After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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? Use dplyr to answer those questions.
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.
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.
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.
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.
Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Assess grouped mutate operations and window functions to compute intra-group metrics such as year-over-year changes
- Differentiate between count(), group_by + summarize(), and slice_min/slice_max when aggregating or extracting extreme observations
- Evaluate multi-step dplyr pipelines that integrate several verbs to generate analytical insights from the counties and babynames datasets.
- Identify the appropriate dplyr verb to perform specific data transformations involving selection, filtering, arrangement, and mutation
- Recognize how select helpers, rename(), and relocate() alter column selection, naming, and ordering within a tibble
Prerequisites
Curriculum
Course outline
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 dplyr50 XP
- Understanding your data50 XP
- Selecting columns100 XP
- The filter and arrange verbs50 XP
- Arranging observations100 XP
- Filtering for conditions100 XP
- Filtering and arranging100 XP
- The mutate() verb50 XP
- Calculating the number of government employees100 XP
- Calculating the percentage of women in a county100 XP
- Mutate, filter, and arrange100 XP
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.
R
Data Manipulation with dplyr
Course
Complete
