = c("region"="entity"))
```
# code after 30 minutes
create similar animated map for mobile
```{r, eval=FALSE}
world_map <- map_data("world")
mobile %>%
drop_na(mobile_subs) %>%
group_by(entity) %>%
fill(total_pop, gdp_per_cap, .direction = "up") %>%
ggplot() +
geom_map(map = world_map,
aes(map_id = entity,
fill = mobile_subs,
group = year)) +
#This assures the map looks decently nice:
expand_limits(x = world_map$long, y = world_map$lat) +
theme_map() +
theme(legend.background = element_blank(),
plot.background = element_rect("#F8F3ED")) +
transition_states(year) +
labs(title = "Mobile subscriptions per 100 people",
subtitle = "Year: {closest_state}",
caption = "Data from Ourworldindata.org, viz by @lisalendway")
anim_save("mobile_over_time.gif", path = "images/")
```
Using `geom_polygon()` instead of `geom_map()` - this kept crashing my computer and I don't have time to debug.
```{r, eval=FALSE}
world_map <- map_data("world")
landline %>%
drop_na(landline_subs) %>%
group_by(entity) %>%
fill(total_pop, gdp_per_cap, .direction = "up") %>%
inner_join(world_map,
by = c("entity"="region")) %>%
# filter(year == 2010) %>%
ggplot() +
geom_polygon(aes(x = long,
y = lat,
group = group,
fill = landline_subs)) +
#This assures the map looks decently nice:
expand_limits(x = world_map$long,
y = world_map$lat) +
theme_map() +
theme(legend.background = element_blank(),
plot.background = element_rect("gray70")) +
transition_time(year) +
labs(title = "Landlines subscriptions per 100 people",
subtitle = "Year: {frame_time}",
caption = "Data from Ourworldindata.org, viz by @lisalendway")
```