Part 1 of the series of practical hands-on tutorials on performing correlation analysis in R. In this part, I will provide a simple overview of what correlation coefficients are, what they tell and don’t tell us, when they are meaningful (aka statistically significant), and what is their practical significance.
An R package containing themes for ggplot2 that have been optimized for blogs and similar online usage: theme_web_bw, theme_web_classic, and theme_web_void.
Sooner or later (usually sooner) a data analyst runs into a situation where one needs to make multiple visualizations on the same subject, which can be slow and time-consuming. By writing simple functions, it is possible to automate most of the work, thus greatly simplifying and speeding up repetitive plotting tasks.
A detailed tutorial on visualizing Canadian Census data using R.
An interactive dashboard to explore the Government of Canada’s official statistics on the spread of the COVID-19 disease. Built with R Shiny, Leaflet, and plotly. Auto-updated every six hours.
A detailed tutorial on using R to retrieve, and work with Canadian Census data.
How to set up the ‘cancensus’ R package – the best tool to work with the Canadian Census data.
A detailed example of using R to visualize Statistics Canada CANSIM data, step-by-step.
A detailed tutorial on using R to retrieve and clean Statistics Canada CANSIM data, step-by-step.
A collection of links to some great R-spatial resources, sources of geospatial data, and other GIS-related stuff that you may find useful.
CANSIM, also known as Statistics Canada Data, stands for Canadian Socioeconomic Information Management System. Here I’ll address some of the key features of CANSIM data and explain how to set up the R tools required to work with it.
An introduction to the series on working with Statistics Canada data in the R language. The goal of the series is to provide some examples (accompanied by detailed in-depth explanations) of working with Statistics Canada data in R.