Everyday R: tips and tricks for data science with R

Few days ago, I installed a 'fresh' Windows and used this opportunity to migrate to the newest version of R ( 4.0.3 ) and R Studio ( 1.3.1093 ). At the same time, as most users, I wanted to keep my library collection from the previous installations. There are many ways to achieve that: you may copy your old library folder to the 'fresh' one and update the packages in R, for example. But, because each version of base R has obviously a bit changed internals, you can be almost sure, that the update will fail for a quite number of them.   This time I took it as a little challenge and set off to develop a simple tool which 'migrates' my libraries to the new R version. The first step was to get a list of the installed packages in the old R. Provided you it is still up, you may simply retrieve a named matrix with names of the installed packages by calling installed.packages() function. The output is a bit verbose, n fact, you just need the 'Package' column to

There are many blogs and community portals devoted to data science, data crunching, statistics and coding with the workhorses of data scientists, R and Python. So you may wonder what motivates me to found another one.   The first reason is to share my experiences with real-life data analysis and, often unusual,  R (and in part Python) programing solutions which may make your life of a data professional or hobbyist easier. I suppose, you are not looking for programming basics - if so, there are better addresses in the Web, like Stack Overflow , the generic R or R Studio help . If you are looking for detailed information on tools, their code and bugs, you'll better visit GitHub . This blog is rather about coding solutions which may help make your scripts clearer, more efficient and versatile and present your projects' results in a visually compelling way. The second reason is to aid you at understanding some phenomena of our data-oriented world. The very special COV-19 year 2020