Finding: Wealthier neighborhoods with higher median household income generally have higher-rated POIs, particularly for Yelp ratings of 3 and 4. However, there is a decrease in median income for neighborhoods with a Yelp rating of 5.

plot1 <- ggplot(data = coffee) +
  geom_boxplot(aes(x=avg_rating, y=hhincome),
               color="black", fill="white") +
  scale_x_discrete(limits = c("1", "2", "3", "4", "5")) +
  labs(title = "Plot 1.", 
       subtitle = "Variables used - avg_rating, hhincome", 
       x = "Average Yelp Rating", 
       y = "Median Annual Household Income ($)")

ggplot(data = coffee) +
  geom_boxplot(aes(x=as.factor(avg_rating), y=hhincome),
               color="black", fill="white") +
  labs(title = "Plot 1. Variables used - avg_rating, hhincome", 
       x = "Average Yelp Rating", 
       y = "Median Annual Household Income ($)")

plotly::ggplotly(plot1)

Finding: Higher Yelp ratings generally correlate with higher median household incomes, though the strength of this relationship varies by county. Fulton, Cobb, DeKalb, and Gwinnett show higher ratings linked to wealthier areas but experience drops in the 5-star category, while Clayton has consistently lower incomes across all ratings.

ggplot(data = coffee) +
  geom_boxplot(aes(x = as.factor(avg_rating), y = hhincome),
               color = "black", fill = "white") +
  facet_wrap(~ county) +
  labs(title = "Plot 2.", 
       subtitle = "Variables used - avg_rating, hhincome, county", 
       x = "Average Yelp Rating", 
       y = "Median Annual Household Income ($)")

plot2 <- ggplot(data = coffee) +
  geom_boxplot(aes(x = as.factor(avg_rating), y = hhincome),
               color = "black", fill = "white") +
  facet_wrap(~ county) +
  labs(title = "Plot 2. Variables used - avg_rating, hhincome, county", 
       x = "Average Yelp Rating", 
       y = "Median Annual Household Income ($)")

plotly::ggplotly(plot2)

# Convert to interactive Plotly plot
#plotly::ggplotly(plot2)

Finding: Higher-income areas, especially in Fulton and Cobb counties, have a higher proportion of white residents, while lower-income areas like Clayton County show a lower proportion.

ggplot(data = coffee) +
  geom_point(mapping = aes(x=review_count_log, y=hhincome, color = pct_white), alpha=0.7, size=1.5) +
  facet_wrap(~ county) +
  labs(x = "Review Count (log)",
       y = "Median Annual Household Income",
       color = "Proportion of residents\nwho self-identified as white",
       title = "Scatterplot: Review Count vs. Household Income") +
  scale_color_gradient(low="blue", high="red") + 
  theme_bw()+ 
  theme(
    plot.title = element_text(size= 12, color = "black"),
    legend.title = element_text(size = 6),
    legend.text = element_text(size = 5),
    legend.key.height = unit(0.4, 'cm'),
    legend.key.width = unit(0.4, 'cm'),
    axis.title.x = element_text(size = 8),
    axis.title.y = element_text(size = 8),
    axis.text.x = element_text(size = 6),
    axis.text.y = element_text(size = 6),
    
    # Adjust facet label size
    strip.text = element_text(size = 6)
  )

Finding: The strongest correlation is with the percentage of white residents, showing that neighborhoods with more white residents tend to have more popular POIs. There is a weaker positive link with median income, while higher poverty rates are negatively associated with POI reviews. Total population has no significant impact on POI attractiveness.

library(ggpubr)

coffee_long <- coffee %>%
  pivot_longer(cols = c(pct_pov_log, hhincome, pct_white, pop),
               names_to = "variable",
               values_to = "value")

labels <- c(
  hhincome = "Median Annual Household Income ($)",
  pct_pov_log = "Percent Residents Under Poverty",
  pct_white = "Percent White Resident",
  pop = "Total Population"
)

ggplot(data = coffee_long, aes(x = review_count_log, y = value)) +
  geom_point(aes(color = county), size = 1) +
  geom_smooth(aes(color = county), method = "lm", se = FALSE, size = 0.5) +
  facet_wrap(~ variable, scales = "free_y", , labeller = as_labeller(labels)) +
  labs(
    title = "Scatterplot between logged review count & neighborhood characteristics",
    subtitle = "Using Yelp data in Five Counties Around Atlanta, GA",
    x = "Review Count Logged",
    y = "Values",
    color = "County"
  ) +
  stat_cor(aes(x = review_count_log, y = value), method = "pearson", size = 2) +
  theme(
    plot.title = element_text(size= 12, color = "black"),
    plot.subtitle = element_text(size = 10, hjust = 0.5),
    axis.title.x = element_text(size = 8),
    axis.title.y = element_text(size = 8),
    axis.text.x = element_text(size = 6),
    axis.text.y = element_text(size = 6),
    legend.title = element_text(size = 6),
    legend.text = element_text(size = 4),
    legend.position = "right"
  )+
  theme_bw()

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `geom_smooth()` using formula = 'y ~ x'

library(ggpubr)

labels <- c(
  hhincome = "Median Annual Household Income ($)",
  pct_pov_log = "Percent Residents Under Poverty",
  pct_white = "Percent White Resident",
  pop = "Total Population"
)


coffee %>%
  pivot_longer(cols = c(pct_pov_log, hhincome, pct_white, pop),
               names_to = "variable",
               values_to = "value") %>%
  ggplot(aes(x = review_count_log, y = value)) +
  geom_point(aes(color = county),size = 1) + 
  geom_smooth(aes(color = county), method = "lm", se = FALSE, size = 0.5) +
  facet_wrap(~ variable, scales = "free_y", labeller = as_labeller(labels)) + 
  labs(
    title = "Scatterplot between logged review count & neighborhood characteristics",
    subtitle = "Using Yelp data in Five Counties Around Atlanta, GA",
    x = "Review Count Logged",
    y = "Values",
    color = "County"
  ) +
  stat_cor(aes(x = review_count_log, y = value), method = "pearson", size = 2) + 
  theme(
    plot.title = element_text(size = 12, color = "black"),
    plot.subtitle = element_text(size = 10, hjust = 0.5),
    axis.title.x = element_text(size = 8),
    axis.title.y = element_text(size = 8),
    axis.text.x = element_text(size = 6),
    axis.text.y = element_text(size = 6),
    legend.title = element_text(size = 6),
    legend.text = element_text(size = 5),
    legend.position = "right"
  )+
  theme_bw()

## `geom_smooth()` using formula = 'y ~ x'