I've been using R for a few years now, since I got into statistics and data science. I started using python after I got a job, and in general, r is much more convenient than python for data analysis. the best thing about r is that it's very flexible for data manipulation, especially the packages with different functions, which make it very easy to experience one-stop input and get results without having to do it yourself step by step. Most of the statistical algorithms have corresponding packages that can be used directly. In particular, we recommend the dplyr package, which can help you analyze data like a charm. If you're using rstudio, it's a step up from writing python with the paid version of pycharm. If you're writing a dashbpard, you can get a prototype to iterate very quickly with shiny. People used to say that r was slow, but with the incremental improvements to r, that's no longer an issue. My overall experience has been one of blissful, fly-by-the-seat-of-my-pants joy.

Using R to compile four or five years, summed up to say.1. After skilled R, a lot of small data analysis tasks, your idea is to read in the data, how to use the existing Cran on the R package, how to further improve the algorithm, how to use the GG plot2 and so on visualization package to present the results of the data analysis.2. For big data analysis tasks, the use of parallel computing tools and RCPP and other packages to optimize the program computational speed is the primary consideration. 3. sas is the bad company that messes with the fees!!!! If you use R and some Python, C, the calculation will be significantly free and faster than Sas, and free! 4. Using tools such as knitr and rmarkdown can efficiently write reproducible web pages, pdfs and beamer, etc.. Unlike other general-purpose languages, R is a language that focuses on data analysis, i.e., domain-specific language. from this point of view and matlab, SAS is more similar. Therefore, rather than being proficient in R, it is better to be proficient in data analysis methods. But data analysis is divided into areas, different domains have different data analysis methods. And look at the official website, there are dozens of task view, from biological information data analysis to natural language processing data analysis, everything. You need to have at least a basic level of expertise before entering different areas of data analytics, otherwise you won't be able to understand the meaning of the data.