# Getting started with ggseg

If you work with neuroimaging data, you’ve probably spent time wrestling region-level results into brain plots. ggseg handles that plumbing for you. It stores brain atlas geometries as simple features and plots them as ggplot2 layers, so you get brain figures with the same code you’d use for any other ggplot.

    library(ggseg)
    library(ggplot2)

## What’s in a brain atlas

A `brain_atlas` object bundles four things: a name, a type (cortical, subcortical, or tract), region geometry stored as simple features, and an optional colour palette.

    dk
    #> function ()
    #> .dk_atlas
    #> <bytecode: 0x119a5d248>
    #> <environment: namespace:ggseg.formats>

The `dk` atlas (Desikan-Killiany) ships with the package. Call `plot()` for a quick look:

    plot(dk())

Quick overview of the Desikan-Killiany cortical atlas.

Two helpers pull out the names you’ll need for matching data:

    ggseg.formats::atlas_regions(dk())
    #>  [1] "banks of superior temporal sulcus" "caudal anterior cingulate"
    #>  [3] "caudal middle frontal"             "corpus callosum"
    #>  [5] "cuneus"                            "entorhinal"
    #>  [7] "frontal pole"                      "fusiform"
    #>  [9] "inferior parietal"                 "inferior temporal"
    #> [11] "insula"                            "isthmus cingulate"
    #> [13] "lateral occipital"                 "lateral orbitofrontal"
    #> [15] "lingual"                           "medial orbitofrontal"
    #> [17] "middle temporal"                   "paracentral"
    #> [19] "parahippocampal"                   "pars opercularis"
    #> [21] "pars orbitalis"                    "pars triangularis"
    #> [23] "pericalcarine"                     "postcentral"
    #> [25] "posterior cingulate"               "precentral"
    #> [27] "precuneus"                         "rostral anterior cingulate"
    #> [29] "rostral middle frontal"            "superior frontal"
    #> [31] "superior parietal"                 "superior temporal"
    #> [33] "supramarginal"                     "temporal pole"
    #> [35] "transverse temporal"
    ggseg.formats::atlas_labels(dk())
    #>  [1] "lh_bankssts"                 "lh_caudalanteriorcingulate"
    #>  [3] "lh_caudalmiddlefrontal"      "lh_corpuscallosum"
    #>  [5] "lh_cuneus"                   "lh_entorhinal"
    #>  [7] "lh_frontalpole"              "lh_fusiform"
    #>  [9] "lh_inferiorparietal"         "lh_inferiortemporal"
    #> [11] "lh_insula"                   "lh_isthmuscingulate"
    #> [13] "lh_lateraloccipital"         "lh_lateralorbitofrontal"
    #> [15] "lh_lingual"                  "lh_medialorbitofrontal"
    #> [17] "lh_middletemporal"           "lh_paracentral"
    #> [19] "lh_parahippocampal"          "lh_parsopercularis"
    #> [21] "lh_parsorbitalis"            "lh_parstriangularis"
    #> [23] "lh_pericalcarine"            "lh_postcentral"
    #> [25] "lh_posteriorcingulate"       "lh_precentral"
    #> [27] "lh_precuneus"                "lh_rostralanteriorcingulate"
    #> [29] "lh_rostralmiddlefrontal"     "lh_superiorfrontal"
    #> [31] "lh_superiorparietal"         "lh_superiortemporal"
    #> [33] "lh_supramarginal"            "lh_temporalpole"
    #> [35] "lh_transversetemporal"       "rh_bankssts"
    #> [37] "rh_caudalanteriorcingulate"  "rh_caudalmiddlefrontal"
    #> [39] "rh_corpuscallosum"           "rh_cuneus"
    #> [41] "rh_entorhinal"               "rh_frontalpole"
    #> [43] "rh_fusiform"                 "rh_inferiorparietal"
    #> [45] "rh_inferiortemporal"         "rh_insula"
    #> [47] "rh_isthmuscingulate"         "rh_lateraloccipital"
    #> [49] "rh_lateralorbitofrontal"     "rh_lingual"
    #> [51] "rh_medialorbitofrontal"      "rh_middletemporal"
    #> [53] "rh_paracentral"              "rh_parahippocampal"
    #> [55] "rh_parsopercularis"          "rh_parsorbitalis"
    #> [57] "rh_parstriangularis"         "rh_pericalcarine"
    #> [59] "rh_postcentral"              "rh_posteriorcingulate"
    #> [61] "rh_precentral"               "rh_precuneus"
    #> [63] "rh_rostralanteriorcingulate" "rh_rostralmiddlefrontal"
    #> [65] "rh_superiorfrontal"          "rh_superiorparietal"
    #> [67] "rh_superiortemporal"         "rh_supramarginal"
    #> [69] "rh_temporalpole"             "rh_transversetemporal"

## Your first brain plot

`geom_brain()` works like any ggplot2 geom. Pass it an atlas and it draws the regions:

    ggplot() +
      geom_brain(atlas = dk())

Default brain plot using geom_brain() with the dk atlas.

## Arranging views

Brain atlases have multiple views (lateral, medial) and hemispheres. `position_brain()` controls how they’re laid out, using formula syntax borrowed from `facet_grid()`:

    ggplot() +
      geom_brain(atlas = dk(), position = position_brain(hemi ~ view))

Brain views arranged using formula syntax in position_brain().

Or reorder views with a character vector:

    ggplot() +
      geom_brain(
        atlas = dk(),
        position = position_brain(c(
          "right lateral", "right medial",
          "left lateral", "left medial"
        ))
      )

Brain views arranged using a character vector to specify order.

See `vignette("positioning-views")` for the full set of layout options, including grid layouts for subcortical and tract atlases.

## Showing only certain hemispheres or views

    ggplot() +
      geom_brain(atlas = dk(), view = "lateral")

Brain plot showing only lateral views.

    ggplot() +
      geom_brain(atlas = dk(), hemi = "left")

Brain plot showing only the left hemisphere.

For subcortical atlases, views are slice identifiers. Check what’s available with `ggseg.formats::atlas_views()`:

    ggseg.formats::atlas_views(aseg())
    #> [1] "axial_3"   "axial_4"   "axial_5"   "axial_6"   "coronal_1" "coronal_2"
    #> [7] "sagittal"

## Plotting your own data

Create a data frame with at least one column that matches the atlas (typically `region` or `label`). `geom_brain()` joins your data to the atlas automatically:

    library(dplyr)

    some_data <- tibble(
      region = c(
        "transverse temporal", "insula", "precentral", "superior parietal"
      ),
      p = sample(seq(0, .5, .001), 4)
    )

    ggplot(some_data) +
      geom_brain(
        atlas = dk(),
        position = position_brain(hemi ~ view),
        aes(fill = p)
      ) +
      scale_fill_viridis_c(option = "cividis", direction = -1) +
      theme_void()
    #> Merging atlas and data by region.

Brain plot coloured by external p-values using a viridis scale.

See `vignette("external-data")` for more on data preparation and matching.

## Faceting

ggplot2 faceting works as you’d expect. `geom_brain()` detects faceting variables and replicates the full atlas in each panel:

    some_data <- tibble(
      region = rep(c(
        "transverse temporal", "insula", "precentral", "superior parietal"
      ), 2),
      p = sample(seq(0, .5, .001), 8),
      group = c(rep("A", 4), rep("B", 4))
    )

    ggplot(some_data) +
      geom_brain(
        atlas = dk(),
        position = position_brain(hemi ~ view),
        aes(fill = p)
      ) +
      facet_wrap(~group)
    #> Merging atlas and data by region.

Brain plots faceted by group, each panel showing the full atlas.

No `group_by()` needed – the geom handles it.

## Highlighting specific regions

To colour a few regions with their atlas colours, use two columns: one for the join (`region`) and one for the fill aesthetic:

    data <- data.frame(
      region = ggseg.formats::atlas_regions(dk())[1:3],
      reg_col = ggseg.formats::atlas_regions(dk())[1:3]
    )

    ggplot(data) +
      geom_brain(atlas = dk(), aes(fill = reg_col)) +
      scale_fill_brain_manual(dk()$palette[data$region])
    #> Merging atlas and data by region.

Highlighting selected regions using the atlas colour palette.

## Scales and themes

Atlas colour palettes are applied automatically by `geom_brain()`. For custom palettes, use `scale_fill_brain_manual()`.

Built-in themes strip axes and grids for cleaner figures:

    ggplot() +
      geom_brain(atlas = dk()) +
      theme_brain()

## Finding more atlases

ggseg ships with `dk` (cortical), `aseg` (subcortical), and `tracula` (white matter tracts). Many more are available through the ggsegverse r-universe:

    install.packages("ggsegYeo2011", repos = "https://ggsegverse.r-universe.dev")