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@ -0,0 +1,741 @@ |
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Let's do a deep dive and start visualizing my life using Fitbit and |
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Matplotlib. |
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# What is Fitbit |
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[Fitbit](https://www.fitbit.com) is a fitness watch that tracks your sleep, heart rate, and activity. |
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Fitbit is able to track your steps, however, it is also able to detect multiple types of activity |
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like running, walking, "sport" and biking. |
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# What is Matplotlib |
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[Matplotlib](https://matplotlib.org/) is a python visualization library that enables you to create bar graphs, line graphs, distributions and many more things. |
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Being able to visualize your results is essential to any person working with data at any scale. |
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Although I like [GGplot](https://ggplot2.tidyverse.org/) in R more than Matplotlib, Matplotlib is still my go to graphing library for Python. |
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# Getting Your Fitbit Data |
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There are two main ways that you can get your Fitbit data: |
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- Fitbit API |
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- Data Archival Export |
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Since connecting to the API and setting up all the web hooks can be a |
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pain, I'm just going to use the data export option because this is |
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only for one person. You can export your data here: |
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[https://www.fitbit.com/settings/data/export](https://www.fitbit.com/settings/data/export). |
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The Fitbit data archive was very organized and kept meticulous records |
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of everything. All of the data was organized in separate JSON files |
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labeled by date. Fitbit keeps around 1MB of data on you per day; most |
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of this data is from the heart rate sensors. Although 1MB of data may |
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sound like a ton of data, it is probably a lot less if you store it in |
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formats other than JSON. When I downloaded the compressed file it was |
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20MB, but when I extracted it, it was 380MB! I've only been using |
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Fitbit for 11 months at this point. |
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## Sleep |
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Sleep is something fun to visualize. No matter how much of it you get |
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you still feel tired as a college student. In the "sleep_score" folder |
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of the exported data you will find a single CSV file with your resting |
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heart rate and Fitbit's computed sleep scores. Interesting enough, |
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this is the only file that comes in the CSV format, everything else is |
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JSON file. |
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We can read in all the data using a single liner with the |
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[Pandas](https://pandas.pydata.org/) python library. |
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```python |
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import matplotlib.pyplot as plt |
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import pandas as pd |
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sleep_score_df = pd.read_csv('data/sleep/sleep_score.csv') |
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``` |
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```python |
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print(sleep_score_df) |
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``` |
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sleep_log_entry_id timestamp overall_score \ |
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0 26093459526 2020-02-27T06:04:30Z 80 |
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1 26081303207 2020-02-26T06:13:30Z 83 |
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2 26062481322 2020-02-25T06:00:30Z 82 |
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3 26045941555 2020-02-24T05:49:30Z 79 |
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4 26034268762 2020-02-23T08:35:30Z 75 |
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.. ... ... ... |
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176 23696231032 2019-09-02T07:38:30Z 79 |
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177 23684345925 2019-09-01T07:15:30Z 84 |
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178 23673204871 2019-08-31T07:11:00Z 74 |
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179 23661278483 2019-08-30T06:34:00Z 73 |
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180 23646265400 2019-08-29T05:55:00Z 80 |
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composition_score revitalization_score duration_score \ |
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0 20 19 41 |
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1 22 21 40 |
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2 22 21 39 |
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3 17 20 42 |
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4 20 16 39 |
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.. ... ... ... |
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176 20 20 39 |
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177 22 21 41 |
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178 18 21 35 |
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179 17 19 37 |
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180 21 21 38 |
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deep_sleep_in_minutes resting_heart_rate restlessness |
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0 65 60 0.117330 |
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1 85 60 0.113188 |
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2 95 60 0.120635 |
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3 52 61 0.111224 |
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4 43 59 0.154774 |
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.. ... ... ... |
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176 88 56 0.170923 |
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177 95 56 0.133268 |
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178 73 56 0.102703 |
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179 50 55 0.121086 |
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180 61 57 0.112961 |
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[181 rows x 9 columns] |
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With the Pandas library you can generate Matplotlib graphs. Although |
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you can directly use Matplotlib, the wrapper functions using Pandas |
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makes it easier to use. |
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## Sleep Score Histogram |
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```python |
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sleep_score_df.hist(column='overall_score') |
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``` |
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array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc2c0a270d0>]], |
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dtype=object) |
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## Heart Rate |
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Fitbit keeps their calculated heart rates in the sleep scores file |
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rather than heart. Knowing your resting heart rate is useful because |
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it is a good indicator of your overall health. |
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```python |
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sleep_score_df.hist(column='resting_heart_rate') |
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``` |
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array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc2917a6090>]], |
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dtype=object) |
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## Resting Heart Rate Time Graph |
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Using the pandas wrapper we can quickly create a heart rate graph over |
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time. |
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```python |
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sleep_score_df.plot(kind='line', y='resting_heart_rate', x ='timestamp', legend=False, title="Resting Heart Rate(BPM)") |
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``` |
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<matplotlib.axes._subplots.AxesSubplot at 0x7fc28f609b50> |
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However, as we notice with the graph above, the time axis is wack. In |
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the pandas data frame everything was stored as a string timestamp. We |
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can convert this into a datetime object by telling pandas to parse the |
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date as it reads it. |
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```python |
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sleep_score_df = pd.read_csv('data/sleep/sleep_score.csv', parse_dates=[1]) |
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sleep_score_df.plot(kind='line', y='resting_heart_rate', x ='timestamp', legend=False, title="Resting Heart Rate(BPM)") |
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``` |
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<matplotlib.axes._subplots.AxesSubplot at 0x7fc28f533510> |
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To fully manipulate the graphs, we need to use some matplotlib code to |
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do things like setting the axis labels or make multiple plots right |
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next to each other. We can create grab the current axis being used by |
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matplotlib by using plt.gca(). |
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```python |
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ax = plt.gca() |
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sleep_score_df.plot(kind='line', y='resting_heart_rate', x ='timestamp', legend=False, title="Resting Heart Rate Graph", ax=ax, figsize=(10, 5)) |
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plt.xlabel("Date") |
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plt.ylabel("Resting Heart Rate (BPM)") |
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plt.show() |
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#plt.savefig('restingHeartRate.svg') |
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``` |
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The same thing can be done with sleep scores. It is interesting to |
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note that the sleep scores rarely vary anything between 75 and 85. |
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```python |
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ax = plt.gca() |
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sleep_score_df.plot(kind='line', y='overall_score', x ='timestamp', legend=False, title="Sleep Score Time Series Graph", ax=ax) |
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plt.xlabel("Date") |
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plt.ylabel("Fitbit's Sleep Score") |
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plt.show() |
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``` |
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Using Pandas we can generate a new column with a specific date |
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attribute like year, day, month, or weekday. If we add a new column |
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for weekday, we can then group by weekday and collapse them all into a |
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single column by summing or averaging the value. |
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```python |
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temp = pd.DatetimeIndex(sleep_score_df['timestamp']) |
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sleep_score_df['weekday'] = temp.weekday |
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print(sleep_score_df) |
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``` |
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sleep_log_entry_id timestamp overall_score \ |
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0 26093459526 2020-02-27 06:04:30+00:00 80 |
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1 26081303207 2020-02-26 06:13:30+00:00 83 |
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2 26062481322 2020-02-25 06:00:30+00:00 82 |
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3 26045941555 2020-02-24 05:49:30+00:00 79 |
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4 26034268762 2020-02-23 08:35:30+00:00 75 |
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.. ... ... ... |
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176 23696231032 2019-09-02 07:38:30+00:00 79 |
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177 23684345925 2019-09-01 07:15:30+00:00 84 |
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178 23673204871 2019-08-31 07:11:00+00:00 74 |
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179 23661278483 2019-08-30 06:34:00+00:00 73 |
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180 23646265400 2019-08-29 05:55:00+00:00 80 |
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composition_score revitalization_score duration_score \ |
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0 20 19 41 |
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1 22 21 40 |
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2 22 21 39 |
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3 17 20 42 |
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4 20 16 39 |
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.. ... ... ... |
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176 20 20 39 |
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177 22 21 41 |
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178 18 21 35 |
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179 17 19 37 |
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180 21 21 38 |
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deep_sleep_in_minutes resting_heart_rate restlessness weekday |
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0 65 60 0.117330 3 |
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1 85 60 0.113188 2 |
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2 95 60 0.120635 1 |
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3 52 61 0.111224 0 |
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4 43 59 0.154774 6 |
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.. ... ... ... ... |
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176 88 56 0.170923 0 |
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177 95 56 0.133268 6 |
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178 73 56 0.102703 5 |
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179 50 55 0.121086 4 |
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180 61 57 0.112961 3 |
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[181 rows x 10 columns] |
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```python |
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print(sleep_score_df.groupby('weekday').mean()) |
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``` |
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sleep_log_entry_id overall_score composition_score \ |
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weekday |
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0 2.483733e+10 79.576923 20.269231 |
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1 2.485200e+10 77.423077 20.423077 |
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2 2.490383e+10 80.880000 21.120000 |
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3 2.483418e+10 76.814815 20.370370 |
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4 2.480085e+10 79.769231 20.961538 |
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5 2.477002e+10 78.840000 20.520000 |
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6 2.482581e+10 77.230769 20.269231 |
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revitalization_score duration_score deep_sleep_in_minutes \ |
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weekday |
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0 19.153846 40.153846 88.000000 |
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1 19.000000 38.000000 83.846154 |
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2 19.400000 40.360000 93.760000 |
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3 19.037037 37.407407 82.592593 |
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4 19.346154 39.461538 94.461538 |
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5 19.080000 39.240000 93.720000 |
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6 18.269231 38.692308 89.423077 |
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resting_heart_rate restlessness |
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weekday |
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0 58.576923 0.139440 |
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1 58.538462 0.142984 |
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2 58.560000 0.138661 |
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3 58.333333 0.135819 |
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4 58.269231 0.129791 |
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5 58.080000 0.138315 |
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6 58.153846 0.147171 |
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## Sleep Score Based on Day |
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```python |
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ax = plt.gca() |
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sleep_score_df.groupby('weekday').mean().plot(kind='line', y='overall_score', ax = ax) |
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plt.ylabel("Sleep Score") |
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plt.title("Sleep Scores on Varying Days of Week") |
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plt.show() |
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``` |
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## Sleep Score Based on Days of Week |
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```python |
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ax = plt.gca() |
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sleep_score_df.groupby('weekday').mean().plot(kind='line', y='resting_heart_rate', ax = ax) |
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plt.ylabel("Resting heart rate (BPM)") |
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plt.title("Resting Heart Rate Varying Days of Week") |
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plt.show() |
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``` |
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# Calories |
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Fitbit keeps all of their calorie data in JSON files representing |
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sequence data at 1 minute increments. To extrapolate calorie data we |
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need to group by day and then sum the days to get the total calories |
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burned per day. |
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```python |
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calories_df = pd.read_json("data/calories/calories-2019-07-01.json", convert_dates=True) |
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``` |
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```python |
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print(calories_df) |
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``` |
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dateTime value |
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0 2019-07-01 00:00:00 1.07 |
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1 2019-07-01 00:01:00 1.07 |
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2 2019-07-01 00:02:00 1.07 |
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3 2019-07-01 00:03:00 1.07 |
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4 2019-07-01 00:04:00 1.07 |
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... ... ... |
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43195 2019-07-30 23:55:00 1.07 |
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43196 2019-07-30 23:56:00 1.07 |
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43197 2019-07-30 23:57:00 1.07 |
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43198 2019-07-30 23:58:00 1.07 |
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43199 2019-07-30 23:59:00 1.07 |
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[43200 rows x 2 columns] |
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```python |
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import datetime |
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calories_df['date_minus_time'] = calories_df["dateTime"].apply( lambda calories_df : |
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datetime.datetime(year=calories_df.year, month=calories_df.month, day=calories_df.day)) |
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calories_df.set_index(calories_df["date_minus_time"],inplace=True) |
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print(calories_df) |
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``` |
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dateTime value date_minus_time |
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date_minus_time |
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2019-07-01 2019-07-01 00:00:00 1.07 2019-07-01 |
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2019-07-01 2019-07-01 00:01:00 1.07 2019-07-01 |
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2019-07-01 2019-07-01 00:02:00 1.07 2019-07-01 |
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2019-07-01 2019-07-01 00:03:00 1.07 2019-07-01 |
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2019-07-01 2019-07-01 00:04:00 1.07 2019-07-01 |
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... ... ... ... |
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2019-07-30 2019-07-30 23:55:00 1.07 2019-07-30 |
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2019-07-30 2019-07-30 23:56:00 1.07 2019-07-30 |
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2019-07-30 2019-07-30 23:57:00 1.07 2019-07-30 |
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2019-07-30 2019-07-30 23:58:00 1.07 2019-07-30 |
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2019-07-30 2019-07-30 23:59:00 1.07 2019-07-30 |
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[43200 rows x 3 columns] |
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```python |
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calories_per_day = calories_df.resample('D').sum() |
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print(calories_per_day) |
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``` |
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value |
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date_minus_time |
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2019-07-01 3422.68 |
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2019-07-02 2705.85 |
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2019-07-03 2871.73 |
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2019-07-04 4089.93 |
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2019-07-05 3917.91 |
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2019-07-06 2762.55 |
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2019-07-07 2929.58 |
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2019-07-08 2698.99 |
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2019-07-09 2833.27 |
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2019-07-10 2529.21 |
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2019-07-11 2634.25 |
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2019-07-12 2953.91 |
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2019-07-13 4247.45 |
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2019-07-14 2998.35 |
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2019-07-15 2846.18 |
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2019-07-16 3084.39 |
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2019-07-17 2331.06 |
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2019-07-18 2849.20 |
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2019-07-19 2071.63 |
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2019-07-20 2746.25 |
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2019-07-21 2562.11 |
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2019-07-22 1892.99 |
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2019-07-23 2372.89 |
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2019-07-24 2320.42 |
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2019-07-25 2140.87 |
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2019-07-26 2430.38 |
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2019-07-27 3769.04 |
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2019-07-28 2036.24 |
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2019-07-29 2814.87 |
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2019-07-30 2077.82 |
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```python |
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ax = plt.gca() |
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calories_per_day.plot(kind='hist', title="Calorie Distribution", legend=False, ax=ax) |
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plt.show() |
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``` |
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```python |
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ax = plt.gca() |
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calories_per_day.plot(kind='line', y='value', legend=False, title="Calories Per Day", ax=ax) |
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plt.xlabel("Date") |
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plt.ylabel("Calories") |
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plt.show() |
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``` |
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## Calories Per Day Box Plot |
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Using this data we can turn this into a boxplot to make it easier to |
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visualize the distribution of calories burned during the month of |
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July. |
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```python |
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ax = plt.gca() |
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ax.set_title('Calorie Distribution for July') |
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ax.boxplot(calories_per_day['value'], vert=False,manage_ticks=False, notch=True) |
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plt.xlabel("Calories Burned") |
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ax.set_yticks([]) |
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plt.show() |
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``` |
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# Steps |
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Fitbit is known for taking the amount of steps someone takes per day. |
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Similar to calories burned, steps taken is stored in time series data |
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at 1 minute increments. Since we are interested at the day level data, |
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we need to first remove the time component of the dataframe so that we |
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can group all the data by date. Once we have everything grouped by |
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date, we can sum and produce steps per day. |
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```python |
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steps_df = pd.read_json("data/steps-2019-07-01.json", convert_dates=True) |
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steps_df['date_minus_time'] = steps_df["dateTime"].apply( lambda steps_df : |
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datetime.datetime(year=steps_df.year, month=steps_df.month, day=steps_df.day)) |
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steps_df.set_index(steps_df["date_minus_time"],inplace=True) |
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print(steps_df) |
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``` |
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dateTime value date_minus_time |
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date_minus_time |
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2019-07-01 2019-07-01 04:00:00 0 2019-07-01 |
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2019-07-01 2019-07-01 04:01:00 0 2019-07-01 |
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2019-07-01 2019-07-01 04:02:00 0 2019-07-01 |
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2019-07-01 2019-07-01 04:03:00 0 2019-07-01 |
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2019-07-01 2019-07-01 04:04:00 0 2019-07-01 |
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... ... ... ... |
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2019-07-31 2019-07-31 03:55:00 0 2019-07-31 |
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2019-07-31 2019-07-31 03:56:00 0 2019-07-31 |
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2019-07-31 2019-07-31 03:57:00 0 2019-07-31 |
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2019-07-31 2019-07-31 03:58:00 0 2019-07-31 |
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2019-07-31 2019-07-31 03:59:00 0 2019-07-31 |
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[41116 rows x 3 columns] |
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```python |
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steps_per_day = steps_df.resample('D').sum() |
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print(steps_per_day) |
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``` |
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value |
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date_minus_time |
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2019-07-01 11285 |
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2019-07-02 4957 |
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2019-07-03 13119 |
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2019-07-04 16034 |
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2019-07-05 11634 |
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2019-07-06 6860 |
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2019-07-07 3758 |
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2019-07-08 9130 |
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2019-07-09 10960 |
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2019-07-10 7012 |
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2019-07-11 5420 |
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2019-07-12 4051 |
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2019-07-13 15980 |
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2019-07-14 23109 |
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2019-07-15 11247 |
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2019-07-16 10170 |
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2019-07-17 4905 |
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2019-07-18 10769 |
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2019-07-19 4504 |
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2019-07-20 5032 |
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2019-07-21 8953 |
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2019-07-22 2200 |
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2019-07-23 9392 |
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2019-07-24 5666 |
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2019-07-25 5016 |
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2019-07-26 5879 |
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2019-07-27 19492 |
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2019-07-28 4987 |
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2019-07-29 9943 |
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2019-07-30 3897 |
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2019-07-31 166 |
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## Steps Per Day Histogram |
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After the data is in the form that we want, graphing the data is |
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straight forward. Two added things I like to do for normal box plots |
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is to set the displays to horizontal add the notches. |
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```python |
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ax = plt.gca() |
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ax.set_title('Steps Distribution for July') |
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ax.boxplot(steps_per_day['value'], vert=False,manage_ticks=False, notch=True) |
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plt.xlabel("Steps Per Day") |
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ax.set_yticks([]) |
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plt.show() |
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``` |
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Wrapping that all into a single function we get something like this: |
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```python |
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def readFileIntoDataFrame(fName): |
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steps_df = pd.read_json(fName, convert_dates=True) |
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steps_df['date_minus_time'] = steps_df["dateTime"].apply( lambda steps_df : |
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datetime.datetime(year=steps_df.year, month=steps_df.month, day=steps_df.day)) |
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steps_df.set_index(steps_df["date_minus_time"],inplace=True) |
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return steps_df.resample('D').sum() |
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def graphBoxAndWhiskers(data, title, xlab): |
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ax = plt.gca() |
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ax.set_title(title) |
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ax.boxplot(data['value'], vert=False, manage_ticks=False, notch=True) |
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plt.xlabel(xlab) |
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ax.set_yticks([]) |
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plt.show() |
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``` |
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```python |
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graphBoxAndWhiskers(readFileIntoDataFrame("data/steps-2020-01-27.json"), "Steps In January", "Steps Per Day") |
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``` |
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That is cool, but, what if we could view the distribution for each |
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month in the same graph? Based on the two previous graphs, my step |
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distribution during July looked distinctly different from my step |
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distribution in January. The first difficultly would be to read in |
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all the files since Fitbit creates a new file for every month. The |
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next thing would be to group them by month and then graph it. |
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```python |
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import os |
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files = os.listdir("data") |
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print(files) |
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``` |
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['steps-2019-04-02.json', 'steps-2019-08-30.json', 'steps-2020-02-26.json', 'steps-2019-10-29.json', 'steps-2019-07-01.json', 'steps-2020-01-27.json', 'steps-2019-07-31.json', 'steps-2019-06-01.json', 'steps-2019-09-29.json', '.ipynb_checkpoints', 'steps-2019-12-28.json', 'steps-2019-05-02.json', 'calories', 'steps-2019-11-28.json', 'sleep'] |
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```python |
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dfs = [] |
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for file in files: # this can take 15 seconds |
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if "steps" in file: # finds the steps files |
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dfs.append(readFileIntoDataFrame("data/" + file)) |
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``` |
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```python |
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stepsPerDay = pd.concat(dfs) |
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graphBoxAndWhiskers(stepsPerDay, "Steps Per Day Last 11 Months", "Steps per Day") |
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``` |
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```python |
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print(type(stepsPerDay['value'].to_numpy())) |
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print(stepsPerDay['value'].keys()) |
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stepsPerDay['month'] = pd.DatetimeIndex(stepsPerDay['value'].keys()).month |
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stepsPerDay['week_day'] = pd.DatetimeIndex(stepsPerDay['value'].keys()).weekday |
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print(stepsPerDay) |
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``` |
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<class 'numpy.ndarray'> |
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DatetimeIndex(['2019-04-03', '2019-04-04', '2019-04-05', '2019-04-06', |
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'2019-04-07', '2019-04-08', '2019-04-09', '2019-04-10', |
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'2019-04-11', '2019-04-12', |
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... |
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'2019-12-19', '2019-12-20', '2019-12-21', '2019-12-22', |
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'2019-12-23', '2019-12-24', '2019-12-25', '2019-12-26', |
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'2019-12-27', '2019-12-28'], |
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dtype='datetime64[ns]', name='date_minus_time', length=342, freq=None) |
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value month week_day |
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date_minus_time |
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2019-04-03 510 4 2 |
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2019-04-04 11453 4 3 |
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2019-04-05 12684 4 4 |
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2019-04-06 12910 4 5 |
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2019-04-07 3368 4 6 |
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... ... ... ... |
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2019-12-24 5779 12 1 |
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2019-12-25 4264 12 2 |
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2019-12-26 4843 12 3 |
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2019-12-27 9609 12 4 |
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2019-12-28 2218 12 5 |
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[342 rows x 3 columns] |
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## Graphing Steps by Month |
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Now that we have columns for the total amount of steps per day and the |
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months, we can plot all the data on a single plot using the group by |
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operator in the plotting library. |
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```python |
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ax = plt.gca() |
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ax.set_title('Steps Distribution for July\n') |
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stepsPerDay.boxplot(column=['value'], by='month',ax=ax, notch=True) |
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plt.xlabel("Month") |
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plt.ylabel("Steps Per Day") |
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plt.show() |
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``` |
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```python |
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ax = plt.gca() |
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ax.set_title('Steps Distribution By Week Day\n') |
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stepsPerDay.boxplot(column=['value'], by='week_day',ax=ax, notch=True) |
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plt.xlabel("Week Day") |
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plt.ylabel("Steps Per Day") |
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plt.show() |
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``` |
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## Future Work |
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Moving forward with this I would like to do more visualizations with |
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sleep data and heart rate. |
