This course is ideal for anyone who knows basic R concepts and would like to prepare for real-world data analysis with R.
Have you ever had a problem with importing data into R? Was every file in a different format? Did you have trouble picking the right packages for your needs? And then, after all that headache, you run into errors or missing data. Perhaps you've also struggled with strings or dates in data sets.
Working with real-world datasets can be tough. It's certainly different than working with data sets from courses, which have usually been cleaned ahead of time and sometimes contain fictitious data. In this course, you'll learn how to handle problems with data so you're prepared for real-world data analysis with R.
We'll focus on reading and writing into files in different formats, such as CSV, Excel, and others. The course will teach you the best solutions and practices for the task at hand, including which packages you should use.
After we import our data into R, we'll learn how to prepare it for analysis. We'll learn how to clean data, change the structure of data (from wide to long format, using the spread() and gather() functions), remove redundant information (using filter()), and handle duplicates (using duplicated()).
Additionally, we'll take a look at special data types like strings and dates that tend to cause problems in data analysis. For example, have you ever tried converting a string into a date or removing duplicate characters from a string? If so, then you probably know that it can be tricky. After you take this course, you should have a good grasp on how to work with these data types.
Toward the end of the course, we'll focus on findings errors and accounting for missing values. Finally, you will get the opportunity to review everything you learned with a cumulative quiz.