This example will examine how to plot time series wind measurements stored as NetCDF datasets, using Python3 (for info on installing Python3 and packages, see our previous blog).
In this series of technical blog posts, I will be looking at using open source tools to examine energy and climate data.
I am currently working for a non profit organisation, working on enhancing the interaction between the energy industry and the weather, climate and broader environmental sciences community.
Not coming from a climate science background, I had to become quickly accustomed to the terminology and technologies associated with this field of research. A commonly used file types produced to represent weather and climate data, are Network Common Data Files (aka NetCDF files).
Just completed the advanced Google analytics training at Google Academy!
I recently completed some website maintenance work for Norwich based opticians Dipple & Conway
As I’ve been using Google Analytics daily for the past year, I thought it was high time I got properly certified. I completed the beginners course on Analytics Academy today and picked up some useful tips I hadn’t been utilising. Will be doing the intermediate and advanced courses in the coming weeks.
Recent contract to create an eCommerce site for UK based children’s party suppliers, Marvellous Cat.
They requested a clean website to replace their existing big cartel shop, with provision for a shop, blog, free downloads and more.
Sixteen47 Ltd (Dawn French and Helen Teague) hired me to develop an online marketing strategy and social media campaign for their exclusive plus size clothing brand. This included creating a blog website to host archive material and discussion topics relevant to the companies interests. I worked closely with Sixteen47 to ensure the design was suitably customised to match their distinctive brand aesthetics.
Recently I have been looking into new and interesting ways of visualising data. I stumbled across an excellent suite of open source tools called nodebox (http://nodebox.net) so decided to have a play.
I have experimenting with nodebox v3 for a couple of days and found it really quick and intuitive to make some nice generative animations. You can watch them at: https://www.instagram.com/mrlukesanger/
Wha initially interested me in nodebox is the fact it can accept CSV files. So the next step is to load up some datasets and seeing what kind of data visualisations are possible.
The beta of my web app for performing sentiment analysis on tweets is online:
Simple to use. Just type in a word you want to search, press the button and wait for the result. The app collects the most recent 1000 tweets containing your keyword.
The Word Cloud tab displays the most commonly featured words that appear in tweets alongside the search keyword.
# ggplot2 bar chart design adapted from an excellent tutorial by J.Silge (http://juliasilge.com/blog/Joy-to-the-World/)
# word cloud adapted from shiny word cloud tutorial (http://shiny.rstudio.com/gallery/word-cloud.html)
# tweet data cleansing adapted from sentiment package tutorial (https://sites.google.com/site/miningtwitter/questions/sentiment/sentiment)
# sentiment analysis process adapted from syuzhet tutorial (https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html)