library(tidyverse)
library(httr)
library(jsonlite)
library(ggpubr)

How does Life Compare in Los Angeles vs San Luis Obispo?

As a native of Los Angeles and going to college in San Luis Obispo, I decided to explore the difference in quality of life between these two cities. I predict that Los Angeles is going to have a higher average salary than San Luis Obispo; however, I am unsure and curious as to how their scores on certain aspects of life will differ.

#LA Score
LA_score <- GET("https://api.teleport.org/api/urban_areas/slug:los-angeles/scores/")
LA_score_data <- fromJSON(rawToChar(LA_score$content))
LA_categories <- LA_score_data$categories %>%
  select(name, score_out_of_10) %>%
  rename("Category" = name,
         "Score" = score_out_of_10)

#LA Salaries
LA_salary <- GET("https://api.teleport.org/api/urban_areas/slug:los-angeles/salaries/")
LA_salary_data <- fromJSON(rawToChar(LA_salary$content))
LA_jobs <- LA_salary_data$salaries$job %>%
  select(title)

LA_sals <- LA_salary_data$salaries$salary_percentiles
LA_med_sals <- data.frame(LA_sals$percentile_50)
LA_salaries <- cbind(LA_jobs, LA_med_sals) %>%
  rename("Job" = title,
         "Median_sal" = LA_sals.percentile_50) %>%
  mutate(City = "LA")

#SLO Score
SLO_score <- GET("https://api.teleport.org/api/urban_areas/slug:san-luis-obispo/scores/")
SLO_score_data <- fromJSON(rawToChar(SLO_score$content))
SLO_categories <- SLO_score_data$categories %>%
  select(name, score_out_of_10) %>%
  rename("Category" = name,
         "Score" = score_out_of_10) 

#SLO Salaries
SLO_salary <- GET("https://api.teleport.org/api/urban_areas/slug:san-luis-obispo/salaries/")
SLO_salary_data <- fromJSON(rawToChar(SLO_salary$content))
SLO_jobs <- SLO_salary_data$salaries$job %>%
  select(title)
SLO_sals <- SLO_salary_data$salaries$salary_percentiles
SLO_med_sals <- data.frame(SLO_sals$percentile_50)
SLO_salaries <- cbind(SLO_jobs, SLO_med_sals) %>%
  rename("Job" = title,
         "Median_sal" = SLO_sals.percentile_50) %>%
  mutate(City = "SLO")

sals <- rbind(LA_salaries, SLO_salaries)
score <- merge(LA_categories, SLO_categories, by = "Category") %>%
  rename("LA_Score" = Score.x,
         "SLO_Score" = Score.y)

#comparing salary plot
s <- ggplot(sals, aes(Median_sal, fill = City)) +
  geom_density(alpha = 0.5) +
  labs(title = "Median Salaries of Jobs in LA vs. SLO", 
       x = "Median Salary ($)")+ 
  theme(axis.title.y = element_blank(), 
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank()) + 
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black")) +
  scale_fill_manual(values=c("blue4", "darksalmon"))

#comparing score plot
q <- ggplot(score) +
    theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black")) +
  theme(axis.title = element_blank(),
        axis.ticks.y = element_blank(),
        axis.line = element_blank()) +
  geom_point(aes(x = 12, y = Category), 
             size = 0, col = "white") + 
  geom_hline(yintercept = 1:17, col = "grey80") +
  geom_point(aes(x = LA_Score, y = Category), 
             size = 7, col = "blue4") +
  geom_point(aes(x = SLO_Score, y = Category),
             size = 7, col = "darksalmon") + 
  geom_text(size = 3.2, aes(x = LA_Score, y = Category, 
                label = paste0(round(LA_Score,1))),
            col = "white") +
  geom_text(size = 3.2, aes(x = SLO_Score, y = Category, 
                label = paste0(round(SLO_Score,1))),
            col = "black") +
  scale_x_continuous(breaks = c(0, 2, 4, 6, 8, 10), 
                     labels = c("0", "2", "4", "6", "8", "10")) +
  scale_y_discrete(expand = c(0.05, 0)) +
  labs(title = "Quality of Life in LA vs. SLO")

Below, I created an overlapping density plot to easily visualize the difference between median salaries in these two cities. Moreover, I created a horizontal dot plot to display the scores that each city was given about certain aspects.

ggarrange(s, q, nrow = 1, hjust = -5)

In conclusion, Los Angeles has a much higher median salary than San Luis Obispo as I expected. San Luis Obispo’s average median salary was around $40,000 while Los Angeles had an average median salary of closer to $70,000. Regarding the scores on aspects of life. They differ quite a lot in most categories. Each city possesses their own negative qualities that drive people away; however, they both also contain positive qualities that make them desirable.