Small changes.
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@ -43,15 +43,8 @@ us_urban_joined <- left_join(urban_areas_geom,
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us_urban,
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us_urban,
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by = c("GEOID10" = "GEOID"))
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by = c("GEOID10" = "GEOID"))
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# Prove you can plot the largest cities
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ggplot(us_urban_joined %>% filter(estimate > 5000000)) +
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geom_sf(aes(fill = estimate),
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color = NA) +
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theme_minimal() +
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scale_fill_viridis()
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# Define range of populations to include
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# Define range of populations to include
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gfk_pop <- us_urban_clean %>%
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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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filter(NAME == "Grand Forks, ND--MN Urbanized Area (2010)") %>%
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mutate(high = estimate + (estimate * 0.1),
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mutate(high = estimate + (estimate * 0.1),
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low = estimate - (estimate * 0.1))
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low = estimate - (estimate * 0.1))
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@ -63,6 +56,13 @@ us_urban_clean <- us_urban_joined %>%
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filter(estimate < gfk_pop[['high']],
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filter(estimate < gfk_pop[['high']],
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estimate > gfk_pop[['low']])
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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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# TODO bring in climate data
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