Скачать или смотреть How to Combine Lists and DataFrames in R Using Raster Values for Landcover Analysis

Learn how to effectively combine lists and dataframes in R, particularly focusing on summarizing landcover classes using raster data within buffers.
---
This video is based on the question https://stackoverflow.com/q/73298736/ asked by the user 'Nick Masto' ( https://stackoverflow.com/u/15418910/ ) and on the answer https://stackoverflow.com/a/73309724/ provided by the user 'Nick Masto' ( https://stackoverflow.com/u/15418910/ ) 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: combining lists and dataframes in R from raster values

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
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.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Combine Lists and DataFrames in R Using Raster Values for Landcover Analysis

In the realm of Geographic Information Systems (GIS) and data analysis, efficiently summarizing landcover classes within designated buffers can often present challenges, especially when dealing with large datasets. In this guide, we will explore a specific problem faced while analyzing landcover data in R using a raster stack and how to resolve it effectively.

The Problem: Summarizing Landcover Classes

The task at hand involves using a raster stack named hab_stack that contains discrete landcover values (ranging from 1 to 6) across three layers representing different years. Additionally, we have a substantial dataset with over 800,000 locations (the dat_sf object) from which we have extracted raster values around each location within a 400-meter buffer. The goal is to summarize the proportions of various landcover classes represented in these buffers.

Given the large size of the data and the fact that not all landcover classes are represented in each of the 800,000 buffers, the challenge lies in creating a comprehensive summary that includes all classes, even those with zero occurrences. An attempt to perform this using a looping structure (lapply) resulted in errors related to data lengths and variable handling.

The Solution: Using a Function to Combine Data

The core of the solution lies in creating a well-defined function that accurately processes each element in the list and constructs a data frame that summarizes the proportions for each landcover class. Here’s a breakdown of the final function used for processing:

Step 1: Define the Function

We start by defining a function named sum_class_function, which takes each element of the list as input (named x):

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

Step 2: Summarize Using lapply

Once the function is defined, we use lapply to apply the function across all elements of our list, effectively summarizing the landcover proportions for each buffer:

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

Step 3: Understanding the Transformations

Creating the Dummy Data Frame:

The true_names data frame initializes landcover classes (1 to 6) with an initial frequency of zero, ensuring that we include all classes in our output.

Calculating Class Proportions:

The prop.table(table(x)) function calculates the proportions of each landcover class in the current buffer.

Merging Results:

The use of anti_join allows us to identify classes that were not present in the current buffer while bind_rows consolidates these results with the existing counts.

Final Output

The output of the sum_class list contains data frames with the proportions of landcover classes for each buffer, structured consistently even when some classes have no occurrences.

Conclusion

By defining a structured function and utilizing the power of R's data manipulation tools, we can effectively summarize proportions of landcover classes in vast datasets. This method not only resolves the issues encountered with variable length errors but also provides a robust framework for future analysis involving raster and buffer data. If you are facing similar challenges, leveraging functions like sum_class_function can streamline your workflow significantly.



With this understanding, you're now better equipped to handle similar tasks in R, enhancing both your data analysis skills and your ability to work with complex geospatial datasets. If you have any questions or need further clarification, feel free to reach out!