rstats

Using {pollen} and {vegperiod} to analyze temperature, GDD, and vegetation period

{pollen} and {vegperiod} are two R packages that can be used to analyze temperature, Growing Degree Days (GDD), and vegetation period. In this analysis, we explore historical temperature records, GDD trends, and vegetation period changes in Chaudières-Appalaches, Quebec, using these packages. By combining data visualization and exploratory data analysis (EDA) techniques, we uncover key patterns and anomalies that shed light on climate-driven changes in the region.

Aminated Visualisation for Centre-du-Québec’s Precipitation

Building upon previous analyses and predictive modeling, I details the process of creating this visualization, including data preparation, disaggregation to daily levels, and kriging for enhanced spatial resolution. The post culminates in an animated map that illustrates precipitation trends and anomalies over time, providing valuable insights for climate analysis, agriculture, and water resource management.

30 Years of Precipitation for Centre-du-Québec: Trends, Patterns & Anomalies

Understanding long-term precipitation patterns is crucial for climate research, agriculture, and water resource management. In this post, we analyze 30 years of high-resolution precipitation data from the AgERA5 dataset, focusing on a single administrative region in Quebec. Using exploratory data analysis (EDA) techniques, we uncover trends, seasonal variations, and anomalies to gain deeper insights into precipitation dynamics

St. Lawrence Lowlands Precipitation Data: 30-Year Trends Prediction

In this phase of the analysis, we aim to model precipitation patterns in the St. Lawrence Lowlands using machine learning techniques, leveraging historical climate and environmental data. We will compare Random Forest, XGBoost, and Mars models to assess their ability to capture complex relationships and predict precipitation trends. Model performance will be evaluated using cross-validation and regression metrics to determine the most effective approach.

St. Lawrence Lowlands Precipitation Data: 30-Year Trends & Anomalies

Understanding long-term precipitation patterns is essential for climate research, agriculture, and water resource management. In this post, we analyze 30 years of precipitation data from the AgERA5 dataset for St. Lawrence Lowlands, using exploratory data analysis (EDA) techniques to uncover trends, seasonal variations, and anomalies.

TyT2024W21 - VIZ:Carbon Majors Emissions Data

This week we are exploring historical emissions data from Carbon Majors. They have complied a database of emissions data going back to 1854. In the first and second part I did some EDA and created a spatio-temporal machine learning model. In this part, I’m creating an animated vizualisation of the data including the prediction.

TyT2024W21 - ML:Carbon Majors Emissions Data

This week we’re exploring historical emissions data from Carbon Majors. They have complied a database of emissions data going back to 1854. In this second part, I’m predicting carbon emission over space and time.

TyT2024W21 - EDA:Carbon Majors Emissions Data

This week we are exploring historical emissions data from Carbon Majors. They have complied a database of emissions data going back to 1854. In this first part, I start with some exploratory data analysis.

IRDA soil data

I recently prepare FADQ data to make some predictives models. Those are great spatial data, but I can’t go without a bit of soil information.

FADQ historical crops data

I have a lot of things to try… so I need a lot of data to play with. Here I summarize you how I extract FADQ historic data. I’m going to place the tidy data in a repro on github and play with it for my next blogs.

TyT2019W47 - Treemap

Initially publish it on my wordpress blog. I put it here for reference purpose.

TyT2019W46 - Radar

Initially publish it on my wordpress blog. I put it here for reference purpose.

TyT2019W29 - R4DS

Initially publish it on my wordpress blog. I put it here for reference purpose.