Spatial Intersections with R and sf Package

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This article will explore the use of the sf package in R for spatial data manipulation, focusing on looping through a list of values and adding it to designated areas within spatial parameters.

Introduction

The sf package is an R package that provides a simple way to work with spatial data. It offers a convenient interface for common spatial operations, such as geometry creation, spatial joins, and intersection calculations. This article will use the sf package in conjunction with the map() function from the purrr package to loop through a list of values and perform spatial intersections.

Background

Before we dive into the code, let’s take a look at the background behind this problem. The original question involves creating three data frames (Data_String1, Data_String2, and Data_String3) using spatial joins with a base data frame (DF2). The goal is to create new columns in each of these data frames by checking if there are any overlapping areas between the corresponding string values and the geometry in DF2.

The original solution uses a loop to iterate over the list of string values, perform the spatial join, and create the desired data frames. However, this approach can be cumbersome and may not scale well for larger lists.

Using map() with sf

A better approach is to use the map() function from the purrr package in conjunction with the sf package. The map() function applies a given function to each element of an input list, returning a new list with the results.

Here’s an example of how we can use map() to loop through a list of string values and perform spatial intersections:

library(sf)
library(purrr)

# Create a sample data frame (DataFrame) with geometry
data <- st_make_point(c(0, 1, 2))
df_data <- data.frame(
  ID = c("A", "B", "C"),
  geometry = st_simplify(data)
)

# Define the list of string values
string_values <- c("String1", "String2", "String3")

# Map over the list of string values and perform spatial intersections
df_lst <- map(string_values, ~ df_data %>% filter(ID == .x))

# Extract the results into a data frame
result_df <- bind_rows(lapply(df_lst, function(x) {
  with(as.list(x), mutate(New_String = if_else(lengths(st_intersects(geometry, string_value, join = st_within)) > 0, ID, "N")))
}))

Extending from Previous Solution

As shown in the answer to the original question, an alternative approach is to extend from the previous solution. This involves creating a list of data frames (df_lst) and then mapping over it to perform the spatial intersections:

library(sf)

# Create sample data frame (DataFrame) with geometry
data <- st_make_point(c(0, 1, 2))
df_data <- data.frame(
  ID = c("A", "B", "C"),
  geometry = st_simplify(data)
)

# Define the list of string values
string_values <- c("String1", "String2", "String3")

# Create a list of data frames (df_lst) and map over it to perform spatial intersections
df_lst <- map(string_values, ~ DataFrame %>% filter(ID == .x))

df_lst <- map(df_lst, ~ DF2 %>% mutate(`New String`= if_else(
  lengths(st_intersects(DF2, .x, join = st_within)) > 0,
  .x$ID,
  "N")) %&gt;% 
filter(`New String`!="N"))

# Extract the results into a data frame
result_df <- bind_rows(lapply(df_lst, function(x) {
  with(as.list(x), mutate(New_String = if_else(lengths(st_intersects(geometry, string_value, join = st_within)) > 0, ID, "N")))
}))

Conclusion

In this article, we’ve explored how to loop through a list of values and add it to designated areas within spatial parameters using the sf package in R. We discussed two approaches: one using the map() function from the purrr package and another extending from the previous solution.

The use of the map() function provides a concise and elegant way to perform spatial operations on lists of values. By leveraging this approach, you can write more efficient and scalable code for working with spatial data in R.

I hope this article has provided you with a deeper understanding of how to work with spatial data in R using the sf package. If you have any questions or would like further clarification, please don’t hesitate to ask!

Last modified on 2023-05-12