Introduction Data Overview Load Data Data Cleaning Exploratory Data Analysis Animation Conclusions References Introduction I went to my first basketball game on 27 January 2019 to watch the Skycity Breakers versus Brisbane Bullets. The final score was Breakers 109-96 Bullets. Since it was a close game I wondered if the type of shots attempted got closer to the hoop under pressure near the end of time, assuming that shorter range shots have a better success rate ?
This post is of the the folder structure of a blogdown website, to get a basic understanding of how it works and to also remind myself next time I face a similar publishing issue! Publishing issue I have been updating my blogdown website with posts, and then I hit an issue. The about link would not update. I tried multiple amendments and multiple commits to no avail. I then re-created the whole website and reinstalled the theme.
Introduction Data Overview Load Data Data Manipulation Data Exploration Data Visualisation Conclusions Introduction The Kaikoura earthquake happened just after midnight on 14 November 2016. After reading this article Astonishing Nasa photos show Kaikoura land raised by earthquake I thought it would interesting to look at what open data is available to view the land changes from the article. This analysis has the following objectives:
Introduction Data Data Cleaning Visualisations Conclusions Introduction The motivations for cycling around town can be for fitness, economic or environmental reasons. However in Auckland the weather and the 53 odd volcanoes could be deterrents to would-be cyclists. Auckland Transport (AT) publishes daily and monthly bike count data from counters around Auckland now available under CC BY 4.0 license. Kia ora, us again!
This blog was using the creative portfolio theme until the number of posts grew. The tiled effect became busy and the website required a different website format including an archive list. I have changed the theme to Blackburn following this post by Mike Treglia. So far so good….
Introduction Load the data Summary of data Clean Data Exploratory Data Analysis Feature Engineering Training Create Scorecard Conclusions Introduction Credit Risk modeling predicts whether a customer or applicant may or may not default on a loan. These models include predictor variables that are categorical or numeric. One of the outputs in the modeling process is a credit scorecard with attributes to allocate scores. The objectives of this post are as follow:
Introduction This tutorial was inspired by the R Curious tutorial at useR! 2018, and follows on thematically from the R Curious workshop notes as an extension. It is aimed at those with a background in Excel, who would also like to use R for data analysis. This tutorial compares the things you would normally do in Excel, but with an equivalent function in R. This introductory level tutorial assumes you have already installed R and R studio and had a brief introduction to the R basics and R Markdown.
Introduction Data Clean Data Success Factors Specific Events Resulting in Internships Social Impact Conclusion Introduction We were looking for a real data from a non-profit organisation for the R-Ladies Auckland Dataviz meetup. We were approached by Summer of Tech(SoT) who volunteered their data for the group to explore. SoT is a non-profit organisation that connects employers with students and graduates for paid work experience and graduate jobs.
Recently I have been working on an Natural Language Processing (NLP) client project. This field appears to extensively use Python packages so I used the opportunity to go on an NLP journey in Python, starting with a Jupyter notebook. The Python packages included here are the research tool NLTK, gensim then the more recent spaCy. The purpose of this post is the next step in the journey to produce a pipeline for the NLP areas of text mining and Named Entity Recognition (NER) using the Python spaCy NLP Toolkit, in R.
Version control of code seems similar to creating, updating and sharing recipes. Since we were trying out crepe recipes at home, I decided to use this personal connection to work through a crepes made with git example to validate my understanding of Git from the command line. Inspired by Reflections on 4 months of GitHub: my advice to beginners by Suzan Baert, I also started a personal Git cheatsheet, which will be incrementally updated with gems from future projects.