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Happy One Year Anniversary

The 26 August 2017 marks exactly one year since embarking on learning the R programming language. A year ago I had neither the foresight nor will strength of believing I would b this engrossed in the new found love for programming. But it didn’t take long before I found myself spending at least an hour a day playing with different skills and knowledge I had acquired. Coming from the background of playing with VBA in Excel. I got hooked every time I discovered I could do something more efficiently and, less painful in R than other data analysis packages I had used.

Although R has a steep learning curve, its syntax is quite intuitive, and once you master the underlying language specific issues - such as, how it deals with different data types and some of the strengths at the core of the language; like its vectorised operations. It becomes quite easy to write code for relatively easy operations quickly and in a few lines of code.

What have I learned in the last year?

In terms of learning, it’s been a decent year considering I have had to juggle between my other commitments and sparing time to learn and practice. Thankfully, the R community has grown both in size and commitment to developing new tools that not only make it easier to solve data problems but also makes it easy to report your findings.

Within the R ecosystem so many tools have been built,for me the most outstanding are the following: Thanks to Yihui Xie, his package, blogdown allows one to build a website making use of the static site generator hugo. The official package documentation can be found here. This website is a product of this package, built with a cactus theme. In this brief post I have given some details on how I built my website.

The second package from Yihui Xie I use often is bookdown. A great package for authoring books,articles and just able everything you can imagine. Both technical and non-technical documents. I beat myself whenever I recall the trauma I had to go through writing my undergraduate thesis with SPSS as a tool of choice for analysis. Picture this, I would have to manually copy my graphs and analyses into Word.Then, after I realise I have a mistake in one of the figures, you can probably guess the next step, going back step by step through the painful process. Thanks to bookdown, which implements Rmarkdown, all it takes is writing the prose with code embedded in the same plain text document. With the use of pandoc, it allows you to convert to various output formats such as HTML, Word, and PDF. You can check the package documentation and the accompanying book.. I have managed to author two full technical documents and many more brief articles.

Since I started R, mainly just to learn enough to be able to do simple data analyses, I later realised, there is a more interesting field beyond mere simple analyses. I started taking both paid and free on-line courses in Data Science. DataCamp and Coursera are the two paid platforms I am using this far. But of the two, Coursera stands out for me. Their model incorporates top universities as partners and financial aid is available for those that can’t afford to pay. Of course, with the usual scholarship questions to be answered. I’m happy to have received my first financial aid to pursue a course in probability and data offered by Duke University. But the learning doesn’t end there, Hadley Wickham and Garret Grolemund of RStudio have a free on-line book on R for Data Science. This book implements a collection of R packages under the tidyverse banner, developed to make working with data less painful and more smoother- plus it contains many real world data science problems.

In hindsight, learning how to program with R is one of the best decisions I made. I have been exposed to a lot and learnt many things, which 12 months ago was all Greek mythology.

I can only imagine what is in store in the next 12 months.

In closing, I have realised git and github as tools for version control mean life to code crunchers…