R/summarize_nmap_s2_self_report.R
In PennStateDEPENdLab/dependlab: Useful functions for the UNC Chapel Hill DEPENd Lab (Director: Michael Hallquist)
Defines functions
summarize_nmap_s2_self_report
Documented in
summarize_nmap_s2_self_report
#' Summarize Self-Report Completion for NMAP Session 2
#'
#' This function processes and summarizes self-report data for NMAP session 2. It calculates
#' the number of complete items, missing items, and the percentage of completion for each
#' psychological measure per participant. The function integrates data from Qualtrics,
#' filters based on session-specific criteria, and excludes certain IDs as specified.
#'
#' @details
#' The function performs the following operations:
#' - Checks for the presence of Qualtrics API credentials.
#' - Fetches raw self-report and session 3 data from Qualtrics.
#' - Excludes specific IDs based on predefined criteria (e.g., PSU IDs).
#' - Calculates completion metrics (# complete, # missing, % completion) for various measures
#' such as IIP, CTQ, PANAS, PID, and others.
#' - Returns a cleaned dataset with summarized completion metrics per participant.
#'
#' @return
#' A data frame containing the following columns for each participant:
#' - `id`: Participant ID.
#' - Completion metrics for each measure:
#' - Number of complete items (`*_complete`).
#' - Number of missing items (`*_missing`).
#' - Percentage of completion (`*_percent`).
#'
#' @note
#' Ensure that Qualtrics API credentials are set in the environment using `qualtrics_api_credentials()`.
#'
#' @examples
#' \dontrun{
#' # Run the function to summarize self-report completion:
#' results <- summarize_nmap_s2_self_report()
#' head(results)
#' }
#'
#' @importFrom dplyr mutate rename select filter arrange
#' @importFrom qualtRics fetch_survey
#' @importFrom magrittr %>%
#' @author Rachel Velasquez
#' @export summarize_nmap_s2_self_report
summarize_nmap_s2_self_report <- function(...) {
# checks if Qualtrics API Key is defined in environment
if (Sys.getenv("QUALTRICS_API_KEY") <= 1 | Sys.getenv("QUALTRICS_BASE_URL") <= 1) {
warning("Qualtrics API Key and/or Base URL has/have not been set up. \n Use 'qualtrics_api_credentials()' to configure.")
return(invisible(NULL))
}
# import all self report data from Qualtrics
self_report <- suppressMessages(qualtRics::fetch_survey(surveyID = "SV_2gwGMWFcer4Sl37", verbose = TRUE, breakout_sets = FALSE))
self_report <- self_report |>
dplyr::rename(id = Intro_ID) |>
dplyr::mutate(id = as.numeric(id)) |>
dplyr::select(
-c(Q322, Q323, Q328, Q329_1, Q329_2, Q655_1, Q655_2, Q2_1, Q2_2, Q325_1, Q657_1, Q326_1, Q326_2, matches("_TEXT")) # remove attention checks and text fields
) |>
dplyr::arrange(id) |>
dplyr::select(RecordedDate, 18:867)
# pull self-reports for only IDs who contributed behavioral data (S3+)
## import s3 report
s3_report <- suppressMessages(qualtRics::fetch_survey(surveyID = "SV_8ixbUxRgNWtxuHH", verbose = TRUE, breakout_sets = FALSE))
s3_report <- s3_report |> select(RecordedDate, s3_sessioninfo_1:s3neigh_notesOLD) |> rename(id = s3_sessioninfo_4) |> arrange(id)
## filter out PSU IDs
psu_ids <- c(
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 59, 60,
61, 62, 63, 65, 66, 68, 69, 70, 71, 73, 75, 77, 80, 81, 82, 83, 87, 90, 93, 94, 97, 98, 103, 107, 108, 109, 111, 112, 123
)
s3_report <- s3_report[!s3_report$id %in% psu_ids, ]
## further cleaning
s3_report_clean <- s3_report %>%
filter(
!(id %in% c("0", "Invalid Number")),
!(S3_visit_occurred %in% c("No-show", "Last minute cancellation")),
is.na(S3_visit_occurred) | S3_visit_occurred != "No-show" & S3_visit_occurred != "Last minute cancellation",
as.Date(RecordedDate) > as.Date("2021-01-01")
)
# calculate # complete, # missing, and % completion per measure and totals per ID ---
self_report_complete <- self_report |> filter(id %in% unique(s3_report_clean$id))
self_report_complete <- self_report_complete |> dplyr::mutate(
# count of complete items
iip_complete = 90 - rowSums(is.na(self_report_complete[, 6: 95])),
ctq_complete = 28 - rowSums(is.na(self_report_complete[, 96:123])),
panas_complete = 20 - rowSums(is.na(self_report_complete[, 124:143])),
pid_complete = 100 - rowSums(is.na(self_report_complete[, 144:243])),
asr_complete = 134 - rowSums(is.na(self_report_complete[, 244:377])),
bpq_complete = 80 - rowSums(is.na(self_report_complete[, 378:457])),
fs_complete = 8 - rowSums(is.na(self_report_complete[, 458:465])),
dusi_complete = 15 - rowSums(is.na(self_report_complete[, 466:480])),
isc_complete = 64 - rowSums(is.na(self_report_complete[, 481:544])),
uppsp_complete = 59 - rowSums(is.na(self_report_complete[, 545:603])),
bfi_complete = 60 - rowSums(is.na(self_report_complete[, 604:663])),
cts_complete = 40 - rowSums(is.na(self_report_complete[, 664:703])),
emotb_percstrs_complete = 10 - rowSums(is.na(self_report_complete[, 704:713])),
emotb_self_complete = 10 - rowSums(is.na(self_report_complete[, 714:723])),
emotb_mp_complete = 7 - rowSums(is.na(self_report_complete[, 724:730])),
emotb_sad_complete = 8 - rowSums(is.na(self_report_complete[, 731:738])),
emotb_gls_complete = 5 - rowSums(is.na(self_report_complete[, 739:743])),
emotb_posaf_complete = 15 - rowSums(is.na(self_report_complete[, 744:758])),
emotb_angaf_complete = 5 - rowSums(is.na(self_report_complete[, 759:763])),
emotb_angpa_complete = 5 - rowSums(is.na(self_report_complete[, 764:768])),
emotb_anghost_complete = 5 - rowSums(is.na(self_report_complete[, 769:773])),
emotb_emosup_complete = 8 - rowSums(is.na(self_report_complete[, 774:781])),
emotb_dls_complete = 13 - rowSums(is.na(self_report_complete[, 782:794])),
emotb_fearsoma_complete = 6 - rowSums(is.na(self_report_complete[, 795:800])),
emotb_fearaf_complete = 7 - rowSums(is.na(self_report_complete[, 801:807])),
emotb_friend_complete = 8 - rowSums(is.na(self_report_complete[, 808:815])),
emotb_instrsup_complete = 8 - rowSums(is.na(self_report_complete[, 816:823])),
emotb_lone_complete = 5 - rowSums(is.na(self_report_complete[, 824:828])),
emotb_perchost_complete = 8 - rowSums(is.na(self_report_complete[, 829:836])),
emotb_percrej_complete = 8 - rowSums(is.na(self_report_complete[, 837:844])),
emotb_apathy_complete = 7 - rowSums(is.na(self_report_complete[, 845:851])),
total_complete = 846 - rowSums(is.na(self_report_complete[, 6:851])),
# count of missing items
iip_missing = rowSums(is.na(self_report_complete[, 6: 95])),
ctq_missing = rowSums(is.na(self_report_complete[, 96:123])),
panas_missing = rowSums(is.na(self_report_complete[, 124:143])),
pid_missing = rowSums(is.na(self_report_complete[, 144:243])),
asr_missing = rowSums(is.na(self_report_complete[, 244:377])),
bpq_missing = rowSums(is.na(self_report_complete[, 378:457])),
fs_missing = rowSums(is.na(self_report_complete[, 458:465])),
dusi_missing = rowSums(is.na(self_report_complete[, 466:480])),
isc_missing = rowSums(is.na(self_report_complete[, 481:544])),
uppsp_missing = rowSums(is.na(self_report_complete[, 545:603])),
bfi_missing = rowSums(is.na(self_report_complete[, 604:663])),
cts_missing = rowSums(is.na(self_report_complete[, 664:703])),
emotb_percstrs_missing = rowSums(is.na(self_report_complete[, 704:713])),
emotb_self_missing = rowSums(is.na(self_report_complete[, 714:723])),
emotb_mp_missing = rowSums(is.na(self_report_complete[, 724:730])),
emotb_sad_missing = rowSums(is.na(self_report_complete[, 731:738])),
emotb_gls_missing = rowSums(is.na(self_report_complete[, 739:743])),
emotb_posaf_missing = rowSums(is.na(self_report_complete[, 744:758])),
emotb_angaf_missing = rowSums(is.na(self_report_complete[, 759:763])),
emotb_angpa_missing = rowSums(is.na(self_report_complete[, 764:768])),
emotb_anghost_missing = rowSums(is.na(self_report_complete[, 769:773])),
emotb_emosup_missing = rowSums(is.na(self_report_complete[, 774:781])),
emotb_dls_missing = rowSums(is.na(self_report_complete[, 782:794])),
emotb_fearsoma_missing = rowSums(is.na(self_report_complete[, 795:800])),
emotb_fearaf_missing = rowSums(is.na(self_report_complete[, 801:807])),
emotb_friend_missing = rowSums(is.na(self_report_complete[, 808:815])),
emotb_instrsup_missing = rowSums(is.na(self_report_complete[, 816:823])),
emotb_lone_missing = rowSums(is.na(self_report_complete[, 824:828])),
emotb_perchost_missing = rowSums(is.na(self_report_complete[, 829:836])),
emotb_percrej_missing = rowSums(is.na(self_report_complete[, 837:844])),
emotb_apathy_missing = rowSums(is.na(self_report_complete[, 845:851])),
total_missing = rowSums(is.na(self_report_complete[, 6:851])),
# calculate % complete
iip_percent = 1 - rowSums(is.na(self_report_complete[, 6: 95])) / 90,
ctq_percent = 1 - rowSums(is.na(self_report_complete[, 96:123])) / 28,
panas_percent = 1 - rowSums(is.na(self_report_complete[, 124:143])) / 20,
pid_percent = 1 - rowSums(is.na(self_report_complete[, 144:243])) / 100,
asr_percent = 1 - rowSums(is.na(self_report_complete[, 244:377])) / 134,
bpq_percent = 1 - rowSums(is.na(self_report_complete[, 378:457])) / 80,
fs_percent = 1 - rowSums(is.na(self_report_complete[, 458:465])) / 8,
dusi_percent = 1 - rowSums(is.na(self_report_complete[, 466:480])) / 15,
isc_percent = 1 - rowSums(is.na(self_report_complete[, 481:544])) / 64,
uppsp_percent = 1 - rowSums(is.na(self_report_complete[, 545:603])) / 59,
bfi_percent = 1 - rowSums(is.na(self_report_complete[, 604:663])) / 60,
cts_percent = 1 - rowSums(is.na(self_report_complete[, 664:703])) / 40,
emotb_percstrs_percent = 1 - rowSums(is.na(self_report_complete[, 704:713])) / 10,
emotb_self_percent = 1 - rowSums(is.na(self_report_complete[, 714:723])) / 10,
emotb_mp_percent = 1 - rowSums(is.na(self_report_complete[, 724:730])) / 7,
emotb_sad_percent = 1 - rowSums(is.na(self_report_complete[, 731:738])) / 8,
emotb_gls_percent = 1 - rowSums(is.na(self_report_complete[, 739:743])) / 5,
emotb_posaf_percent = 1 - rowSums(is.na(self_report_complete[, 744:758])) / 15,
emotb_angaf_percent = 1 - rowSums(is.na(self_report_complete[, 759:763])) / 5,
emotb_angpa_percent = 1 - rowSums(is.na(self_report_complete[, 764:768])) / 5,
emotb_anghost_percent = 1 - rowSums(is.na(self_report_complete[, 769:773])) / 5,
emotb_emosup_percent = 1 - rowSums(is.na(self_report_complete[, 774:781])) / 8,
emotb_dls_percent = 1 - rowSums(is.na(self_report_complete[, 782:794])) / 13,
emotb_fearsoma_percent = 1 - rowSums(is.na(self_report_complete[, 795:800])) / 6,
emotb_fearaf_percent = 1 - rowSums(is.na(self_report_complete[, 801:807])) / 7,
emotb_friend_percent = 1 - rowSums(is.na(self_report_complete[, 808:815])) / 8,
emotb_instrsup_percent = 1 - rowSums(is.na(self_report_complete[, 816:823])) / 8,
emotb_lone_percent = 1 - rowSums(is.na(self_report_complete[, 824:828])) / 5,
emotb_perchost_percent = 1 - rowSums(is.na(self_report_complete[, 829:836])) / 8,
emotb_percrej_percent = 1 - rowSums(is.na(self_report_complete[, 837:844])) / 8,
emotb_apathy_percent = 1 - rowSums(is.na(self_report_complete[, 845:851])) / 7,
total_percent = 1 - rowSums(is.na(self_report_complete[, 6:851])) / 846
) |>
dplyr::select(id, 852:947)
return(self_report_complete)
}
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Defines functions summarize_nmap_s2_self_report
Documented in summarize_nmap_s2_self_report
#' Summarize Self-Report Completion for NMAP Session 2
#'
#' This function processes and summarizes self-report data for NMAP session 2. It calculates
#' the number of complete items, missing items, and the percentage of completion for each
#' psychological measure per participant. The function integrates data from Qualtrics,
#' filters based on session-specific criteria, and excludes certain IDs as specified.
#'
#' @details
#' The function performs the following operations:
#' - Checks for the presence of Qualtrics API credentials.
#' - Fetches raw self-report and session 3 data from Qualtrics.
#' - Excludes specific IDs based on predefined criteria (e.g., PSU IDs).
#' - Calculates completion metrics (# complete, # missing, % completion) for various measures
#' such as IIP, CTQ, PANAS, PID, and others.
#' - Returns a cleaned dataset with summarized completion metrics per participant.
#'
#' @return
#' A data frame containing the following columns for each participant:
#' - `id`: Participant ID.
#' - Completion metrics for each measure:
#' - Number of complete items (`*_complete`).
#' - Number of missing items (`*_missing`).
#' - Percentage of completion (`*_percent`).
#'
#' @note
#' Ensure that Qualtrics API credentials are set in the environment using `qualtrics_api_credentials()`.
#'
#' @examples
#' \dontrun{
#' # Run the function to summarize self-report completion:
#' results <- summarize_nmap_s2_self_report()
#' head(results)
#' }
#'
#' @importFrom dplyr mutate rename select filter arrange
#' @importFrom qualtRics fetch_survey
#' @importFrom magrittr %>%
#' @author Rachel Velasquez
#' @export summarize_nmap_s2_self_report
summarize_nmap_s2_self_report <- function(...) {
# checks if Qualtrics API Key is defined in environment
if (Sys.getenv("QUALTRICS_API_KEY") <= 1 | Sys.getenv("QUALTRICS_BASE_URL") <= 1) {
warning("Qualtrics API Key and/or Base URL has/have not been set up. \n Use 'qualtrics_api_credentials()' to configure.")
return(invisible(NULL))
}
# import all self report data from Qualtrics
self_report <- suppressMessages(qualtRics::fetch_survey(surveyID = "SV_2gwGMWFcer4Sl37", verbose = TRUE, breakout_sets = FALSE))
self_report <- self_report |>
dplyr::rename(id = Intro_ID) |>
dplyr::mutate(id = as.numeric(id)) |>
dplyr::select(
-c(Q322, Q323, Q328, Q329_1, Q329_2, Q655_1, Q655_2, Q2_1, Q2_2, Q325_1, Q657_1, Q326_1, Q326_2, matches("_TEXT")) # remove attention checks and text fields
) |>
dplyr::arrange(id) |>
dplyr::select(RecordedDate, 18:867)
# pull self-reports for only IDs who contributed behavioral data (S3+)
## import s3 report
s3_report <- suppressMessages(qualtRics::fetch_survey(surveyID = "SV_8ixbUxRgNWtxuHH", verbose = TRUE, breakout_sets = FALSE))
s3_report <- s3_report |> select(RecordedDate, s3_sessioninfo_1:s3neigh_notesOLD) |> rename(id = s3_sessioninfo_4) |> arrange(id)
## filter out PSU IDs
psu_ids <- c(
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 59, 60,
61, 62, 63, 65, 66, 68, 69, 70, 71, 73, 75, 77, 80, 81, 82, 83, 87, 90, 93, 94, 97, 98, 103, 107, 108, 109, 111, 112, 123
)
s3_report <- s3_report[!s3_report$id %in% psu_ids, ]
## further cleaning
s3_report_clean <- s3_report %>%
filter(
!(id %in% c("0", "Invalid Number")),
!(S3_visit_occurred %in% c("No-show", "Last minute cancellation")),
is.na(S3_visit_occurred) | S3_visit_occurred != "No-show" & S3_visit_occurred != "Last minute cancellation",
as.Date(RecordedDate) > as.Date("2021-01-01")
)
# calculate # complete, # missing, and % completion per measure and totals per ID ---
self_report_complete <- self_report |> filter(id %in% unique(s3_report_clean$id))
self_report_complete <- self_report_complete |> dplyr::mutate(
# count of complete items
iip_complete = 90 - rowSums(is.na(self_report_complete[, 6: 95])),
ctq_complete = 28 - rowSums(is.na(self_report_complete[, 96:123])),
panas_complete = 20 - rowSums(is.na(self_report_complete[, 124:143])),
pid_complete = 100 - rowSums(is.na(self_report_complete[, 144:243])),
asr_complete = 134 - rowSums(is.na(self_report_complete[, 244:377])),
bpq_complete = 80 - rowSums(is.na(self_report_complete[, 378:457])),
fs_complete = 8 - rowSums(is.na(self_report_complete[, 458:465])),
dusi_complete = 15 - rowSums(is.na(self_report_complete[, 466:480])),
isc_complete = 64 - rowSums(is.na(self_report_complete[, 481:544])),
uppsp_complete = 59 - rowSums(is.na(self_report_complete[, 545:603])),
bfi_complete = 60 - rowSums(is.na(self_report_complete[, 604:663])),
cts_complete = 40 - rowSums(is.na(self_report_complete[, 664:703])),
emotb_percstrs_complete = 10 - rowSums(is.na(self_report_complete[, 704:713])),
emotb_self_complete = 10 - rowSums(is.na(self_report_complete[, 714:723])),
emotb_mp_complete = 7 - rowSums(is.na(self_report_complete[, 724:730])),
emotb_sad_complete = 8 - rowSums(is.na(self_report_complete[, 731:738])),
emotb_gls_complete = 5 - rowSums(is.na(self_report_complete[, 739:743])),
emotb_posaf_complete = 15 - rowSums(is.na(self_report_complete[, 744:758])),
emotb_angaf_complete = 5 - rowSums(is.na(self_report_complete[, 759:763])),
emotb_angpa_complete = 5 - rowSums(is.na(self_report_complete[, 764:768])),
emotb_anghost_complete = 5 - rowSums(is.na(self_report_complete[, 769:773])),
emotb_emosup_complete = 8 - rowSums(is.na(self_report_complete[, 774:781])),
emotb_dls_complete = 13 - rowSums(is.na(self_report_complete[, 782:794])),
emotb_fearsoma_complete = 6 - rowSums(is.na(self_report_complete[, 795:800])),
emotb_fearaf_complete = 7 - rowSums(is.na(self_report_complete[, 801:807])),
emotb_friend_complete = 8 - rowSums(is.na(self_report_complete[, 808:815])),
emotb_instrsup_complete = 8 - rowSums(is.na(self_report_complete[, 816:823])),
emotb_lone_complete = 5 - rowSums(is.na(self_report_complete[, 824:828])),
emotb_perchost_complete = 8 - rowSums(is.na(self_report_complete[, 829:836])),
emotb_percrej_complete = 8 - rowSums(is.na(self_report_complete[, 837:844])),
emotb_apathy_complete = 7 - rowSums(is.na(self_report_complete[, 845:851])),
total_complete = 846 - rowSums(is.na(self_report_complete[, 6:851])),
# count of missing items
iip_missing = rowSums(is.na(self_report_complete[, 6: 95])),
ctq_missing = rowSums(is.na(self_report_complete[, 96:123])),
panas_missing = rowSums(is.na(self_report_complete[, 124:143])),
pid_missing = rowSums(is.na(self_report_complete[, 144:243])),
asr_missing = rowSums(is.na(self_report_complete[, 244:377])),
bpq_missing = rowSums(is.na(self_report_complete[, 378:457])),
fs_missing = rowSums(is.na(self_report_complete[, 458:465])),
dusi_missing = rowSums(is.na(self_report_complete[, 466:480])),
isc_missing = rowSums(is.na(self_report_complete[, 481:544])),
uppsp_missing = rowSums(is.na(self_report_complete[, 545:603])),
bfi_missing = rowSums(is.na(self_report_complete[, 604:663])),
cts_missing = rowSums(is.na(self_report_complete[, 664:703])),
emotb_percstrs_missing = rowSums(is.na(self_report_complete[, 704:713])),
emotb_self_missing = rowSums(is.na(self_report_complete[, 714:723])),
emotb_mp_missing = rowSums(is.na(self_report_complete[, 724:730])),
emotb_sad_missing = rowSums(is.na(self_report_complete[, 731:738])),
emotb_gls_missing = rowSums(is.na(self_report_complete[, 739:743])),
emotb_posaf_missing = rowSums(is.na(self_report_complete[, 744:758])),
emotb_angaf_missing = rowSums(is.na(self_report_complete[, 759:763])),
emotb_angpa_missing = rowSums(is.na(self_report_complete[, 764:768])),
emotb_anghost_missing = rowSums(is.na(self_report_complete[, 769:773])),
emotb_emosup_missing = rowSums(is.na(self_report_complete[, 774:781])),
emotb_dls_missing = rowSums(is.na(self_report_complete[, 782:794])),
emotb_fearsoma_missing = rowSums(is.na(self_report_complete[, 795:800])),
emotb_fearaf_missing = rowSums(is.na(self_report_complete[, 801:807])),
emotb_friend_missing = rowSums(is.na(self_report_complete[, 808:815])),
emotb_instrsup_missing = rowSums(is.na(self_report_complete[, 816:823])),
emotb_lone_missing = rowSums(is.na(self_report_complete[, 824:828])),
emotb_perchost_missing = rowSums(is.na(self_report_complete[, 829:836])),
emotb_percrej_missing = rowSums(is.na(self_report_complete[, 837:844])),
emotb_apathy_missing = rowSums(is.na(self_report_complete[, 845:851])),
total_missing = rowSums(is.na(self_report_complete[, 6:851])),
# calculate % complete
iip_percent = 1 - rowSums(is.na(self_report_complete[, 6: 95])) / 90,
ctq_percent = 1 - rowSums(is.na(self_report_complete[, 96:123])) / 28,
panas_percent = 1 - rowSums(is.na(self_report_complete[, 124:143])) / 20,
pid_percent = 1 - rowSums(is.na(self_report_complete[, 144:243])) / 100,
asr_percent = 1 - rowSums(is.na(self_report_complete[, 244:377])) / 134,
bpq_percent = 1 - rowSums(is.na(self_report_complete[, 378:457])) / 80,
fs_percent = 1 - rowSums(is.na(self_report_complete[, 458:465])) / 8,
dusi_percent = 1 - rowSums(is.na(self_report_complete[, 466:480])) / 15,
isc_percent = 1 - rowSums(is.na(self_report_complete[, 481:544])) / 64,
uppsp_percent = 1 - rowSums(is.na(self_report_complete[, 545:603])) / 59,
bfi_percent = 1 - rowSums(is.na(self_report_complete[, 604:663])) / 60,
cts_percent = 1 - rowSums(is.na(self_report_complete[, 664:703])) / 40,
emotb_percstrs_percent = 1 - rowSums(is.na(self_report_complete[, 704:713])) / 10,
emotb_self_percent = 1 - rowSums(is.na(self_report_complete[, 714:723])) / 10,
emotb_mp_percent = 1 - rowSums(is.na(self_report_complete[, 724:730])) / 7,
emotb_sad_percent = 1 - rowSums(is.na(self_report_complete[, 731:738])) / 8,
emotb_gls_percent = 1 - rowSums(is.na(self_report_complete[, 739:743])) / 5,
emotb_posaf_percent = 1 - rowSums(is.na(self_report_complete[, 744:758])) / 15,
emotb_angaf_percent = 1 - rowSums(is.na(self_report_complete[, 759:763])) / 5,
emotb_angpa_percent = 1 - rowSums(is.na(self_report_complete[, 764:768])) / 5,
emotb_anghost_percent = 1 - rowSums(is.na(self_report_complete[, 769:773])) / 5,
emotb_emosup_percent = 1 - rowSums(is.na(self_report_complete[, 774:781])) / 8,
emotb_dls_percent = 1 - rowSums(is.na(self_report_complete[, 782:794])) / 13,
emotb_fearsoma_percent = 1 - rowSums(is.na(self_report_complete[, 795:800])) / 6,
emotb_fearaf_percent = 1 - rowSums(is.na(self_report_complete[, 801:807])) / 7,
emotb_friend_percent = 1 - rowSums(is.na(self_report_complete[, 808:815])) / 8,
emotb_instrsup_percent = 1 - rowSums(is.na(self_report_complete[, 816:823])) / 8,
emotb_lone_percent = 1 - rowSums(is.na(self_report_complete[, 824:828])) / 5,
emotb_perchost_percent = 1 - rowSums(is.na(self_report_complete[, 829:836])) / 8,
emotb_percrej_percent = 1 - rowSums(is.na(self_report_complete[, 837:844])) / 8,
emotb_apathy_percent = 1 - rowSums(is.na(self_report_complete[, 845:851])) / 7,
total_percent = 1 - rowSums(is.na(self_report_complete[, 6:851])) / 846
) |>
dplyr::select(id, 852:947)
return(self_report_complete)
}
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