# Advanced Split, Map, and Reduce

#### 2026-04-16

This article is now the older, lower-level companion to `vignette("AnalysisWorkflows")`. Read that vignette first if you want the shortest path to ROI extraction, searchlights, and split-map-reduce patterns. Use this article when you want more raw examples of composing those operations with `purrr` and direct list-processing.

# Pipelining operations with a functional style

`neuroim2` exposes several operations that split an image into pieces, let you map a function over those pieces, and then reduce the results back into a vector, list, or derived image.

The ROI- and searchlight-based split-map-reduce patterns now live in `vignette("AnalysisWorkflows")`. This article keeps the lower-level iteration helpers that are still useful when you want to split by slices, volumes, or voxel vectors directly.

    file_name <- system.file("extdata", "global_mask_v4.nii", package = "neuroim2")
    vol <- read_vol(file_name)

## Mapping a function over every slice of a `NeuroVol`

Suppose we want to split up an image volume by slice and apply a function to each slice. We can use the `slices` function to achieve this as follows:

    slice_means <- vol %>% slices %>% map_dbl(~ mean(.))
    plot(slice_means, type='l', ylab="mean intensity", xlab="slice number")

## Mapping a function over each volume of a `NeuroVec` object

    vec <- concat(vol,vol,vol,vol,vol)
    vec
    #> <DenseNeuroVec> [3.9 Mb]
    #> ── Spatial ─────────────────────────────────────────────────────────────────────
    #>   Dimensions    : 64 x 64 x 25 (5 timepoints)
    #>   Spacing       : 3.5 x 3.5 x 3.7
    #>   Origin        : 112, -108, -46.2
    #>   Orientation   : LAS
    #> ── Data ────────────────────────────────────────────────────────────────────────
    #>   Mean +/- SD   : 0.288 +/- 0.453 (t=1)
    #>   Label         : none
    mean_vec <- vec %>% vols %>% map_dbl(~ mean(.))
    sd_vec <- vec %>% vols %>% map_dbl(~ sd(.))
    stopifnot(length(mean_vec) == dim(vec)[4])
    stopifnot(length(sd_vec) == dim(vec)[4])

## Mapping a function over each vector of a `NeuroVec` object

    vec <- concat(vol,vol,vol,vol,vol)
    vec
    #> <DenseNeuroVec> [3.9 Mb]
    #> ── Spatial ─────────────────────────────────────────────────────────────────────
    #>   Dimensions    : 64 x 64 x 25 (5 timepoints)
    #>   Spacing       : 3.5 x 3.5 x 3.7
    #>   Origin        : 112, -108, -46.2
    #>   Orientation   : LAS
    #> ── Data ────────────────────────────────────────────────────────────────────────
    #>   Mean +/- SD   : 0.288 +/- 0.453 (t=1)
    #>   Label         : none
    mean_vol <- vec %>% vectors() %>% map_dbl(~ mean(.)) %>% NeuroVol(., space=space(vol))
    stopifnot(all(dim(mean_vol) == dim(vol)))