Unless you’ve been living under a (virtual) rock online, chances are that you’ve already heard of the famous Team Trees fundraising challenge. So I thought – why not use this opportunity to spread more awareness about this challenge AND teach people some cool data visualization while I’m at it?
So in this post, I’ll tell you about #teamtrees initiative and teach show you how to use the visual called ‘Treemap’ to visualize the Team Trees donation count. Let’s go!
I like to read. Like a LOT. But I’m not limited to just books. I read everything that comes my way – books, articles, Reddit threads, tweets and what not. Consuming information by audio (podcasts, audio books) or video is just not my thing. Text is how I like it – and to keep track of all the articles and posts I have to read (but can’t at the moment), I use a very popular app called Pocket. Whenever I come across any interesting article that needs to be saved for reading later, I just save it to my Pocket account. It’s a very handy app – you can save articles from your phone, within apps or from your browser. You can then go back to it later and read the articles in a distraction-free way, offline.
Being the data-curious person that I am, I thought, why not use data analysis to gain deeper insights on my internet reading habits using my Pocket data? So this is what this post is about – I explore trends on how frequently I add articles to my Pocket, how frequently I read them and what those articles are about. I use the Pocket API and Python language to do this analysis. Let’s go!
Not many people know this but apart from being a data analyst, I am an artist too. This means that I regularly create art and post it on my Instagram account. Making art, just like doing an analysis, takes a lot of time and effort. And it makes me sad when I’m not able to get enough social validation in the form of likes, comments or new followers on my posts.
So I keep trying out different methods to increase my following and post engagement. One of the methods I use is to include relevant Instagram hashtags in my posts. But the biggest struggle is finding the most relevant hashtags for a particular post. How do I know if the hashtags I’m using are effective enough or not? Therefore I decided to tackle this problem doing what I do best (apart from making art!) – I decided to write a Python code for doing my own Instagram hashtag analysis!
Twitter has become a toxic place. There, I said it. It is no longer the fun and happy place it used to be a few years back, certainly not in India. It is now full of trolls, rude and nasty people, politicians and companies busy trying to sell their products or spreading propaganda – manipulating people’s opinion using fake Twitter trends and tweets.
But I still love Twitter. Partly because it is not Facebook (that’s a good enough reason). However it pains me see the negativity every time I visit it. As a user, it appears that the Twitter team isn’t moving fast and hard enough to eliminate the problem of trolls and propaganda. So I decided to approach this problem on my own, doing what I do best – data analysis. In this post, I use the Twitter data to perform a basic data analysis in R to analyze a very specific part of the problem – unnaturally trending hashtags and trends (or as others call them – fake Twitter trends)