![]() Otherwise you can use print(flights, width = Inf) to show all columns, or use glimpse(): ![]() If you’re using RStudio, the most convenient is probably View(flights), which will open an interactive scrollable and filterable view. There are a few options to see everything. The most important difference between tibbles and data frames is the way tibbles print they are designed for large datasets, so they only show the first few rows and only the columns that fit on one screen. This dataset contains all 336,776 flights that departed from New York City in 2013. To explore the basic dplyr verbs, we’re going to use nycflights13::flights. However, knowing the package can help you find help and find related functions, so when we need to be precise about which package a function comes from, we’ll use the same syntax as R: packagename::functionname(). So far we’ve mostly ignored which package a function comes from because most of the time it doesn’t matter. ![]() If you want to use the base version of these functions after loading dplyr, you’ll need to use their full names: stats::filter() and stats::lag(). It tells you that dplyr overwrites some functions in base R. Take careful note of the conflicts message that’s printed when you load the tidyverse. We’ll illustrate the key ideas using data from the nycflights13 package, and use ggplot2 to help us understand the data. In this chapter we’ll focus on the dplyr package, another core member of the tidyverse. We will end the chapter with a case study that showcases these functions in action and we’ll come back to the functions in more detail in later chapters, as we start to dig into specific types of data (e.g., numbers, strings, dates). We will then introduce the ability to work with groups. We’ll start with functions that operate on rows and then columns of a data frame, then circle back to talk more about the pipe, an important tool that you use to combine verbs. The goal of this chapter is to give you an overview of all the key tools for transforming a data frame. You’ll learn how to do all that (and more!) in this chapter, which will introduce you to data transformation using the dplyr package and a new dataset on flights that departed from New York City in 2013. Often you’ll need to create some new variables or summaries to answer your questions with your data, or maybe you just want to rename the variables or reorder the observations to make the data a little easier to work with. Visualization is an important tool for generating insight, but it’s rare that you get the data in exactly the right form you need to make the graph you want.
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