Introduction
The American statistician Edward Deming once said:
"Without data you are just another man with an opinion."
I think this great quote is enough to highlight the importance of the data acquisition phase of every data analysis project. This phase is exactly where we are going to start from. This chapter will give you tools for scraping the Web, accessing data via web APIs, and importing nearly every kind of file you will probably have to work with quickly, thanks to the magic package rio.
All the recipes in this book are based on the great and popular packages developed and maintained by the members of the R community.
After reading this section, you will be able to get all your data into R to start your data analysis project, no matter where it comes from.
Before starting the data acquisition process, you should gain a clear understanding of your data needs. In other words, what data do you need in order to get solutions to your problems?
A rule of thumb to solve this problem is to look at the process that you are investigating—from input to output—and outline all the data that will go in and out during its development.
In this data, you will surely have that chunk of data that is needed to solve your problem.
In particular, for each type of data you are going to acquire, you should define the following:
- The source: This is where data is stored
- The required authorizations: This refers to any form of authorization/authentication that is needed in order to get the data you need
- The data format: This is the format in which data is made available
- The data license: This is to check whether there is any license covering data utilization/distribution or whether there is any need for ethics/privacy considerations
After covering these points for each set of data, you will have a clear vision of future data acquisition activities. This will let you plan ahead the activities needed to clearly define resources, steps, and expected results.
