Although at first glance it seems Chinese, this free and open source tool is so multifunctional that once you learn how to use it, you will not want to use anything else. R is a flexible programming language designed to make it easy to explore data analysis with high-level graphics. It has a large number of libraries, which makes it a very powerful ally for statistics, data mining and web analytics . Index of contents Its growing popularity is not the only reason to learn R. If you want to truly learn data science, you need to have the basic skills to understand it: knowledge of data manipulation, visualization, and machine learning. It is necessary to select a language that is capable of taking full advantage of these abilities, as well as that this language has resources to be able to learn to handle it completely. You have to learn to think about solving problems , and all this with R is much simpler.
Although there are many voices that indicate that R is becoming the lingua franca for data science , it is not for all audiences. Keep in mind that at first it is very difficult to understand and some basic knowledge is required to get started, in addition to the large number of packages and functions that make it up, the number of which is increasing, make it seem extremely complex. learn R Learn to program in R It is almost always recommended to start using this tool with RStudio , to handle a nicer and more understandable environment. Apart from all the information that can number list be obtained through the internet, three basic points are also recommended: know statistics (something obvious in this case), have basic knowledge of programming (something that not many people think of at first) and a good user manual. reference to always have at hand -not to be read from beginning to end, but to open when a doubt arises or to see if some operations are possible-. And most importantly: stop using any other programs and start using R , since troubleshooting is the best way to prepare.
There are thousands of ways to get to know this program on the internet, such as through manuals, specialized blogs, videos on YouTube or even examining other people's codes to understand how it works. It's also a good way to try to understand how the different features work. You can write the name of these, without the parentheses, hit enter and analyze the code to see what it does. The debug() function can also help you understand how things work. Previous knowledge to program in R If you already know other systems like SPSS or SAS , the best advice is to dive straight into R every time you want to do some work, even if you have to force yourself to do so. It is true that it will be hard, but asking google and even comparing the result with SPSS or SAS , little by little one will be taking control of the environment and handling it faster and faster. Reading specialized blogs can also help you learn the tricks and improve your knowledge of this tool, and there are even some dedicated to the relationship between R and these other programs. Although these are not programming languages like R, it is advisable to repeat problems that have already been solved in them with these tools.