# Adding external data to ggseg plotting

## Introduction

Once you have covered the main functionality in `ggseg` you will want to use it to plot the results of your data. In order to do this, your data must adhere to certain specifications, so that `ggseg` can manage to merge your data with the atlas you are using. This means you need to be able to inspect and locate the way the regions you are working with are names in the internal atlas files. This vignette should provide the tools you need to figure these features out, and to manipulate your data to fit these requirements.

## Inspecting the atlas labels

There are several ways you can inspect what the data in the atlas looks like. While each atlas has some small differences, they all share six main columns:
1. .long - x-axis
2. .lat - y-axis
3. .region - name of region/network
4. .hemi - hemisphere (left or right)
5. .side - side of view (medial, lateral, sagittal or axial)

Most atlases also have a `label` column, which are raw names assigned from the program run to segment/extract data. TO inspect the atlases, call them in the console.

    library(ggseg)
    library(dplyr)

    ##
    ## Attaching package: 'dplyr'

    ## The following objects are masked from 'package:stats':
    ##
    ##     filter, lag

    ## The following objects are masked from 'package:base':
    ##
    ##     intersect, setdiff, setequal, union

    library(ggplot2)

    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
    ## # ℹ 76 more rows

Further inspection of the atlas data can be explored by turning them into tibbles (or data.frames)

    as_tibble(dk)

    ## # A tibble: 90 × 8
    ##    atlas type     hemi  side    region     label roi                    geometry
    ##    <chr> <chr>    <chr> <chr>   <chr>      <chr> <chr>            <MULTIPOLYGON>
    ##  1 dk    cortical left  lateral <NA>       <NA>  0001  (((84.32563 34.46407, 84…
    ##  2 dk    cortical left  lateral bankssts   lh_b… 0002  (((214.8215 108.8139, 21…
    ##  3 dk    cortical left  lateral caudal mi… lh_c… 0004  (((106.16 184.3144, 93.6…
    ##  4 dk    cortical left  lateral fusiform   lh_f… 0008  (((256.5481 48.35713, 24…
    ##  5 dk    cortical left  lateral inferior … lh_i… 0009  (((218.4373 161.6233, 21…
    ##  6 dk    cortical left  lateral inferior … lh_i… 0010  (((250.7745 70.75764, 24…
    ##  7 dk    cortical left  lateral lateral o… lh_l… 0012  (((277.4615 115.0523, 27…
    ##  8 dk    cortical left  lateral lateral o… lh_l… 0013  (((66.26648 69.56474, 56…
    ##  9 dk    cortical left  lateral middle te… lh_m… 0016  (((238.0128 91.25816, 23…
    ## 10 dk    cortical left  lateral pars oper… lh_p… 0019  (((79.03391 126.496, 74.…
    ## # ℹ 80 more rows

Here you can see information about the `dk` atlas, and the main attributes of this atlas. If you want to use external data with your `ggseg` plot, you will need to make sure that your data has at least one column corresponding in name and content with another in the atlas you are using.

## Structuring data for merging

For instance, here we make some data for the “default” and “visual” networks in the `dk` atlas, and two p values for those two networks.

    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

Notice you we have spelled both the column name and the region names exactly as they appear in the data. This is necessary for the merging within the `ggseg` function to work properly. This merge can be attempted before supplying the data to `ggseg` to see if there are any errors.

    dk |>
      as_tibble() |>
      left_join(some_data)

    ## Joining with `by = join_by(region)`

    ## # A tibble: 90 × 9
    ##    atlas type     hemi  side  region label roi                    geometry     p
    ##    <chr> <chr>    <chr> <chr> <chr>  <chr> <chr>            <MULTIPOLYGON> <dbl>
    ##  1 dk    cortical left  late… <NA>   <NA>  0001  (((84.32563 34.46407, 84… NA
    ##  2 dk    cortical left  late… banks… lh_b… 0002  (((214.8215 108.8139, 21… NA
    ##  3 dk    cortical left  late… cauda… lh_c… 0004  (((106.16 184.3144, 93.6… NA
    ##  4 dk    cortical left  late… fusif… lh_f… 0008  (((256.5481 48.35713, 24… NA
    ##  5 dk    cortical left  late… infer… lh_i… 0009  (((218.4373 161.6233, 21… NA
    ##  6 dk    cortical left  late… infer… lh_i… 0010  (((250.7745 70.75764, 24… NA
    ##  7 dk    cortical left  late… later… lh_l… 0012  (((277.4615 115.0523, 27… NA
    ##  8 dk    cortical left  late… later… lh_l… 0013  (((66.26648 69.56474, 56…  0.05
    ##  9 dk    cortical left  late… middl… lh_m… 0016  (((238.0128 91.25816, 23… NA
    ## 10 dk    cortical left  late… pars … lh_p… 0019  (((79.03391 126.496, 74.… NA
    ## # ℹ 80 more rows

No errors! Yes, the `p` column is seemingly full of `NA`s, but that is just because the top of the data is the somatomotor network, which we did not supply any p values for, so it has been populated with `NA`s. We can sort the data differently, so we can see the `p`has been added correctly.

    dk |>
      as_tibble() |>
      left_join(some_data) |>
      arrange(p)

    ## Joining with `by = join_by(region)`

    ## # A tibble: 90 × 9
    ##    atlas type     hemi  side  region label roi                    geometry     p
    ##    <chr> <chr>    <chr> <chr> <chr>  <chr> <chr>            <MULTIPOLYGON> <dbl>
    ##  1 dk    cortical left  late… super… lh_s… 0031  (((207.634 112.8177, 195…  0.03
    ##  2 dk    cortical right late… super… rh_s… 0031  (((1201.097 114.4479, 12…  0.03
    ##  3 dk    cortical left  late… later… lh_l… 0013  (((66.26648 69.56474, 56…  0.05
    ##  4 dk    cortical left  medi… later… lh_l… 0013  (((571.041 20.11423, 571…  0.05
    ##  5 dk    cortical right medi… later… rh_l… 0013  (((770.9836 30.46747, 77…  0.05
    ##  6 dk    cortical right late… later… rh_l… 0013  (((1321.97 75.4817, 1320…  0.05
    ##  7 dk    cortical left  late… prece… lh_p… 0025  (((157.1559 204.0537, 13…  0.6
    ##  8 dk    cortical left  medi… prece… lh_p… 0025  (((476.8878 196.7183, 48…  0.6
    ##  9 dk    cortical right medi… prece… rh_p… 0025  (((923.7245 201.5463, 92…  0.6
    ## 10 dk    cortical right late… prece… rh_p… 0025  (((1226.19 203.939, 1230…  0.6
    ## # ℹ 80 more rows

If you need your data to be matched on several columns, the approach is the same. Add the column you want to match on, with the exact same name, and make sure it’s content matches the content of the same column in the data.

    some_data$hemi <- rep("left", nrow(some_data))
    some_data

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

    dk |>
      as_tibble() |>
      left_join(some_data) |>
      arrange(p)

    ## Joining with `by = join_by(hemi, region)`

    ## # A tibble: 90 × 9
    ##    atlas type     hemi  side  region label roi                    geometry     p
    ##    <chr> <chr>    <chr> <chr> <chr>  <chr> <chr>            <MULTIPOLYGON> <dbl>
    ##  1 dk    cortical left  late… super… lh_s… 0031  (((207.634 112.8177, 195…  0.03
    ##  2 dk    cortical left  late… later… lh_l… 0013  (((66.26648 69.56474, 56…  0.05
    ##  3 dk    cortical left  medi… later… lh_l… 0013  (((571.041 20.11423, 571…  0.05
    ##  4 dk    cortical left  late… prece… lh_p… 0025  (((157.1559 204.0537, 13…  0.6
    ##  5 dk    cortical left  medi… prece… lh_p… 0025  (((476.8878 196.7183, 48…  0.6
    ##  6 dk    cortical left  late… <NA>   <NA>  0001  (((84.32563 34.46407, 84… NA
    ##  7 dk    cortical left  late… banks… lh_b… 0002  (((214.8215 108.8139, 21… NA
    ##  8 dk    cortical left  late… cauda… lh_c… 0004  (((106.16 184.3144, 93.6… NA
    ##  9 dk    cortical left  late… fusif… lh_f… 0008  (((256.5481 48.35713, 24… NA
    ## 10 dk    cortical left  late… infer… lh_i… 0009  (((218.4373 161.6233, 21… NA
    ## # ℹ 80 more rows

Notice how the message now states that it is joining `by = c("region", "hemi")`. The merge function has recognized that there are two equally named columns, and assumes (in this case correctly) that these are equivalent.
Notice that everything is case-sensitive, so writing `Region` or `Left` will not result in matching.

## Providing data to `ggseg`

When you have managed to create data that merges nicely with the atlas, you can go ahead and supply it to the function.

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

    ## merging atlas and data by 'region', 'hemi'

You can actually also supply it directly as an atlas. For instance, if you had saved the merged data from the previous steps, you can supply this directly to the `atlas` option.

    new_atlas <- dk |>
      as_tibble() |>
      left_join(some_data) |>
      as_brain_atlas()

    ## Joining with `by = join_by(hemi, region)`

    ggplot() +
      geom_brain(
        atlas = new_atlas,
        mapping = aes(fill = p),
        position = position_brain(hemi ~ side)
      )

It is this possibility of supplying a custom atlas that gives you particular flexibility, though a little tricky to begin with. Lets do a recap of the unwanted results:

    some_data <- data.frame(
      region = rep(
        c("transverse temporal", "insula", "precentral", "superior parietal"),
        2
      ),
      p = sample(seq(0, .5, .001), 8),
      AgeG = c(rep("Young", 4), rep("Old", 4)),
      stringsAsFactors = FALSE
    )

    ggplot(some_data) +
      geom_brain(atlas = dk, colour = "white", mapping = aes(fill = p)) +
      facet_wrap(~AgeG, ncol = 1) +
      theme(legend.position = "bottom")

    ## merging atlas and data by 'region'

See how you have three facets, when you only have 2 groups, and that the “background” brain is not printed in your two groups. This is because for ggplot, that is what the data looks like. For this to work, you can supply already grouped data to `ggseg`, but you must make sure they are grouped by the columns you will use for faceting, or else it will not work.

    # If you group_by the columns you will facet by, this will work well.
    some_data <- some_data |>
      group_by(AgeG)

    # We can now supply the new_atlas as an atlas to ggseg
    ggplot(some_data) +
      geom_brain(atlas = dk, colour = "white", mapping = aes(fill = p)) +
      facet_wrap(~AgeG, ncol = 1) +
      theme(legend.position = "bottom") +
      scale_fill_gradientn(
        colours = c("royalblue", "firebrick", "goldenrod"),
        na.value = "grey"
      )

    ## merging atlas and data by 'region'

This whole procedure can be piped together, so you don’t have to save all the intermediate steps.

    some_data |>
      group_by(AgeG) |>

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

    ## merging atlas and data by 'region'