tacookson

### Load packages -------------------------------------------------------------
library(tidyverse) # General-purpose cleaning
library(janitor) # For the clean_names() function

### Import data ---------------------------------------------------------------
hbcu_all <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-02/hbcu_all.csv') %>%
  # clean_names() converts field names to snake_case
  clean_names()

tacookson

### Load libraries
library(tidyverse)

### Load data
hike_data_raw <- read_rds(url('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-24/hike_data.rds'))

### Clean hike_data
hike_data <- hike_data_raw %>%
  # De-duplicate Cap Sante Park
  distinct(name, location, .keep_all = TRUE) %>%

tacookson

### Load libraries
library(dplyr)      # For data manipulation
library(readr)      # For reading CSV and parsing numbers
library(ggplot2)    # For graphing
library(ggthemes)   # For ggplot2 theme_map()
library(gganimate)  # For animation

### Import data
### https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-10-27/readme.md
wind_turbine <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-27/wind-turbine.csv')

tacookson

library(ggplot2) # For graphing
library(mapproj) # For the polar orthographic map projection
library(ggthemes) # For theme_map()

# Get geospatial data for Antarctica only
antarctica <- map_data("world", region = "Antarctica")

ggplot(antarctica, aes(long, lat, group = group)) +
  geom_polygon(fill = "#506B8E") +
  # This is where the magic happens