74 lines
2.2 KiB
R
74 lines
2.2 KiB
R
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# This project is to try to figure out similarity among
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# Grand Forks/East Grand Forks and US cities of similar size
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# in similar climate. The results will then be used to reseearch
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# what those cities are doing for bike infra and how it is
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# working out for them.
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# TODO make this Rmd file
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library(tidycensus)
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library(tidyverse)
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library(ggplot2)
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library(tigris)
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library(viridis)
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library(ini)
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library(sf)
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# Load the census API key, the ini file should be of format
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# [keys]
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# key = your_key_here
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census_api_key(read.ini('census_api_key.ini')$keys$key)
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# Get a list of variables
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vars <- load_variables("2018", "acs5")
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# This includes a broader area
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# us_metro <- get_acs(geography = "metropolitan statistical area/micropolitan statistical area",
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# variables = "B01001_001",
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# geometry = T)
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# This is just urban
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# Code is for total population, all ages
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# https://www.census.gov/data/developers/data-sets/acs-1year/notes-on-acs-api-variable-formats.html
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us_urban <- get_acs(geography = "urban area",
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variables = "B01001_001")
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# Geometry is not in tidycensus yet, so we have to get it this way
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# Download it once and save locally
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#urban_areas_geom <- urban_areas(class = "sf")
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#save(urban_areas_geom, file = "data/urban_areas_tigris.RData")
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# Load saved variable
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load("data/urban_areas_tigris.RData")
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us_urban_joined <- left_join(urban_areas_geom,
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us_urban,
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by = c("GEOID10" = "GEOID"))
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# Define range of populations to include
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gfk_pop <- us_urban_joined %>%
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filter(NAME == "Grand Forks, ND--MN Urbanized Area (2010)") %>%
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mutate(high = estimate + (estimate * 0.1),
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low = estimate - (estimate * 0.1))
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# Clean data
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us_urban_clean <- us_urban_joined %>%
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select(NAME, variable, estimate, moe) %>%
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st_centroid() %>%
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filter(estimate < gfk_pop[['high']],
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estimate > gfk_pop[['low']])
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plot(us_urban_clean['estimate'])
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ggplot(us_urban_clean) +
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geom_sf(aes(color = estimate)) +
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scale_color_viridis() +
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theme_minimal()
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# TODO bring in climate data
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