Introduction Data Exploratory Data Analysis Data Visualisation Conclusions References Introduction Mount Ruepehu is an active volcano on the North Island which is skied in winter and walked in summer. This analysis looks at whether anyone should swim in the crater lake at the top of the mountain. Data We can access volcano field time series temperature observation results data from GeoNet.
Introduction Data Data Summary Exploratory Data Analysis Data Cleaning Data Visualisation Conclusions References Introduction I was inspired to analyse and visualise global fishing open data by the story produced by Jon Olav Eikenes. This is also a sequel to my previous shark analysis post Is Swimming with Sharks Dangerous?, digging further into the potential reduction in shark numbers due to commercial fishing. # Load packages library(tidyverse) library(sf) library(rnaturalearth) library(biscale) library(cowplot) Data We will use the Global Fishing Watch Vessel Identity open data available with the Vessels metadata available under the Creative Commons Attribution-ShareAlike 4.
Introduction Data Data Summary Exploratory Data Analysis Data Cleaning Data Visualisation Conclusions References Introduction We recently went swimming with sharks with a Marine Biologist. We asked the question “Is swimming with sharks dangerous?” in the dive brief about how to swim with sharks. This is a spatial analysis of historical shark attacks. # Load packages library(tidyverse) library(sf) library(rnaturalearth) Data We will use the International Shark Attack File (ISAF) global shark attack csv dataset from 1580 until July 26, 2018.
Introduction Data Summary Exploratory Data Analysis Data Cleaning Data Visualisation Conclusions References Introduction The Geocomputation with R book has a great example of transport analysis in Bristol. I decided this is a chance to get to know Open Street Map (OSM) and its data better given the flurry of bike route construction. This is a bicycle network analysis of Auckland using open source spatial and bike counter data for 2018.
Introduction Data Fundamentals of Data Visualisation Final Visualisations Conclusions References Introduction This post creates and reviews at visualisation objects considering the fundamentals of data visualisation book; Data visualization is part art and part science. The challenge is to get the art right without getting the science wrong and vice versa Learning R is a bit of a journey over time so here goes Part 2 of an interactive visualisation project using tidyverse and ggplot compatible1 packages.
Introduction Data Exploratory Data Analysis First Roadmap Conclusions References Introduction Recently one of the problems I was trying to solve required matrix algebra. This falls under the umbrella of need to know R basics so I realised I needed a way to track and refresh my R skills, recognise gaps and ultimately create a reference cheat-sheet.