Visualizing time series data I

Lecture 8

Dr. Mine Çetinkaya-Rundel

Duke University
STA 313 - Spring 2023

Warm up

Announcements

Project 1 proposal due Friday, 5pm
RQ 3 due Tuesday by class, covers everything since last reading quiz
Project 1 presentations – Feb 22 in lab, 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

AQI Basics for Ozone and Particle Pollution

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 Durham-Chapel Hill, NC 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 Durham-Chapel Hill

Load data

dch_2022 <- read_csv(here::here("data/durham-chapel-hill/ad_aqi_tracker_data-2022.csv"))

Metadata

dim(dch_2022)

[1] 365  11

names(dch_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

dch_2022 <- dch_2022 |>
  janitor::clean_names()

names(dch_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(dch_2022, aes(x = date, y = aqi_value, group = 1)) +
  geom_line()

Peek at data

dch_2022 |>
  select(date, aqi_value, site_name, site_id)

# A tibble: 365 × 4
   date       aqi_value site_name     site_id    
   <chr>      <chr>     <chr>         <chr>      
 1 01/01/2022 22        Durham Armory 37-063-0015
 2 01/02/2022 12        Durham Armory 37-063-0015
 3 01/03/2022 10        Durham Armory 37-063-0015
 4 01/04/2022 21        Durham Armory 37-063-0015
 5 01/05/2022 35        Durham Armory 37-063-0015
 6 01/06/2022 29        Durham Armory 37-063-0015
 7 01/07/2022 15        Durham Armory 37-063-0015
 8 01/08/2022 28        Durham Armory 37-063-0015
 9 01/09/2022 28        Durham Armory 37-063-0015
10 01/10/2022 15        Durham Armory 37-063-0015
# … with 355 more rows

Transforming date

Using lubridate::mdy():

dch_2022 |>
  mutate(date = mdy(date))

# A tibble: 365 × 11
   date       aqi_value main_pol…¹ site_…² site_id source x20_y…³
   <date>     <chr>     <chr>      <chr>   <chr>   <chr>    <dbl>
 1 2022-01-01 22        PM2.5      Durham… 37-063… AQS        111
 2 2022-01-02 12        PM2.5      Durham… 37-063… AQS         76
 3 2022-01-03 10        PM2.5      Durham… 37-063… AQS         66
 4 2022-01-04 21        PM2.5      Durham… 37-063… AQS         61
 5 2022-01-05 35        PM2.5      Durham… 37-063… AQS         83
 6 2022-01-06 29        PM2.5      Durham… 37-063… AQS         71
 7 2022-01-07 15        PM2.5      Durham… 37-063… AQS         75
 8 2022-01-08 28        PM2.5      Durham… 37-063… AQS         76
 9 2022-01-09 28        PM2.5      Durham… 37-063… AQS         57
10 2022-01-10 15        PM2.5      Durham… 37-063… AQS         71
# … with 355 more rows, 4 more variables:
#   x20_year_low_2000_2019 <dbl>,
#   x5_year_average_2015_2019 <dbl>, date_of_20_year_high <chr>,
#   date_of_20_year_low <chr>, and abbreviated variable names
#   ¹main_pollutant, ²site_name, ³x20_year_high_2000_2019

Transforming AQI values

What does this warning mean?

dch_2022 |>
  mutate(aqi_value = as.numeric(aqi_value))

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `aqi_value = as.numeric(aqi_value)`.
Caused by warning:
! NAs introduced by coercion

# A tibble: 365 × 11
  date     aqi_v…¹ main_…² site_…³ site_id source x20_y…⁴ x20_y…⁵
  <chr>      <dbl> <chr>   <chr>   <chr>   <chr>    <dbl>   <dbl>
1 01/01/2…      22 PM2.5   Durham… 37-063… AQS        111      10
2 01/02/2…      12 PM2.5   Durham… 37-063… AQS         76       8
3 01/03/2…      10 PM2.5   Durham… 37-063… AQS         66      14
# … with 362 more rows, 3 more variables:
#   x5_year_average_2015_2019 <dbl>, date_of_20_year_high <chr>,
#   date_of_20_year_low <chr>, and abbreviated variable names
#   ¹aqi_value, ²main_pollutant, ³site_name,
#   ⁴x20_year_high_2000_2019, ⁵x20_year_low_2000_2019

Investigating AQI values

Take a peek at distinct values of AQI

dch_2022 |>
  distinct(aqi_value) |>
  pull()

 [1] "22" "12" "10" "21" "35" "29" "15" "28" "25" "36" "49" "19"
[13] "24" "46" "38" "48" "34" "47" "57" "26" "45" "42" "13" "31"
[25] "39" "62" "40" "30" "20" "18" "27" "67" "41" "56" "37" "32"
[37] "43" "51" "50" "44" "33" "54" "52" "77" "74" "53" "58" "64"
[49] "61" "93" "16" "17" "23" "5"  "6"  "."  "9"  "14" "59"

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

Rewind, and start over

dch_2022 <- read_csv(
  here::here("data/durham-chapel-hill/ad_aqi_tracker_data-2022.csv"),
  na = c(".", "")
)

glimpse(dch_2022)

Rows: 365
Columns: 11
$ Date                         <chr> "01/01/2022", "01/02/2022"…
$ `AQI Value`                  <dbl> 22, 12, 10, 21, 35, 29, 15…
$ `Main Pollutant`             <chr> "PM2.5", "PM2.5", "PM2.5",…
$ `Site Name`                  <chr> "Durham Armory", "Durham A…
$ `Site ID`                    <chr> "37-063-0015", "37-063-001…
$ Source                       <chr> "AQS", "AQS", "AQS", "AQS"…
$ `20-year High (2000-2019)`   <dbl> 111, 76, 66, 61, 83, 71, 7…
$ `20-year Low (2000-2019)`    <dbl> 10, 8, 14, 9, 8, 15, 18, 1…
$ `5-year Average (2015-2019)` <dbl> 39.2, 36.8, 38.2, 30.4, 26…
$ `Date of 20-year High`       <chr> "01/01/2000", "01/02/2005"…
$ `Date of 20-year Low`        <chr> "01/01/2007", "01/02/2012"…

Data cleaning

dch_2022 <- dch_2022 |>
  janitor::clean_names() |>
  mutate(date = mdy(date))

dch_2022

# A tibble: 365 × 11
   date       aqi_value main_pol…¹ site_…² site_id source x20_y…³
   <date>         <dbl> <chr>      <chr>   <chr>   <chr>    <dbl>
 1 2022-01-01        22 PM2.5      Durham… 37-063… AQS        111
 2 2022-01-02        12 PM2.5      Durham… 37-063… AQS         76
 3 2022-01-03        10 PM2.5      Durham… 37-063… AQS         66
 4 2022-01-04        21 PM2.5      Durham… 37-063… AQS         61
 5 2022-01-05        35 PM2.5      Durham… 37-063… AQS         83
 6 2022-01-06        29 PM2.5      Durham… 37-063… AQS         71
 7 2022-01-07        15 PM2.5      Durham… 37-063… AQS         75
 8 2022-01-08        28 PM2.5      Durham… 37-063… AQS         76
 9 2022-01-09        28 PM2.5      Durham… 37-063… AQS         57
10 2022-01-10        15 PM2.5      Durham… 37-063… AQS         71
# … with 355 more rows, 4 more variables:
#   x20_year_low_2000_2019 <dbl>,
#   x5_year_average_2015_2019 <dbl>, date_of_20_year_high <chr>,
#   date_of_20_year_low <chr>, and abbreviated variable names
#   ¹main_pollutant, ²site_name, ³x20_year_high_2000_2019

Another look

ggplot(dch_2022, aes(x = date, y = aqi_value, group = 1)) +
  geom_line()

How would you improve this visualization?

Visualizing Durham AQI

Recreate the following visualization.