Visualizing time series data I

Lecture 8

Dr. Greg Chism

University of Arizona
INFO 526 - Spring 2024

Warm up

Announcements

• Final Project 1 due Monday, March 11th, 11am

• Project 1 presentations – Monday March 11th in class, all team members must be there

Setup

# load packages
library(countdown)
library(tidyverse)
library(lubridate)
library(janitor)
library(colorspace)
library(broom)
library(fs)

# set theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14))

# set width of code output
options(width = 65)

# set figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 7, # 7" width
  fig.asp = 0.618, # the golden ratio
  fig.retina = 3, # dpi multiplier for displaying HTML output on retina
  fig.align = "center", # center align figures
  dpi = 300 # higher dpi, sharper image
)

Working with dates

Air Quality Index

• The AQI is the Environmental Protection Agency’s index for reporting air quality

• Higher values of AQI indicate worse air quality

Source: https://www.airnow.gov/aqi-basics

AQI levels

The previous graphic in tibble form, to be used later…

aqi_levels <- tribble(
  ~aqi_min, ~aqi_max, ~color,    ~level,
  0,        50,       "#D8EEDA", "Good",
  51,       100,      "#F1E7D4", "Moderate",
  101,      150,      "#F8E4D8", "Unhealthy for sensitive groups",
  151,      200,      "#FEE2E1", "Unhealthy",
  201,      300,      "#F4E3F7", "Very unhealthy",
  301,      400,      "#F9D0D4", "Hazardous"
)

AQI data

• Source: EPA’s Daily Air Quality Tracker

• 2016 - 2022 AQI (Ozone and PM2.5 combined) for Tucson, AZ core-based statistical area (CBSA), one file per year

• 2016 - 2022 AQI (Ozone and PM2.5 combined) for San Francisco-Oakland-Hayward, CA CBSA, one file per year

2022 Tucson, AZ

• Load data

tuc_2022 <- read_csv(here::here("data/tucson/ad_aqi_tracker_data-2022.csv"))

• Metadata

dim(tuc_2022)

[1] 365  11

names(tuc_2022)

 [1] "Date"                       "AQI Value"                 
 [3] "Main Pollutant"             "Site Name"                 
 [5] "Site ID"                    "Source"                    
 [7] "20-year High (2000-2019)"   "20-year Low (2000-2019)"   
 [9] "5-year Average (2015-2019)" "Date of 20-year High"      
[11] "Date of 20-year Low"       

Clean variable names

tuc_2022 <- tuc_2022 |>
  janitor::clean_names()

names(tuc_2022)

 [1] "date"                      "aqi_value"                
 [3] "main_pollutant"            "site_name"                
 [5] "site_id"                   "source"                   
 [7] "x20_year_high_2000_2019"   "x20_year_low_2000_2019"   
 [9] "x5_year_average_2015_2019" "date_of_20_year_high"     
[11] "date_of_20_year_low"      

First look

This plot looks quite bizarre. What might be going on?

ggplot(tuc_2022, aes(x = date, y = aqi_value, group = 1)) +
  geom_line()

Peek at data

tuc_2022 |>
  select(date, aqi_value, site_name, site_id)

# A tibble: 365 × 4
   date       aqi_value site_name    site_id    
   <chr>          <dbl> <chr>        <chr>      
 1 01/01/2022        40 GERONIMO     04-019-1113
 2 01/02/2022        55 GERONIMO     04-019-1113
 3 01/03/2022        55 GERONIMO     04-019-1113
 4 01/04/2022        48 GERONIMO     04-019-1113
 5 01/05/2022        50 GERONIMO     04-019-1113
 6 01/06/2022        45 GERONIMO     04-019-1113
 7 01/07/2022        42 SAGUARO PARK 04-019-0021
 8 01/08/2022        42 SAGUARO PARK 04-019-0021
 9 01/09/2022        41 GERONIMO     04-019-1113
10 01/10/2022        38 FAIRGROUNDS  04-019-1020
# ℹ 355 more rows

Transforming date

Using lubridate::mdy():

tuc_2022 |>
  mutate(date = mdy(date))

# A tibble: 365 × 11
   date       aqi_value main_pollutant site_name   site_id source
   <date>         <dbl> <chr>          <chr>       <chr>   <chr> 
 1 2022-01-01        40 PM2.5          GERONIMO    04-019… AQS   
 2 2022-01-02        55 PM2.5          GERONIMO    04-019… AQS   
 3 2022-01-03        55 PM2.5          GERONIMO    04-019… AQS   
 4 2022-01-04        48 PM2.5          GERONIMO    04-019… AQS   
 5 2022-01-05        50 PM2.5          GERONIMO    04-019… AQS   
 6 2022-01-06        45 PM2.5          GERONIMO    04-019… AQS   
 7 2022-01-07        42 Ozone          SAGUARO PA… 04-019… AQS   
 8 2022-01-08        42 Ozone          SAGUARO PA… 04-019… AQS   
 9 2022-01-09        41 PM2.5          GERONIMO    04-019… AQS   
10 2022-01-10        38 Ozone          FAIRGROUNDS 04-019… AQS   
# ℹ 355 more rows
# ℹ 5 more variables: x20_year_high_2000_2019 <dbl>,
#   x20_year_low_2000_2019 <dbl>,
#   x5_year_average_2015_2019 <dbl>, date_of_20_year_high <chr>,
#   date_of_20_year_low <chr>

Investigating AQI values

• Take a peek at distinct values of AQI

tuc_2022 |>
  distinct(aqi_value) |>
  pull()

 [1]  40  55  48  50  45  42  41  38  37  43  44  35  46  52  49
[16]  58  64  51  47  61  71  54  77  84  90  67  97 101  87 100
[31]  93  53  74  80 105 119 108  94  31  39  34  59  32  28  27
[46]  33  36  60  81

• "." likely indicates NA, and it’s causing the entire column to be read in as characters

Rewind, and start over

tuc_2022 <- read_csv(
  here::here("data/tucson/ad_aqi_tracker_data-2022.csv"),
  na = c(".", "")
)

glimpse(tuc_2022)

Rows: 365
Columns: 11
$ Date                         <chr> "01/01/2022", "01/02/2022"…
$ `AQI Value`                  <dbl> 40, 55, 55, 48, 50, 45, 42…
$ `Main Pollutant`             <chr> "PM2.5", "PM2.5", "PM2.5",…
$ `Site Name`                  <chr> "GERONIMO", "GERONIMO", "G…
$ `Site ID`                    <chr> "04-019-1113", "04-019-111…
$ Source                       <chr> "AQS", "AQS", "AQS", "AQS"…
$ `20-year High (2000-2019)`   <dbl> 115, 57, 67, 55, 52, 55, 5…
$ `20-year Low (2000-2019)`    <dbl> 35, 31, 29, 27, 28, 25, 31…
$ `5-year Average (2015-2019)` <dbl> 62.2, 43.2, 43.6, 40.4, 36…
$ `Date of 20-year High`       <chr> "01/01/2018", "01/02/2015"…
$ `Date of 20-year Low`        <chr> "01/01/2001", "01/02/2016"…

Data cleaning

tuc_2022 <- tuc_2022 |>
  janitor::clean_names() |>
  mutate(date = mdy(date))

tuc_2022

# A tibble: 365 × 11
   date       aqi_value main_pollutant site_name   site_id source
   <date>         <dbl> <chr>          <chr>       <chr>   <chr> 
 1 2022-01-01        40 PM2.5          GERONIMO    04-019… AQS   
 2 2022-01-02        55 PM2.5          GERONIMO    04-019… AQS   
 3 2022-01-03        55 PM2.5          GERONIMO    04-019… AQS   
 4 2022-01-04        48 PM2.5          GERONIMO    04-019… AQS   
 5 2022-01-05        50 PM2.5          GERONIMO    04-019… AQS   
 6 2022-01-06        45 PM2.5          GERONIMO    04-019… AQS   
 7 2022-01-07        42 Ozone          SAGUARO PA… 04-019… AQS   
 8 2022-01-08        42 Ozone          SAGUARO PA… 04-019… AQS   
 9 2022-01-09        41 PM2.5          GERONIMO    04-019… AQS   
10 2022-01-10        38 Ozone          FAIRGROUNDS 04-019… AQS   
# ℹ 355 more rows
# ℹ 5 more variables: x20_year_high_2000_2019 <dbl>,
#   x20_year_low_2000_2019 <dbl>,
#   x5_year_average_2015_2019 <dbl>, date_of_20_year_high <chr>,
#   date_of_20_year_low <chr>

Another look

ggplot(tuc_2022, aes(x = date, y = aqi_value, group = 1)) +
  geom_line()

How would you improve this visualization?

Visualizing Tucson AQI

Livecoding

Reveal below for code developed during live coding session.

aqi_levels <- aqi_levels |>
  mutate(aqi_mid = ((aqi_min + aqi_max) / 2))

tuc_2022 |>
  filter(!is.na(aqi_value)) |>
  ggplot(aes(x = date, y = aqi_value, group = 1)) +
  geom_rect(
    data = aqi_levels,
    aes(
      ymin = aqi_min, ymax = aqi_max,
      xmin = as.Date(-Inf), xmax = as.Date(Inf),
      fill = color, y = NULL, x = NULL
    )
  ) +
  geom_line(linewidth = 1) +
  scale_fill_identity() +
  scale_x_date(
    name = NULL, date_labels = "%b",
    limits = c(ymd("2022-01-01"), ymd("2023-03-01"))
  ) +
  geom_text(
    data = aqi_levels,
    aes(x = ymd("2023-02-28"), y = aqi_mid, label = level),
    hjust = 1, size = 6, fontface = "bold", color = "white"
  ) +
  annotate(
    geom = "text",
    x = c(ymd("2022-01-01"), ymd("2023-03-01")), y = -100,
    label = c("2022", "2023"), size = 4
  ) +
  coord_cartesian(clip = "off", ylim = c(0, 400)) +
  labs(
    x = NULL, y = "AQI",
    title = "Ozone and PM2.5 Daily AQI Values",
    subtitle = "Tucson, AZ",
    caption = "\nSource: EPA Daily Air Quality Tracker"
  ) +
  theme(
    plot.title.position = "plot",
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    plot.margin = unit(c(1, 1, 3, 1), "lines")
  )