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Data Manipulation with dplyr
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อัปเดตแล้ว 12/2568RData Manipulation4 ชม.13 videos44 Exercises3,700 เอ็กซ์พี160K+คำแถลงแสดงความสำเร็จ
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First steps: Transforming data with dplyr
This course is designed to teach users how to efficiently manipulate and transform data using the dplyr package in R.First, explore fundamental data transformation techniques, including the use of key dplyr verbs like select, filter, arrange, and mutate. These functions will teach you how to modify datasets by selecting specific columns, filtering rows based on conditions, sorting data, and creating new calculated columns.
Aggregating data with dplyr
Next, the course covers data aggregation, teaching users how to summarize and condense data for better interpretation.You’ll understand how to make your data more interpretable and manageable. Functions such as count, group_by, and summarize are introduced to perform operations that aggregate many observations into meaningful summaries, essential for data analysis and reporting.
Selecting and transforming data
Finally, you will learn advanced data selection and transformation techniques, such as using select helpers and the rename verb. You will also get to apply your skills to a real-world case study and practice grouped mutates, window functions, and data visualization with ggplot2.By the end of the course, you will have developed robust data manipulation skills using dplyr, enabling more efficient and effective data analysis—a vital capability for any data analyst or scientist.
ข้อกำหนดเบื้องต้น
Introduction to the Tidyverse1
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.
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.
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.
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.
Data Manipulation with dplyr
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