Code
targets::tar_load(volume_demog_df, store = "../_targets")
demo_df <- volume_demog_df |>
dplyr::distinct()Demographics
This page summarizes the demographic characteristics of participants in shared volumes on Databrary.
Owner data
All owners.
Note
TODO: Fix importing of owners data.
Code
owner_df <- load_owner_csvs("csv", fn_suffix = "-owners")
Note
The session-level CSV data are stored in a local directory that is not synched with GitHub. To generate the report, one must generate and save the data locally.
Overall
Databrary has demographic data for ~ n= 10725 individual participant-sessions. This number is an underestimate because the number of unshared volumes is 3-4x the number of shared volumes.
Code
demo_df <- volume_demog_df |>
dplyr::mutate(vol_url = paste0("https://nyu.databrary.org/volume/", as.numeric(vol_id)))Age
Volumes and session
Code
age_df <- demo_df |>
dplyr::filter(!is.na(age_days)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
age_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00008 | https://nyu.databrary.org/volume/8 | 1112 |
| 00564 | https://nyu.databrary.org/volume/564 | 459 |
| 00090 | https://nyu.databrary.org/volume/90 | 312 |
| 00739 | https://nyu.databrary.org/volume/739 | 295 |
| 00011 | https://nyu.databrary.org/volume/11 | 236 |
| 00087 | https://nyu.databrary.org/volume/87 | 231 |
| 01020 | https://nyu.databrary.org/volume/1020 | 220 |
| 00322 | https://nyu.databrary.org/volume/322 | 173 |
| 00359 | https://nyu.databrary.org/volume/359 | 169 |
| 01042 | https://nyu.databrary.org/volume/1042 | 161 |
| 01364 | https://nyu.databrary.org/volume/1364 | 157 |
| 00030 | https://nyu.databrary.org/volume/30 | 155 |
| 01075 | https://nyu.databrary.org/volume/1075 | 138 |
| 00005 | https://nyu.databrary.org/volume/5 | 133 |
| 00088 | https://nyu.databrary.org/volume/88 | 132 |
| 00184 | https://nyu.databrary.org/volume/184 | 129 |
| 00226 | https://nyu.databrary.org/volume/226 | 129 |
| 00989 | https://nyu.databrary.org/volume/989 | 127 |
| 00563 | https://nyu.databrary.org/volume/563 | 118 |
| 00271 | https://nyu.databrary.org/volume/271 | 115 |
| 00089 | https://nyu.databrary.org/volume/89 | 114 |
| 01526 | https://nyu.databrary.org/volume/1526 | 111 |
| 00083 | https://nyu.databrary.org/volume/83 | 109 |
| 00114 | https://nyu.databrary.org/volume/114 | 105 |
| 00084 | https://nyu.databrary.org/volume/84 | 104 |
| 00988 | https://nyu.databrary.org/volume/988 | 101 |
| 00140 | https://nyu.databrary.org/volume/140 | 97 |
| 00162 | https://nyu.databrary.org/volume/162 | 93 |
| 00070 | https://nyu.databrary.org/volume/70 | 91 |
| 00460 | https://nyu.databrary.org/volume/460 | 91 |
| 00152 | https://nyu.databrary.org/volume/152 | 84 |
| 01273 | https://nyu.databrary.org/volume/1273 | 82 |
| 00484 | https://nyu.databrary.org/volume/484 | 81 |
| 00434 | https://nyu.databrary.org/volume/434 | 79 |
| 00868 | https://nyu.databrary.org/volume/868 | 77 |
| 00308 | https://nyu.databrary.org/volume/308 | 76 |
| 01379 | https://nyu.databrary.org/volume/1379 | 76 |
| 01567 | https://nyu.databrary.org/volume/1567 | 74 |
| 00253 | https://nyu.databrary.org/volume/253 | 73 |
| 00207 | https://nyu.databrary.org/volume/207 | 71 |
| 00269 | https://nyu.databrary.org/volume/269 | 71 |
| 00321 | https://nyu.databrary.org/volume/321 | 71 |
| 00081 | https://nyu.databrary.org/volume/81 | 69 |
| 00004 | https://nyu.databrary.org/volume/4 | 67 |
| 00899 | https://nyu.databrary.org/volume/899 | 65 |
| 00950 | https://nyu.databrary.org/volume/950 | 65 |
| 01436 | https://nyu.databrary.org/volume/1436 | 65 |
| 00136 | https://nyu.databrary.org/volume/136 | 62 |
| 00835 | https://nyu.databrary.org/volume/835 | 62 |
| 01103 | https://nyu.databrary.org/volume/1103 | 62 |
| 00837 | https://nyu.databrary.org/volume/837 | 59 |
| 00350 | https://nyu.databrary.org/volume/350 | 58 |
| 00150 | https://nyu.databrary.org/volume/150 | 57 |
| 00007 | https://nyu.databrary.org/volume/7 | 55 |
| 00163 | https://nyu.databrary.org/volume/163 | 55 |
| 00144 | https://nyu.databrary.org/volume/144 | 52 |
| 00192 | https://nyu.databrary.org/volume/192 | 51 |
| 01141 | https://nyu.databrary.org/volume/1141 | 48 |
| 01312 | https://nyu.databrary.org/volume/1312 | 48 |
| 00827 | https://nyu.databrary.org/volume/827 | 47 |
| 01129 | https://nyu.databrary.org/volume/1129 | 47 |
| 01415 | https://nyu.databrary.org/volume/1415 | 47 |
| 00954 | https://nyu.databrary.org/volume/954 | 46 |
| 01026 | https://nyu.databrary.org/volume/1026 | 46 |
| 01551 | https://nyu.databrary.org/volume/1551 | 46 |
| 01108 | https://nyu.databrary.org/volume/1108 | 44 |
| 01328 | https://nyu.databrary.org/volume/1328 | 43 |
| 00400 | https://nyu.databrary.org/volume/400 | 41 |
| 00455 | https://nyu.databrary.org/volume/455 | 41 |
| 00941 | https://nyu.databrary.org/volume/941 | 41 |
| 01448 | https://nyu.databrary.org/volume/1448 | 41 |
| 00854 | https://nyu.databrary.org/volume/854 | 39 |
| 01517 | https://nyu.databrary.org/volume/1517 | 39 |
| 00146 | https://nyu.databrary.org/volume/146 | 38 |
| 00123 | https://nyu.databrary.org/volume/123 | 37 |
| 00476 | https://nyu.databrary.org/volume/476 | 35 |
| 00218 | https://nyu.databrary.org/volume/218 | 34 |
| 00957 | https://nyu.databrary.org/volume/957 | 34 |
| 00979 | https://nyu.databrary.org/volume/979 | 34 |
| 01515 | https://nyu.databrary.org/volume/1515 | 34 |
| 00075 | https://nyu.databrary.org/volume/75 | 33 |
| 00174 | https://nyu.databrary.org/volume/174 | 33 |
| 00444 | https://nyu.databrary.org/volume/444 | 33 |
| 01397 | https://nyu.databrary.org/volume/1397 | 33 |
| 00135 | https://nyu.databrary.org/volume/135 | 32 |
| 00073 | https://nyu.databrary.org/volume/73 | 31 |
| 00098 | https://nyu.databrary.org/volume/98 | 31 |
| 01066 | https://nyu.databrary.org/volume/1066 | 31 |
| 00943 | https://nyu.databrary.org/volume/943 | 30 |
| 01321 | https://nyu.databrary.org/volume/1321 | 30 |
| 01370 | https://nyu.databrary.org/volume/1370 | 30 |
| 01365 | https://nyu.databrary.org/volume/1365 | 28 |
| 00821 | https://nyu.databrary.org/volume/821 | 26 |
| 01128 | https://nyu.databrary.org/volume/1128 | 26 |
| 01363 | https://nyu.databrary.org/volume/1363 | 25 |
| 01143 | https://nyu.databrary.org/volume/1143 | 23 |
| 00452 | https://nyu.databrary.org/volume/452 | 22 |
| 00132 | https://nyu.databrary.org/volume/132 | 19 |
| 00443 | https://nyu.databrary.org/volume/443 | 19 |
| 00996 | https://nyu.databrary.org/volume/996 | 19 |
| 01391 | https://nyu.databrary.org/volume/1391 | 19 |
| 01422 | https://nyu.databrary.org/volume/1422 | 19 |
| 00108 | https://nyu.databrary.org/volume/108 | 17 |
| 01481 | https://nyu.databrary.org/volume/1481 | 17 |
| 00169 | https://nyu.databrary.org/volume/169 | 16 |
| 01008 | https://nyu.databrary.org/volume/1008 | 16 |
| 01376 | https://nyu.databrary.org/volume/1376 | 16 |
| 00171 | https://nyu.databrary.org/volume/171 | 15 |
| 00710 | https://nyu.databrary.org/volume/710 | 15 |
| 00956 | https://nyu.databrary.org/volume/956 | 14 |
| 00124 | https://nyu.databrary.org/volume/124 | 13 |
| 00217 | https://nyu.databrary.org/volume/217 | 13 |
| 00390 | https://nyu.databrary.org/volume/390 | 13 |
| 00966 | https://nyu.databrary.org/volume/966 | 13 |
| 00145 | https://nyu.databrary.org/volume/145 | 12 |
| 00149 | https://nyu.databrary.org/volume/149 | 12 |
| 00761 | https://nyu.databrary.org/volume/761 | 11 |
| 00015 | https://nyu.databrary.org/volume/15 | 10 |
| 00323 | https://nyu.databrary.org/volume/323 | 10 |
| 01590 | https://nyu.databrary.org/volume/1590 | 10 |
| 00148 | https://nyu.databrary.org/volume/148 | 9 |
| 01442 | https://nyu.databrary.org/volume/1442 | 9 |
| 01459 | https://nyu.databrary.org/volume/1459 | 7 |
| 01663 | https://nyu.databrary.org/volume/1663 | 7 |
| 01400 | https://nyu.databrary.org/volume/1400 | 6 |
| 01576 | https://nyu.databrary.org/volume/1576 | 6 |
| 01656 | https://nyu.databrary.org/volume/1656 | 6 |
| 00002 | https://nyu.databrary.org/volume/2 | 4 |
| 00254 | https://nyu.databrary.org/volume/254 | 4 |
| 00241 | https://nyu.databrary.org/volume/241 | 3 |
| 00881 | https://nyu.databrary.org/volume/881 | 3 |
| 00009 | https://nyu.databrary.org/volume/9 | 2 |
| 00760 | https://nyu.databrary.org/volume/760 | 2 |
| 00982 | https://nyu.databrary.org/volume/982 | 2 |
| 01023 | https://nyu.databrary.org/volume/1023 | 2 |
| 01362 | https://nyu.databrary.org/volume/1362 | 2 |
| 01596 | https://nyu.databrary.org/volume/1596 | 2 |
| 00101 | https://nyu.databrary.org/volume/101 | 1 |
| 00116 | https://nyu.databrary.org/volume/116 | 1 |
| 00142 | https://nyu.databrary.org/volume/142 | 1 |
| 00876 | https://nyu.databrary.org/volume/876 | 1 |
| 01073 | https://nyu.databrary.org/volume/1073 | 1 |
| 01509 | https://nyu.databrary.org/volume/1509 | 1 |
| 01624 | https://nyu.databrary.org/volume/1624 | 1 |
| 01657 | https://nyu.databrary.org/volume/1657 | 1 |
| 01688 | https://nyu.databrary.org/volume/1688 | 1 |
| 01705 | https://nyu.databrary.org/volume/1705 | 1 |
There are n= 147 shared volumes reporting age_days.
n participants and age distribution
The following summarizes the number of individual participant-sessions for whom there are data.
Code
age_df <- demo_df |>
dplyr::mutate(age_grp = cut(as.numeric(demo_df$age_days), c(0, 90, 180, 365.25, 2*365.25, 3*365.25, 4*365.25, 5*365.25, 15*365.25, 20*365.25, 25*365.25, 100*365.25), c("<3m", "3-6m", "6m-1y", "1-2y", "2-3y", "3-4y", "4-5y", "5-15y", "15-20y", "20-25y", ">25y")))
xtabs(formula = ~ age_grp, data = age_df)age_grp
<3m 3-6m 6m-1y 1-2y 2-3y 3-4y 4-5y 5-15y 15-20y 20-25y >25y
103 157 1176 3390 875 474 897 1527 74 308 411 Code
demo_df |>
dplyr::filter(age_days <= 365.24*5) |>
ggplot2::ggplot() +
ggplot2::aes(age_days) +
ggplot2::geom_histogram() +
ggplot2::ggtitle("Age at test (days) for 5-year-olds and younger")Don't know how to automatically pick scale for object of type <difftime>.
Defaulting to continuous.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Code
demo_df |>
dplyr::filter(age_days > 365.24*5,
age_days <= 365.24*15) |>
ggplot2::ggplot() +
ggplot2::aes(age_days) +
ggplot2::geom_histogram() +
ggplot2::ggtitle("Age at test (days) for 5-15 year-olds")Don't know how to automatically pick scale for object of type <difftime>.
Defaulting to continuous.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Code
demo_df |>
dplyr::filter(age_days > 365.24*15) |>
ggplot2::ggplot() +
ggplot2::aes(age_days) +
ggplot2::geom_histogram() +
ggplot2::ggtitle("Age at test (days) 15+ year-olds")Don't know how to automatically pick scale for object of type <difftime>.
Defaulting to continuous.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Gender
Volumes and sessions
Code
gender_df <- demo_df |>
dplyr::filter(!is.na(participant_gender)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
gender_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00008 | https://nyu.databrary.org/volume/8 | 1318 |
| 00564 | https://nyu.databrary.org/volume/564 | 459 |
| 00090 | https://nyu.databrary.org/volume/90 | 312 |
| 00739 | https://nyu.databrary.org/volume/739 | 295 |
| 00011 | https://nyu.databrary.org/volume/11 | 236 |
| 00087 | https://nyu.databrary.org/volume/87 | 231 |
| 01020 | https://nyu.databrary.org/volume/1020 | 220 |
| 00359 | https://nyu.databrary.org/volume/359 | 216 |
| 00400 | https://nyu.databrary.org/volume/400 | 182 |
| 00322 | https://nyu.databrary.org/volume/322 | 173 |
| 00184 | https://nyu.databrary.org/volume/184 | 161 |
| 01042 | https://nyu.databrary.org/volume/1042 | 161 |
| 00030 | https://nyu.databrary.org/volume/30 | 157 |
| 01364 | https://nyu.databrary.org/volume/1364 | 156 |
| 00149 | https://nyu.databrary.org/volume/149 | 155 |
| 00139 | https://nyu.databrary.org/volume/139 | 142 |
| 01075 | https://nyu.databrary.org/volume/1075 | 138 |
| 00005 | https://nyu.databrary.org/volume/5 | 133 |
| 00088 | https://nyu.databrary.org/volume/88 | 132 |
| 00226 | https://nyu.databrary.org/volume/226 | 129 |
| 00989 | https://nyu.databrary.org/volume/989 | 127 |
| 00563 | https://nyu.databrary.org/volume/563 | 118 |
| 00207 | https://nyu.databrary.org/volume/207 | 116 |
| 00253 | https://nyu.databrary.org/volume/253 | 115 |
| 00271 | https://nyu.databrary.org/volume/271 | 115 |
| 00089 | https://nyu.databrary.org/volume/89 | 114 |
| 00150 | https://nyu.databrary.org/volume/150 | 111 |
| 01526 | https://nyu.databrary.org/volume/1526 | 111 |
| 00083 | https://nyu.databrary.org/volume/83 | 109 |
| 00308 | https://nyu.databrary.org/volume/308 | 109 |
| 00988 | https://nyu.databrary.org/volume/988 | 109 |
| 00114 | https://nyu.databrary.org/volume/114 | 105 |
| 00084 | https://nyu.databrary.org/volume/84 | 104 |
| 00269 | https://nyu.databrary.org/volume/269 | 102 |
| 00140 | https://nyu.databrary.org/volume/140 | 97 |
| 00950 | https://nyu.databrary.org/volume/950 | 95 |
| 00162 | https://nyu.databrary.org/volume/162 | 93 |
| 00460 | https://nyu.databrary.org/volume/460 | 91 |
| 00484 | https://nyu.databrary.org/volume/484 | 90 |
| 01141 | https://nyu.databrary.org/volume/1141 | 90 |
| 01129 | https://nyu.databrary.org/volume/1129 | 89 |
| 00070 | https://nyu.databrary.org/volume/70 | 88 |
| 00152 | https://nyu.databrary.org/volume/152 | 84 |
| 01273 | https://nyu.databrary.org/volume/1273 | 82 |
| 00434 | https://nyu.databrary.org/volume/434 | 79 |
| 00868 | https://nyu.databrary.org/volume/868 | 77 |
| 01379 | https://nyu.databrary.org/volume/1379 | 76 |
| 01567 | https://nyu.databrary.org/volume/1567 | 74 |
| 00321 | https://nyu.databrary.org/volume/321 | 71 |
| 00004 | https://nyu.databrary.org/volume/4 | 67 |
| 00899 | https://nyu.databrary.org/volume/899 | 65 |
| 01436 | https://nyu.databrary.org/volume/1436 | 65 |
| 00081 | https://nyu.databrary.org/volume/81 | 63 |
| 00136 | https://nyu.databrary.org/volume/136 | 62 |
| 00835 | https://nyu.databrary.org/volume/835 | 62 |
| 01103 | https://nyu.databrary.org/volume/1103 | 62 |
| 00350 | https://nyu.databrary.org/volume/350 | 58 |
| 00837 | https://nyu.databrary.org/volume/837 | 58 |
| 00854 | https://nyu.databrary.org/volume/854 | 57 |
| 00007 | https://nyu.databrary.org/volume/7 | 55 |
| 00163 | https://nyu.databrary.org/volume/163 | 55 |
| 00144 | https://nyu.databrary.org/volume/144 | 52 |
| 00192 | https://nyu.databrary.org/volume/192 | 51 |
| 01312 | https://nyu.databrary.org/volume/1312 | 48 |
| 00827 | https://nyu.databrary.org/volume/827 | 47 |
| 01415 | https://nyu.databrary.org/volume/1415 | 47 |
| 00954 | https://nyu.databrary.org/volume/954 | 46 |
| 01026 | https://nyu.databrary.org/volume/1026 | 46 |
| 01551 | https://nyu.databrary.org/volume/1551 | 46 |
| 01108 | https://nyu.databrary.org/volume/1108 | 44 |
| 01328 | https://nyu.databrary.org/volume/1328 | 43 |
| 00941 | https://nyu.databrary.org/volume/941 | 42 |
| 00455 | https://nyu.databrary.org/volume/455 | 41 |
| 01128 | https://nyu.databrary.org/volume/1128 | 41 |
| 01448 | https://nyu.databrary.org/volume/1448 | 41 |
| 00108 | https://nyu.databrary.org/volume/108 | 40 |
| 01517 | https://nyu.databrary.org/volume/1517 | 39 |
| 00146 | https://nyu.databrary.org/volume/146 | 38 |
| 00123 | https://nyu.databrary.org/volume/123 | 37 |
| 00476 | https://nyu.databrary.org/volume/476 | 35 |
| 00218 | https://nyu.databrary.org/volume/218 | 34 |
| 00592 | https://nyu.databrary.org/volume/592 | 34 |
| 00957 | https://nyu.databrary.org/volume/957 | 34 |
| 00979 | https://nyu.databrary.org/volume/979 | 34 |
| 01515 | https://nyu.databrary.org/volume/1515 | 34 |
| 00075 | https://nyu.databrary.org/volume/75 | 33 |
| 00098 | https://nyu.databrary.org/volume/98 | 33 |
| 00135 | https://nyu.databrary.org/volume/135 | 33 |
| 00174 | https://nyu.databrary.org/volume/174 | 33 |
| 00444 | https://nyu.databrary.org/volume/444 | 33 |
| 01397 | https://nyu.databrary.org/volume/1397 | 33 |
| 01321 | https://nyu.databrary.org/volume/1321 | 32 |
| 00073 | https://nyu.databrary.org/volume/73 | 31 |
| 00101 | https://nyu.databrary.org/volume/101 | 31 |
| 01066 | https://nyu.databrary.org/volume/1066 | 31 |
| 00943 | https://nyu.databrary.org/volume/943 | 30 |
| 01370 | https://nyu.databrary.org/volume/1370 | 30 |
| 01365 | https://nyu.databrary.org/volume/1365 | 28 |
| 00452 | https://nyu.databrary.org/volume/452 | 26 |
| 00821 | https://nyu.databrary.org/volume/821 | 26 |
| 01363 | https://nyu.databrary.org/volume/1363 | 25 |
| 01143 | https://nyu.databrary.org/volume/1143 | 24 |
| 00443 | https://nyu.databrary.org/volume/443 | 20 |
| 00132 | https://nyu.databrary.org/volume/132 | 19 |
| 00996 | https://nyu.databrary.org/volume/996 | 19 |
| 01391 | https://nyu.databrary.org/volume/1391 | 19 |
| 01422 | https://nyu.databrary.org/volume/1422 | 19 |
| 00390 | https://nyu.databrary.org/volume/390 | 18 |
| 00171 | https://nyu.databrary.org/volume/171 | 17 |
| 01481 | https://nyu.databrary.org/volume/1481 | 17 |
| 00169 | https://nyu.databrary.org/volume/169 | 16 |
| 00710 | https://nyu.databrary.org/volume/710 | 16 |
| 01008 | https://nyu.databrary.org/volume/1008 | 16 |
| 01376 | https://nyu.databrary.org/volume/1376 | 16 |
| 00956 | https://nyu.databrary.org/volume/956 | 14 |
| 00124 | https://nyu.databrary.org/volume/124 | 13 |
| 00217 | https://nyu.databrary.org/volume/217 | 13 |
| 00966 | https://nyu.databrary.org/volume/966 | 13 |
| 00015 | https://nyu.databrary.org/volume/15 | 12 |
| 00145 | https://nyu.databrary.org/volume/145 | 12 |
| 01442 | https://nyu.databrary.org/volume/1442 | 12 |
| 00143 | https://nyu.databrary.org/volume/143 | 11 |
| 00761 | https://nyu.databrary.org/volume/761 | 11 |
| 00323 | https://nyu.databrary.org/volume/323 | 10 |
| 01590 | https://nyu.databrary.org/volume/1590 | 10 |
| 00148 | https://nyu.databrary.org/volume/148 | 9 |
| 01459 | https://nyu.databrary.org/volume/1459 | 7 |
| 01663 | https://nyu.databrary.org/volume/1663 | 7 |
| 00351 | https://nyu.databrary.org/volume/351 | 6 |
| 01400 | https://nyu.databrary.org/volume/1400 | 6 |
| 01576 | https://nyu.databrary.org/volume/1576 | 6 |
| 01656 | https://nyu.databrary.org/volume/1656 | 6 |
| 01419 | https://nyu.databrary.org/volume/1419 | 5 |
| 00002 | https://nyu.databrary.org/volume/2 | 4 |
| 00254 | https://nyu.databrary.org/volume/254 | 4 |
| 01206 | https://nyu.databrary.org/volume/1206 | 4 |
| 00241 | https://nyu.databrary.org/volume/241 | 3 |
| 00881 | https://nyu.databrary.org/volume/881 | 3 |
| 00009 | https://nyu.databrary.org/volume/9 | 2 |
| 00760 | https://nyu.databrary.org/volume/760 | 2 |
| 00982 | https://nyu.databrary.org/volume/982 | 2 |
| 01023 | https://nyu.databrary.org/volume/1023 | 2 |
| 01362 | https://nyu.databrary.org/volume/1362 | 2 |
| 01596 | https://nyu.databrary.org/volume/1596 | 2 |
| 00116 | https://nyu.databrary.org/volume/116 | 1 |
| 00142 | https://nyu.databrary.org/volume/142 | 1 |
| 00876 | https://nyu.databrary.org/volume/876 | 1 |
| 01073 | https://nyu.databrary.org/volume/1073 | 1 |
| 01509 | https://nyu.databrary.org/volume/1509 | 1 |
| 01624 | https://nyu.databrary.org/volume/1624 | 1 |
| 01657 | https://nyu.databrary.org/volume/1657 | 1 |
| 01688 | https://nyu.databrary.org/volume/1688 | 1 |
| 01705 | https://nyu.databrary.org/volume/1705 | 1 |
There are n= 153 shared volumes reporting participant_gender.
n participants
Code
xtabs(formula = ~ participant_gender, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_gender | Freq |
|---|---|
| Female | 5355 |
| Male | 5255 |
| Non binary | 2 |
| Non-binary | 1 |
| Other | 1 |
| Unknown | 1 |
| Unknown or not reported | 1 |
Race
Volumes and sessions
Code
race_df <- demo_df |>
dplyr::filter(!is.na(participant_race)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
race_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00008 | https://nyu.databrary.org/volume/8 | 1318 |
| 00564 | https://nyu.databrary.org/volume/564 | 459 |
| 00011 | https://nyu.databrary.org/volume/11 | 236 |
| 00087 | https://nyu.databrary.org/volume/87 | 231 |
| 01020 | https://nyu.databrary.org/volume/1020 | 220 |
| 00359 | https://nyu.databrary.org/volume/359 | 216 |
| 00400 | https://nyu.databrary.org/volume/400 | 177 |
| 00322 | https://nyu.databrary.org/volume/322 | 173 |
| 00184 | https://nyu.databrary.org/volume/184 | 157 |
| 00149 | https://nyu.databrary.org/volume/149 | 155 |
| 01364 | https://nyu.databrary.org/volume/1364 | 151 |
| 00139 | https://nyu.databrary.org/volume/139 | 141 |
| 00030 | https://nyu.databrary.org/volume/30 | 134 |
| 00005 | https://nyu.databrary.org/volume/5 | 133 |
| 00088 | https://nyu.databrary.org/volume/88 | 132 |
| 00226 | https://nyu.databrary.org/volume/226 | 129 |
| 00989 | https://nyu.databrary.org/volume/989 | 127 |
| 01042 | https://nyu.databrary.org/volume/1042 | 122 |
| 00563 | https://nyu.databrary.org/volume/563 | 118 |
| 00089 | https://nyu.databrary.org/volume/89 | 114 |
| 00207 | https://nyu.databrary.org/volume/207 | 114 |
| 00271 | https://nyu.databrary.org/volume/271 | 113 |
| 00253 | https://nyu.databrary.org/volume/253 | 112 |
| 00150 | https://nyu.databrary.org/volume/150 | 110 |
| 01526 | https://nyu.databrary.org/volume/1526 | 110 |
| 00083 | https://nyu.databrary.org/volume/83 | 109 |
| 00988 | https://nyu.databrary.org/volume/988 | 109 |
| 00308 | https://nyu.databrary.org/volume/308 | 107 |
| 00070 | https://nyu.databrary.org/volume/70 | 105 |
| 00114 | https://nyu.databrary.org/volume/114 | 105 |
| 00269 | https://nyu.databrary.org/volume/269 | 105 |
| 00084 | https://nyu.databrary.org/volume/84 | 104 |
| 00140 | https://nyu.databrary.org/volume/140 | 97 |
| 00950 | https://nyu.databrary.org/volume/950 | 94 |
| 00162 | https://nyu.databrary.org/volume/162 | 93 |
| 00460 | https://nyu.databrary.org/volume/460 | 91 |
| 00152 | https://nyu.databrary.org/volume/152 | 84 |
| 01273 | https://nyu.databrary.org/volume/1273 | 82 |
| 00434 | https://nyu.databrary.org/volume/434 | 79 |
| 00868 | https://nyu.databrary.org/volume/868 | 77 |
| 01379 | https://nyu.databrary.org/volume/1379 | 76 |
| 01567 | https://nyu.databrary.org/volume/1567 | 74 |
| 00321 | https://nyu.databrary.org/volume/321 | 70 |
| 00004 | https://nyu.databrary.org/volume/4 | 67 |
| 00899 | https://nyu.databrary.org/volume/899 | 65 |
| 01436 | https://nyu.databrary.org/volume/1436 | 65 |
| 00136 | https://nyu.databrary.org/volume/136 | 62 |
| 00835 | https://nyu.databrary.org/volume/835 | 62 |
| 01103 | https://nyu.databrary.org/volume/1103 | 62 |
| 00350 | https://nyu.databrary.org/volume/350 | 58 |
| 00837 | https://nyu.databrary.org/volume/837 | 56 |
| 00854 | https://nyu.databrary.org/volume/854 | 56 |
| 00163 | https://nyu.databrary.org/volume/163 | 55 |
| 00007 | https://nyu.databrary.org/volume/7 | 54 |
| 00192 | https://nyu.databrary.org/volume/192 | 51 |
| 01312 | https://nyu.databrary.org/volume/1312 | 48 |
| 00827 | https://nyu.databrary.org/volume/827 | 47 |
| 00954 | https://nyu.databrary.org/volume/954 | 46 |
| 01026 | https://nyu.databrary.org/volume/1026 | 44 |
| 01108 | https://nyu.databrary.org/volume/1108 | 43 |
| 00941 | https://nyu.databrary.org/volume/941 | 42 |
| 00455 | https://nyu.databrary.org/volume/455 | 41 |
| 01128 | https://nyu.databrary.org/volume/1128 | 41 |
| 01448 | https://nyu.databrary.org/volume/1448 | 41 |
| 00108 | https://nyu.databrary.org/volume/108 | 40 |
| 01517 | https://nyu.databrary.org/volume/1517 | 39 |
| 00146 | https://nyu.databrary.org/volume/146 | 38 |
| 00123 | https://nyu.databrary.org/volume/123 | 37 |
| 00476 | https://nyu.databrary.org/volume/476 | 35 |
| 00218 | https://nyu.databrary.org/volume/218 | 34 |
| 00957 | https://nyu.databrary.org/volume/957 | 34 |
| 00979 | https://nyu.databrary.org/volume/979 | 34 |
| 01515 | https://nyu.databrary.org/volume/1515 | 34 |
| 00135 | https://nyu.databrary.org/volume/135 | 33 |
| 00174 | https://nyu.databrary.org/volume/174 | 33 |
| 00444 | https://nyu.databrary.org/volume/444 | 33 |
| 00592 | https://nyu.databrary.org/volume/592 | 33 |
| 01397 | https://nyu.databrary.org/volume/1397 | 33 |
| 00073 | https://nyu.databrary.org/volume/73 | 31 |
| 01066 | https://nyu.databrary.org/volume/1066 | 31 |
| 00943 | https://nyu.databrary.org/volume/943 | 30 |
| 01370 | https://nyu.databrary.org/volume/1370 | 30 |
| 00452 | https://nyu.databrary.org/volume/452 | 26 |
| 00821 | https://nyu.databrary.org/volume/821 | 26 |
| 01363 | https://nyu.databrary.org/volume/1363 | 25 |
| 01143 | https://nyu.databrary.org/volume/1143 | 24 |
| 00443 | https://nyu.databrary.org/volume/443 | 20 |
| 01365 | https://nyu.databrary.org/volume/1365 | 20 |
| 00132 | https://nyu.databrary.org/volume/132 | 19 |
| 01391 | https://nyu.databrary.org/volume/1391 | 19 |
| 01422 | https://nyu.databrary.org/volume/1422 | 19 |
| 00390 | https://nyu.databrary.org/volume/390 | 18 |
| 00996 | https://nyu.databrary.org/volume/996 | 18 |
| 00171 | https://nyu.databrary.org/volume/171 | 17 |
| 01481 | https://nyu.databrary.org/volume/1481 | 17 |
| 00169 | https://nyu.databrary.org/volume/169 | 16 |
| 01008 | https://nyu.databrary.org/volume/1008 | 16 |
| 01376 | https://nyu.databrary.org/volume/1376 | 16 |
| 00956 | https://nyu.databrary.org/volume/956 | 14 |
| 00124 | https://nyu.databrary.org/volume/124 | 13 |
| 00217 | https://nyu.databrary.org/volume/217 | 13 |
| 00966 | https://nyu.databrary.org/volume/966 | 13 |
| 00015 | https://nyu.databrary.org/volume/15 | 12 |
| 00145 | https://nyu.databrary.org/volume/145 | 12 |
| 01442 | https://nyu.databrary.org/volume/1442 | 12 |
| 00143 | https://nyu.databrary.org/volume/143 | 11 |
| 00761 | https://nyu.databrary.org/volume/761 | 11 |
| 00323 | https://nyu.databrary.org/volume/323 | 10 |
| 01590 | https://nyu.databrary.org/volume/1590 | 10 |
| 00148 | https://nyu.databrary.org/volume/148 | 9 |
| 01459 | https://nyu.databrary.org/volume/1459 | 7 |
| 01663 | https://nyu.databrary.org/volume/1663 | 7 |
| 01400 | https://nyu.databrary.org/volume/1400 | 6 |
| 01576 | https://nyu.databrary.org/volume/1576 | 6 |
| 01656 | https://nyu.databrary.org/volume/1656 | 6 |
| 00351 | https://nyu.databrary.org/volume/351 | 5 |
| 01419 | https://nyu.databrary.org/volume/1419 | 5 |
| 00002 | https://nyu.databrary.org/volume/2 | 4 |
| 00254 | https://nyu.databrary.org/volume/254 | 4 |
| 00241 | https://nyu.databrary.org/volume/241 | 3 |
| 00881 | https://nyu.databrary.org/volume/881 | 3 |
| 00009 | https://nyu.databrary.org/volume/9 | 2 |
| 00760 | https://nyu.databrary.org/volume/760 | 2 |
| 00982 | https://nyu.databrary.org/volume/982 | 2 |
| 01023 | https://nyu.databrary.org/volume/1023 | 2 |
| 01362 | https://nyu.databrary.org/volume/1362 | 2 |
| 01596 | https://nyu.databrary.org/volume/1596 | 2 |
| 00116 | https://nyu.databrary.org/volume/116 | 1 |
| 00142 | https://nyu.databrary.org/volume/142 | 1 |
| 00144 | https://nyu.databrary.org/volume/144 | 1 |
| 00876 | https://nyu.databrary.org/volume/876 | 1 |
| 01073 | https://nyu.databrary.org/volume/1073 | 1 |
| 01624 | https://nyu.databrary.org/volume/1624 | 1 |
| 01657 | https://nyu.databrary.org/volume/1657 | 1 |
| 01688 | https://nyu.databrary.org/volume/1688 | 1 |
| 01705 | https://nyu.databrary.org/volume/1705 | 1 |
There are n= 136 shared volumes reporting participant_race.
n participants
Code
xtabs(formula = ~ participant_race, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_race | Freq |
|---|---|
| 1/2 White, 1/2 Asian | 1 |
| African American and Puerto Rican | 1 |
| African American and White | 1 |
| American Indian or Alaska Native | 8 |
| Arab | 1 |
| Ashkenazi Jewish | 1 |
| Asian | 1185 |
| Asian and Hispanic or Latino | 1 |
| Asian and White | 2 |
| Biracial | 1 |
| Black and Asian | 1 |
| Black or African American | 1225 |
| Black or African American AND Native Hawaiian or Pacific Islander | 1 |
| Black or African American White | 1 |
| Black or African American, White | 1 |
| Black or African-American | 1 |
| Black/Mixed | 1 |
| Chose not to answer | 5 |
| Chose Not To Asnwer | 1 |
| Decline to State | 1 |
| Did Not Answer | 3 |
| Did not report | 1 |
| Dominican American | 1 |
| European White | 38 |
| H | 7 |
| Half Asian | 1 |
| Hawaiian or other Pacific Islander | 1 |
| Hawaiian or Pacific Islander | 1 |
| Hispanic/Mixed Race | 1 |
| I choose not to answer | 4 |
| I choose not to answer this question | 3 |
| Indian | 3 |
| Latino, Native American, white | 1 |
| Middle East | 1 |
| Mixed | 3 |
| More than one | 943 |
| More than one race | 1 |
| More than one race: Asian and White | 2 |
| More than one race: North african and asian | 1 |
| More than one race: White and Asian | 1 |
| More than one race: White and middle eastern | 1 |
| More than one race: White, Asian | 1 |
| More than one race: White, Jewish, Latinx | 1 |
| More than one race: White/ Asian | 1 |
| More than one: Black or African and White | 1 |
| Native American or Alaskan Native | 1 |
| Native Hawaiian or Other Pacific Islander | 2 |
| Nepall | 1 |
| Not reported | 2 |
| Not Reported | 2 |
| Other | 58 |
| Pacific Islander | 1 |
| Refused | 3 |
| South Asian | 14 |
| South East Asian/ East Asian | 1 |
| Unknown or not reported | 395 |
| While | 1 |
| White | 5143 |
| White & Mexican American | 1 |
| White and Afro Latina | 12 |
| White and Asian | 7 |
| White and Black | 1 |
| White, American Indian or Alaskan Native, and Hispanic or Latino | 1 |
| White, Asian | 1 |
| White, Asian, Hawaiian | 1 |
| White, Black/African American | 1 |
| White/Asian | 1 |
| White/Mixed | 1 |
| White/Vietnamese | 1 |
Ethnicity
Volumes and sessions
Code
ethnicity_df <- demo_df |>
dplyr::filter(!is.na(participant_ethnicity)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
ethnicity_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00008 | https://nyu.databrary.org/volume/8 | 1318 |
| 00564 | https://nyu.databrary.org/volume/564 | 459 |
| 00011 | https://nyu.databrary.org/volume/11 | 236 |
| 00087 | https://nyu.databrary.org/volume/87 | 231 |
| 01020 | https://nyu.databrary.org/volume/1020 | 220 |
| 00359 | https://nyu.databrary.org/volume/359 | 216 |
| 00322 | https://nyu.databrary.org/volume/322 | 173 |
| 00184 | https://nyu.databrary.org/volume/184 | 158 |
| 01042 | https://nyu.databrary.org/volume/1042 | 155 |
| 00400 | https://nyu.databrary.org/volume/400 | 151 |
| 01364 | https://nyu.databrary.org/volume/1364 | 151 |
| 00139 | https://nyu.databrary.org/volume/139 | 142 |
| 00005 | https://nyu.databrary.org/volume/5 | 133 |
| 00088 | https://nyu.databrary.org/volume/88 | 132 |
| 00226 | https://nyu.databrary.org/volume/226 | 129 |
| 00989 | https://nyu.databrary.org/volume/989 | 121 |
| 00563 | https://nyu.databrary.org/volume/563 | 118 |
| 00089 | https://nyu.databrary.org/volume/89 | 114 |
| 00253 | https://nyu.databrary.org/volume/253 | 114 |
| 00207 | https://nyu.databrary.org/volume/207 | 113 |
| 00271 | https://nyu.databrary.org/volume/271 | 111 |
| 01526 | https://nyu.databrary.org/volume/1526 | 110 |
| 00083 | https://nyu.databrary.org/volume/83 | 109 |
| 00308 | https://nyu.databrary.org/volume/308 | 108 |
| 00114 | https://nyu.databrary.org/volume/114 | 105 |
| 00269 | https://nyu.databrary.org/volume/269 | 105 |
| 00988 | https://nyu.databrary.org/volume/988 | 105 |
| 00084 | https://nyu.databrary.org/volume/84 | 104 |
| 00140 | https://nyu.databrary.org/volume/140 | 97 |
| 00162 | https://nyu.databrary.org/volume/162 | 93 |
| 00460 | https://nyu.databrary.org/volume/460 | 91 |
| 00484 | https://nyu.databrary.org/volume/484 | 90 |
| 00152 | https://nyu.databrary.org/volume/152 | 84 |
| 01273 | https://nyu.databrary.org/volume/1273 | 82 |
| 00150 | https://nyu.databrary.org/volume/150 | 81 |
| 00950 | https://nyu.databrary.org/volume/950 | 80 |
| 00434 | https://nyu.databrary.org/volume/434 | 79 |
| 00868 | https://nyu.databrary.org/volume/868 | 77 |
| 01379 | https://nyu.databrary.org/volume/1379 | 76 |
| 01567 | https://nyu.databrary.org/volume/1567 | 74 |
| 00321 | https://nyu.databrary.org/volume/321 | 70 |
| 00004 | https://nyu.databrary.org/volume/4 | 67 |
| 00899 | https://nyu.databrary.org/volume/899 | 65 |
| 01436 | https://nyu.databrary.org/volume/1436 | 65 |
| 00136 | https://nyu.databrary.org/volume/136 | 62 |
| 00835 | https://nyu.databrary.org/volume/835 | 62 |
| 01103 | https://nyu.databrary.org/volume/1103 | 62 |
| 00070 | https://nyu.databrary.org/volume/70 | 58 |
| 00350 | https://nyu.databrary.org/volume/350 | 58 |
| 00007 | https://nyu.databrary.org/volume/7 | 55 |
| 00163 | https://nyu.databrary.org/volume/163 | 55 |
| 00854 | https://nyu.databrary.org/volume/854 | 54 |
| 00192 | https://nyu.databrary.org/volume/192 | 51 |
| 01312 | https://nyu.databrary.org/volume/1312 | 48 |
| 00827 | https://nyu.databrary.org/volume/827 | 47 |
| 00954 | https://nyu.databrary.org/volume/954 | 46 |
| 01026 | https://nyu.databrary.org/volume/1026 | 45 |
| 00941 | https://nyu.databrary.org/volume/941 | 42 |
| 00455 | https://nyu.databrary.org/volume/455 | 41 |
| 01108 | https://nyu.databrary.org/volume/1108 | 41 |
| 01128 | https://nyu.databrary.org/volume/1128 | 41 |
| 01448 | https://nyu.databrary.org/volume/1448 | 41 |
| 00108 | https://nyu.databrary.org/volume/108 | 40 |
| 01517 | https://nyu.databrary.org/volume/1517 | 39 |
| 00146 | https://nyu.databrary.org/volume/146 | 38 |
| 00123 | https://nyu.databrary.org/volume/123 | 37 |
| 00476 | https://nyu.databrary.org/volume/476 | 35 |
| 00218 | https://nyu.databrary.org/volume/218 | 34 |
| 00957 | https://nyu.databrary.org/volume/957 | 34 |
| 00979 | https://nyu.databrary.org/volume/979 | 34 |
| 01515 | https://nyu.databrary.org/volume/1515 | 34 |
| 00135 | https://nyu.databrary.org/volume/135 | 33 |
| 00444 | https://nyu.databrary.org/volume/444 | 33 |
| 01397 | https://nyu.databrary.org/volume/1397 | 33 |
| 00149 | https://nyu.databrary.org/volume/149 | 32 |
| 00073 | https://nyu.databrary.org/volume/73 | 31 |
| 00174 | https://nyu.databrary.org/volume/174 | 31 |
| 01066 | https://nyu.databrary.org/volume/1066 | 31 |
| 00943 | https://nyu.databrary.org/volume/943 | 30 |
| 01370 | https://nyu.databrary.org/volume/1370 | 30 |
| 00452 | https://nyu.databrary.org/volume/452 | 26 |
| 00821 | https://nyu.databrary.org/volume/821 | 26 |
| 01363 | https://nyu.databrary.org/volume/1363 | 25 |
| 01143 | https://nyu.databrary.org/volume/1143 | 24 |
| 00030 | https://nyu.databrary.org/volume/30 | 23 |
| 00443 | https://nyu.databrary.org/volume/443 | 20 |
| 01365 | https://nyu.databrary.org/volume/1365 | 20 |
| 00132 | https://nyu.databrary.org/volume/132 | 19 |
| 01391 | https://nyu.databrary.org/volume/1391 | 19 |
| 01422 | https://nyu.databrary.org/volume/1422 | 19 |
| 00390 | https://nyu.databrary.org/volume/390 | 18 |
| 00996 | https://nyu.databrary.org/volume/996 | 18 |
| 00171 | https://nyu.databrary.org/volume/171 | 17 |
| 01481 | https://nyu.databrary.org/volume/1481 | 17 |
| 00169 | https://nyu.databrary.org/volume/169 | 16 |
| 01008 | https://nyu.databrary.org/volume/1008 | 16 |
| 01376 | https://nyu.databrary.org/volume/1376 | 16 |
| 00956 | https://nyu.databrary.org/volume/956 | 14 |
| 00124 | https://nyu.databrary.org/volume/124 | 13 |
| 00217 | https://nyu.databrary.org/volume/217 | 13 |
| 00966 | https://nyu.databrary.org/volume/966 | 13 |
| 00015 | https://nyu.databrary.org/volume/15 | 12 |
| 00145 | https://nyu.databrary.org/volume/145 | 12 |
| 01442 | https://nyu.databrary.org/volume/1442 | 12 |
| 00143 | https://nyu.databrary.org/volume/143 | 11 |
| 00761 | https://nyu.databrary.org/volume/761 | 11 |
| 00323 | https://nyu.databrary.org/volume/323 | 10 |
| 01590 | https://nyu.databrary.org/volume/1590 | 10 |
| 00148 | https://nyu.databrary.org/volume/148 | 9 |
| 01459 | https://nyu.databrary.org/volume/1459 | 7 |
| 01663 | https://nyu.databrary.org/volume/1663 | 7 |
| 01400 | https://nyu.databrary.org/volume/1400 | 6 |
| 01576 | https://nyu.databrary.org/volume/1576 | 6 |
| 01656 | https://nyu.databrary.org/volume/1656 | 6 |
| 00351 | https://nyu.databrary.org/volume/351 | 5 |
| 01419 | https://nyu.databrary.org/volume/1419 | 5 |
| 00002 | https://nyu.databrary.org/volume/2 | 4 |
| 00254 | https://nyu.databrary.org/volume/254 | 4 |
| 00241 | https://nyu.databrary.org/volume/241 | 3 |
| 00881 | https://nyu.databrary.org/volume/881 | 3 |
| 00009 | https://nyu.databrary.org/volume/9 | 2 |
| 00760 | https://nyu.databrary.org/volume/760 | 2 |
| 00982 | https://nyu.databrary.org/volume/982 | 2 |
| 01023 | https://nyu.databrary.org/volume/1023 | 2 |
| 01362 | https://nyu.databrary.org/volume/1362 | 2 |
| 01596 | https://nyu.databrary.org/volume/1596 | 2 |
| 00116 | https://nyu.databrary.org/volume/116 | 1 |
| 00142 | https://nyu.databrary.org/volume/142 | 1 |
| 00144 | https://nyu.databrary.org/volume/144 | 1 |
| 00876 | https://nyu.databrary.org/volume/876 | 1 |
| 01073 | https://nyu.databrary.org/volume/1073 | 1 |
| 01624 | https://nyu.databrary.org/volume/1624 | 1 |
| 01657 | https://nyu.databrary.org/volume/1657 | 1 |
| 01688 | https://nyu.databrary.org/volume/1688 | 1 |
| 01705 | https://nyu.databrary.org/volume/1705 | 1 |
There are n= 135 shared volumes reporting participant_ethnicity.
n participants
Code
xtabs(formula = ~ participant_ethnicity, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_ethnicity | Freq |
|---|---|
| Asian | 1 |
| Chinese | 221 |
| Choose not to answer | 2 |
| Chose not to answer | 3 |
| Chose Not To Answer | 1 |
| Decline to State | 2 |
| did not answer | 1 |
| Did Not Answer | 3 |
| Dominican | 393 |
| Hispanic or Latino | 1180 |
| Hispanic or Latinx | 3 |
| I choose not to answer this question | 7 |
| Indigenous | 1 |
| Mexican | 357 |
| More than one | 1 |
| Non-Hispanic | 5 |
| Not Hispanic or Latino | 5613 |
| Not Hispanic or Latinoo | 1 |
| Not Hispanic or Latinx | 2 |
| Not Hispanic/Latinx | 1 |
| Not indigenous | 2 |
| Not reported | 3 |
| Not Reported | 4 |
| Refused | 3 |
| Unknown or not reported | 976 |
Participant language
Volumes and sessions
Code
language_df <- demo_df |>
dplyr::filter(!is.na(participant_language)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
language_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00008 | https://nyu.databrary.org/volume/8 | 1318 |
| 00011 | https://nyu.databrary.org/volume/11 | 468 |
| 00564 | https://nyu.databrary.org/volume/564 | 459 |
| 00090 | https://nyu.databrary.org/volume/90 | 312 |
| 00088 | https://nyu.databrary.org/volume/88 | 262 |
| 00087 | https://nyu.databrary.org/volume/87 | 231 |
| 01020 | https://nyu.databrary.org/volume/1020 | 220 |
| 00359 | https://nyu.databrary.org/volume/359 | 216 |
| 00400 | https://nyu.databrary.org/volume/400 | 182 |
| 00460 | https://nyu.databrary.org/volume/460 | 175 |
| 00322 | https://nyu.databrary.org/volume/322 | 173 |
| 00184 | https://nyu.databrary.org/volume/184 | 161 |
| 01042 | https://nyu.databrary.org/volume/1042 | 161 |
| 01364 | https://nyu.databrary.org/volume/1364 | 161 |
| 00149 | https://nyu.databrary.org/volume/149 | 155 |
| 00226 | https://nyu.databrary.org/volume/226 | 129 |
| 00989 | https://nyu.databrary.org/volume/989 | 127 |
| 00207 | https://nyu.databrary.org/volume/207 | 118 |
| 00563 | https://nyu.databrary.org/volume/563 | 118 |
| 00253 | https://nyu.databrary.org/volume/253 | 115 |
| 00089 | https://nyu.databrary.org/volume/89 | 114 |
| 00150 | https://nyu.databrary.org/volume/150 | 111 |
| 01526 | https://nyu.databrary.org/volume/1526 | 111 |
| 00083 | https://nyu.databrary.org/volume/83 | 109 |
| 00308 | https://nyu.databrary.org/volume/308 | 109 |
| 00988 | https://nyu.databrary.org/volume/988 | 109 |
| 00114 | https://nyu.databrary.org/volume/114 | 105 |
| 00269 | https://nyu.databrary.org/volume/269 | 105 |
| 00084 | https://nyu.databrary.org/volume/84 | 104 |
| 00140 | https://nyu.databrary.org/volume/140 | 97 |
| 00950 | https://nyu.databrary.org/volume/950 | 96 |
| 00162 | https://nyu.databrary.org/volume/162 | 93 |
| 00152 | https://nyu.databrary.org/volume/152 | 84 |
| 01273 | https://nyu.databrary.org/volume/1273 | 82 |
| 00434 | https://nyu.databrary.org/volume/434 | 79 |
| 00868 | https://nyu.databrary.org/volume/868 | 77 |
| 01379 | https://nyu.databrary.org/volume/1379 | 76 |
| 01567 | https://nyu.databrary.org/volume/1567 | 74 |
| 00321 | https://nyu.databrary.org/volume/321 | 71 |
| 01436 | https://nyu.databrary.org/volume/1436 | 67 |
| 00899 | https://nyu.databrary.org/volume/899 | 65 |
| 00136 | https://nyu.databrary.org/volume/136 | 62 |
| 00835 | https://nyu.databrary.org/volume/835 | 62 |
| 01103 | https://nyu.databrary.org/volume/1103 | 62 |
| 00837 | https://nyu.databrary.org/volume/837 | 59 |
| 00854 | https://nyu.databrary.org/volume/854 | 59 |
| 00350 | https://nyu.databrary.org/volume/350 | 58 |
| 01312 | https://nyu.databrary.org/volume/1312 | 48 |
| 00827 | https://nyu.databrary.org/volume/827 | 47 |
| 00954 | https://nyu.databrary.org/volume/954 | 46 |
| 01026 | https://nyu.databrary.org/volume/1026 | 46 |
| 01108 | https://nyu.databrary.org/volume/1108 | 44 |
| 00941 | https://nyu.databrary.org/volume/941 | 42 |
| 01448 | https://nyu.databrary.org/volume/1448 | 41 |
| 01517 | https://nyu.databrary.org/volume/1517 | 39 |
| 00123 | https://nyu.databrary.org/volume/123 | 37 |
| 00476 | https://nyu.databrary.org/volume/476 | 35 |
| 00218 | https://nyu.databrary.org/volume/218 | 34 |
| 00592 | https://nyu.databrary.org/volume/592 | 34 |
| 00957 | https://nyu.databrary.org/volume/957 | 34 |
| 00979 | https://nyu.databrary.org/volume/979 | 34 |
| 01515 | https://nyu.databrary.org/volume/1515 | 34 |
| 00075 | https://nyu.databrary.org/volume/75 | 33 |
| 00444 | https://nyu.databrary.org/volume/444 | 33 |
| 01397 | https://nyu.databrary.org/volume/1397 | 33 |
| 00073 | https://nyu.databrary.org/volume/73 | 31 |
| 01066 | https://nyu.databrary.org/volume/1066 | 31 |
| 00943 | https://nyu.databrary.org/volume/943 | 30 |
| 01370 | https://nyu.databrary.org/volume/1370 | 30 |
| 01365 | https://nyu.databrary.org/volume/1365 | 28 |
| 00452 | https://nyu.databrary.org/volume/452 | 26 |
| 00821 | https://nyu.databrary.org/volume/821 | 26 |
| 01363 | https://nyu.databrary.org/volume/1363 | 26 |
| 01143 | https://nyu.databrary.org/volume/1143 | 24 |
| 00132 | https://nyu.databrary.org/volume/132 | 19 |
| 00996 | https://nyu.databrary.org/volume/996 | 19 |
| 01391 | https://nyu.databrary.org/volume/1391 | 19 |
| 01422 | https://nyu.databrary.org/volume/1422 | 19 |
| 00390 | https://nyu.databrary.org/volume/390 | 18 |
| 00171 | https://nyu.databrary.org/volume/171 | 17 |
| 01376 | https://nyu.databrary.org/volume/1376 | 17 |
| 01481 | https://nyu.databrary.org/volume/1481 | 17 |
| 00169 | https://nyu.databrary.org/volume/169 | 16 |
| 00710 | https://nyu.databrary.org/volume/710 | 16 |
| 01008 | https://nyu.databrary.org/volume/1008 | 16 |
| 00956 | https://nyu.databrary.org/volume/956 | 14 |
| 00124 | https://nyu.databrary.org/volume/124 | 13 |
| 00217 | https://nyu.databrary.org/volume/217 | 13 |
| 00966 | https://nyu.databrary.org/volume/966 | 13 |
| 00145 | https://nyu.databrary.org/volume/145 | 12 |
| 01442 | https://nyu.databrary.org/volume/1442 | 12 |
| 00143 | https://nyu.databrary.org/volume/143 | 11 |
| 00761 | https://nyu.databrary.org/volume/761 | 11 |
| 00323 | https://nyu.databrary.org/volume/323 | 10 |
| 01590 | https://nyu.databrary.org/volume/1590 | 10 |
| 00148 | https://nyu.databrary.org/volume/148 | 9 |
| 01459 | https://nyu.databrary.org/volume/1459 | 7 |
| 01663 | https://nyu.databrary.org/volume/1663 | 7 |
| 01400 | https://nyu.databrary.org/volume/1400 | 6 |
| 01576 | https://nyu.databrary.org/volume/1576 | 6 |
| 01656 | https://nyu.databrary.org/volume/1656 | 6 |
| 01419 | https://nyu.databrary.org/volume/1419 | 5 |
| 00254 | https://nyu.databrary.org/volume/254 | 4 |
| 00194 | https://nyu.databrary.org/volume/194 | 3 |
| 00241 | https://nyu.databrary.org/volume/241 | 3 |
| 00881 | https://nyu.databrary.org/volume/881 | 3 |
| 00982 | https://nyu.databrary.org/volume/982 | 3 |
| 00009 | https://nyu.databrary.org/volume/9 | 2 |
| 00116 | https://nyu.databrary.org/volume/116 | 2 |
| 00760 | https://nyu.databrary.org/volume/760 | 2 |
| 01023 | https://nyu.databrary.org/volume/1023 | 2 |
| 01362 | https://nyu.databrary.org/volume/1362 | 2 |
| 01596 | https://nyu.databrary.org/volume/1596 | 2 |
| 00142 | https://nyu.databrary.org/volume/142 | 1 |
| 00876 | https://nyu.databrary.org/volume/876 | 1 |
| 01073 | https://nyu.databrary.org/volume/1073 | 1 |
| 01624 | https://nyu.databrary.org/volume/1624 | 1 |
| 01657 | https://nyu.databrary.org/volume/1657 | 1 |
| 01688 | https://nyu.databrary.org/volume/1688 | 1 |
| 01705 | https://nyu.databrary.org/volume/1705 | 1 |
There are n= 120 shared volumes reporting participant_language.
n participants
Code
xtabs(formula = ~ participant_language, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_language | Freq |
|---|---|
| Arabic | 1 |
| Arabic, English | 2 |
| Bengali, English | 1 |
| Bulgarian, English, Spanish | 2 |
| Cantonese | 118 |
| Cantonese - in Mandarin | 56 |
| Catalan | 1 |
| Chinese | 1 |
| Engish | 1 |
| Englidh | 1 |
| english | 2 |
| English | 6759 |
| English - ESL | 9 |
| English & Arabic | 1 |
| English & French | 1 |
| English & Greek | 2 |
| English & Japanese | 1 |
| English & Spanish | 4 |
| English & Spanish & German | 1 |
| English & Swedish | 1 |
| English & Tagalog | 1 |
| English 100% | 14 |
| English 50%, Russian 50% | 1 |
| English 90%, German 10% | 1 |
| English 90%, Vietnamese 5%, Chinese 5% | 1 |
| English and Bengali | 1 |
| English and Malayalam | 1 |
| English and Mandarin | 1 |
| English and Spanish | 6 |
| English Chinese | 4 |
| English Danish | 1 |
| English German | 1 |
| English Mandarin | 1 |
| English Other | 1 |
| English, Japanese | 1 |
| English, Albanian | 2 |
| English, Arabic | 6 |
| English, ASL | 1 |
| English, Bosnian, Spanish | 9 |
| English, Bulgarian, French | 1 |
| English, Cantonese | 14 |
| English, Cantonese, Japanese | 1 |
| English, Cantonese, Mandarin | 1 |
| English, Catalan | 2 |
| English, Catalonian, Spanish, Tamil | 3 |
| English, Chinese | 13 |
| English, Chinese, Cantanese | 1 |
| English, Chinese, Portuguese | 1 |
| English, Danish | 2 |
| English, Dutch, Mandarin | 2 |
| English, Estonian | 1 |
| English, Farsi | 1 |
| English, Filipino | 11 |
| English, French | 18 |
| English, French, Bulgarian | 1 |
| English, French, Spanish | 7 |
| English, French, Thai | 1 |
| English, German | 7 |
| English, German, Spanish | 6 |
| English, Greek | 7 |
| English, Hebrew | 1 |
| English, Hebrew, Spanish | 1 |
| English, Hindi | 4 |
| English, Hindi, Punjab | 2 |
| English, Hindi, Spanish | 1 |
| English, Hindi, Urdu, Punjabi, Spanish | 1 |
| English, Hungarian | 3 |
| English, Ilonggo | 2 |
| English, Italian | 17 |
| English, Italian, French | 3 |
| English, Italian, Spanish | 4 |
| English, Japanese | 5 |
| English, Japanese, Spanish | 3 |
| English, Kannada | 11 |
| English, Korean | 10 |
| English, Korean, Spanish | 1 |
| English, Mandarin | 13 |
| English, Mandarin, Cantonese | 3 |
| English, Mandarin, Cantonese, French | 2 |
| English, Mandarin, Norwegian | 1 |
| English, Marwadi | 4 |
| English, Nepal | 1 |
| English, Norwegian, Mandarin | 1 |
| English, Other | 2 |
| English, Polish | 8 |
| English, Polish, French | 1 |
| English, Polish, Hebrew | 2 |
| English, Polish, Spanish | 1 |
| English, Portuguese | 11 |
| English, Russian | 17 |
| English, Russian, Spanish | 1 |
| English, Serbian, Italian | 1 |
| English, Sign | 1 |
| English, some Spanish | 2 |
| English, Spanish | 262 |
| English, Spanish, Arabic | 1 |
| English, Spanish, Farsi | 2 |
| English, Spanish, French | 5 |
| English, Spanish, Hebrew | 1 |
| English, Spanish, Hindi | 12 |
| English, Spanish, Italian | 1 |
| English, Spanish, Polish | 1 |
| English, Spanish, Portuguese | 1 |
| English, Swedish | 6 |
| English, Swedish, Spanish, Italian, French, Mandarin | 1 |
| English, Sweedish | 1 |
| English, Tagalog | 7 |
| English, Tegalog | 1 |
| English, Thai | 6 |
| English, Thai, French | 1 |
| English, Thai, Spanish | 1 |
| English, Turkish | 4 |
| English, Ukranian, Cantonese | 13 |
| English, Urdu | 2 |
| English, Vietnamese, Korean | 1 |
| English,Spanish | 2 |
| English; Spanish | 2 |
| Enlgish | 1 |
| French | 2 |
| French & English | 1 |
| French 70%, English 25%, Spanish 5% | 1 |
| French, English | 2 |
| French, English, Russian | 1 |
| French, Spanish, English | 1 |
| Fulanu, English | 1 |
| Greek, English | 1 |
| Hebrew, Russian, English | 7 |
| Japanese | 2 |
| Japanese, English | 1 |
| Kazakh, English | 1 |
| Korean | 13 |
| Korean, English, Spanish | 24 |
| Mandarin | 51 |
| Mandarin, Cantonese, English | 1 |
| Mandarin, English | 6 |
| None | 1 |
| other | 1 |
| Other | 2 |
| Polish | 2 |
| Polish, English, Spanish | 1 |
| Portuguese, English | 2 |
| Russian | 2 |
| Russian, English | 1 |
| Russian, English, Ukrainian | 3 |
| spanish | 126 |
| Spanish | 596 |
| Spanish, English | 23 |
| Spanish, English, Hebrew | 2 |
| Spanish, German | 1 |
| Tajik | 348 |
| Tseltal | 3 |
| Turkish, English | 2 |
| Ukranian, English, Cantonese, Chinese | 1 |
| Unknown | 71 |
Pregnancy term
Volumes and sessions
Code
participant_pregnancy_term_df <- demo_df |>
dplyr::filter(!is.na(participant_pregnancy_term)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
participant_pregnancy_term_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00564 | https://nyu.databrary.org/volume/564 | 459 |
| 00087 | https://nyu.databrary.org/volume/87 | 231 |
| 00322 | https://nyu.databrary.org/volume/322 | 173 |
| 00005 | https://nyu.databrary.org/volume/5 | 133 |
| 00114 | https://nyu.databrary.org/volume/114 | 105 |
| 00140 | https://nyu.databrary.org/volume/140 | 97 |
| 00162 | https://nyu.databrary.org/volume/162 | 93 |
| 00868 | https://nyu.databrary.org/volume/868 | 77 |
| 00004 | https://nyu.databrary.org/volume/4 | 67 |
| 00136 | https://nyu.databrary.org/volume/136 | 62 |
| 00163 | https://nyu.databrary.org/volume/163 | 55 |
| 00827 | https://nyu.databrary.org/volume/827 | 47 |
| 01026 | https://nyu.databrary.org/volume/1026 | 46 |
| 00455 | https://nyu.databrary.org/volume/455 | 41 |
| 00123 | https://nyu.databrary.org/volume/123 | 37 |
| 00476 | https://nyu.databrary.org/volume/476 | 35 |
| 00135 | https://nyu.databrary.org/volume/135 | 33 |
| 00132 | https://nyu.databrary.org/volume/132 | 19 |
| 00124 | https://nyu.databrary.org/volume/124 | 13 |
| 00323 | https://nyu.databrary.org/volume/323 | 10 |
| 00351 | https://nyu.databrary.org/volume/351 | 6 |
There are 21 shared volumes reporting participant_pregnancy_term.
n participants
Code
xtabs(formula = ~ participant_pregnancy_term, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_pregnancy_term | Freq |
|---|---|
| 30 weeks | 2 |
| 34 weeks | 1 |
| 36 weeks | 1 |
| 37 | 1 |
| 37 weeks | 2 |
| 37.5 weeks | 1 |
| 38 weeks | 2 |
| 39 weeks | 6 |
| 41 weeks | 2 |
| 42 weeks | 1 |
| Full term | 1807 |
| Fullterm | 1 |
| Not full term | 2 |
| Preterm | 7 |
| Unknown | 3 |
Birthweight
Volumes and sessions
Code
participant_birth_weight_df <- demo_df |>
dplyr::filter(!is.na(participant_birth_weight)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
participant_birth_weight_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00827 | https://nyu.databrary.org/volume/827 | 41 |
| 00455 | https://nyu.databrary.org/volume/455 | 35 |
| 00444 | https://nyu.databrary.org/volume/444 | 19 |
There are n= 3 shared volumes reporting participant_birth_weight.
n participants
Code
xtabs(formula = ~ participant_birth_weight, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_birth_weight | Freq |
|---|---|
| 10.3 | 1 |
| 2.7 | 1 |
| 3.38 | 1 |
| 4.11 | 1 |
| 5.11 | 1 |
| 5.3 | 1 |
| 5.4 | 2 |
| 5.7 | 1 |
| 5.8 | 2 |
| 5.9 | 1 |
| 6 | 2 |
| 6.1 | 1 |
| 6.10 | 1 |
| 6.15 | 1 |
| 6.25 | 1 |
| 6.3 | 2 |
| 6.375 | 1 |
| 6.4 | 1 |
| 6.44 | 1 |
| 6.5 | 1 |
| 6.7 | 3 |
| 6.75 | 1 |
| 6.8 | 1 |
| 6.9 | 2 |
| 7 | 6 |
| 7.0 | 3 |
| 7.1 | 4 |
| 7.11 | 1 |
| 7.12 | 1 |
| 7.2 | 3 |
| 7.25 | 3 |
| 7.31 | 1 |
| 7.375 | 1 |
| 7.4 | 3 |
| 7.56 | 1 |
| 7.6 | 4 |
| 7.7 | 3 |
| 7.75 | 1 |
| 7.76 | 1 |
| 7.8 | 2 |
| 7.875 | 1 |
| 7.9 | 1 |
| 8 | 3 |
| 8.0 | 1 |
| 8.125 | 1 |
| 8.13 | 1 |
| 8.14 | 1 |
| 8.15 | 1 |
| 8.2 | 1 |
| 8.25 | 1 |
| 8.3 | 2 |
| 8.375 | 2 |
| 8.4 | 1 |
| 8.44 | 1 |
| 8.5 | 1 |
| 8.56 | 1 |
| 8.6 | 1 |
| 8.75 | 1 |
| 9 | 3 |
| 9.44 | 1 |
Histogram
Code
demo_df |>
ggplot2::ggplot() +
ggplot2::aes(x = as.numeric(participant_birth_weight)) +
ggplot2::geom_histogram(bins = 15)Warning: Removed 11599 rows containing non-finite outside the scale range
(`stat_bin()`).
Disability
Volumes and sessions
Code
participant_disability_df <- demo_df |>
dplyr::filter(!is.na(participant_disability)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
participant_disability_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00011 | https://nyu.databrary.org/volume/11 | 468 |
| 00564 | https://nyu.databrary.org/volume/564 | 459 |
| 00088 | https://nyu.databrary.org/volume/88 | 262 |
| 00087 | https://nyu.databrary.org/volume/87 | 231 |
| 01020 | https://nyu.databrary.org/volume/1020 | 220 |
| 00359 | https://nyu.databrary.org/volume/359 | 216 |
| 00400 | https://nyu.databrary.org/volume/400 | 182 |
| 00460 | https://nyu.databrary.org/volume/460 | 175 |
| 00322 | https://nyu.databrary.org/volume/322 | 173 |
| 01042 | https://nyu.databrary.org/volume/1042 | 161 |
| 00005 | https://nyu.databrary.org/volume/5 | 133 |
| 00226 | https://nyu.databrary.org/volume/226 | 129 |
| 00989 | https://nyu.databrary.org/volume/989 | 127 |
| 00563 | https://nyu.databrary.org/volume/563 | 118 |
| 00271 | https://nyu.databrary.org/volume/271 | 115 |
| 00089 | https://nyu.databrary.org/volume/89 | 114 |
| 01526 | https://nyu.databrary.org/volume/1526 | 111 |
| 00083 | https://nyu.databrary.org/volume/83 | 109 |
| 00308 | https://nyu.databrary.org/volume/308 | 109 |
| 00988 | https://nyu.databrary.org/volume/988 | 109 |
| 00114 | https://nyu.databrary.org/volume/114 | 105 |
| 00084 | https://nyu.databrary.org/volume/84 | 104 |
| 00140 | https://nyu.databrary.org/volume/140 | 97 |
| 00950 | https://nyu.databrary.org/volume/950 | 96 |
| 00162 | https://nyu.databrary.org/volume/162 | 93 |
| 00152 | https://nyu.databrary.org/volume/152 | 84 |
| 01273 | https://nyu.databrary.org/volume/1273 | 82 |
| 00434 | https://nyu.databrary.org/volume/434 | 79 |
| 00868 | https://nyu.databrary.org/volume/868 | 77 |
| 01379 | https://nyu.databrary.org/volume/1379 | 76 |
| 01567 | https://nyu.databrary.org/volume/1567 | 74 |
| 00321 | https://nyu.databrary.org/volume/321 | 71 |
| 00004 | https://nyu.databrary.org/volume/4 | 67 |
| 01436 | https://nyu.databrary.org/volume/1436 | 67 |
| 00136 | https://nyu.databrary.org/volume/136 | 62 |
| 00835 | https://nyu.databrary.org/volume/835 | 62 |
| 00837 | https://nyu.databrary.org/volume/837 | 59 |
| 00854 | https://nyu.databrary.org/volume/854 | 59 |
| 00350 | https://nyu.databrary.org/volume/350 | 58 |
| 00007 | https://nyu.databrary.org/volume/7 | 55 |
| 00163 | https://nyu.databrary.org/volume/163 | 55 |
| 00192 | https://nyu.databrary.org/volume/192 | 51 |
| 01312 | https://nyu.databrary.org/volume/1312 | 48 |
| 00827 | https://nyu.databrary.org/volume/827 | 47 |
| 00954 | https://nyu.databrary.org/volume/954 | 46 |
| 01026 | https://nyu.databrary.org/volume/1026 | 46 |
| 01108 | https://nyu.databrary.org/volume/1108 | 44 |
| 00941 | https://nyu.databrary.org/volume/941 | 42 |
| 01448 | https://nyu.databrary.org/volume/1448 | 41 |
| 00123 | https://nyu.databrary.org/volume/123 | 37 |
| 00476 | https://nyu.databrary.org/volume/476 | 35 |
| 00592 | https://nyu.databrary.org/volume/592 | 34 |
| 00957 | https://nyu.databrary.org/volume/957 | 34 |
| 00979 | https://nyu.databrary.org/volume/979 | 34 |
| 00444 | https://nyu.databrary.org/volume/444 | 33 |
| 00943 | https://nyu.databrary.org/volume/943 | 30 |
| 01365 | https://nyu.databrary.org/volume/1365 | 28 |
| 00452 | https://nyu.databrary.org/volume/452 | 26 |
| 00821 | https://nyu.databrary.org/volume/821 | 26 |
| 01363 | https://nyu.databrary.org/volume/1363 | 26 |
| 01143 | https://nyu.databrary.org/volume/1143 | 24 |
| 00132 | https://nyu.databrary.org/volume/132 | 19 |
| 00390 | https://nyu.databrary.org/volume/390 | 18 |
| 01376 | https://nyu.databrary.org/volume/1376 | 17 |
| 00169 | https://nyu.databrary.org/volume/169 | 16 |
| 00956 | https://nyu.databrary.org/volume/956 | 14 |
| 00124 | https://nyu.databrary.org/volume/124 | 13 |
| 00217 | https://nyu.databrary.org/volume/217 | 13 |
| 00145 | https://nyu.databrary.org/volume/145 | 12 |
| 01442 | https://nyu.databrary.org/volume/1442 | 12 |
| 00143 | https://nyu.databrary.org/volume/143 | 11 |
| 00761 | https://nyu.databrary.org/volume/761 | 11 |
| 00323 | https://nyu.databrary.org/volume/323 | 10 |
| 00148 | https://nyu.databrary.org/volume/148 | 9 |
| 00351 | https://nyu.databrary.org/volume/351 | 6 |
| 01419 | https://nyu.databrary.org/volume/1419 | 5 |
| 00194 | https://nyu.databrary.org/volume/194 | 3 |
| 00241 | https://nyu.databrary.org/volume/241 | 3 |
| 00881 | https://nyu.databrary.org/volume/881 | 3 |
| 00009 | https://nyu.databrary.org/volume/9 | 2 |
| 00116 | https://nyu.databrary.org/volume/116 | 2 |
| 01023 | https://nyu.databrary.org/volume/1023 | 2 |
| 00142 | https://nyu.databrary.org/volume/142 | 1 |
| 00876 | https://nyu.databrary.org/volume/876 | 1 |
| 01073 | https://nyu.databrary.org/volume/1073 | 1 |
| 01688 | https://nyu.databrary.org/volume/1688 | 1 |
| 01705 | https://nyu.databrary.org/volume/1705 | 1 |
There are n= 87 shared volumes reporting participant_disability.
n participants
Code
xtabs(formula = ~ participant_disability, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_disability | Freq |
|---|---|
| Acid Reflux | 1 |
| ASD | 225 |
| atypical | 3 |
| Atypical | 1 |
| atypical hand | 1 |
| baby did not pass hearing test for the right ear when he was born | 1 |
| Beckwith-Weidemenn Syndrome (no delay) | 1 |
| CMT | 1 |
| delayed speech | 1 |
| evaluated for speech | 2 |
| gross motor delay | 1 |
| Hip arthritis | 1 |
| No | 23 |
| none | 3 |
| Sprained ankle (3 wks ago) | 1 |
| ty | 1 |
| typical | 5805 |
| Typical | 162 |
| Unknown | 7 |
Country
Volumes and sessions
Code
participant_country_df <- demo_df |>
dplyr::filter(!is.na(participant_country)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
participant_country_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00564 | https://nyu.databrary.org/volume/564 | 459 |
| 00390 | https://nyu.databrary.org/volume/390 | 18 |
There are n= 2 shared volumes reporting participant_country.
n participants
Code
xtabs(formula = ~ participant_country, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_country | Freq |
|---|---|
| ARG | 3 |
| Australia | 224 |
| CA | 3 |
| MX | 3 |
| UK | 3 |
| US | 241 |
State
Volumes and sessions
Code
participant_state_df <- demo_df |>
dplyr::filter(!is.na(participant_state)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
participant_state_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00184 | https://nyu.databrary.org/volume/184 | 161 |
| 00149 | https://nyu.databrary.org/volume/149 | 155 |
| 00207 | https://nyu.databrary.org/volume/207 | 115 |
| 00253 | https://nyu.databrary.org/volume/253 | 113 |
| 00150 | https://nyu.databrary.org/volume/150 | 109 |
| 00269 | https://nyu.databrary.org/volume/269 | 105 |
| 00073 | https://nyu.databrary.org/volume/73 | 31 |
| 00171 | https://nyu.databrary.org/volume/171 | 17 |
| 00390 | https://nyu.databrary.org/volume/390 | 15 |
There are n= 9 shared volumes reporting participant_state.
n participants
Code
xtabs(formula = ~ participant_state, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_state | Freq |
|---|---|
| Bs.As | 1 |
| Bs.As. | 2 |
| CA | 3 |
| CP | 3 |
| MB | 3 |
| NJ | 2 |
| NY | 3 |
| PA | 804 |
Setting
Volumes and sessions
Code
participant_setting_df <- demo_df |>
dplyr::filter(!is.na(participant_setting)) |>
dplyr::group_by(vol_id, vol_url) |>
dplyr::summarize(n_sessions = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::mutate(n_vols_w_demo = dplyr::n())`summarise()` has grouped output by 'vol_id'. You can override using the
`.groups` argument.Code
participant_setting_df |>
dplyr::select(vol_id, vol_url, n_sessions) |>
dplyr::arrange(desc(n_sessions)) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| vol_id | vol_url | n_sessions |
|---|---|---|
| 00322 | https://nyu.databrary.org/volume/322 | 173 |
| 00073 | https://nyu.databrary.org/volume/73 | 31 |
There are n= 2 shared volumes reporting participant_setting.
n participants
Code
xtabs(formula = ~ participant_setting, data = demo_df) |>
knitr::kable("html") |>
kableExtra::kable_styling() |>
kableExtra::scroll_box(width = "100%", height = "300px")| participant_setting | Freq |
|---|---|
| Lab | 204 |