Exploration of the different variables

Compostion of the three cities over time

Race/ethnicity by overall and by city

### Race ethnicity by class

### Education

### Education 

edu_all <- ggplot(data = ses, aes(x = year, 
                         y = pedu,
                         na.rm = T)) +
  geom_line(stat = "summary") +
    xlab("Years") + ylab("Percent with 4+ years college") +
  ggtitle("Overall Percentage of people with 4+ yeas of college") +
    theme_dark() 

edu_city <- ggplot(data = ses, aes(x = year, 
                         y = pedu,
                         group = city,
                         color = city,
                         na.rm = T)) +
  geom_line(stat = "summary")  +
    xlab("Years") + ylab("Percent with 4+ years college") +
  ggtitle("Percentage of people with 4+ yeas of college by City") +
    theme_dark() 

edu_class <- ggplot(data = ses, aes(x = year, 
                         y = pedu,
                         group = class,
                         color = class,
                         na.rm = T)) +
  geom_line(stat = "summary")  +
  facet_grid(~city)+
    xlab("Years") + ylab("Percent with 4+ years college") +
  ggtitle("Percentage of people with 4+ yeas of college by City by class") +
    theme_dark()
edu_all

edu_city

edu_class

### Income
## Contextual Variables

Land Development

### Access to food
### Building Permits

A colllection of the graphs by class

  • Questions/notes to talk about
    • Why won’t overall graphs show line? (left code exposed for education for that one)
    • I’m having a hard time determining which classes to explore? (class 4 and 6?).
    • Do you think the access to food variable is worth keeping in the analysis. Is it worth rerunning everything to look at results without it? (I hesitated doing this as I didn’t want to wander down a rabit hole)
    • Wasn’t quite sure where to put population
    • Experimented with and was somewhat successful with annotation
    • Thoughts on the patchwork package