A First Experiment with Evidence
I came across Evidence while looking through a list of YCombinator backed companies operating out of Canada. As someone who is a lot more comfortable with data (as opposed to web development), this was an incredibly fulfilling experience to be able to create data analyses and publish it, without writing a single line of JS
The Dataset
I didn't want to spend time hooking up a postgres DB, so I decided to go with a CSV file from Guilherme Samora containing data regarding alcohol consumption across countries and continents (which I've duly named drinks.csv
).
I'm currently a graduate student at McGill University. As someone who went to college in the United States, I thought I was accustomed to large-scale college drinking. I should admit that drinking culture seems a lot more ingrained into the social culture here at McGill: I don't mean to be judgemental at all; in fact, I love beer (especially when you can get one for $2 on university grounds).
Data Table
Taking a look at the data as-is.
Country | Beer Servings | Spirit Servings | Wine Servings | Total Litres of Pure Alcohol | Continent |
---|---|---|---|---|---|
Afghanistan | 0 | 0 | 0 | 0.00 | AS |
Albania | 89 | 132 | 54 | 4.90 | EU |
Algeria | 25 | 0 | 14 | 0.70 | AF |
Andorra | 245 | 138 | 312 | 12.40 | EU |
Angola | 217 | 57 | 45 | 5.90 | AF |
Antigua & Barbuda | 102 | 128 | 45 | 4.90 | NA |
I absolutely love this - to get the table above, I can just simply run SQL directly from a markdown document!
Trying a CASE Statement: Available Continents
I'm generating a list of continents that are represented in this dataset using a new SQL query as can be seen below. I'm doing this here specifically to see if my usual way of writing CASE statements will work without any issues.
Continent Code | Continent Name |
---|---|
AS | Asia |
EU | Europe |
AF | Africa |
NA | North America |
SA | South America |
OC | Oceania |
Using an Aggregation: Finding the Top 10 Consumers of Beer
Taking a look at the top 10 consumers of beer as per the available data. Using a simple SQL GROUP BY and ORDER BY with a LIMIT 10 clause.
Country | Beer Consumed |
---|---|
Namibia | 376 |
Czech Republic | 361 |
Gabon | 347 |
Germany | 346 |
Lithuania | 343 |
Poland | 343 |
Venezuela | 333 |
Ireland | 313 |
Palau | 306 |
Romania | 297 |
Pretty surprised to see Namibia leading this list (though I do have to admit that I have been trying to get my hands on a bottle of Windhoek Beer for over two years with no luck). Czechia isn't as much of a surprise considering they're renowned for Pilsners. Wondering what the trend is if the aggregation is performed by continent:
Continent | Beer Consumed |
---|---|
EU | 8,720 |
NA | 3,345 |
AF | 3,258 |
SA | 2,101 |
AS | 1,630 |
OC | 1,435 |
Average Beer Consumption by Continent
Continent | Average Beer Consumption |
---|---|
EU | 194 |
SA | 175 |
NA | 145 |
OC | 90 |
AF | 61 |
AS | 37 |
Ranking Top 5 Consumers of Beer by Continent
Attempting the use of Window Functions to get the top 5 consumers of beer by continent which can be chosen using a filter:
Country | Continent | Continent Rank | Beer Servings |
---|---|---|---|
Russian Federation | AS | 1 | 247 |
South Korea | AS | 2 | 140 |
Kazakhstan | AS | 3 | 124 |
Vietnam | AS | 4 | 111 |
Thailand | AS | 5 | 99 |
Playing around with available visualization components
Creating a Bar Plot of Beer Servings per year
This is so neat! Especially considering tooltips that already come along with a beautiful plot!
Creating a Big Value KPI Card
Total Beer Servings
Total Wine Servings
Total Spirit Servings