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df <- read.csv("~/Downloads/stat436/e_1/urban_connectivity.csv")
head(df)

I am interested in this dataset because it is useful information for looking at walkability, and associations with other variables such as demographics, accessibility of urban areas, and income. It may help answer questions around which cities or states are most accessible, how correlated walkability is with other indicators and equity measures.

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

state_walk_avg <- df %>%
  group_by(State) %>%
  summarise(
    avg_walk_score = mean(Walk.Score, na.rm = TRUE)
  ) %>%
  arrange(desc(avg_walk_score))

state_walk_avg

library(ggplot2)
ggplot(state_walk_avg, aes(reorder(State, avg_walk_score), y = avg_walk_score)) +
  coord_flip() +
  geom_col() +
  labs(title = "Avg Walk Score by State",
       x = "State",
       y = "Avg Walk Score") +
  theme_minimal()

I wanted to make a visualization for the various states and their average walk scores–which states came up with high vs low scores, as well as the range and distribution of the scores.

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