Data Manipulation with dplyr
Learn to transform and manipulate your data using dplyr.
- 4 hours
- 13 Videos
- 46 Exercises
- 9,819 Participants
- 3,850 XP
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Course Description
Say you've found a great dataset and would like to learn more about it. How can you start to answer the questions you have about the data? You can use dplyr to answer those questions—it can also help with basic transformations of your data. You'll also learn to aggregate your data and add, remove, or change the variables. Along the way, you'll explore a dataset containing information about counties in the United States. You'll finish the course by applying these tools to the babynames dataset to explore trends of baby names in the United States.
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1
Transforming Data with dplyr
FreeLearn 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.
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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 and transmute verbs.
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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 top_n.
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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.
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1
Transforming Data with dplyr
FreeLearn 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.
-
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 top_n.
-
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 and transmute verbs.
-
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.
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Lloyd's Banking Group
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Harvard Business School
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Chris Cardillo
Data Scientist at DataCamp
Chris is a Generalist, and actually learned programming for data science on DataCamp prior to joining the company. He is extremely passionate about helping others find the joy of coding to alleviate repetitive and time-consuming tasks. Previously, Chris was the Associate Director of Strategy at M&C Saatchi Mobile, a Data Scientist at DataCamp, and graduated with a B.S./M.B.A. from Drexel University in Philadelphia.
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Collaborators
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Amy Peterson