By block

boston_blocks20 <- boston_blocks20 %>% filter(total > 0)
boston_blocks10 <- boston_blocks10 %>% filter(total > 0)
boston_blocks00 <- boston_blocks00 %>% filter(total > 0)

ggplot() + geom_sf(data = boston_blocks20,aes(fill=black/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  ggtitle("Black Population 2020")+
  scale_fill_continuous(limits = c(0, 1))

ggplot() + geom_sf(data = boston_blocks10,aes(fill=black/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Black Population 2010")

ggplot() + geom_sf(data = boston_blocks00,aes(fill=black/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Black Population 2000")

ggplot() + geom_sf(data = boston_blocks20,aes(fill=white/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("White Population 2020")

ggplot() + geom_sf(data = boston_blocks10,aes(fill=white/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("White Population 2010")

ggplot() + geom_sf(data = boston_blocks00,aes(fill=white/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("White Population 2000")

ggplot() + geom_sf(data = boston_blocks20,aes(fill=hispanic/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Hispanic Population 2020")

ggplot() + geom_sf(data = boston_blocks10,aes(fill=hispanic/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Hispanic Population 2010")

ggplot() + geom_sf(data = boston_blocks00,aes(fill=hispanic/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Hispanic Population 2000")

By tract

ggplot() + geom_sf(data = boston_tracts20,aes(fill=black/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Black Population 2020")

ggplot() + geom_sf(data = boston_tracts10,aes(fill=black/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Black Population 2010")

ggplot() + geom_sf(data = boston_tracts00,aes(fill=black/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Black Population 2000")

ggplot() + geom_sf(data = boston_tracts20,aes(fill=white/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("White Population 2020")

ggplot() + geom_sf(data = boston_tracts10,aes(fill=white/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("White Population 2010")

ggplot() + geom_sf(data = boston_tracts00,aes(fill=white/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("White Population 2000")

ggplot() + geom_sf(data = boston_tracts20,aes(fill=hispanic/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Hispanic Population 2020")

ggplot() + geom_sf(data = boston_tracts10,aes(fill=hispanic/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Hispanic Population 2010")

ggplot() + geom_sf(data = boston_tracts00,aes(fill=hispanic/total),color=NA) + geom_sf(data = planning_districts, color="yellow",fill = NA)+theme_map()+
  scale_fill_continuous(limits = c(0, 1))+
  ggtitle("Hispanic Population 2000")

Between neighborhoods over time

#comparison between neighborhoods
neighborhoods <- read_csv("C:/Users/liste/OneDrive - Harvard University/Studio/Assignment C/neighborhoods.csv")
## Rows: 3542 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Neighborhood, Variable, Value
## dbl (2): Year, PCT
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
neighborhoods$Value <- as.double(neighborhoods$Value)
## Warning: NAs introduced by coercion
black <- neighborhoods %>% filter(Variable == "Black/ African American") %>%
  ggplot(aes(Year,PCT,color=Neighborhood))+geom_line()+geom_point()+
  labs(title="% Black")
ggplotly(black)
white <- neighborhoods %>% filter(Variable == "White") %>%
  ggplot(aes(Year,PCT,color=Neighborhood))+geom_line()+geom_point()+
  labs(title="% White")
ggplotly(white)
hispanic <- neighborhoods %>% filter(Variable == "Hispanic") %>%
  ggplot(aes(Year,PCT,color=Neighborhood))+geom_line()+geom_point()+
  labs(title="% Hispanic")
ggplotly(hispanic)
renter <- neighborhoods %>% filter(Variable == "Owner-occupied") %>%
  ggplot(aes(Year,PCT,color=Neighborhood))+geom_line()+geom_point()+
  labs(title="% Owner-Occupied")
ggplotly(renter)
foreign <- neighborhoods %>% filter(Variable == "Foreign Born") %>%
  ggplot(aes(Year,PCT,color=Neighborhood))+geom_line()+geom_point()+
  labs(title="% Foreign-born")
ggplotly(foreign)
bachelors <- neighborhoods %>% filter(Variable == "Bachelor's Degree or Higher") %>%
  ggplot(aes(Year,PCT,color=Neighborhood))+geom_line()+geom_point()+
  labs(title="% Bachelors")
ggplotly(bachelors)
female <- neighborhoods %>% filter(Variable == "Female") %>%
  ggplot(aes(Year,PCT,color=Neighborhood))+geom_line()+geom_point()+
  labs(title="% Female Workforce")
ggplotly(female)
stackedbar <-  neighborhoods %>% filter(Variable == "Population") %>%
  ggplot(aes(Year,Value,fill=Neighborhood))+geom_bar(stat = "identity")
ggplotly(stackedbar)
## Warning: Removed 1 rows containing missing values (position_stack).

Block

##City comparison

v20 <- load_variables(2020, "acs5", cache = TRUE)
acs_vars <- c(
  pop = "B03002_001",
  white ="B03002_003",
  black = "B03002_004",
  asian = "B03002_006",
  other = "B03002_008",
  mixed = "B03002_009",
  hispanic = "B03002_012",
  income = "B19013_001",
  income_white = "B19013A_001",
  income_black = "B19013B_001",
  income_hispanic = "B19013I_001",
  value = "B25077_001",
  rent_income = "B25071_001",
  less_than_hs = "B06009_002",
  hs = "B06009_003",
  some_college= "B06009_004",
  bachelors= "B06009_005",
  grad = "B06009_006"
)

acs_vars_ancestry <- c(
  pop = "B03002_001",
  english = "B06007_003",
  spanish = "B06007_002",
  spanish_english_not_well = "B06007_005",
  other_language = "B06007_006",
  foreign_born =  "B05002_013",
  west_indian = "B04006_094",
  haitian = "B04006_101",
  ssafrica = "B04006_073",
  capeverde = "B04006_074",
  ethiopia = "B04006_075",
  ghanian = "B04006_077",
  nigerian = "B04006_079",
  somalia = "B04006_082",
  mexican = "B03001_004",
  boricua = "B03001_005",
  dominican = "B03001_007",
  central="B03001_008"
)
boston <- "2507000"
gateway <- c(
  "2553960",
  "2530840",
  "2567000",
  "2523875",
  "2582000",
  "2537000",
  "2529405",
  "2534550",
  "2509000",
  "2523000",
  "2545000",
  "2502690",
  "2503690",
  "2513205",
  "2513660",
  "2521990",
  "2535075",
  "2537490",
  "2537875",
  "2540710",
  "2552490",
  "2555745",
  "2556585",
  "2559105",
  "2569170",
  "2576030"
)
ma_place <- get_acs(year = 2020, geography = "place",state = "MA", variables = acs_vars,output = "wide")
## Getting data from the 2016-2020 5-year ACS
ma_ancestry_place <- get_acs(year = 2020, geography = "place",state = "MA", variables = acs_vars_ancestry,output = "wide")
## Getting data from the 2016-2020 5-year ACS
gateway_places <- ma_place %>% left_join(ma_ancestry_place) %>% filter(GEOID %in% gateway | GEOID == "2507000") %>%
  mutate(NAME=str_remove(NAME," city, Massachusetts"))  %>%
  mutate(geography="City")
## Joining, by = c("GEOID", "NAME", "popE", "popM")
ma <- get_acs(year = 2020, geography = "state",state = "MA", variables = acs_vars,output = "wide")
## Getting data from the 2016-2020 5-year ACS
ma_ancestry <- get_acs(year = 2020, geography = "state",state = "MA", variables = acs_vars_ancestry,output = "wide")
## Getting data from the 2016-2020 5-year ACS
ma %>% left_join(ma_ancestry)  %>%
  mutate(geography="State")-> ma
## Joining, by = c("GEOID", "NAME", "popE", "popM")
boston_tract <- get_acs(year = 2020, geography = "tract",state = "MA",county = "Suffolk", variables = acs_vars,output = "wide")
## Getting data from the 2016-2020 5-year ACS
boston_ancestry <- get_acs(year = 2020, geography = "tract",state = "MA",county = "Suffolk", variables = acs_vars_ancestry,output = "wide")
## Getting data from the 2016-2020 5-year ACS
boston_nhood <- get_acs(year = 2020, geography = "tract",county = "Suffolk",state = "MA", variables = acs_vars,output = "wide")
## Getting data from the 2016-2020 5-year ACS
boston_nhood_ancestry <- get_acs(year = 2020, geography = "tract",state = "MA",county = "Suffolk", variables = acs_vars_ancestry,output = "wide")
## Getting data from the 2016-2020 5-year ACS
boston_nhood %>% left_join(boston_nhood_ancestry) %>% left_join(xwalk,by = c("GEOID"="GEOID10")) %>%
  filter(!is.na(Neighborho))-> boston_nhood_join
## Joining, by = c("GEOID", "NAME", "popE", "popM")
boston_nhood_join %>% st_drop_geometry() %>%  group_by(Neighborho) %>% summarise(
  popE =sum(popE),
  whiteE=sum(whiteE),
  blackE=sum(blackE),
  asianE=sum(asianE),
  otherE=sum(otherE),
  mixedE=sum(mixedE),
  hispanicE=sum(hispanicE)) %>% rename(NAME=Neighborho) %>%
  mutate(geography="Neighborhood")-> boston_nhood_join


data_cities <- bind_rows(ma,gateway_places,boston_nhood_join)

##gatewaygraphs

hisp_black <- ggplot(data = data_cities,aes(shape=geography,x=blackE/popE,y=hispanicE/popE,color=NAME))+geom_point()+geom_hline(yintercept = 0.12)+geom_vline(xintercept =0.0678)
ggplotly(hisp_black)
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.