TL;DR:

Describes a data analysis project tracking skateboarding trick consistency over 100 days. The key components include:

• The author collected data on 21 skateboarding tricks, scoring them from 0-5 based on consistency
• Data analysis was performed using R programming language to analyze trick performance over time
• The study used various statistical methods including:
  • Descriptive statistics to analyze trick consistency
  • TSNE dimensionality reduction to find patterns between tricks
  • Visual analysis through interactive plots
• Key findings showed that some basic tricks like ollies were highly consistent (93% of attempts scored 5), while more complex tricks needed improvement
• The project resulted in the creation of a dashboard to track ongoing trick progress

Introduction

In 2015, I realized that I generate a lot of data from skateboarding and I had difficulty figuring out which tricks I was good at and kept losing in skateboarding competitions. The reasons for failure were not only my lack of a variety of tricks but also, how to keep the tricks consistent. I don't trust my gut feeling completely when it comes to figuring out what tricks I am good at. I prefer to follow a set of tricks that I know with some certainty that I will be able to do with ease. Thus, the skate project was born, I decided for a short period of time that I would record the number of times — I'd do a trick consistently. It was in a scale of 0-5, I even made a playlist where I explain some bits of the project which you can follow up in the video below; It is a playlist with 9 videos approximately 2-3 minutes long.

https://www.youtube.com/playlist?list=PLwteCCP_K4M6AinSMeSZL2ULwMnHps4ki

If you don't fancy checking out videos, below is the codebook that explains the experiment as a whole, how I collected the data and the tricks under study.

Codebook

• Click arrow to see codebook

Importing and cleaning data

This involves loading the data in your preferred programming language environment; It is then followed by removing unnecessary columns, removing data inconsistencies or replacing them with something more usable. For example if the columns were in uppercase and changing them to lowercase, maybe there's just an empty space in one of the rows of the dataframe and then you delete that row.