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Update import.Rmd (#250) 

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S'busiso Mkhondwane 2016-08-12 22:40:24 +02:00 committed by Hadley Wickham
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## Introduction
Working with data provided by R packages is a great way to learn the tools of data science, but at some point you want to stop learning and start working with your own data. In this chapter, you'll learn how to read plain-text rectangular files into R. Here, we'll only scratch surface of data import, but many of the principles will translate to the other forms of data. We'll finish with a few pointers to packages that useful for other types of data.
Working with data provided by R packages is a great way to learn the tools of data science, but at some point you want to stop learning and start working with your own data. In this chapter, you'll learn how to read plain-text rectangular files into R. Here, we'll only scratch surface of data import, but many of the principles will translate to the other forms of data. We'll finish with a few pointers to packages that are useful for other types of data.
### Prerequisites
@ -30,7 +30,7 @@ Most of readr's functions are concerned with turning flat files into data frames
[webreadr](https://github.com/Ironholds/webreadr) which is built on top
of `read_log()` and provides many more helpful tools.)
These functions all have similar syntax: once you've mastered one, you can use the others with ease. For the rest of this chapter we'll focus on `read_csv()`. Not onl are csv files one of the most common forms of data storage, but once you understand `read_csv()`, you can easily apply your knowledge to all the other functions in readr.
These functions all have similar syntax: once you've mastered one, you can use the others with ease. For the rest of this chapter we'll focus on `read_csv()`. Not only are csv files one of the most common forms of data storage, but once you understand `read_csv()`, you can easily apply your knowledge to all the other functions in readr.
The first argument to `read_csv()` is the most important: it's the path to the file to read.