I recently had the pleasure of presenting at useR2018 about practical workflows with R. One of the things that I commented about was how to get data out of R into other software easily. The majority of my clients work with excel when dealing with data, and as such will need some of my outputs in excel so they can use them. Unfortunately R doesn’t natively provide the simplest techniques to do this.
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Over the past 6 months I’ve been asked by several people in the charity sector, how did you get started with R? The other question that comes with that is, why R over Python? Some background One of the key approaches to my work in data analytics is that I strive to make my life as easy as possible. I first looked at trying to learn R about 10 years ago.
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In 2013 the ACNC conducted some research into Public trust and confidence in Australian charities. They kindly published the data to the http://data.gov.au website. The survey was created to understand the public attitudes and current levels of trust surrounding charities in Australia. It also collected information about awareness of and support for a national regulator (the ACNC). The following analysis aims to see where Australian confidence was in 2013. I’ll then aim to follow up with some further analysis based on the survey that was conduted in 2015, and then when data is released see if we can start to plot some trends with the inclusion of the 2017 data.
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In my last post I looked at how charities are using Facebook to post content and engage with their fans (and hopefully donors). At the end of the post I started to have a look at some text based analysis and sentiment to see how often donations were being mentioned or solicited and how positive, negative or neutral posts were over time. I thought for the next post in the series it might be worthwhile having a brief look at different keywords that might get used within posts and the importance of those words to the three charities I had chosen to look at.
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One of the things that amazes me about R is the incredible eco-system of packages that are available to make data analysis easier, and sometimes to even just make things that little more aesthetically pleasing that what base R plots can provide. Much of what is posted within this blog is leveraging the incredible skills of people like Hadley Wickham, Yihui Xie, and Bob Rudis to name just a few. One of the great things about having such a rich eco-system, is that if there’s ever anything you’re wanting to do someone may have already written a package to help you do it.
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UPDATE: It isn’t really a 299 minute read, but if you choose to explore the map at the end you could well spend 299 minutes. Australia currently has 53,043 charities registered as at 19 January, 2017. The ACNC is the national regulator of these charities. Of the many things they’re tasked with one of them is to: maintain, protect and enhance public trust and confidence in the sector through increased accountability and transparency.
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I’ve been wanting to do some analysis on some weather data for a while. Mostly to see whether (pun not intended) there are any longer term trends that are observable from figures recorded by the Bureau of Meteorology. There are a lot of historic stations, and a lot of data that could be assessed. To keep things contained I’ve decided to look at a single city, albeit built from several recording weather stations due to the long term availability of the data.
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