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Скачать или смотреть How to Split a Time Series Data by Date Range in R

  • vlogize
  • 2025-08-16
  • 0
How to Split a Time Series Data by Date Range in R
How to split a time series data by range of date in R?dataframesplitdplyr
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Описание к видео How to Split a Time Series Data by Date Range in R

Learn how to effectively divide your time series data into separate periods before and after COVID-19 using R. This quick guide will walk you through the process step-by-step!
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This video is based on the question https://stackoverflow.com/q/64327143/ asked by the user 'Albert' ( https://stackoverflow.com/u/14438952/ ) and on the answer https://stackoverflow.com/a/64331002/ provided by the user 'Ronak Shah' ( https://stackoverflow.com/u/3962914/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to split a time series data by range of date in R?

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The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

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Splitting Time Series Data by Date Range in R

Working with time series data can be quite overwhelming, especially if you're new to R. One common task is splitting the data into specific date ranges—this can be essential for analyzing trends before and after significant events, such as the COVID-19 pandemic. In this guide, we will guide you through a straightforward method to separate your dataset into two distinct periods: before COVID-19 (2016-01 to 2020-01) and after COVID-19 (2020-02 to 2020-09).

Understanding the Dataset

Let’s set the stage. You have a dataset that contains monthly data from various locations, detailing production sectors and their respective values. Your select data columns look like this:

[[See Video to Reveal this Text or Code Snippet]]

Your dataset spans from January 2016 to September 2020 and includes multiple entries from 13 different locations.

The Task at Hand

Your goal is to split this data into two periods based on the date:

Before COVID-19: From January 2016 to January 2020

After COVID-19: From February 2020 to September 2020

Step-by-Step Guide to Splitting the Data

Step 1: Load Required Libraries

First, ensure you have the dplyr library loaded, as it offers essential functions for data manipulation in R.

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Convert Dates

It’s crucial to convert the 'Date' column to the date format that R understands. This will allow you to effectively use date-related functions. Use the mutate function to create a new Date column:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Create the 'Period' Column

With your date correctly formatted, you can now categorize the data into periods. Using the case_when function in dplyr, add a new column named period that defines whether the entry falls before or after COVID-19.

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Verify Your Changes

Finally, you may want to check the first few rows of your dataframe to ensure that the new period classifications have been correctly added:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By following these steps, you have successfully split your time series data into two critical periods regarding the impact of COVID-19. This can be pivotal for further analyses or visualizations you plan to conduct.

Whether you’re analyzing trends or comparing production values across time, understanding how to manipulate and categorize your data is an invaluable skill in data analysis with R.



We hope this guide has helped clarify how to split your time series data by date range effectively. Keep practicing, and soon you'll be navigating your data with confidence!

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