Quick blog post basé sur les données des véhicules en circulation de la SAAQ 2013-2019 (https://www.donneesquebec.ca/recherche/dataset/vehicules-en-circulation).
Le but c’est de voir quelle marque a le plus de véhicules sur la route après quelques années. Sans surprise les toyota et honda de ce monde dominent largement. Deux curiosités: les F150 disparaissent (exportés?) ainsi que les volkswagen (dieselgate?)
Reasons I’ll come back to this post: {showtext} and font_add_google. Mise en page du dernier graphique (palette, polices) inspirée de https://github.
(code volé à un dude qui a fait la même chose pour les costco à Anchorage, Alaska)
Petit projet avec utilisation de: - osmdata pour trouver les ashton - (new to me) de mapboxapi pour les isochrones - et raster/fasterize pour combiner les multiples isochrones
suppressMessages( suppressWarnings({ library(mapboxapi) library(tidyverse) library(mapdeck) library(osmdata) library(leaflet) library(sf) library(raster) library(rgeos) library(fasterize) library(rgdal) library(sf) library(dplyr) library(purrr) library(mapview) }) ) step 1 : trouver les ashton avec osmdata
Petit post rapide qui utilise les données de Statistique Canada pour regarder l’évolution du taux de participation pendant et après la pandémie.
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Quick post inspired by the winning / nearly there / need action graphs by @yaneerbaryam at https://www.endcoronavirus.org/countries.
Data Health regions date is compiled by Isha Berry & friends github. Montreal boroughs data is published daily. They only keep the total and keep no history, so @bouchecl visits them every day and compiles the data in this google sheet
Code I went a bit over the top for this one and created an R package you can install to recreate all the graphs and fetch the data.
I made a twitter survey a couple of months before the apocalypse to help me pick my next blog post topic and all 3 members of the crowd overwhelmingly agreed that I should use bike gps data and graphhopper to find out how far cyclists are willing to go to use safer infrastructure.
This is awesome, because I had been looking for a use for this open data that contains GPS data for ~ 5000 bike trips in Montreal for a while.
I’m building my first tweedie model, and I’m finally trying the {recipes} package.
We will try to predict the pure premium of car insurance policy. This can be done directly with a tweedie model, or by multiplying two separates models: a frequency (Poisson) and a severity (Gamma) model. We wil be using “lift charts” and “double lift charts” to evaluate the model performance .
Here’s is the plan:
Pre-process the train and test data using recipes.
I found life expectancy at birth data for “health regions” in Canada for 2015-2017 and in “census tracts” in the USA for 2010-2015.
Here is a map of these two countries, excluding areas with a life expectancy at birth lower than 0.
Data sources and shapefiles: Canada mortality. Canada shapefiles. USA mortality. USA shapefiles downloaded using the tigris package. Libraries The usual data wrangling libraries for spatial data (tidyverse, sf), mapped using mapview and leaflet.
