Chuck's Bucks

2022 was the year of tracking dollar bills. I "created" 365 Chuck Bucks by adding a stamp to my tracking website and a QR sticker to easily be able to scan it and pre-populate the dollar's serial number. For the first 73 days of the year I handed out 5 Chuck Bucks to various people and places to try and get them to spread throughout the world. I thought it went surprising well and am still seeing check-ins come in sporadically. All the data displayed will continue to use the live app I use for tracking, but the numbers below are as of May 9th, 2023.

Total Distance Chuck Bucks Traveled: 54,987 Miles (88,493 km) which is about 2.2 times around the world
Longest Distance Traveled from a Single Check-in to Check-in: 9,030 Miles (14,532 km) with serial number F17317947D
Longest Distance Traveled From a Single Chuck Buck: 11,285 Miles (18,162 km) with serial number E72974408A
Total Number of Check-ins: 483 which is about 1.5 check-ins per Chuck Buck
Distinct Chuck Bucks Check-ins: 318/365 (87%)
Largest number of Check-ins From a Single Chuck Buck: 5 from these serial numbers A52233859C, C87665168C, E72974408A, J41218200C

Data visualization can be found here.

Disclaimer: May not work well on mobile. 

Guinness Money

2020 was a strange year to try and track data about myself, but fortunately I had picked tasks that were still doable even with a pandemic going on. You can see the twin post of this where I rated tacos every Tuesday, but in this post its all about how much money I spent on Guinness for the year. 

Pre-COVID I had spent $1,915.33, and was on pace to spend $7,661.32 for the year. Which is $2,404.08 more than what I actually spent, so you can see how much that changed things. 

Average spent per day on Guinness: $14.40 

Total Spent: $5,257.24

Most Money Spent at One Place: $3,364.75 at D.D. Peckers

Data visualization can be found here.
Disclaimer: May not work well on mobile.

Taco Tuesday

2020 was a strange year to try and track data about myself, but fortunately I had picked tasks that were still doable even with a pandemic going on. You can see the twin post of this where I tracked how much I spent on Guinness for the year, but in this post its all about Taco Tuesday.

 
Every Tuesday during 2020, I tried out a different restaurant's tacos and rated the best one. With the pandemic going on this made it a little more challenging to go outside of Charleston for tacos (I did get one in from South Dakota), but I think with everyone more open to delivery it gave me a chance to try a lot more places in Charleston as well. 58% of tacos I ate were delivered so you can see I took full advantage of it.

Favorite Tacos: 

Locals - Korean BBQ 

Minero - Cauliflower 

Micho - El Dorado 

Las Lupitas - Chorizo 

SOL - Verde Carnitas

Least Favorite Tacos: 

The Wickliffe House - Smoked Chicken

Average Taco Rating: 3.3 stars

 
Data visualization can be found here.
Disclaimer: May not work well on mobile.

How much do I have to walk to burn off my Guinness calories?



This was a real question I had. Fortunately, its pretty simple algebra and I know how to do that so lets start with gathering a couple of numbers.

1. How many calories do I burn per minute by walking? 7.7 I walk quite vigorously
   • $230/30=7.7$ Calories per minute
2. How many calories do I take in per ounce of Guinness? 10.4 It might be 10.5 but this is what I got
   • $125/12=10.4$ Calories per ounce

Now we know every minute I walk burns 7.7 calories and every ounce of Guinness I drink gains me 10.4 calories. So the next question is how many minutes do I have to walk to burn off 1 ounce of Guinness. More math.

• $10.4/7.7=1.35$ minutes (1 minute 21 seconds) walked to burn 1 ounce of Guinness Calories

So the closest bar to me (shouts to DD Peckers) is ~20 mins in one direction. How many Guinness can I have to break even by walking there and back. (Its unfortunately not very many)
Given a Guinness is 20 oz (imperial pint) and my walk to and from the bar is ~40 mins:

• $40*7.7/10.4/20=1.48$ Guinness

Visualized as:

%matplotlib notebook
import matplotlib.pyplot as plt
plt.plot([[7.7*40] for x in range(100)], linewidth=1, label='random')
plt.plot([[x*10.4] for x in range(100)], linewidth=1, label='random')
plt.scatter([7.7*40/10.4], [7.7*40])
plt.annotate("Break even point", (30, 310))
plt.show()

The orange line in this case is the increase of calories per ounce drank and the blue line is a constant of calories burned if I walk vigorously for 40 mins. The intersection gives us the amount of ounces it takes to equal the number of calories I burned walking 40 mins. Which shows x as 29.6 ounces divided by 20 is 1.48 Guinness.

I wish my story problems as a kid were as interesting as this, but alas they were not, so here we are making up my own.

5 Million Step Man

Another year of Chuck does Data Science. For the 2019 year, I challenged myself to walk 5 million steps for the year which broke down to ~13000 steps per day. Crushed the goal and ended up going over 6 million steps for the year (6098464 to be exact) which was ~16000. Calculating that 2000 steps is a mile for me, I walked about 3050 miles. You see a pretty major dip in the number of steps in November because I strained my calf playing basketball during the early part of the month.

For the data visualizations this year I mostly worked on using the GPS data to get some interesting stats from the year. For instance, the number of times I walked the same routes. By far my favorite route is the Chuck route which I only got to do once, but is fun to look at. Below are a breakdown of step stats from the year and a link to the visualizations.

Total steps for the year: 6,098,464
Average steps per day: 16,708
Average steps per week: 117,278
Average steps per month: 508,205
Most steps in a month: 604,290 (August)
Least steps in a month: 318,127 (November)
Most steps in a day: 37,687
Least steps in a day: 1,123
Percentage of days meeting goal of 13000 steps: 297/365 81%
Sunday Steps Total (Avg):        734,654 (14,128)
Monday Steps Total (Avg):       943,700 (18,148)
Tuesday Steps Total (Avg):       996,785 (18,807)
Wednesday Steps Total (Avg):  909,695 (17,494)
Thursday Steps Total (Avg):     950,875 (18,286)
Friday Steps Total (Avg):          822,101 (15,810)
Saturday Steps Total (Avg):      740,654 (14,243)
Most walked route: Bar Walk (300 times)


Data visualization can be found here.
Disclaimer: May not work well on mobile.

Chuck Rates Beer

The 5th year for Chuck does Data Science, I challenged myself to drink a different beer everyday and rate it. The challenge was completed with no missing days (no applause necessary). I figured there could be some interesting findings from this data including what types of beer do I rate the highest/lowest, do I tend towards one type of beer, and could I make a model to predict whether I would like a new beer not in the current data set. If you follow the link you should be able to try it for yourself by following the link below. Just type in a beer select the correct one and it will be rated by Robo Chuck. There is also a calendar displaying each beer as its picture for that day. You can hover over it to see details and click on it to take you to Untappd for more info.

Data visualization and Robo Chuck can be found  here.
Disclaimer: May not work well on mobile.

Some interesting stats:

Highest rated beers (5 star rated):
Guinness - Guinness Draught
Great Divide Brewing Co. - Velvet Yeti Nitro
D9 Brewing Co. - Brown Sugar Brown Cow
Edmund's Oast - Peanut Butter & Jelly
Holy City Brewing - Pluff Mud Porter
Frothy Beard - Back From the Dead
Brauerei Lemke - Lemke Original
Anderson Valley Brewing Co. - Barney Flats Oatmeal Stout

Lowest rated beers (0.5 star rated):
Dogfish Head Craft Brewery - SeaQuench Ale
SweetWater Brewing Co. - Grass Monkey
Boston Beer Co. - Samuel Adams Pumpkin Ale

Breakdown of all ratings (0-5 in 0.5 increments):
0.0:   0
0.5:   3
1.0:   9
1.5:   30
2.0:   56
2.5:   84
3.0:   66
3.5:   58
4.0:   40
4.5:   11
5.0:   8

Breakdown of styles (number of beers I had in that style):
IPA - American 40
Pale Ale - American 20
Stout - Milk / Sweet 16
Lager - North American Adjunct 13
Lager - Pale 13
Porter - American 12
Lager - American Light 10
Hefeweizen 9
Witbier 9
Lager - Vienna 9
Brown Ale - American 8
Brown Ale - English 8
IPA - New England 8
Red Ale - American Amber / Red 8
Fruit Beer 7
Blonde Ale 7
IPA - Imperial / Double 7
Stout - American 7
Stout - Irish Dry 7
Golden Ale 6
Pilsner - Czech 5
Pale Ale - English 5
Cream Ale 5
Shandy / Radler 4
Lager - Euro 4
Lager - American Amber / Red 4
Sour - Gose 4
Pale Wheat Ale - American 4
Pilsner - German 4
Stout - American Imperial / Double 4
Porter - Coffee 4
IPA - Session / India Session Ale 4
Stout - Oatmeal 4
Scotch Ale / Wee Heavy 3
Lager - Dark 3
Extra Special / Strong Bitter 3
Sour - Ale 3
Lager - Helles 3
Stout - Foreign / Export 3
Porter - Other 3
Pilsner - Other 2
Kölsch 2
Saison / Farmhouse Ale 2
Cider - Other 2
Belgian Tripel 2
Festbier 2
Dark Ale 2
Märzen 2
Strong Ale - American 2
Lager - Munich Dunkel 2
Bock - Single / Traditional 2
Lager - Japanese Rice 2
Lager - Amber 2
Pumpkin / Yam Beer 2
Porter - English 2
Brown Ale - Other 2
Belgian Strong Dark Ale 1
Belgian Dubbel 1
Scottish Export Ale 1
Winter Warmer 1
Belgian Quad 1
Porter - Baltic 1
Rye Beer 1
Dunkelweizen 1
Pale Ale - Belgian 1
Stout - Imperial Milk / Sweet 1
Stout - English 1
Lager - IPL (India Pale Lager) 1
Mead - Braggot 1
Stout - Other 1
Cider - Sweet 1
English Bitter 1
Spiced / Herbed Beer 1
Bock - Maibock / Heller (Helles) / Lentebock 1
Brown Ale - Imperial / Double 1
Stout - Russian Imperial 1
Winter Ale 1
Other 1
IPA - Brown 1
Red Ale - Irish 1
Red Ale - Imperial / Double 1
Scottish Ale 1
Lager - Winter 1
Porter - Imperial / Double 1
Altbier 1
Malt Liquor 1
Smoked Beer 1
IPA - Triple 1
Stout - Oyster 1

Type of beer I had the most of:
IPA - American (40)

Breakdown of Major Styles (number of beers I had in that style):
Lager 67
IPA 61
Stout 46
Pale Ale 26
Porter 23
Brown Ale 19
Pilsner 11
Red Ale 10
Witbier 9
Hefeweizen 9
Sour 7
Fruit Beer 7
Blonde Ale 7
Golden Ale 6
Cream Ale 5
Shandy / Radler 4
Pale Wheat Ale 4
Scotch Ale / Wee Heavy 3
Cider 3
Extra Special / Strong Bitter 3
Bock 3
Strong Ale 2
Kölsch 2
Saison / Farmhouse Ale 2
Dark Ale 2
Festbier 2
Märzen 2
Pumpkin / Yam Beer 2
Belgian Tripel 2
Spiced / Herbed Beer 1
Rye Beer 1
Smoked Beer 1
Dunkelweizen 1
Scottish Ale 1
Belgian Quad 1
English Bitter 1
Altbier 1
Winter Warmer 1
Belgian Strong Dark Ale 1
Mead 1
Winter Ale 1
Scottish Export Ale 1
Other 1
Malt Liquor 1
Belgian Dubbel 1

Over the Wire

I meant to post this awhile ago like right after I gave the talk, but that didn't happen so I'm doing it now. Not too long ago I gave a talk at DakotaCon 2018 on executing Python in memory and some tips and tricks for doing so.

Here is the video.

Here are the slides.