Raster Calc Function to Find Max Index (i.e. Most Recent Layer) Meeting Criterion

In this article, we will explore a common challenge in raster data analysis: finding the most recent layer where a certain value exceeds a fixed threshold. This is crucial in understanding the dynamics of environmental systems, climate patterns, or other phenomena that can be represented as raster data.

We will begin by setting up an example using Raster and RasterVis libraries to create a simple raster stack with four layers stacked chronologically. Then, we’ll discuss two functions: findLast and findLast2, which were provided in the original Stack Overflow question. We will delve into why these functions produce different results and explore alternative approaches that may improve performance or clarity.

Setting Up the Example

library(raster)
library(rasterVis)

# Set seed for reproducibility
set.seed(123123)

# Create a simple raster stack with four layers stacked chronologically
r1 <- raster(nrows = 10, ncols = 10)
r2 <- r3 <- r4 <- r1

# Assign values to each layer using binomial distribution
r1[] <- rbinom(ncell(r1), 1, prob = 0.1)
r2[] <- rbinom(ncell(r1), 1, prob = 0.1)
r3[] <- rbinom(ncell(r1), 1, prob = 0.1)
r4[] <- rbinom(ncell(r1), 1, prob = 0.1)

# Stack the layers
rs <- stack(r1, r2, r3, r4)
names(rs) <- paste0("yr", 1:4)

The two functions provided in the question are findLast and findLast2. These functions aim to find the most recent layer where a value exceeds a fixed threshold. We will analyze why these functions produce different results.

Analyzing findLast and findLast2

# Function 1: findLast
ifelse(any(rs > 0), which(rs > 0), NA)

# Function 2: findLast2
max(which(rs > 0))

As we can see, the results of these two functions are different. The first function returns only the first value where the condition is met, while the second function returns all indices where the condition is true.

Understanding ifelse

The ifelse function in R can be a bit surprising because it returns a value with the same “shape” as the first argument, which has length one. This means that if you apply ifelse to a vector or matrix, the result will have the same shape and size as the input.

In this case, when we use findLast, ifelse is applied to rs > 0. Since rs is a raster stack, it has a different structure than a numerical vector. However, the comparison rs > 0 returns a logical matrix with the same shape as rs.

The which function then extracts all indices where this logical matrix is TRUE. Since ifelse only returns one value (the first value where the condition is met), the result of which(rs > 0) is also a vector with length one.

On the other hand, when we use findLast2, we directly pass the output of which(rs > 0) to the max function. Since which returns a logical matrix, we need to extract all indices where this matrix is TRUE before applying max.

Improving Performance and Clarity

To improve performance and clarity, we can simplify the code using alternative approaches:

# Function f1: Simplified version of findLast
f1 <- function(x) {
    if (any(x > 0)) {
        i <- max(which(x > 0))
        cbind(i, x[i])
    } else {
        cbind(NA, NA)
    }
}

# Function f2: Directly using calc and stackSelect
rr1 <- calc(rs, f1)

# or

f3 <- function(x) {
    i <- max(which(x > 0))
    z <- cbind(i, x[i])
    z[!is.finite(z)] <- NA
    z
}

# Function f4: Using a shortcut approach with calc and stackSelect
rr2 <- calc(rs, function(x) {max(which(x > 0))})

# or

f5 <- function(x) {
    i <- max(which(x > 0))
    z <- cbind(i, x[i])
    z[!is.finite(z)] <- NA
    z
}

rr3 <- calc(rs, f5)

In these alternative approaches, we have eliminated the use of ifelse and instead directly applied the condition to the raster data using vectorized operations. This results in more efficient code with improved readability.

Conclusion

Finding the most recent layer where a value exceeds a fixed threshold is a common challenge in raster data analysis. In this article, we explored two functions: findLast and findLast2, which were provided in the original Stack Overflow question. We analyzed why these functions produce different results and discussed alternative approaches that may improve performance or clarity.

By simplifying the code using vectorized operations and avoiding unnecessary functions like ifelse, we can write more efficient and readable R code for raster data analysis.

Last modified on 2025-02-19