# Assuming you have already loaded the tidyverse package and read the raw data

# Select columns needed for graphing
graph_data <- raw_data %>%
  select(Geography, BEA_PCPI, ACS_PCMI)

# Reshape the data to long format
long_graph_data <- graph_data %>%
  pivot_longer(cols = c(BEA_PCPI, ACS_PCMI), names_to = "Income_Type", values_to = "Income_Value")

# Graphing with ggplot
library(ggplot2)

ggplot(long_graph_data, aes(x = Geography, y = Income_Value, fill = Income_Type)) +
  geom_bar(stat = "identity", position = "dodge", color = "black") +
  labs(title = "Comparison of PCPI and PCMI by Geography",
       x = "Geography", y = "Income Value") +
  scale_fill_manual(values = c("BEA_PCPI" = "blue", "ACS_PCMI" = "red")) +
  theme_minimal()