R

As part of an analytics project for a client, I used R and various libraries to import, clean, and analyze datasets related to labor market statistics in British Columbia. This project involves reading data from CSV files, performing data cleansing operations, merging datasets, and generating visualizations such as box plots and a bar chart showing the monthly change in employment across Canadian provinces. The analysis focuses on specific sectors, including accommodation and food services, wholesale and retail trade, and other services, providing valuable insights into the labor market dynamics. In this post, we’ll walk through the steps to analyze employment data across various sectors and provinces in Canada using R. ...

Choosing between long and wide data formats can make or break your workflow—especially when it comes to analysis, visualization, or preparing reports. This guide will help you figure out the best structure for your dataset based on your goals and tools. 🧠 Start With the Right Questions Before reshaping your data, ask yourself: 1. What are you trying to do with the data? Analyze it (e.g., run time-series or trend models)? Visualize it (e.g., bar charts, line graphs)? Use it in tools like R, Python, Excel, or Tableau? 🔧 Pro tip: Tools like ggplot2 in R or pandas in Python often prefer long format. ...