Summary

In this chapter, we covered essential functions and techniques for data transformation, aggregation, and merging. For data transformation at the row level, we learned about common utility functions such as filter(), mutate(), select(), arrange(), top_n(), and transmute(). For data aggregation, which summarizes the raw dataset into a smaller and more concise summary view, we introduced functions such as count(), group_by(), and summarize(). For data merging, which combines multiple datasets into one, we learned about different joining methods, including inner_join(), left_join(), right_join(), and full_join(). Although there are other more advanced joining functions, the essential tools we covered in our toolkit are enough for us to achieve the same task. Finally, we went through a case study based on the Stack Overflow dataset. The skills we learned in this chapter will come in very handy in many data analysis tasks.

In the next chapter, we will cover a more advanced topic...