Learning R: Previous Knowledge and Resources

Although at first glance it seems Chinese, this free and open source tool is so multifunctional that once you learn to drive,you don’t want to use anything else. R is a flexible programming language designed to facilitate exploration in data analysis, with high-level graphics. It has a large number of libraries, which places it very much as a powerful ally for statistics, data mining and web analytics.

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Its growing popularity is not the only reason to learn R. If you want to learn data science for real, you need to have the basic skills to understand it: knowledge of data manipulation, visualization and machine learning. You have to select a language that is able to take full advantage of these skills, 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 indicating that R is becoming the lingua franca for data science,it is not for all audiences. It should be borne 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 integrate it, whose number is increasing, make it seem extremely complex.

Learn to program in R

It is almost always recommended to start using this tool with RStudio,to handle a pleasant and more understandable environment. Apart from all the information that can be obtained through the internet, three basic points are also advised: knowing statistics (something obvious in this case), having basic programming knowledge (something that not so many people think about at first) and a good reference manual to always have at hand -not to be read from beginning to end, but to open when a doubt arises or see if some operations are possible. And most importantly: stop using any other program and start using R,as troubleshooting is the best way to prepare. There are thousands of ways to start getting to know this program on the internet, such as through manuals, specialized blogs, videos on youtube or even examine other people’s codes to understand how it works. It’s also a good way to try to understand how different functions work. You can write the name of these, without the parentheses, give the enter and analyze the code to see what it carries out. The debug() function can also help you understand how everything works.

Previous knowledge to program in R

If you already know other systems such as SPSS or SAS,the best advice is to dive directly into R every time you want to do some work, even if you have to force yourself to do it. It is true that it will be hard, but by asking google and even comparing the result with SPSS or SAS,little by little one will get the environment and handle it faster and faster. Reading specialized blogs can also help to know the tricks and perfect the knowledge of this tool, and there are even some dedicated to the relationship that exists between R and these other programs. Although these are not programming languages such as R, it is advisable to repeat problems that have already been solved in them with these tools, to get loose in R, step by step. Thus, no mistakes will be made with new projects and you will know that you are doing well, since you have the right answer and you can easily see the difference between the different programs. It is also advisable to have some experience with languages such as Python or C,since it is vital to know the most basics of programming if you want to start working with R, as well as keep in mind that it takes a long time to master this tool, so it is best to have patience and tenasion.

Material for programming in R

There are endless pages where we can find manuals and references to learn this language. It all depends on what we want to achieve when facing this tool.

• If the intention to use R is clear, a suitable manual can be found in this list of 129 books on R and S on the project page.
• If one does not dare on their own, the coursera platform offers a course through Johns Hopkins University.
• To get started with R, a simple page is Quick-R.
• Through rseek you can find any type of content related to R.
• On the RStudio page you can find a lot of information and resources to learn how to use the program. It is also advisable to visit the R Projectpage.
• For users with knowledge of SAS and SPSS, on this page you will find help getting started in R, as well as in the SAS and R blog.
• As progress is made, doubts will arise and at the same time new discoveries will be made. Therefore, it is interesting to be informed at the moment of everything that happens through R-bloggers or follow the Stack Overflowcommunity.
• Something that makes R special is its use of vectors, so it’s important to learn the different ways to handle them.