课程

使用 dplyr 进行数据处理

基础技能水平

更新时间 2025年12月

通过学习如何使用 dplyr 转换和处理数据，提升 Tidyverse 技能。

免费开始课程

RData Manipulation

4小时

13 视频

44 道练习

3,700 XP

170K+

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课程描述

假设您找到一个很棒的数据集，并想更深入地了解它。如何开始回答您对数据的疑问？您可以使用 dplyr 来解答这些问题——它还能帮助完成数据的基本变换。您还将学习如何聚合数据，以及添加、移除或修改变量。过程中，您会探索一个包含美国各县信息的数据集。课程最后，您将把这些工具应用到 babynames 数据集，探索美国婴儿姓名的趋势。视频包含实时字幕，您可以点击视频左下角的 "Show transcript" 展开查看。课程术语表位于右侧的资源区域。若要获得 CPE 学分，您需要完成课程并在合格测评中达到 70% 的分数。您可以点击右侧的 CPE 学分提示进入测评。

先决条件

Introduction to the Tidyverse

1

Transforming Data with dplyr

Learn verbs you can use to transform your data, including select, filter, arrange, and mutate. You'll use these functions to modify the counties dataset to view particular observations and answer questions about the data.

Exploring data with dplyr

Understanding your data

Selecting columns

The filter and arrange verbs

Arranging observations

Filtering for conditions

Filtering and arranging

The mutate() verb

Calculating the number of government employees

Calculating the percentage of women in a county

Mutate, filter, and arrange

2

Aggregating Data

Now that you know how to transform your data, you'll want to know more about how to aggregate your data to make it more interpretable. You'll learn a number of functions you can use to take many observations in your data and summarize them, including count, group_by, summarize, ungroup, and slice_min/slice_max.

The count verb

Counting by region

Counting citizens by state

Mutating and counting

The group_by, summarize, and ungroup verbs

Summarizing

Summarizing by state

Summarizing by state and region

The slice_min and slice_max verbs

Selecting a county from each region

Finding the lowest-income state in each region

Using summarize, slice_max, and count together

3

Selecting and Transforming Data

Learn advanced methods to select and transform columns. Also, learn about select helpers, which are functions that specify criteria for columns you want to choose, as well as the rename verb.

Selecting columns

Select helpers

The rename verb

Renaming a column after count

Renaming a column as part of a select

The relocate verb

Using relocate

Choosing among the four verbs

4

Case Study: The babynames Dataset

Work with a new dataset that represents the names of babies born in the United States each year. Learn how to use grouped mutates and window functions to ask and answer more complex questions about your data. And use a combination of dplyr and ggplot2 to make interesting graphs to further explore your data.

The babynames data

Filtering and arranging for one year

Finding the most popular names each year

Visualizing names with ggplot2

Grouped mutates

Finding the year each name is most common

Adding the total and maximum for each name

Visualizing the normalized change in popularity

Window functions

Using ratios to describe the frequency of a name

Biggest jumps in a name

Congratulations!

使用 dplyr 进行数据处理

课程完成

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