Return pre and post counts in all the ROIs given bodyids innervate.

  dataset = NULL,
  all_segments = FALSE,
  chunk = TRUE,
  progress = FALSE,
  conn = NULL,



the body IDs for neurons/segments (bodies) you wish to query. This can be in any form understood by neuprint_ids.


optional, a dataset you want to query. If NULL, the default specified by your R environ file is used or, failing that the current connection, is used. See neuprint_login for details.


if TRUE, all bodies are considered, if FALSE, only 'Neurons', i.e. bodies with a status roughly traced status.


A logical specifying whether to split the query into multiple chunks or an integer specifying the size of those chunks (which defaults to 2000 when chunk=TRUE).


default FALSE. If TRUE, the API is called separately for each neuron and you can assess its progress, if an error is thrown by any one bodyid, that bodyid is ignored


optional, a neuprintr connection object, which also specifies the neuPrint server. If NULL, the defaults set in your .Rprofile or .Renviron are used. See neuprint_login for details.


methods passed to neuprint_login


a dataframe, one row for each given body id, columns ROI_pre and ROI_post for every ROI. If data is missing, NA is returned.


# \donttest{
neuprint_get_roiInfo(c(818983130, 1796818119))
#> # A tibble: 2 × 97
#>       bodyid `LH(R).pre` `LH(R).post` `LH(R).downstream` `LH(R).upstream`
#>        <int>       <int>        <int>              <int>            <int>
#> 1  818983130         211           97               2082               97
#> 2 1796818119         284          117               2554              117
#> # ℹ 92 more variables: `LH(R).mito` <int>, `LH(R).dark` <int>,
#> #   `LH(R).medium` <int>, `SNP(R).pre` <int>, `SNP(R).post` <int>,
#> #   `SNP(R).downstream` <int>, `SNP(R).upstream` <int>, `SLP(R).pre` <int>,
#> #   `SLP(R).post` <int>, `SLP(R).downstream` <int>, `SLP(R).upstream` <int>,
#> #   `MB(R).pre` <int>, `MB(R).post` <int>, `MB(R).downstream` <int>,
#> #   `MB(R).upstream` <int>, `MB(R).mito` <int>, `MB(R).dark` <int>,
#> #   `MB(R).light` <int>, `MB(R).medium` <int>, `CA(R).pre` <int>, …
# }