Data Draft of West Oak Cliff

df <-read_xlsx("C:/Users/Dulce Si/Documents/PSL/Oak Cliff/Tract_SDOH_COMBINE.xlsx")

Oak Cliff Data

Hi comrades! I hope this works. It is a set of graphs that show basic information on West Oak Cliff. The data is presented as census tracts and separated as North, Central, and South Oak Cliff

Please try to identify common tracts that have high levels of poverty, GINI coefficients, or other indicators of need.

Tract numbers 1 - 16 = Central Oak Cliff Tract numbers 17 - 26 = North Oak Cliff Tract numbers 27 - 36 = South Oak Cliff

Total Population

Census tracts by total population in each tract.

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = TOT_POP_WT,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Population") +
  xlab("Tract") +
  ylab("Population") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

## Poverty Tract by total civilian population (18+) whose poverty status is determined

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = TOT_CIVIL_POP_POV,
             colour = `AREA NAME`,
             )) +
  geom_col() +
  ggtitle("Tract by Civil Population") +
  xlab("Tract") +
  ylab("Civil Population") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

Citizenship

Tract by percentage of population who are NOT U.S. citizens

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_NON_CITIZEN,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Non-US Citizen") +
  xlab("Tract") +
  ylab("Percent Non-Citizen") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

Native

Percent of population reporting American Indian and Alaska Native alone

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_AIAN,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Native Population") +
  xlab("Tract") +
  ylab("Percent Native") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

Black

Percent of population reporting Black or African American race alone

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_BLACK,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Black Population") +
  xlab("Tract") +
  ylab("Percent Black") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

Latinx

Percent of population reporting Hispanic/Latinx ethnicity

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_HISPANIC,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Latinx Population") +
  xlab("Tract") +
  ylab("Percent Latinx") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

Mixed Race

Percent of population reporting multi-racial

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_MULT_RACE,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Mixed Race Population") +
  xlab("Tract") +
  ylab("Percent Mixed Race") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

White

Percent of population reporting White race alone

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_WHITE_NONHISP,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by White Population") +
  xlab("Tract") +
  ylab("Percent White") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

GINI Index

Gini index of income inequality. This number from 0-1 represents income inequality in an area. The higher the number the more unequal the area is.

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = GINI_INDEX,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by GINI Index") +
  xlab("Tract") +
  ylab("GINI Index") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

House Hold Income

Census tract by median household income in dollars.

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = MEDIAN_HH_INC,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Household Income") +
  xlab("Tract") +
  ylab("Median Household Income") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

Food Stamps/SNAP

census tract by percentage of households that received food stamps/SNAP, in the past 12 months

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_HH_FOOD_STMP,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Food Stamp/SNAP use") +
  xlab("Tract") +
  ylab("Percent of Households using SNAP") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

Only High School Diploma

Census tract by percentage of population with ONLY a HS diploma (ages 25 and over)

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_HS_GRADUATE,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by High School Diploma") +
  xlab("Tract") +
  ylab("Percent with Only Highschool Diploma") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

Rent Cost

Census tract by median gross rent cost. This graph might be better visualized in another way I will fix later.

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = MEDIAN_RENT,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Rent Cost") +
  xlab("Tract") +
  ylab("Median Gross Rent Cost") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )

No Health Insurance

Census tract by percentage of population with no health insurance coverage

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_UNINSURED,
             colour = `AREA NAME`)) +
  geom_col() +
  ggtitle("Tract by Uninsured Population") +
  xlab("Tract") +
  ylab("Percent Uninsured") +
  theme(
    plot.title = element_text(vjust = 1, hjust = .5)
  )