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2019-09-17

readme.md

National Park Visits

This week's data is from dataisplural/data.world.

There are some additional datasets that may be interesting in relation to this data - namely US population and gas prices over time.

There's also some shapefiles you might be able to use with the sf package.

Lastly, a fivethirtyeight article covered this dataset with lots of great visualizations to copy or build off of.

Get the data!

park_visits <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-09-17/national_parks.csv")
state_pop <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-09-17/state_pop.csv")
gas_price <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-09-17/gas_price.csv")

Data Dictionary

national_parks.csv

variable class description
year_raw integer Year of record
gnis_id character ID for shapefile and long-lat lookup
geometry character Geometric shape for shapefile
metadata character URL to metadata about the park
number_of_records double Number of records
parkname character Full park name
region character US Region where park is located
state character State abbreviation
unit_code character Park code abbreviation
unit_name character Park Unit name
unit_type character Park unit type
visitors double Number of visitors

state_pop.csv

variable class description
year integer Jan 1st of year
state character State abbreviation
pop double Population

gas_price.csv

variable class description
year double Year (Jan 1st)
gas_current double Gas price in that year (dollars/gallon)
gas_constant double Gas price (constant 2015 dollars/gallon)

locations.csv

variable class description
lon double longitude
lat double latitude
gnis_id character ID for shapefile and long-lat lookup

Cleaning Script


library(tidyverse)
library(rvest)

df_raw <- read_csv(here::here("2019/2019-09-17/All National Parks Visitation 1904-2016.csv")) 

df <- df_raw %>% 
  janitor::clean_names() %>%
  mutate(date = lubridate::mdy_hms(year)) %>% 
  select(date, gnis_id, geometry:year_raw)

df %>% 
  write_csv(here::here("2019/2019-09-17/national_parks.csv"))


# Get pop data

url <- "https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_historical_population"

raw_html <- url %>% 
  read_html() %>% 
  html_table()

pop_df <- raw_html %>% 
  chuck(5) %>% 
  gather(key = "state", value = "pop", AL:DC) %>% 
  rename("year" = 1) %>% 
  mutate(pop = str_remove_all(pop, ","),
         pop = as.double(pop))

pop_df %>% 
  write_csv(here::here("2019/2019-09-17", "state_pop.csv"))

# Get gas prices

url2 <- "https://www.energy.gov/eere/vehicles/fact-915-march-7-2016-average-historical-annual-gasoline-pump-price-1929-2015"

raw_gas <- url2 %>% 
  read_html() %>% 
  html_table()

gas <- raw_gas %>% 
  chuck(1) %>% 
  set_names(nm = c("year", "gas_current", "gas_constant")) %>%   
  as_tibble() %>% 
  filter(!str_detect(year, "Source")) %>% 
  mutate(year = as.double(year),
         gas_current = as.double(gas_current),
         gas_constant = as.double(gas_constant))