R Tutorial : Exploring raw data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/cleaning-data-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- The first step in the data cleaning process is exploring your raw data. We can think of data exploration itself as a three-step process consisting of understanding the structure of your data, looking at your data, and visualizing your data. To understand the structure of your data, you have several tools at your disposal in R. Here, we read in a simple dataset called lunch, which contains information on the number of free, reduced price, and full price school lunches served in the US from 1969 through 2014. First, we check the class of the lunch object to verify that it's a data frame, or a two-dimensional table consisting of rows and columns, of which each column is a single data type such as numeric, character, etc. We then view the dimensions of the dataset with the dim() function. This particular dataset has 46 rows and 7 columns. dim() always displays the number of rows first, followed by the number of columns. Next, we take a look at the column names of lunch with the names() function. Each of the 7 columns has a name: year, avg_free, avg_reduced, and so on. Okay, so we're starting to get a feel for things, but let's dig deeper. The str() (for "structure") function is one of the most versatile and useful functions in the R language because it can be called on any object and will normally provide a useful and compact summary of its internal structure. When passed a data frame, as in this case, str() tells us how many rows and columns we have. Actually, the function refers to rows as observations and columns as variables, which, strictly speaking, is true in a tidy dataset, but not always the case as you'll see in the next chapter. In addition, you see the name of each column, followed by its data type and a preview of the data contained in it. The