In this primer, you will explore the popularity of different names over time. To succeed, you will need to master some common tools for manipulating data with R:
View(), which let you inspect raw data
filter(), which let you extract rows and columns from a data frame
arrange(), which lets yuo reorder the rows in your data
%>%, which organizes your code into reader-friendly “pipes”
summarize(), which help you use your data to compute new variables and summary statistics
These are some of the most useful R functions for data science, and the tutorials that follow will provide you everything you need to learn them.
In the tutorials, we’ll use a dataset named
babynames, which comes in a package that is also named
babynames, you will find information about almost every name given to children in the United States since 1880.
This tutorial introduces
babynames as well as a new data structure that makes working with data in R easy: the tibble.
In addition to
babynames, this tutorial uses the core tidyverse packages, including ggplot2, tibble, and dplyr. All of these packages have been pre-installed for your convenience. But they haven’t been pre-loaded—something you will soon learn more about!
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