Challenge: Pipe Your Data Manipulation
In analytics, you often need to perform several data manipulation steps in sequence—such as selecting columns, filtering rows, creating new variables, and arranging results. Rather than writing separate commands, you can use the pipe operator '%>%' to chain these operations together, making your code cleaner and easier to follow. This approach is especially useful when cleaning and preparing data for analysis, as it allows you to express a series of transformations as a single, readable pipeline. Now, you will practice chaining multiple dplyr verbs using pipes to achieve a real-world data cleaning task.
Swipe to start coding
You are given an orders data frame containing order information. Your goal is to clean this data using a single pipeline with pipes and dplyr verbs.
- Select only the columns
order_id,customer,amount, andstatus. - Filter for orders where
statusis"Completed"andamountis greater than 100. - Create a new column
amount_usdby multiplyingamountby 1.1. - Arrange the resulting data in descending order of
amount_usd.
Assign your final data frame to cleaned_orders.
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In analytics, you often need to perform several data manipulation steps in sequence—such as selecting columns, filtering rows, creating new variables, and arranging results. Rather than writing separate commands, you can use the pipe operator '%>%' to chain these operations together, making your code cleaner and easier to follow. This approach is especially useful when cleaning and preparing data for analysis, as it allows you to express a series of transformations as a single, readable pipeline. Now, you will practice chaining multiple dplyr verbs using pipes to achieve a real-world data cleaning task.
Swipe to start coding
You are given an orders data frame containing order information. Your goal is to clean this data using a single pipeline with pipes and dplyr verbs.
- Select only the columns
order_id,customer,amount, andstatus. - Filter for orders where
statusis"Completed"andamountis greater than 100. - Create a new column
amount_usdby multiplyingamountby 1.1. - Arrange the resulting data in descending order of
amount_usd.
Assign your final data frame to cleaned_orders.
Рішення
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