|
|
|
@ -0,0 +1,576 @@ |
|
|
|
Health trackers are the current craze. After I bought a Fitbit, I |
|
|
|
wanted to determine what exactly could I do with my Fitbit data. Can we |
|
|
|
learn something from this data that we did not know before? |
|
|
|
Most people don't need a watch to tell them that they walked a lot |
|
|
|
today or that they got a ton of sleep. We typically have a pretty good |
|
|
|
gauge of our basic physical health. I am interested in figuring out |
|
|
|
how we can use data science to look at our health data over a longer |
|
|
|
period of time and learn something useful. |
|
|
|
|
|
|
|
Lets first look at a few things that people typically use Fitbit data for |
|
|
|
before we jump into the weeds. |
|
|
|
|
|
|
|
- Setting Goals |
|
|
|
- Motivation |
|
|
|
- Tracking Progress |
|
|
|
|
|
|
|
Ever since I bought a Fitbit, I found that I went to the gym a lot |
|
|
|
more frequently. Having something which keeps track of your progress |
|
|
|
is a great motivator. Not only is your daily steps recorded for your |
|
|
|
own viewing, you can share that data with your friends as a |
|
|
|
competition. Although I only have one friend on Fitbit, I found that was |
|
|
|
a good motivator to hit ten thousand steps per day. |
|
|
|
|
|
|
|
Goals which are not concrete never get accomplished. Simply |
|
|
|
saying that "I will get in shape" is a terrible goal. In order for you |
|
|
|
to actually accomplish your goals, they need to be quantifiable, reasonable, and |
|
|
|
measurable. Rather than saying "I will improve my health this year", |
|
|
|
you can say "I will loose ten pounds this year by increasing my daily |
|
|
|
step count to twelve thousand and going to the gym twice a week". One |
|
|
|
goal is wishy washy where the other is concrete and measurable. Having |
|
|
|
concrete data from Fitbit allows you to quantify your goals and set |
|
|
|
milestones for you to accomplish. Along the way to achieving your |
|
|
|
goal, you can easily track your progress. |
|
|
|
|
|
|
|
Simply knowing your Fitbit data can help you make some better educated |
|
|
|
decisions about your fitness. By comparing your data against what is |
|
|
|
healthy you can tweak your lifestyle. For example: if you notice that |
|
|
|
you are only getting 6 hours of sleep per night, you can look up the |
|
|
|
recommended amount of sleep and tweak your sleep routine until you hit |
|
|
|
that target. |
|
|
|
|
|
|
|
Alright, lets do some data science! |
|
|
|
|
|
|
|
 |
|
|
|
|
|
|
|
# Getting The Data |
|
|
|
|
|
|
|
There are two options that we can use to fetch data from Fitbit. |
|
|
|
|
|
|
|
|
|
|
|
## Using Fitbit's API |
|
|
|
|
|
|
|
Fitbit has an [OAuth 2.0 web |
|
|
|
API](https://dev.fitbit.com/build/reference/web-api/) that you can |
|
|
|
use. You first have to register your application on Fitbit's website |
|
|
|
to receive a client ID and a client secret. |
|
|
|
|
|
|
|
I decided to fetch the Fitbit data using an Express app with node. |
|
|
|
Fetching the data this way will make it really easy to use on a |
|
|
|
live website. Node has tons of NPM modules which makes connecting to |
|
|
|
Fitbit's API really easy. I'm using Passport which is a pretty common |
|
|
|
authentication middleware for Express. |
|
|
|
|
|
|
|
|
|
|
|
```javascript |
|
|
|
/** express app */ |
|
|
|
const express = require("express"); |
|
|
|
|
|
|
|
/** Manages oauth 2.0 w/ fitbit */ |
|
|
|
const passport = require('passport'); |
|
|
|
|
|
|
|
/** Used to make API calls */ |
|
|
|
const unirest = require('unirest'); |
|
|
|
|
|
|
|
/** express app */ |
|
|
|
const app = express(); |
|
|
|
|
|
|
|
app.use(passport.initialize()); |
|
|
|
app.use(passport.session({ |
|
|
|
resave: false, |
|
|
|
saveUninitialized: true |
|
|
|
})); |
|
|
|
|
|
|
|
|
|
|
|
var FitbitStrategy = require( 'passport-fitbit-oauth2' ).FitbitOAuth2Strategy; |
|
|
|
|
|
|
|
|
|
|
|
var accessTokenTemp = null; |
|
|
|
passport.use(new FitbitStrategy({ |
|
|
|
clientID: config.clientID, |
|
|
|
clientSecret: config.clientSecret, |
|
|
|
callbackURL: config.callbackURL |
|
|
|
}, |
|
|
|
function(accessToken, refreshToken, profile, done) |
|
|
|
{ |
|
|
|
console.log(accessToken); |
|
|
|
accessTokenTemp = accessToken; |
|
|
|
done(null, { |
|
|
|
accessToken: accessToken, |
|
|
|
refreshToken: refreshToken, |
|
|
|
profile: profile |
|
|
|
}); |
|
|
|
} |
|
|
|
)); |
|
|
|
|
|
|
|
passport.serializeUser(function(user, done) { |
|
|
|
done(null, user); |
|
|
|
}); |
|
|
|
|
|
|
|
passport.deserializeUser(function(obj, done) { |
|
|
|
done(null, obj); |
|
|
|
}); |
|
|
|
|
|
|
|
passport.authenticate('fitbit', { scope: |
|
|
|
['activity','heartrate','location','profile'] |
|
|
|
}); |
|
|
|
``` |
|
|
|
|
|
|
|
Since our authentication middlware is all set up, we just need to add |
|
|
|
the express routes which are required when authenticating. |
|
|
|
|
|
|
|
```javascript |
|
|
|
app.get('/auth/fitbit', |
|
|
|
passport.authenticate('fitbit', { scope: |
|
|
|
['activity','heartrate','location','profile'] } |
|
|
|
)); |
|
|
|
|
|
|
|
app.get( '/auth/fitbit/callback', passport.authenticate( 'fitbit', { |
|
|
|
successRedirect: '/', |
|
|
|
failureRedirect: '/error' |
|
|
|
})); |
|
|
|
|
|
|
|
|
|
|
|
app.get('/error', (request, result) => |
|
|
|
{ |
|
|
|
result.write("Error authenticating with Fitbit API"); |
|
|
|
result.end(); |
|
|
|
}); |
|
|
|
``` |
|
|
|
|
|
|
|
Now that we are authenticated with Fitbit, we can finally make |
|
|
|
queries. I created a helper function called queryAPI which attempts |
|
|
|
to authenticate if it is not already authenticated and then fetches |
|
|
|
the API result from a provided URL. |
|
|
|
|
|
|
|
```javascript |
|
|
|
const queryAPI = function(result, path) |
|
|
|
{ |
|
|
|
return new Promise((resolve, reject)=> |
|
|
|
{ |
|
|
|
if(accessTokenTemp == null) |
|
|
|
{ |
|
|
|
result.redirect('/auth/fitbit'); |
|
|
|
resolve(false); |
|
|
|
} |
|
|
|
|
|
|
|
unirest.get(path) |
|
|
|
.headers({'Accept': 'application/json', 'Content-Type': 'application/json', Authorization: "Bearer " + accessTokenTemp}) |
|
|
|
.end(function (response) |
|
|
|
{ |
|
|
|
if(response.hasOwnProperty("success") && response.success == false) |
|
|
|
{ |
|
|
|
result.redirect('/auth/fitbit'); |
|
|
|
resolve(false); |
|
|
|
} |
|
|
|
resolve(response.body); |
|
|
|
}); |
|
|
|
}); |
|
|
|
}; |
|
|
|
|
|
|
|
app.get('/steps', (request, result)=> |
|
|
|
{ |
|
|
|
queryAPI(result, 'https://api.fitbit.com/1/user/-/activities/tracker/steps/date/today/1m.json').then((data)=> |
|
|
|
{ |
|
|
|
if(data != false) |
|
|
|
{ |
|
|
|
result.writeHead(200, {'Content-Type': 'text/html'}); |
|
|
|
result.write(JSON.stringify(data)); |
|
|
|
result.end(); |
|
|
|
} |
|
|
|
else |
|
|
|
{ |
|
|
|
console.log("Validating with API"); |
|
|
|
} |
|
|
|
}); |
|
|
|
}); |
|
|
|
``` |
|
|
|
|
|
|
|
## Exporting Data from Website |
|
|
|
|
|
|
|
On [Fitbit's website](https://www.fitbit.com/settings/data/export) |
|
|
|
there is a nice page where you can export your data. |
|
|
|
|
|
|
|
 |
|
|
|
|
|
|
|
The on demand export is pretty useless because it can only go back a |
|
|
|
month. On top of that, you don't get to download any heart rate data. |
|
|
|
The only data that you do get is aggregated by day. This might be fine |
|
|
|
for some use cases; however, this will not suffice for any interesting |
|
|
|
analysis. |
|
|
|
|
|
|
|
I decided to try the account archive option out of curiosity. |
|
|
|
|
|
|
|
 |
|
|
|
|
|
|
|
The Fitbit data archive was very organized and kept meticulous records |
|
|
|
of everything. All of the data was in JSON format and was organized |
|
|
|
in separate files labeled by date. Fitbit keeps around 1MB |
|
|
|
of data on you per day; most of this data is from the heart rate |
|
|
|
sensors. Although 1MB of data may sound like a ton of data, it is probably a |
|
|
|
lot less if you store it in a format other than JSON. Since Fitbit |
|
|
|
hires a lot of people for hadoop and SQL development, they are most |
|
|
|
likely using [Apache Hive](https://hive.apache.org/) to store user |
|
|
|
information on the backend. Distributing the data to users as JSON is |
|
|
|
really convenient since it makes learning the data schema very easy. |
|
|
|
|
|
|
|
# Visualizing The Data |
|
|
|
|
|
|
|
Since the Data Archive is far easier, I'm going to start visualizing the |
|
|
|
data retrieved from the JSON archive. In the future I may |
|
|
|
use the Fitbit API if I decide to make this a live website or something. |
|
|
|
Using R to visualize this would be easy, however; I want to use some |
|
|
|
pretty javascript graphs so I can host this as a demo on my website. |
|
|
|
|
|
|
|
## Heart Rate |
|
|
|
|
|
|
|
My biggest quirk with the Fitbit website is that it only displays your continuous |
|
|
|
heart rate in one day intervals. If you zoom out to the week or month view, it aggregates it |
|
|
|
as the number of minutes you are in each heart rate zone. |
|
|
|
This is fine for the fitbit app where you have limited screen space and no good ways of zooming in |
|
|
|
and out of the graphs. |
|
|
|
|
|
|
|
 |
|
|
|
|
|
|
|
 |
|
|
|
|
|
|
|
I really want to be able to view my heart rate over the course of a |
|
|
|
few days. To view my continuous heart rate I'm going to |
|
|
|
use [VisJS](http://visjs.org/docs/graph2d/) because |
|
|
|
it works really well with time series data. |
|
|
|
|
|
|
|
This is some Javascript code which imports user selected JSON files |
|
|
|
to the web page and parses it as Javascript objects. |
|
|
|
|
|
|
|
```html |
|
|
|
<div class="col-4 shadow-lg p-3 bg-white rounded"> |
|
|
|
<label>Heart Rate JSON Files</label> |
|
|
|
<input type="file" id="files" name="files[]" multiple /> |
|
|
|
<output id="list"></output> |
|
|
|
</div> |
|
|
|
... |
|
|
|
<script> |
|
|
|
function handleFileSelect(evt) |
|
|
|
{ |
|
|
|
fetchFilesAsJSONArray(evt).then((data)=> |
|
|
|
{ |
|
|
|
generateHeartRateGraph(data); |
|
|
|
}) |
|
|
|
} |
|
|
|
document.getElementById('files').addEventListener('change', handleFileSelect, false); |
|
|
|
|
|
|
|
function fetchFilesAsJSONArray(evt) |
|
|
|
{ |
|
|
|
return new Promise((res, rej)=> |
|
|
|
{ |
|
|
|
var files = evt.target.files; // FileList object |
|
|
|
|
|
|
|
var promises = []; |
|
|
|
|
|
|
|
for (var i = 0, f; f = files[i]; i++) |
|
|
|
{ |
|
|
|
promises.push(new Promise((resolve, reject)=> |
|
|
|
{ |
|
|
|
var reader = new FileReader(); |
|
|
|
|
|
|
|
reader.onload = function(e) |
|
|
|
{ |
|
|
|
resolve(JSON.parse(reader.result)); |
|
|
|
}; |
|
|
|
|
|
|
|
reader.onerror= function(e) |
|
|
|
{ |
|
|
|
reject(e); |
|
|
|
}; |
|
|
|
reader.readAsBinaryString(files[i]); |
|
|
|
})); |
|
|
|
} |
|
|
|
Promise.all(promises).then((data)=> |
|
|
|
{ |
|
|
|
res(data); |
|
|
|
}).catch((error)=> |
|
|
|
{ |
|
|
|
console.log(error); |
|
|
|
console.log("Unable to Load Data"); |
|
|
|
rej(error); |
|
|
|
}) |
|
|
|
}); |
|
|
|
} |
|
|
|
</script> |
|
|
|
``` |
|
|
|
|
|
|
|
The actual Javascript objects look like this: |
|
|
|
|
|
|
|
```json |
|
|
|
[{ |
|
|
|
"dateTime" : "04/22/19 04:00:05", |
|
|
|
"value" : { |
|
|
|
"bpm" : 69, |
|
|
|
"confidence" : 2 |
|
|
|
} |
|
|
|
},{ |
|
|
|
"dateTime" : "04/22/19 04:00:10", |
|
|
|
"value" : { |
|
|
|
"bpm" : 70, |
|
|
|
"confidence" : 2 |
|
|
|
} |
|
|
|
} |
|
|
|
... |
|
|
|
] |
|
|
|
``` |
|
|
|
|
|
|
|
I found it interesting that each point had a confidence score associated with it. I wonder |
|
|
|
how Fitbit is using that confidence information. Since it does not directly appear anywhere in the app, |
|
|
|
they may be using it to exclude inaccurate points from the heart rate graphs to make it smoother. |
|
|
|
A really annoying thing about this data is that the time stamps don't contain any information on the |
|
|
|
timezone. When graphing this data, I will shift the times by 4 hours so that it aligns |
|
|
|
with eastern standard time. |
|
|
|
|
|
|
|
|
|
|
|
After we read the data from the user selected heart rate files, we can treat this object as an array |
|
|
|
of arrays. Each array represents a file or an entire days worth of heart rate data. Each day is an |
|
|
|
array of time stamped points with heart rate information. Using the code from the |
|
|
|
[VisJS example](http://visjs.org/docs/graph2d/), it is relatively straightforward to plot this data. |
|
|
|
|
|
|
|
```javascript |
|
|
|
function generateHeartRateGraph(jsonFiles) |
|
|
|
{ |
|
|
|
var items = []; |
|
|
|
for(var i = 0; i < jsonFiles.length; i++) |
|
|
|
{ |
|
|
|
console.log(jsonFiles[i].length); |
|
|
|
for(var j = 0; j < jsonFiles[i].length; j++) |
|
|
|
{ |
|
|
|
var localTime = new Date(jsonFiles[i][j].dateTime); |
|
|
|
items.push({y:jsonFiles[i][j].value.bpm, x:localTime.setHours(localTime.getHours() - 4)}); |
|
|
|
} |
|
|
|
} |
|
|
|
var dataset = new vis.DataSet(items); |
|
|
|
var options = { |
|
|
|
dataAxis: { |
|
|
|
showMinorLabels: true, |
|
|
|
left: { |
|
|
|
title: { |
|
|
|
text: "Heart Rate" |
|
|
|
} |
|
|
|
} |
|
|
|
} |
|
|
|
}; |
|
|
|
var container = document.getElementById("heartRateGraph"); |
|
|
|
var graph2d = new vis.Graph2d(container, dataset, options); |
|
|
|
graph2d.on('rangechanged', graphMoved); |
|
|
|
graphsOnPage.push(graph2d); |
|
|
|
} |
|
|
|
``` |
|
|
|
|
|
|
|
It works! As an example, this is what my heart rate looks like over a week. |
|
|
|
|
|
|
|
 |
|
|
|
|
|
|
|
|
|
|
|
## Time Line |
|
|
|
|
|
|
|
Fitbit does a pretty good job of detecting and recording health related activities. |
|
|
|
The two major things that Fitbit detects is sleep and workout activities. |
|
|
|
Although the app does a good job at informing you about these activities, the app is lacking |
|
|
|
a comprehensive timeline. Rather than provide a timeline for these activities, |
|
|
|
the app only displays a simple list. |
|
|
|
|
|
|
|
 |
|
|
|
|
|
|
|
The JSON files for sleep store a ton of data! For the sake of the time line I am only interested |
|
|
|
in the start and finish times. Unlike the heart rate data, this actually stores the time zone. |
|
|
|
|
|
|
|
```json |
|
|
|
[{ |
|
|
|
"logId" : 22128553286, |
|
|
|
"dateOfSleep" : "2019-04-28", |
|
|
|
"startTime" : "2019-04-27T23:09:00.000", |
|
|
|
"endTime" : "2019-04-28T07:33:30.000", |
|
|
|
"duration" : 30240000, |
|
|
|
"minutesToFallAsleep" : 0, |
|
|
|
"minutesAsleep" : 438, |
|
|
|
"minutesAwake" : 66, |
|
|
|
"minutesAfterWakeup" : 1, |
|
|
|
"timeInBed" : 504, |
|
|
|
"efficiency" : 86, |
|
|
|
"type" : "stages", |
|
|
|
"infoCode" : 0, |
|
|
|
"levels" : { |
|
|
|
"summary" : { |
|
|
|
"deep" : { |
|
|
|
"count" : 4, |
|
|
|
"minutes" : 103, |
|
|
|
"thirtyDayAvgMinutes" : 89 |
|
|
|
}, |
|
|
|
"wake" : { |
|
|
|
"count" : 33, |
|
|
|
"minutes" : 66, |
|
|
|
"thirtyDayAvgMinutes" : 65 |
|
|
|
}, |
|
|
|
"light" : { |
|
|
|
"count" : 24, |
|
|
|
"minutes" : 214, |
|
|
|
"thirtyDayAvgMinutes" : 221 |
|
|
|
}, |
|
|
|
"rem" : { |
|
|
|
"count" : 16, |
|
|
|
"minutes" : 121, |
|
|
|
"thirtyDayAvgMinutes" : 93 |
|
|
|
} |
|
|
|
}, |
|
|
|
"data" : [{ |
|
|
|
"dateTime" : "2019-04-27T23:09:00.000", |
|
|
|
"level" : "wake", |
|
|
|
"seconds" : 30 |
|
|
|
},{ |
|
|
|
"dateTime" : "2019-04-27T23:09:30.000", |
|
|
|
"level" : "light", |
|
|
|
"seconds" : 900 |
|
|
|
}, |
|
|
|
``` |
|
|
|
|
|
|
|
The JSON file for each activity stores a lot of information on heart rate. |
|
|
|
Similar to the heart rate file, this date format does not take into account time zones. Grr! |
|
|
|
Rather than storing a finish date like the sleep JSON file, this keeps track of the total duration |
|
|
|
of the event in milliseconds. |
|
|
|
|
|
|
|
```json |
|
|
|
[{ |
|
|
|
"logId" : 21092332392, |
|
|
|
"activityName" : "Run", |
|
|
|
"activityTypeId" : 90009, |
|
|
|
"activityLevel" : [{ |
|
|
|
"minutes" : 0, |
|
|
|
"name" : "sedentary" |
|
|
|
},{ |
|
|
|
"minutes" : 0, |
|
|
|
"name" : "lightly" |
|
|
|
},{ |
|
|
|
"minutes" : 1, |
|
|
|
"name" : "fairly" |
|
|
|
},{ |
|
|
|
"minutes" : 30, |
|
|
|
"name" : "very" |
|
|
|
}], |
|
|
|
"averageHeartRate" : 149, |
|
|
|
"calories" : 306, |
|
|
|
"duration" : 1843000, |
|
|
|
"activeDuration" : 1843000, |
|
|
|
"steps" : 4510, |
|
|
|
"logType" : "auto_detected", |
|
|
|
"manualValuesSpecified" : { |
|
|
|
"calories" : false, |
|
|
|
"distance" : false, |
|
|
|
"steps" : false |
|
|
|
}, |
|
|
|
"heartRateZones" : [{ |
|
|
|
"name" : "Out of Range", |
|
|
|
"min" : 30, |
|
|
|
"max" : 100, |
|
|
|
"minutes" : 0 |
|
|
|
},{ |
|
|
|
"name" : "Fat Burn", |
|
|
|
"min" : 100, |
|
|
|
"max" : 140, |
|
|
|
"minutes" : 6 |
|
|
|
},{ |
|
|
|
"name" : "Cardio", |
|
|
|
"min" : 140, |
|
|
|
"max" : 170, |
|
|
|
"minutes" : 24 |
|
|
|
},{ |
|
|
|
"name" : "Peak", |
|
|
|
"min" : 170, |
|
|
|
"max" : 220, |
|
|
|
"minutes" : 1 |
|
|
|
}], |
|
|
|
"lastModified" : "04/06/19 17:51:30", |
|
|
|
"startTime" : "04/06/19 17:11:48", |
|
|
|
"originalStartTime" : "04/06/19 17:11:48", |
|
|
|
"originalDuration" : 1843000, |
|
|
|
"hasGps" : false, |
|
|
|
"shouldFetchDetails" : false |
|
|
|
} |
|
|
|
``` |
|
|
|
|
|
|
|
After we import both the sleep files and activity files from the user we can use the VisJS library |
|
|
|
to construct a timeline. |
|
|
|
|
|
|
|
|
|
|
|
```javascript |
|
|
|
function generateTimeline(jsonFiles) |
|
|
|
{ |
|
|
|
var items = []; |
|
|
|
|
|
|
|
for(var i = 0; i < jsonFiles.length; i++) |
|
|
|
{ |
|
|
|
for(var j = 0; j < jsonFiles[i].length; j++) |
|
|
|
{ |
|
|
|
if(jsonFiles[i][j].hasOwnProperty("dateOfSleep")) |
|
|
|
{ |
|
|
|
var startT = new Date(jsonFiles[i][j].startTime); |
|
|
|
var finishT = new Date(jsonFiles[i][j].endTime); |
|
|
|
items.push({content: "Sleep", |
|
|
|
start:startT, end:finishT, group:0}); |
|
|
|
} |
|
|
|
else |
|
|
|
{ |
|
|
|
var localTime = new Date(jsonFiles[i][j].startTime); |
|
|
|
var timeAdjusted = localTime.setHours(localTime.getHours() - 4); |
|
|
|
var timeFinish = localTime.setMilliseconds( |
|
|
|
localTime.getMilliseconds() + jsonFiles[i][j].activeDuration); |
|
|
|
items.push({content: jsonFiles[i][j].activityName, |
|
|
|
start:timeAdjusted, end:timeFinish, group:0}); |
|
|
|
} |
|
|
|
} |
|
|
|
} |
|
|
|
console.log("Finished Loading Heart Rate Data Into Graph"); |
|
|
|
|
|
|
|
var dataset = new vis.DataSet(items); |
|
|
|
var options = |
|
|
|
{ |
|
|
|
margin: |
|
|
|
{ |
|
|
|
item:20, |
|
|
|
axis:40 |
|
|
|
}, |
|
|
|
showCurrentTime: false |
|
|
|
}; |
|
|
|
|
|
|
|
var grpups = new vis.DataSet([ |
|
|
|
{id: 0, content:"Activity", value:0} |
|
|
|
]); |
|
|
|
|
|
|
|
var container = document.getElementById("heartRateGraph"); |
|
|
|
var graph2d = new vis.Timeline(container, dataset, options); |
|
|
|
graph2d.setGroups(grpups); |
|
|
|
graph2d.on('rangechanged', graphMoved); |
|
|
|
graphsOnPage.push(graph2d); |
|
|
|
} |
|
|
|
``` |
|
|
|
|
|
|
|
To make both the heart rate graph and the activity timeline focused on the same region at the |
|
|
|
same time, I used the 'rangechanged' event to move the other graphs's window of view. |
|
|
|
|
|
|
|
```javascript |
|
|
|
function graphMoved(moveEvent) |
|
|
|
{ |
|
|
|
graphsOnPage.forEach((g)=> |
|
|
|
{ |
|
|
|
g.setWindow(moveEvent.start, moveEvent.end); |
|
|
|
}) |
|
|
|
} |
|
|
|
``` |
|
|
|
|
|
|
|
I am pretty pleased with how these two graphs turned out. When you zoom too far out of the graph, the |
|
|
|
events get really small, but, it does a pretty good job at visualizing a few days worth of data at a time. |
|
|
|
|
|
|
|
 |
|
|
|
|
|
|
|
 |
|
|
|
|
|
|
|
# Pulling Outside Data |
|
|
|
|
|
|
|
|
|
|
|
# Analysis |
jrtechs
6 years ago
