added AQI
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4 changed files with 202 additions and 32 deletions
2
.gitignore
vendored
2
.gitignore
vendored
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@ -6,3 +6,5 @@
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#exclude API keys
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api_keys/*
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#exclude data
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data/*
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16
cities.csv
16
cities.csv
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@ -1,8 +1,8 @@
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City,Type,State,Country
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Madison,city,WI,United States
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Bellingham,city,WA,United States
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Portland,city,OR,United States
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Port Angeles,city,WA,United States
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Boston,city,MA,United States
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Boise City,city,ID,United States
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Salt Lake City,city,UT,United States
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City,Type,State,Country,CBSA-Code
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Madison,city,WI,United States,31540
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Bellingham,city,WA,United States,13380
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Portland,city,OR,United States,38900
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Port Angeles,city,WA,United States,38820
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Boston,city,MA,United States,14460
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Boise City,city,ID,United States,14260
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Salt Lake City,city,UT,United States,41620
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@ -11,3 +11,5 @@ Encoding: UTF-8
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RnwWeave: Sweave
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LaTeX: pdfLaTeX
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BuildType: Makefile
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214
city_compare.Rmd
214
city_compare.Rmd
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@ -36,7 +36,7 @@ census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
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## Date ranges
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```{r date_range, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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date_start <- "2010-01-01"
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date_start <- "2014-01-01"
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date_end <- "2024-12-31"
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```
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@ -44,42 +44,42 @@ date_end <- "2024-12-31"
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```{r cities, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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cities <- read_csv(file = "cities.csv")
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cities <- cities %>%
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cities <- cities |>
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mutate(city_name = paste0(City, " ", Type))
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```
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# Get data
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# Data
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## Census data
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```{r census, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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populations <- list(NULL)
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for(city in cities$city_name){
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state <- cities %>% filter(city_name == city) %>% pull(State)
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state <- cities |> filter(city_name == city) |> pull(State)
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populations[[city]] <- get_acs(
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geography = "place",
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variables = "B01003_001",
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state = state,
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year = 2023,
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geometry = TRUE
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) %>%
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) |>
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filter(str_detect(NAME, city))
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}
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populations <- bind_rows(populations)
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city_center <- populations %>%
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st_centroid() %>%
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st_transform(4326) %>% # Convert to WGS84 (standard lat/lon)
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city_center <- populations |>
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st_centroid() |>
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st_transform(4326) %>%
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mutate(
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lon = st_coordinates(.)[,1],
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lat = st_coordinates(.)[,2]
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) %>%
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st_drop_geometry() %>%
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) |>
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st_drop_geometry() |>
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select(lat, lon)
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cities <- bind_cols(cities, populations, city_center)
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cities <- cities %>% mutate(
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cities <- cities |> mutate(
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density = estimate/as.double(st_area(geometry))*1000000
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)
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@ -120,12 +120,12 @@ ggplot(data = cities) +
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```{r weather, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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weather <- list(NULL)
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for(city in cities$City){
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city_info <- cities %>% filter(City == city)
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city_info <- cities |> filter(City == city)
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city_run <- weather_history(
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location = c(city_info %>% pull(lat), city_info %>% pull(lon)),
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location = c(city_info |> pull(lat), city_info |> pull(lon)),
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start = date_start,
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end = date_end,
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daily = c("apparent_temperature_max", "apparent_temperature_min"),
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daily = c("apparent_temperature_max", "apparent_temperature_min", "precipitation_hours"),
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response_units = list(temperature_unit = "fahrenheit")
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)
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city_run$city <- city
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@ -134,23 +134,27 @@ for(city in cities$City){
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weather <- bind_rows(weather)
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weather_summary <- weather %>%
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weather |>
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mutate(year = year(ymd(date)),
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month = month(ymd(date))) %>%
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group_by(year, city) %>%
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summarise(days_above_80 = sum(daily_apparent_temperature_max > 80)) %>%
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group_by(city) %>%
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summarise(median_days_above_80 = median(days_above_80))
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ggplot(data = weather_summary) +
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month = month(ymd(date))) |>
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group_by(year, city) |>
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summarise(days_above_80 = sum(daily_apparent_temperature_max > 80)) |>
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group_by(city) |>
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summarise(median_days_above_80 = median(days_above_80)) |>
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ggplot() +
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geom_col(aes(x = city,
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y = median_days_above_80)) +
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labs(title = "Days above 80°F",
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labs(title = "Days above 80°F (apparent temperature)",
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y = "Median days per year",
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x = "City",
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fill = NULL)
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ggplot(data = weather %>% pivot_longer(cols = starts_with("daily"), names_to = "max_min", values_to = "temperature") %>% filter(max_min %in% c("daily_apparent_temperature_min", "daily_apparent_temperature_max"))) +
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weather |>
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pivot_longer(cols = starts_with("daily"),
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names_to = "max_min",
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values_to = "temperature") |>
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filter(max_min %in% c("daily_apparent_temperature_min", "daily_apparent_temperature_max")) |>
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ggplot() +
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geom_violin(aes(x = city,
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y = temperature,
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fill = max_min)) +
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@ -160,5 +164,167 @@ ggplot(data = weather %>% pivot_longer(cols = starts_with("daily"), names_to = "
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y = "°F",
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x = "City",
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fill = NULL)
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weather |>
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mutate(year = year(ymd(date)),
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month = month(ymd(date))) |>
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group_by(year, city) |>
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summarise(days_above_4hr = sum(daily_precipitation_hours > 4)) |>
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group_by(city) |>
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summarise(median_days_above_4hr = median(days_above_4hr)) |>
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ggplot() +
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geom_col(aes(x = city,
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y = median_days_above_4hr)) +
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labs(title = "Days with more than 4 hrs of rain",
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y = "Median days per year",
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x = "City",
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fill = NULL)
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weather |>
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mutate(year = year(ymd(date)),
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month = month(ymd(date))) |>
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group_by(year, month, city) |>
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summarise(days_above_4hr = sum(daily_precipitation_hours > 4)) |>
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group_by(city, month) |>
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summarise(median_days_above_4hr = median(days_above_4hr)) |>
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ggplot() +
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geom_line(aes(x = month,
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y = median_days_above_4hr,
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color = city)) +
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labs(title = "Days with more than 4 hrs of rain",
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y = "Median days per month",
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x = "Month",
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color = "City")
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```
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## Air Quality
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```{r air_quality, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# download data
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# Create data directory if it doesn't exist
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if (!dir.exists("data")) {
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dir.create("data")
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}
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# Define years
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years <- seq(year(ymd(date_start)), year(ymd(date_end)), 1)
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# Initialize empty list to store dataframes
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aqi_list <- list()
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# Download and process files for each year
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for (year in years) {
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# Construct URL
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url <- paste0("https://aqs.epa.gov/aqsweb/airdata/daily_aqi_by_cbsa_", year, ".zip")
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# Define local file paths
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zip_file <- file.path("data", paste0("daily_aqi_by_cbsa_", year, ".zip"))
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csv_file <- file.path("data", paste0("daily_aqi_by_cbsa_", year, ".csv"))
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# Download zip if it doesn't exist
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if (file.exists(zip_file)) {
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cat(paste0("Zip file for year ", year, " already exists. Skipping download.\n"))
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} else {
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cat(paste0("Downloading data for year ", year, "...\n"))
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tryCatch({
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download.file(url, zip_file, mode = "wb")
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cat(paste0("Successfully downloaded data for year ", year, "\n"))
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}, error = function(e) {
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cat(paste0("Error downloading data for year ", year, ": ", e$message, "\n"))
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next
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})
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}
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# Extract zip if CSV doesn't exist
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if (file.exists(zip_file)) {
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if (file.exists(csv_file)) {
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cat(paste0("CSV for year ", year, " already extracted. Skipping extraction.\n"))
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} else {
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cat(paste0("Extracting zip for year ", year, "...\n"))
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tryCatch({
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unzip(zip_file, exdir = "data")
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cat(paste0("Successfully extracted data for year ", year, "\n"))
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}, error = function(e) {
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cat(paste0("Error extracting data for year ", year, ": ", e$message, "\n"))
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next
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})
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}
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}
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# Read CSV if it exists
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if (file.exists(csv_file)) {
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cat(paste0("Reading CSV for year ", year, "...\n"))
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tryCatch({
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aqi_year <- read.csv(csv_file)
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aqi_list[[as.character(year)]] <- aqi_year
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cat(paste0("Successfully read ", nrow(aqi_year), " rows for year ", year, "\n"))
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}, error = function(e) {
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cat(paste0("Error reading CSV for year ", year, ": ", e$message, "\n"))
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})
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}
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}
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# Combine all dataframes
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aqi_data <- bind_rows(aqi_list)
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aqi_data <- aqi_data |>
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filter(CBSA.Code %in% (cities |> pull(`CBSA-Code`))) |>
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mutate(year = year(ymd(Date)),
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month = month(ymd(Date)))
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aqi_threshhold <- 50
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aqi_data |>
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group_by(year, CBSA) |>
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summarise(hours_above = sum(AQI >= aqi_threshhold, na.rm = TRUE)) |>
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group_by(CBSA) |>
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summarise(mean_hours_above = mean(hours_above, na.rm = TRUE)) |>
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ggplot() +
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geom_col(aes(x = CBSA,
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y = mean_hours_above)) +
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labs(title = "Hours above 50 AQI",
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y = "Mean hours per year",
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x = "City",
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fill = NULL)
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aqi_data |>
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group_by(year, CBSA) |>
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summarise(hours_above = sum(AQI >= 2*aqi_threshhold, na.rm = TRUE)) |>
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group_by(CBSA) |>
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summarise(mean_hours_above = mean(hours_above, na.rm = TRUE)) |>
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ggplot() +
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geom_col(aes(x = CBSA,
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y = mean_hours_above)) +
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labs(title = paste0("Hours above ", aqi_threshhold * 2," AQI"),
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y = "Mean hours per year",
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x = "City",
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fill = NULL)
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aqi_data |>
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ggplot() +
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geom_violin(aes(x = CBSA,
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y = AQI)) +
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labs(title = "AQI",
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y = "AQI",
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x = "City",
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fill = NULL) +
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coord_cartesian(ylim = (c(0,500)))
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aqi_data |>
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group_by(year, month, CBSA) |>
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summarise(hours_above = sum(AQI >= aqi_threshhold, na.rm = TRUE)) |>
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group_by(CBSA, month) |>
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summarise(mean_hours_above = mean(hours_above, na.rm = TRUE)) |>
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ggplot() +
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geom_line(aes(x = month,
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y = mean_hours_above,
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color = CBSA)) +
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scale_x_continuous(breaks = seq(1,12,1)) +
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scale_color_brewer(type = "qual") +
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labs(title = paste0("Hours with an AQI of greater than or equal to ", aqi_threshhold),
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y = "Mean days per month",
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x = "Month",
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color = "City")
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```
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