library(here)   
library(readr)
library(dplyr)
library(plotly)
library(flexdashboard)

top5_df <- read_rds(here("~/PHW251B/week_10/data/top5_cities.rds"))

Overview (First Page)

Column 1

AQI Bar Chart

ggplot(top5_df, aes(x = reorder(city_ascii, aqi), y = aqi)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(title = "Top 5 California Cities by Average AQI",
       x = "City", y = "Average AQI")

Column 2

AQI Trends Over Time

p <- ggplot(top5_df, aes(x = date, y = aqi, color = city_ascii)) +
  geom_line() +
  labs(
    title = "AQI Trends Over Time",
    x = "Date",
    y = "AQI"
  )

ggplotly(p)

Data_source

top5_cities.rds

Background

This dataset tracks the five California cities with the highest average AQI. Air quality is a critical public health issue, and these data highlight where polution burdens are most severe.

Results

AQI trends over time reveal persistent pollution burdens in Riverside and Los Angeles, while Bishop shows episodic spikes that may reflect environmental events or data irregularities. These patterns underscore the need for both long-term mitigation and rapid response strategies.