I was asked to visualize a set of quarterly KPIs for different companies. The main ask was to make a clear comparison between the companies, with respect to the range of the KPIs.
Given the fairly high number of KPIs, I ended up going for a tabular approach, with small horizontal bullet graphs inside each table cell, using the Sparkline package in R.
However, I quickly experiences that it was not straightforward to place Sparkline objects inside table cells.
This post is simply my previous hockey scripts applied to data for the 2019/2020 season, both the Stavanger Oilers player statistics and aggregated statistics for the Norwegian league.
However, after recently reading the book Storytelling with data, I felt obligated to make some minor changes to the graphs from last season.
Stavanger Oilers player statistics
League statistics
The 3rd International Conference on Econometrics and Statistics (EcoSta 2019) took place at the National Chung Hsing University (NCHU), Taichung, Taiwan 25-27 June 2019. The conference consisted of 10 parallel sessions, each having 14-17 sessions with 3-5 speakers occurring at the same time. The full programme is available here.
Naturally, it was quite the optimization problem to pick which sessions to attend. For parallel sessions where multiple sessions appeared interesting and relevant for my research, my final choice became rather arbitrary.
Continuing from my previous post, I now focus on detailed match statistics, rather than the available aggregate data. By scraping very detailed data from each match of the 2018/2019 Norwegian hockey season, my goal is to present aggregate data that are not available at the source webpage. The data material is scraped from Hockey live.
The code I started by simply downloading the main HTML file manually from the web browser.
I wanted to visualize the personal statistics for the hockey players of Stavanger Oilers, for the 2018/2019 season.
The data material is scraped from both Elite Prospects and Hockey live (regular season and playoffs), using the R-package rvest, as described in this blog post.
The code Scraping the data from Elite Prospects was straightforward, as it is stored as an HTML table. When you want to scrape a table with rvest, you only need to specify an index integer.
This post regards my MS_VAR Github repository, which contains code used in the following paper:
Osmundsen, Kjartan Kloster, Tore Selland Kleppe, and Atle Oglend. “MCMC for Markov-switching models—Gibbs sampling vs. marginalized likelihood.” Communications in Statistics-Simulation and Computation (2019): 1-22.
The model
A Markov-switching vector autoregressive (MS-VAR) model is an autoregressive mixture model governed by a (hidden) finite state Markov chain. In the mentioned paper, the MS-VAR model is expressed as: