The new ggseg-package version has introduced a new way of plotting the brain atlases, through a custom geom_brain
(variant of geom_sf). This has introduced alot of new functionality into the package, in addition to some new custom methods and objects.
library(ggseg)
library(ggplot2)
The first new thing to notice is that we have introduced a new atlas class called brain-atlas
. This class is a special class for ggseg-atlases, that contain information in a specific way. They are objects with 4-levels, each containing important information about the atlas in question.
$atlas
dk#> [1] "dk"
$type
dk#> [1] "cortical"
$palette
dk#> bankssts caudal anterior cingulate
#> "#196428" "#7D64A0"
#> caudal middle frontal corpus callosum
#> "#641900" "#784632"
#> cuneus entorhinal
#> "#DC1464" "#DC140A"
#> fusiform inferior parietal
#> "#B4DC8C" "#DC3CDC"
#> inferior temporal isthmus cingulate
#> "#B42878" "#8C148C"
#> lateral occipital lateral orbitofrontal
#> "#141E8C" "#234B32"
#> lingual medial orbitofrontal
#> "#E18C8C" "#C8234B"
#> middle temporal parahippocampal
#> "#A06432" "#14DC3C"
#> paracentral pars opercularis
#> "#3CDC3C" "#DCB48C"
#> pars orbitalis pars triangularis
#> "#146432" "#DC3C14"
#> pericalcarine postcentral
#> "#78643C" "#DC1414"
#> posterior cingulate precentral
#> "#DCB4DC" "#3C14DC"
#> precuneus rostral anterior cingulate
#> "#A08CB4" "#50148C"
#> rostral middle frontal superior frontal
#> "#4B327D" "#14DCA0"
#> superior parietal superior temporal
#> "#14B48C" "#8CDCDC"
#> supramarginal frontal pole
#> "#50A014" "#640064"
#> temporal pole transverse temporal
#> "#464646" "#9696C8"
#> insula
#> "#FFC020"
$data
dk#> Simple feature collection with 90 features and 5 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 0 ymin: 0 xmax: 1390.585 ymax: 205.4407
#> CRS: NA
#> # A tibble: 90 x 6
#> hemi side region label roi geometry
#> * <chr> <chr> <chr> <chr> <chr> <MULTIPOLYGON>
#> 1 left later… <NA> <NA> 0001 (((84.32563 34.46407, 84.16625 33.33…
#> 2 left later… bankssts lh_banks… 0002 (((214.8215 108.8139, 210.4695 107.6…
#> 3 left later… caudal mi… lh_cauda… 0004 (((106.16 184.3144, 93.61281 180.911…
#> 4 left later… fusiform lh_fusif… 0008 (((256.5481 48.35713, 244.044 45.027…
#> 5 left later… inferior … lh_infer… 0009 (((218.4373 161.6233, 214.914 157.69…
#> 6 left later… inferior … lh_infer… 0010 (((250.7745 70.75764, 246.3042 68.92…
#> 7 left later… lateral o… lh_later… 0012 (((277.4615 115.0523, 277.4682 115.0…
#> 8 left later… lateral o… lh_later… 0013 (((66.26648 69.56474, 56.24012 66.01…
#> 9 left later… middle te… lh_middl… 0016 (((238.0128 91.25816, 230.1394 88.37…
#> 10 left later… pars oper… lh_parso… 0019 (((79.03391 126.496, 74.24232 124.26…
#> # … with 80 more rows
Of these four, only the palette
is an optional part, where some atlases may have this field empty. The data, you might notice, is simple-features data, with a geometry
column that includes all the information needed to plot the data as a simple features object. You can actually call plot
directly on the data, and the standard simple features plot will appear.
plot(dk$data)
Even better, though, you should call plot
directly on the atlas object. This will give you a fast overview of the atlas you are thinking of using.
plot(dk)
You will notice that the new atlas-class has better resolution and default values that what you get from the ggseg-atlas class.
This new class also comes with a new custom printout method, that should give you a better idea of the atlas content. It lists information such as:
And in addition it has a preview of the data content, so you may more easily discern how you might adapt your own data to fit the atlas data.
dk#> # dk cortical brain atlas
#> regions: 35
#> hemispheres: left, right
#> side views: lateral, medial
#> palette: yes
#> use: ggplot() + geom_brain()
#> ----
#> hemi side region label roi
#> <chr> <chr> <chr> <chr> <chr>
#> 1 left lateral bankssts lh_bankssts 0002
#> 2 left lateral caudal middle frontal lh_caudalmiddlefrontal 0004
#> 3 left lateral fusiform lh_fusiform 0008
#> 4 left lateral inferior parietal lh_inferiorparietal 0009
#> 5 left lateral inferior temporal lh_inferiortemporal 0010
#> 6 left lateral lateral occipital lh_lateraloccipital 0012
#> 7 left lateral lateral orbitofrontal lh_lateralorbitofrontal 0013
#> 8 left lateral middle temporal lh_middletemporal 0016
#> 9 left lateral pars opercularis lh_parsopercularis 0019
#> 10 left lateral pars orbitalis lh_parsorbitalis 0020
#> # … with 76 more rows
Some users have also wanted to easier ways of checking the names of regions and labels of an atlas, in order to check if their data fits the atlas data. In order to make this easier, we have added two new functions that should help you with that.
brain_regions(dk)
#> [1] "bankssts" "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"
brain_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"
For other than quick overviews of the atlas using plot
this new atlas class is specifically made to work with the new geom_brain
. Since we have better control over the geom, we have also optimised it so that when plotting just the atlas, without specifying fill
the polygons are automatically filled with the region
column.
ggplot() +
geom_brain(atlas = dk)
This new geom makes it possible for you to also better control the position of the brain slices, using specialised function for this to the position argument. The position_brain
function takes a formula argument similar to that of facet_grid
to alter the positions of the slices.
ggplot() +
geom_brain(atlas = dk, position = position_brain(hemi ~ side))
Of course, as usual, people will have their own data they want to add to the plots, usingcolumns from their own data to the plot aesthetics. By making sure at least one column in your data has the same name and overlapping content as a column in the atlas data, geom_brain will merge your data with the atlas and create your plots.
library(dplyr)
= tibble(
someData region = c("transverse temporal", "insula",
"precentral","superior parietal"),
p = sample(seq(0,.5,.001), 4)
)
someData#> # A tibble: 4 x 2
#> region p
#> <chr> <dbl>
#> 1 transverse temporal 0.396
#> 2 insula 0.47
#> 3 precentral 0.035
#> 4 superior parietal 0.438
And such plots can be further adapted with standard ggplot themes, scales etc, to your liking.
ggplot(someData) +
geom_brain(atlas = dk,
position = position_brain(hemi ~ side),
aes(fill = p)) +
scale_fill_viridis_c(option = "cividis", direction = -1) +
theme_void() +
labs(title = "My awesome title",
subtitle = "of a brain atlas plot",
caption = "I'm pretty happy about this!")
#> merging atlas and data by 'region'
Just like in ggseg, though, you still need to do some double work for facetting to work correctly. Because the atlas and your data need to be merged correctly, you will need to group_by
your data before giving it to ggplot, for facets to work.
<- tibble(
someData region = rep(c("transverse temporal", "insula",
"precentral","superior parietal"), 2),
p = sample(seq(0,.5,.001), 8),
groups = c(rep("g1", 4), rep("g2", 4))
)
someData#> # A tibble: 8 x 3
#> region p groups
#> <chr> <dbl> <chr>
#> 1 transverse temporal 0.377 g1
#> 2 insula 0.331 g1
#> 3 precentral 0.022 g1
#> 4 superior parietal 0.49 g1
#> 5 transverse temporal 0.021 g2
#> 6 insula 0.255 g2
#> 7 precentral 0.041 g2
#> 8 superior parietal 0.338 g2
%>%
someData group_by(groups) %>%
ggplot() +
geom_brain(atlas = dk,
position = position_brain(hemi ~ side),
aes(fill = p)) +
facet_wrap(~groups) +
ggtitle("correct facetting")
#> merging atlas and data by 'region'
You can also plot this new atlas class directly with the ggseg
function, if you are more comfortable with that.
ggseg(someData, atlas = dk,
colour = "black",
size = .1,
position = "stacked",
mapping = aes(fill = p))
#> merging atlas and data by 'region'