In this chapter, you learned how to read and write data in various formats, how to visualize data with plot functions, and how to apply basic models on the data. Now, you know the basic tools and interface of working with data. However, you may learn more data analysis tools from other sources.

For statistical and econometric models, I recommend that you read not only text books of statistics and econometrics but also R books that focus on statistical analysis. For machine learning models such as artificial neural networks, support vector machines, and random forests, I recommend that you read machine learning books and go to CRAN Task View: Machine Learning & Statistical Learning (https://cran.r-project.org/web/views/MachineLearning.html).

Since this book is focused on the R programming language rather than any specific model, we will continue our journey in the next chapter by going deeper into R. If you are not familiar with how R code works, you can hardly predict what will happen, which slows down your coding, and a small issue can waste a lot of your time.

The next few chapters will help you build a concrete understanding of R's evaluation model, metaprogramming facilities, object-oriented systems, and several other mechanisms R chose to facilitate data analysis, which enables you to use more advanced packages of data manipulation and to work on more complicated tasks.