Plotting external data

How matching works

geom_brain() joins your data to the atlas by any columns they share. That means your data needs at least one column with names that match the atlas. The two columns you’ll use most:

• region – human-readable names like “insula” or “precentral”
• label – FreeSurfer labels like “lh_bankssts”

Check what’s available:

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"

Names must match exactly, including case and spacing.

A minimal example

Three regions, three p-values:

some_data <- tibble(
  region = c("superior temporal", "precentral", "lateral orbitofrontal"),
  p = c(.03, .6, .05)
)
some_data

## # A tibble: 3 × 2
##   region                    p
##   <chr>                 <dbl>
## 1 superior temporal      0.03
## 2 precentral             0.6 
## 3 lateral orbitofrontal  0.05

Pass the data to ggplot() and map fill to your variable:

ggplot(some_data) +
  geom_brain(atlas = dk(), mapping = aes(fill = p))

## Merging atlas and data by region.

Brain plot with three regions coloured by p-value.

Regions not in your data appear as NA (grey by default). Regions in your data that don’t match the atlas are silently dropped, so watch your spelling.

Constraining matches with extra columns

If your data is hemisphere-specific, add a hemi column. The join will use both region and hemi, so values only land on the correct side:

some_data$hemi <- "left"

ggplot(some_data) +
  geom_brain(atlas = dk(), mapping = aes(fill = p))

## Merging atlas and data by region and hemi.

Brain plot restricted to the left hemisphere using a hemi column.

The same works for any atlas column – adding view, for instance, would restrict matches to specific views.

Faceting across groups

If your data has a grouping variable, facet_wrap() and facet_grid() work as you’d expect. geom_brain() detects the 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("Young", 4), rep("Old", 4))
)

ggplot(some_data) +
  geom_brain(atlas = dk(), colour = "white", mapping = aes(fill = p)) +
  facet_wrap(~group, ncol = 1) +
  theme(legend.position = "bottom") +
  scale_fill_gradientn(
    colours = c("royalblue", "firebrick", "goldenrod"),
    na.value = "grey"
  )

## Merging atlas and data by region.

Brain plots faceted by age group with a custom colour gradient.

No need to call group_by() first – the geom handles atlas replication automatically. (Explicit group_by() still works for backward compatibility.)

The pre-merged workflow

For full control over faceting or when you need to combine brain data with other sf layers, convert the atlas to a data frame and join manually:

atlas_df <- as.data.frame(dk())
names(atlas_df)

## [1] "label"    "view"     "hemi"     "region"   "lobe"     "geometry" "atlas"   
## [8] "type"     "colour"

Then use a standard join and geom_sf():

some_data <- tibble(
  region = c("superior temporal", "precentral", "lateral orbitofrontal"),
  p = c(.03, .6, .05)
)

atlas_df |>
  left_join(some_data, by = "region") |>
  ggplot() +
  geom_sf(aes(fill = p), colour = "white") +
  facet_grid(hemi ~ view) +
  theme_void()

Brain plot using a manual left_join and geom_sf for full control.

See vignette("geom-sf") for more on this approach.

Troubleshooting

Regions don’t show up. Check spelling and case. ggseg.formats::atlas_regions(dk()) gives you the exact strings the atlas expects.

Data lands on both hemispheres. Add a hemi column with "left" or "right" to constrain the match.

Extra facet panels appear. This is handled automatically by geom_brain(). If you’re using the brain_join() + geom_sf() workflow directly, group_by() your data by the faceting variable before joining.