Kim Fitter

A short blog on regex in Python

As a side project, I am creating a website is to visualise food labeling data from food packaging with JavaScript libraries Node.js and D3, React and possibly using a MongoDB database. This data analysis helps to understand the data behind the web application using visual interactive tables.

Creating a minesweeper game was a challenge in Foundations Sprint 5 at Dev Academy.

Problems are either simple, straightforward to solve, or absolute head scratchers. We can use different techniques, depending on the problem.

Web development is like building a house. The HTML language builds the house, the CSS language paints the house and the JavaScript language switches on the house.

Two of the basic web technologies are HTML, which defines the structure and CSS, which describes the appearance of structural elements. We can use a Mondrian colour palette to explain what CSS does and what it looks like with HTML elements. Piet Mondrian was a Dutch painter famous for his palette of primary colours and drawing lines around boxes.

The first satRdays conference was held in Auckland, New Zealand on 22 February 2020. We had an amazing day pulled together by a great group of volunteers and we hope that we can hold this event annually, and perhaps in cities around New Zealand. Awesome organisers and volunteers.

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.

Personalised logos The new Data Visualisation Society has created personalised logos, see the behind the scenes post by Amy Cesal. It is a visual representation of three key skill areas; data, visualisation and society. See below the key to the logo: There is also a timezone overlay in grey. My personalised logo This is my personalised logo: Purple I have a background in project management and I am involved in community group organisation represented by the purple triangle.

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.

One of the things to reconcile with working in a field is that what interests you and what you need to create or provide as a service commercially is not always the same thing. If doing only one then it’s like throwing one ball - it is quite doable but monotonous. Personal projects Publishing personal side projects as blogs is great practice and ongoing personal learning. I use these to improve the efficiency of my end to end workflow and also to try out different tools, packages and functionality.

Introduction Import Data Data Summary Exploratory Data Analysis Data Cleaning Visualisations Conclusions References Introduction While touring the New Zealand South Island with friends in January 2019, one of our stops was the The University of Canterbury Mt John Observatory. Since it this area is a dark sky reserve, with a clear night and a new moon the midnight sky was lit up with stars and the milky way.