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()

## 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()

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()

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()

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()

Latinx

Percent of population reporting Hispanic/Latinx ethnicity

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_HISPANIC,
             colour = `AREA NAME`)) +
  geom_col()

Mixed Race

Percent of population reporting multi-racial

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_MULT_RACE,
             colour = `AREA NAME`)) +
  geom_col()

White

Percent of population reporting White race alone

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_WHITE_NONHISP,
             colour = `AREA NAME`)) +
  geom_col()

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()

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()

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()

High school grad

Census tract by High School Graduation rate (ages 25 and over)

df %>% 
  ggplot(aes(x = `TRACT NUM`, 
             y = PCT_HS_GRADUATE,
             colour = `AREA NAME`)) +
  geom_col()

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()

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()