02.23.2021 Cooper P: HW3

Part 0

pacman::p_load(tidyverse, usmap, install = FALSE, update = FALSE)

load("G:/My Drive/homework/Cooper P/MA_county_variables_2020.Rdata")
load("G:/My Drive/homework/Cooper P/MA_COVID19_21_02_16.Rdata")
ma_extra <- ma_extra %>% as_tibble()

#
ma_extra %>% dim()

## [1] 14 24

# nearby is a subset of mass
nearby %>% intersect(mass) %>% dim(); nearby %>% dim()

## [1] 669  10

## [1] 669  10

mass %>% dim()

## [1] 5134   10

# total_nearby is not a subset of mass_totals
total_nearby %>% intersect(mass_totals) %>% dim(); total_nearby %>% dim()

## [1] 0 7

## [1] 335   7

totals <- total_nearby %>% union(mass_totals)
totals %>% dim()

## [1] 716   7

# MA data
mass_join <- mass %>% left_join(ma_extra)

## Joining, by = "county"

mass_join %>% dim()

## [1] 5134   33

Part 1

Part (a)

mass %>%
  filter(new_cases >= 0) %>%
  ggplot(aes(date, new_cases)) + 
  geom_point() +
  facet_wrap(vars(county)) +
  labs(title = "New MA COVID Cases by County",
       subtitle = "Fixed Scales",
       x = "Date",
       y = "Cases") +
  theme(axis.text.x=element_text(angle = 90, hjust = 0))

Part (b)

mass %>%
  filter(new_cases >= 0) %>%
  ggplot(aes(date, new_cases)) + 
  geom_point() +
  facet_wrap(vars(county), scales = "free_y") +
  labs(title = "New MA COVID Cases by County",
       subtitle = "Free Scales",
       x = "Date",
       y = "Cases") +
  theme(axis.text.x=element_text(angle = 90, hjust = 0))

Part 2

Part (a)

to_string <- as_labeller(c(`Rural_urban_Continuum_Code_2013` = "Rural Urban Continuum",
                           `Urban_Influence_Code_2013` = "Urban Influence"))

ma_extra %>%
  select(contains("2013")) %>%
  gather() %>%
  ggplot(aes(value)) +
  geom_bar() +
  facet_wrap(vars(key),labeller = to_string) +
  labs(title = "2013 Codes", x = "Category")

Part (b)

ma_extra %>%
  select(contains("2013")) %>%
  gather() %>%
  ggplot(aes(value, fill = key)) +
  geom_bar() +
  labs(title = "2013 Codes", x = "Category", fill = "") +
  scale_fill_discrete(labels = c("Rural Urban Continuum", "Urban Influence"))

Part (d)

mass %>%
  select(county, cases) %>%
  group_by(county) %>% 
  summarize(cases = sum(cases)) %>%
  ggplot(aes(county, cases)) +
  geom_col() +
  theme(axis.text.x=element_text(angle = 90, hjust = 0)) +
  labs(title = "MA COVID Cases by County", x = "", y = "")

Part 3

Part (a)

ma_extra %>%
  ggplot(aes(Urban_rural %>% str_to_title(), people_per_Housing)) +
  geom_boxplot(varwidth = TRUE) +
  labs(title = "People per Dwelling", x = "", y = "Density")

Part (c)

mass %>%
  filter(new_cases >= 0) %>%
  ggplot(aes(county, new_cases)) +
  geom_boxplot(varwidth = TRUE) +
  theme(axis.text.x=element_text(angle = 90, hjust = 0)) +
  labs(title = "MA New Cases by County", x = "", y = "")

Part (d)

nearby %>%
  filter(new_cases >= 0) %>%
  ggplot(aes(county, new_cases)) +
  geom_boxplot() +
  labs(title = "MA New Cases For Two Counties", x = "", y = "")

nearby %>%
  filter(new_cases >= 0) %>%
  ggplot(aes(county, new_cases)) +
  geom_violin() +
  labs(title = "MA New Cases For Two Counties", x = "", y = "")

Part 4

https://github.com/pdil/usmap/blob/master/README.md

mass %>%
  filter(new_cases >= 0) %>%
  group_by(fips) %>%
  summarize(New_Cases = mean(new_cases)) %>%
  # drop_na() %>%
  plot_usmap(regions = "counties",
             include = "MA",
             data = .,
             values = "New_Cases",
             color = "blue",
             labels = TRUE) +
  scale_fill_continuous(name = "Average New Cases") +
  theme(legend.position = "right")

mass %>%
  filter(new_deaths >= 0) %>%
  group_by(fips) %>%
  summarize(New_Deaths = mean(new_deaths)) %>%
  # drop_na() %>%
  plot_usmap(regions = "counties",
             include = "MA",
             data = .,
             values = "New_Deaths",
             color = "blue",
             labels = TRUE) +
  scale_fill_continuous(name = "Average New Deaths") +
  theme(legend.position = "right")

Part 5