Visualizing time series data I

Lecture 6

Dr. Greg Chism

University of Arizona
INFO 526 - Summer 2024

HW 1 lessons

Highlights

• Review HW 1 issues, and show me you reviewed them by closing the issue.

• DO NOT hard code paths! Use the here package to help with relative paths, if you need. e.g., data <- read_csv(here("data", "data.csv"))

• Make sure that all packages are installed/loaded. I suggest the pacman workflow we saw before:

if (!require("pacman")) 
  install.packages("pacman")

pacman::p_load(package1, package2, ...)
pacman::p_load_gh("GitHubPackage1")

HW 1 lessons learned cont…

1. Start very early. No late work exceptions
2. Ask your peers.
   1. Peers will likely have the answer
   2. Peers will likely get to the question before I will.
3. Ask descriptive questions. See this page on asking effective questions.
4. Please respect my work hours. I do not reply to messages after 5pm on work days and at all on weekends.

Setup

# load packages
library(countdown)
library(tidyverse)
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/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

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"
)

tuc_2022 <- read_csv("https://raw.githubusercontent.com/INFO-526-SU24/INFO-526-SU24/main/slides/06/data/tucson/ad_aqi_tracker_data-2022.csv",
                     na = c(".", ""))

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

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")
  )