Working with Dataframes
In this lesson, you'll also get introduced to some R packages designed to make doing data science with dataframes more efficient. Packages are user-contributed extensions that build on R’s base functionalities; they often include functions, code, and data that make working in R easier. While learning about dataframes, you'll learn about two packages,
dplyr, that are part of a "family" of packages collectively referred to by the R community as the tidyverse.
Dataframes are probably the most common structures you'll work with when analyzing data in R, so we'll help you to build a strong foundational understanding of how to manipulate them. Like lists, data frames can contain multiple data types. Unlike lists, though, all elements of a dataframe must be vectors of equal length.
By the end of this lesson, you will have learned how to install packages in R, how to import data into R, filtering a dataframe, what a tibble is, how to index data frames, and how to select a single or multiple dataframe columns.
1. Introduction to Data Frames
2. Installing Packages
3. Importing Data into R
4. Tibbles: Specialized Data Frames
5. Indexing Data Frames
6. Selecting Data Columns
7. Adding a New Column
8. Filtering by a Single Condition
9. Filtering by Multiple Conditions: Meeting At Least One Criterion
10. Filtering by Multiple Conditions
11. Arranging Data Frames by Variables
12. Next Steps