In this post, we are going to explore how to adjust various ggplot plot elements. What can be adjusted, what they are called and how they can be adjusted.

## Customising ggplot2

Customising a ggplot2 plot using the theme function.

Customising a ggplot2 plot using the theme function.

In this post, we are going to explore how to adjust various ggplot plot elements. What can be adjusted, what they are called and how they can be adjusted.

Create an elegant photo calendar completely using R.

I had this idea of using some of my travel photos to create a photo calendar. I would normally go about it using Adobe Photoshop or Adobe Illustrator. But, that would involve a lot of manual work placing dates and days for each month. I would also like to mark some public holidays and friend’s birthdays. So, I wondered if it might be possible to do it with R. After fiddling about with it over the weekend, I managed to make it work. It went better than I expected. And here I am recreating the calendar using some stock photos. All stock photos are royalty-free from Pexels. For the impatient ones, the whole code and images are available at this Github repository. For detailed guide, keep reading.

We scrap Instagram for basic public data using R to help us pick optimal hashtags.

If you are an Instagram user, at some point, you care going to be interested in the various metrics such as followers, number of posts by a certain user etc. You might want to compare these metrics between different users or to find out the number of posts with a certain hashtag etc. The casual way to do it is to go the relevant Instagram page and look at the metric and write it down somewhere, and go to next and so on. Clearly this is not ideal strategy if you want to look at a few hundred pages. It would be neat to get this data in an automated manner.

In the advent of big data such as genomics, running numerous statistical tests is unavoidable. But long comes strange statistical problems. This post investigates issues with multiple statistical testing and its solutions along with simulated data.

In a standard statistical test, one assumes a null hypothesis, performs a statistical test and computes a p-value. The estimated p-value is compared to a predetermined threshold (usually 0.05). If the estimated p-value is greater than 0.05 (say 0.2), it means that there is a 20% chance of obtaining the current result if the null hypothesis is true. Since we decided our threshold as 5%, the 20% is too high to reject the null hypothesis and we accept the null hypothesis. Now, if the estimated p-value was less than 0.05 (say 0.02), there is a 2% probability of obtaining the observed result if the null hypothesis is true. Since 2% is a very low probability and it is below our threshold of 5%, we reject the null hypothesis and accept an alternative hypothesis.

The 5% threshold, although giving us high confidence, is an arbitrary value and does not absolutely guarantee an outcome. There is still the possibility that we are wrong 5% of the time. This is known as the probability of a Type I error. A Type I error occurs when a researcher falsely concludes that an observed difference is real, when in fact, there is no difference.

That was the story of a single statistical test. With large data, it is common for data analysts to do multiple statistical tests on the same data. Similar to a single test, each test in a multiple test has the 5% Type 1 error rate. And this accumulates for the number of tests.

It is easy to be disillusioned and pessimistic about the world we live in. Bad news seems to be followed by worse news. But humanity has come a long way from the disease-ridden, impoverished, war-torn lives of our fore-fathers. Here we look at a few data-driven graphs to convince ourselves of the progress we have made over time in various aspects of life. Slow progress never makes headlines.

It may seem like the world is descending into total chaos, violence, and destruction. War in Syria, Ukraine, Yemen, Islamic state, migrant crisis, Ebola, plane crashes, earthquakes, tsunamis and what-not. The more news you watch, the more worried you will be. This is because the news outlets tend to focus on spectacularly negative instances. Violence, atrocities, and hatred are thrown into the spotlight and into the lives of common people. With the ever increasing digital connectivity, it is easy to disseminate information and to absorb information at an unprecedented level. Relatively smaller incidents have a larger voice. As said by Ray Kurzwil, “The world isn’t getting worse, our information is getting better”. To appreciate the world we live in, we have to put things into a wider context.

The fact is that humanity has never lived in a better time than now in pretty much every aspect you look at; war, violence, diseases, poverty are all at the lowest it has ever been. Of course, there is still a long way to go, but this is the best it has been since the beginning of humankind. To prove my point, here we evaluate human progress using some real data and simple time-series plots. Most of the data and information was obtained from OurWorldInData.