Small changes.

bin/find-small-winter-cities.R

@@ -1,73 +1,73 @@
-
+
-# This project is to try to figure out similarity among
+# This project is to try to figure out similarity among
-# Grand Forks/East Grand Forks and US cities of similar size
+# Grand Forks/East Grand Forks and US cities of similar size
-# in similar climate. The results will then be used to reseearch
+# in similar climate. The results will then be used to reseearch
-# what those cities are doing for bike infra and how it is
+# what those cities are doing for bike infra and how it is
-# working out for them.
+# working out for them.
-
+
-# TODO make this Rmd file
+# TODO make this Rmd file
-
+
-library(tidycensus)
+library(tidycensus)
-library(tidyverse)
+library(tidyverse)
-library(ggplot2)
+library(ggplot2)
-library(tigris)
+library(tigris)
-library(viridis)
+library(viridis)
-library(ini)
+library(ini)
-library(sf)
+library(sf)
-# Load the census API key, the ini file should be of format
+# Load the census API key, the ini file should be of format
-# [keys]
+# [keys]
-# key = your_key_here
+# key = your_key_here
-census_api_key(read.ini('census_api_key.ini')$keys$key)
+census_api_key(read.ini('census_api_key.ini')$keys$key)
-
+
-# Get a list of variables
+# Get a list of variables
-vars <- load_variables("2018", "acs5")
+vars <- load_variables("2018", "acs5")
-
+
-# This includes a broader area
+# This includes a broader area
-# us_metro <- get_acs(geography = "metropolitan statistical area/micropolitan statistical area",
+# us_metro <- get_acs(geography = "metropolitan statistical area/micropolitan statistical area",
-#                      variables = "B01001_001",
+#                      variables = "B01001_001",
-#                     geometry = T)
+#                     geometry = T)
-# This is just urban
+# This is just urban
-# Code is for total population, all ages
+# Code is for total population, all ages
-# https://www.census.gov/data/developers/data-sets/acs-1year/notes-on-acs-api-variable-formats.html
+# https://www.census.gov/data/developers/data-sets/acs-1year/notes-on-acs-api-variable-formats.html
-us_urban <- get_acs(geography = "urban area",
+us_urban <- get_acs(geography = "urban area",
-                    variables = "B01001_001")
+                    variables = "B01001_001")
-
+
-# Geometry is not in tidycensus yet, so we have to get it this way
+# Geometry is not in tidycensus yet, so we have to get it this way
-# Download it once and save locally
+# Download it once and save locally
-#urban_areas_geom <- urban_areas(class = "sf")
+#urban_areas_geom <- urban_areas(class = "sf")
-#save(urban_areas_geom, file = "data/urban_areas_tigris.RData")
+#save(urban_areas_geom, file = "data/urban_areas_tigris.RData")
-# Load saved variable
+# Load saved variable
-load("data/urban_areas_tigris.RData")
+load("data/urban_areas_tigris.RData")
-
+
-us_urban_joined <- left_join(urban_areas_geom,
+us_urban_joined <- left_join(urban_areas_geom,
-                    us_urban,
+                    us_urban,
-                    by = c("GEOID10" = "GEOID"))
+                    by = c("GEOID10" = "GEOID"))
-
+
-# Prove you can plot the largest cities
+# Define range of populations to include
-ggplot(us_urban_joined %>% filter(estimate > 5000000)) +
+gfk_pop <- us_urban_joined %>%
-    geom_sf(aes(fill = estimate),
+                filter(NAME == "Grand Forks, ND--MN Urbanized Area (2010)") %>%
-            color = NA) +
+                mutate(high = estimate + (estimate * 0.1),
-    theme_minimal() +
+                       low = estimate - (estimate * 0.1))
-    scale_fill_viridis()
+
-
+# Clean data
-# Define range of populations to include
+us_urban_clean <- us_urban_joined %>%
-gfk_pop <- us_urban_clean %>%
+                    select(NAME, variable, estimate, moe) %>%
-                filter(NAME == "Grand Forks, ND--MN Urbanized Area (2010)") %>%
+                    st_centroid() %>%
-                mutate(high = estimate + (estimate * 0.1),
+                    filter(estimate < gfk_pop[['high']],
-                       low = estimate - (estimate * 0.1))
+                           estimate > gfk_pop[['low']])
-
+
-# Clean data
+plot(us_urban_clean['estimate'])
-us_urban_clean <- us_urban_joined %>%
+
-                    select(NAME, variable, estimate, moe) %>%
+ggplot(us_urban_clean) +
-                    st_centroid() %>%
+    geom_sf(aes(color = estimate)) +
-                    filter(estimate < gfk_pop[['high']],
+    scale_color_viridis() +
-                           estimate > gfk_pop[['low']])
+    theme_minimal()
-
+
-# TODO bring in climate data
+# TODO bring in climate data
-
+
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