COVID-19 Vaccine Side Effects - a rough night, in data

July 07, 2022

In April 2021 I got my first COVID-19 vaccination. The side effects were pretty hard going - for about 24 hours I was laid out, and woke up in the middle of the night to my smart watch giving me an “extreme heart rate warning”, which is just the kind of thing you want to see after getting injected with a vaccine for a virus sweeping the globe!

Once I felt more alive, I had the idea of extracting the heart rate data from my watch. As it turns out, there is an excellent Python package, fitparse, that allows the parsing of the .FIT files from Fitbit watches. I won’t post the raw data here and the associated code, but it was pretty simple to get a few days’ worth of data from the watch and plot it. Easy to see how bad things were; which made me extremely grateful for getting vaccinated and not the real thing!

I’m posting this because the plot took a little while to do, and has some functions I don’t use often and am always looking up. The trickiest part of this plot was adding annotations to the X axis - because its a datetime object, things need to be expressed also as datetimes. This took some creativity to make a readable attempt, because while the annotate method gives flexibility in how you specify the places to add the text, the raw data coordinates are the easiest approach, and the raw string representation of the dates also messes up the temporal ordering of the plot.

# Imports
import pandas as pd
import seaborn as sns

import matplotlib.pyplot as plt

# Data preprocessing
hr = (pd.read_csv('https://raw.githubusercontent.com/alexjonesphd/alexjonesphd.github.io/master/assets/covid/vaccination_experience.csv') # read
      .assign(date=lambda x: pd.to_datetime(x['timestamp'], format='%Y-%m-%d')) # new column, time as datetime object
      .sort_values(by='date') # sort by date
      .dropna(subset='heart_rate', how='any') # drop missing HR observations
      .query('heart_rate > 50') # Keep obs above 50bpm
      .assign(rolling_30=lambda x: x['heart_rate'].rolling(30).mean()) # new column with 30min rolling average
      .reset_index(drop=True) # clean index
     )

# Display
hr.head()

	timestamp	stress_level	heart_rate	date	rolling_30
0	2021-04-22 23:00:00	30	84.0	2021-04-22 23:00:00	NaN
1	2021-04-22 23:01:00	21	68.0	2021-04-22 23:01:00	NaN
2	2021-04-22 23:02:00	18	67.0	2021-04-22 23:02:00	NaN
3	2021-04-22 23:03:00	19	63.0	2021-04-22 23:03:00	NaN
4	2021-04-22 23:04:00	15	62.0	2021-04-22 23:04:00	NaN

There were also a bunch of things that happened I want to annotate on the plot, so first I’ll make a dictionary that contains them and their associated time points, and my HR at the time (I worked backwards from the plot to figure these out, but it makes the code tidier to include here). These will be used as coorindates for the annotate method of an axis object.

# Event log dictionary, using datetime spec for X locations - the strings will need parsing into datetimes
# Format follows XX, YY...
events = {'Morning dog walk': [('2021-04-23 10:45', 105), ('2021-04-23 10:45', 109)],
          'Vaccinated!': [('2021-04-23 17', 125), ('2021-04-23 18', 135)],
          'Evening dog walk': [('2021-04-23 19:30', 108), ('2021-04-23 21', 125)],
          'Suspiciously high for\njust watching TV...': [('2021-04-23 22:30', 98), ('2021-04-23 22:30', 115)],
          'Sleep': [('2021-04-24 00', 68), ('2021-04-24 00', 55)],
          'Wake up with chills': [('2021-04-24 01', 83), ('2021-04-24 01', 65)],
          'Abnormal heart rate warning': [('2021-04-24 03:55', 134), ('2021-04-24 03:58', 140)],
          'Move downstairs': [('2021-04-24 12:30', 155), ('2021-04-24 13:15', 160)]
         }

Plot and annotate some of the events of the 24 hours and what I was doing, and do it in the xkcd style.

Vaccines are great, and having a heart rate over 150bpm just walking down the stairs is worth it!

# Set up plotting context, style, and fig/ax
sns.set_context('poster', font_scale=2)
with plt.xkcd(scale=0.9, length=100, randomness=200):
    
    # Fig/axis
    fig, ax = plt.subplots(1, 1, figsize=(50, 20))
    sns.despine(fig) # Turn off up/right axis

    # Plot both raw and rolling lines separatey for easier control
    sns.lineplot(data=hr, ax=ax,
                 x='date', y='heart_rate', 
                 alpha=.2, color='black', 
                 label='Raw Heart Rate')

    sns.lineplot(data=hr, ax=ax,
                 x='date', y='rolling_30', 
                 color='black', linestyle='--',
                 linewidth=8,
                 label='Rolling 30 minute average')

    # Add a horizontal line at 100bpm, shift legend, add a shaded block for the 23rd and annotate the 23rd/24th
    ax.axhline(100, color='black', alpha=.5)
    ax.legend(loc='upper left')
    ax.axvspan('2021-04-23 00:00:00', '2021-04-24 00:00:00', color='silver', alpha=.05)
    ax.text(.27, .80, '23rd', transform=ax.transAxes, fontsize=80)
    ax.text(.85, .80, '24th', transform=ax.transAxes, fontsize=80)
    
    # Add the annotations - the X coord needs converting to datetime
    for event, time in events.items():
        
        # Converts the first element - cleanest way I could think of
        time_ = [(pd.to_datetime(t[0]), t[1]) for t in time]
        
        # Add annotation
        ax.annotate(event, *time_, arrowprops={'width': 1}, fontsize=30)

    # Clean up axes, setting an x tick every four hours in date-time format, then relabelling
    ax.set(ylabel='Heart Rate\nbpm', xlabel='Time', ylim=(50, 160),
           xticks=[f'2021-04-{d} {h:02}:00:00' for d in [23, 24] for h in [0, 4, 8, 12, 16, 20]],
           xticklabels=[f'{x:02}:00' for x in [0, 4, 8, 12, 16, 20]]*2)