Summary
This chapter focused on some rather boring, but important tasks that we usually do every day. Importing data is among the first steps of every data science projects, thus mastering data analysis should start with how to load data into the R session in an efficient way.
But efficiency is an ambiguous term in this sense: loading data should be quick in a technical point of view so as not to waste our time, although coding for long hours to speed up the importing process does not make much sense either.
The chapter gave a general overview on the most popular available options to read text files, to interact with databases, and to query subsets of data in R. Now you should be able to deal with all the most often used different data sources, and probably you can also choose which data source would be the ideal candidate in your projects and then do the benchmarks on your own, as we did previously.
The next chapter will extend this knowledge further by providing use cases for fetching data from the Web and different APIs. This simply means that you will be able to use public data in your projects, even if you do not yet have those in binary dataset files or on database backends.
