geocompr: Geocomputation with R: a book

# devtools::install_github('walkerke/tigris')
# devtools::install_github('walkerke/tidycensus')
# devtools::install_github('ateucher/rmapshaper', ref = 'sf')
library(tigris)
library(tidycensus)
library(sf)
library(tidyverse)
library(units)
library(rmapshaper)
options(tigris_class = "sf")

## census data
census_api_key("YOUR API KEY") # http://api.census.gov/data/key_signup.html

# v15 = load_variables(2015, "acs5", cache = TRUE)
# View(v15)
# v15 %>% select(concept) %>% unique() %>% View
# B01003_001E - total pop

total_pop_10 = get_acs(geography = "state", variables = "B01003_001E", endyear = 2010) %>% 
  select(GEOID, total_pop_10 = estimate)
total_pop_15 = get_acs(geography = "state", variables = "B01003_001E", endyear = 2015) %>% 
  select(GEOID, total_pop_15 = estimate)

## groups - Census Bureau-designated regions
## spatial data 
us_states = states() 

us_states49 = us_states %>% 
  filter(DIVISION != 0) %>% 
  filter(NAME != "Alaska", NAME != "Hawaii") %>% 
  ms_simplify(., keep=0.005) %>% 
  select(GEOID, NAME, REGION) %>%
  mutate(REGION = factor(REGION, labels = c("Norteast", "Midwest", "South", "West"))) %>% 
  mutate(AREA = units::set_units(st_area(.), km^2)) %>% 
  left_join(., total_pop_10, by = "GEOID") %>% 
  left_join(., total_pop_15, by = "GEOID")
  
# plot(us_states49["REGION"])

us_states = us_states49
save(us_states, file = "data/us_states.rda")

## non-spatial data
# B06011_001E - Median income in the past 12 months --!!Total:
median_income_10 = get_acs(geography = "state", variables = "B06011_001E", endyear = 2010) %>% 
  select(NAME, median_income_10 = estimate)
median_income_15 = get_acs(geography = "state", variables = "B06011_001E", endyear = 2015) %>% 
  select(NAME, median_income_15 = estimate)
# B17001_002E - Income in the past 12 months below poverty level:
poverty_level_10 = get_acs(geography = "state", variables = "B17001_002E", endyear = 2010) %>% 
  select(NAME, poverty_level_10 = estimate)
poverty_level_15 = get_acs(geography = "state", variables = "B17001_002E", endyear = 2015) %>% 
  select(NAME, poverty_level_15 = estimate)

us_state_eco = median_income_10 %>% 
  filter(NAME != "Puerto Rico") %>% 
  left_join(median_income_15, by = "NAME") %>% 
  left_join(poverty_level_10, by = "NAME") %>% 
  left_join(poverty_level_15, by = "NAME") %>% 
  rename(state = NAME)

us_states_df = us_state_eco
save(us_states_df, file = "data/us_states_df.rda")