R Tutorial: Import your data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/working-with-data-in-the-tidyverse at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, I'm Alison Hill and I'll be your instructor. This course will introduce you to more data science tools from the tidyverse to help you work with your data better using the R programming language. You'll learn to explore, tame, tidy, and transform your data in R. We'll start at the beginning of the data science pipeline by reading data into R, which is an important first step to working with your own data. We'll focus on reading rectangular data into R. In rectangular data, columns hold variables like "series" and "baker". Here, column names are in the first row. Each column holds different kinds of data, like numbers, characters, or dates. Rows correspond to observations on an individual unit. Here the first row holds values corresponding to Natasha from series 3. We are looking at data from a popular television baking competition called "The Great British Bake Off." In it, amateur bakers compete against each other, facing different baking challenges that test their skills. Let's see how this looks in R as a tibble. ...First, this data has a name- "bakers". When we call "bakers", a tibble with rows and columns is printed. A tibble is a special kind of data frame. Data frames are useful because column values in the same row correspond to the same observation. Don't be confused by the terms tibble and data frame- for this course, the most important thing to know is that they both store rectangular data in R. The readr package is for reading rectangular data into R. We'll use the read underscore csv function to read data from a CSV file, which stands for Comma Separated Values. This means that commas separate values within a row, and each row is a new observation. Think of read underscore csv like a recipe...it tells R what to do and