R/score_CCAPS.R
In CCMH-PSU/CCMHr: CCMH Data Cleaning
Defines functions
score_CCAPS
Documented in
score_CCAPS
#' Score the CCAPS subscales from CCAPS items
#'
#' @description Scores the CCAPS subscales for valid CCAPS administrations.
#' @param data A data file containing CCAPS item data
#' @param CCAPS62 A logical value indicating whether the CCAPS62 should be scored. If FALSE, only the CCAPS34 will be scored.
#' @note `score_CCAPS` calls on CCAPS items by name, so they must be named properly in
#' `data`. Variable naming convention is `CCAPS_01`... `CCAPS_70`.
#' If 'data does not contain the proper CCAPS variable names
#' (or the naming convention was changed in the new year's data), the
#' function will return an error.
#' Administrations are considered valid and will be scored if 1/3 or less of the items on any subscale are missing, and if less than
#' 1/2 of the data overall is missing, and if the variance of the items is > 0, indicating that the items were not all marked
#' as the same number.
#' Function does not overwrite CCAPS items to reverse score them. It creates new reverse scored version, which it then deletes.
#' Note that this takes several minutes to run.
#' @return A data frame with all the original data in `data`, and several additional columns: `Has_CCAPS`, `Is_CCAPS62`, `Is_ValidCCAPS`, `Is_ValidCCAPS62`, `Is_CCAPS34`, `Is_ValidCCAPS34`, `Depression34`, `Anxiety34`, `Social_Anxiety34`, `Academics34`, `Eating34`, `Hostility34`, `Alcohol34`, `DI`, `Depression62`, `Eating62`, `Substance62`, `Anxiety62`, `Hostility62`, `Social_Anxiety62`, `Family62`, `Academics62`
#' @export
score_CCAPS <- function(data, CCAPS62 = T) {
if(CCAPS62 == T) {
if(!all(c("CCAPS_01", "CCAPS_03", "CCAPS_04", "CCAPS_05", "CCAPS_06", "CCAPS_08", "CCAPS_09", "CCAPS_10", "CCAPS_11", "CCAPS_13", "CCAPS_14", "CCAPS_15", "CCAPS_16", "CCAPS_17", "CCAPS_18", "CCAPS_19", "CCAPS_21", "CCAPS_22", "CCAPS_23", "CCAPS_24", "CCAPS_25", "CCAPS_26", "CCAPS_27", "CCAPS_28", "CCAPS_29", "CCAPS_30", "CCAPS_31", "CCAPS_32", "CCAPS_33", "CCAPS_34", "CCAPS_35", "CCAPS_36", "CCAPS_37", "CCAPS_38", "CCAPS_39", "CCAPS_40", "CCAPS_41", "CCAPS_43", "CCAPS_44", "CCAPS_45", "CCAPS_46", "CCAPS_47", "CCAPS_48", "CCAPS_49", "CCAPS_50", "CCAPS_51", "CCAPS_52", "CCAPS_53", "CCAPS_54", "CCAPS_56", "CCAPS_57", "CCAPS_58", "CCAPS_59", "CCAPS_60", "CCAPS_61", "CCAPS_63", "CCAPS_64", "CCAPS_65", "CCAPS_66", "CCAPS_68", "CCAPS_69", "CCAPS_70") %in% colnames(data))) stop('CCAPS items not present in data or not properly named. CCAPS items should be named CCAPS_01 through CCAPS_70.')
# Has_CCAPS
data$Has_CCAPS <- rowSums(!is.na(dplyr::select(data, .data$CCAPS_01:.data$CCAPS_70)), na.rm = T)
data$Has_CCAPS[which(data$Has_CCAPS > 0)] <- 1
# Is_CCAPS62 or Is_CCAPS34
data$Is_CCAPS62 <- rowSums(!is.na(dplyr::select(data, .data$CCAPS_01, .data$CCAPS_04, .data$CCAPS_08, .data$CCAPS_09, .data$CCAPS_10, .data$CCAPS_14, .data$CCAPS_15, .data$CCAPS_19, .data$CCAPS_23,.data$CCAPS_25, .data$CCAPS_26, .data$CCAPS_28, .data$CCAPS_32, .data$CCAPS_35, .data$CCAPS_37, .data$CCAPS_38, .data$CCAPS_41, .data$CCAPS_43, .data$CCAPS_44, .data$CCAPS_47, .data$CCAPS_50, .data$CCAPS_53, .data$CCAPS_56, .data$CCAPS_60, .data$CCAPS_61, .data$CCAPS_65, .data$CCAPS_69, .data$CCAPS_70)), na.rm = T)
data$Is_CCAPS62[which(data$Is_CCAPS62 > 0)] <- 1
data$Is_CCAPS34 <- 0
data$Is_CCAPS34[which(data$Has_CCAPS ==1 & data$Is_CCAPS62 == 0)] <- 1
#Number missing per subscale and overall for 34 and 62
data$Depression62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_45, .data$CCAPS_27, .data$CCAPS_51, .data$CCAPS_10, .data$CCAPS_61, .data$CCAPS_70, .data$CCAPS_32, .data$CCAPS_11, .data$CCAPS_15, .data$CCAPS_41, .data$CCAPS_65)), na.rm = T)
data$Eating62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_29, .data$CCAPS_16, .data$CCAPS_35, .data$CCAPS_06, .data$CCAPS_26, .data$CCAPS_23, .data$CCAPS_69, .data$CCAPS_38, .data$CCAPS_53)), na.rm = T)
data$Substance62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_54, .data$CCAPS_30, .data$CCAPS_56, .data$CCAPS_33, .data$CCAPS_63, .data$CCAPS_28)), na.rm = T)
data$Anxiety62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_31, .data$CCAPS_17, .data$CCAPS_05, .data$CCAPS_22, .data$CCAPS_34, .data$CCAPS_37, .data$CCAPS_04, .data$CCAPS_21, .data$CCAPS_44)), na.rm = T)
data$Hostility62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_36, .data$CCAPS_48, .data$CCAPS_64, .data$CCAPS_58, .data$CCAPS_40, .data$CCAPS_68, .data$CCAPS_50)), na.rm = T)
data$Social_Anxiety62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_49, .data$CCAPS_39, .data$CCAPS_60, .data$CCAPS_52, .data$CCAPS_19, .data$CCAPS_46)), na.rm = T)
data$Family62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_25, .data$CCAPS_47, .data$CCAPS_09, .data$CCAPS_01, .data$CCAPS_14, .data$CCAPS_43)), na.rm = T)
data$Academics62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_59, .data$CCAPS_66, .data$CCAPS_18, .data$CCAPS_08, .data$CCAPS_57)), na.rm = T)
data$CCAPS62_Tmiss <- rowSums(is.na(dplyr::select(data, .data$CCAPS_01:.data$CCAPS_70)), na.rm = T)
data$Depression34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_45, .data$CCAPS_51)), na.rm = T)
data$Anxiety34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_05, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_31, .data$CCAPS_34)), na.rm = T)
data$Social_Anxiety34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_39, .data$CCAPS_46, .data$CCAPS_49, .data$CCAPS_52)), na.rm = T)
data$Academics34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_18, .data$CCAPS_57, .data$CCAPS_59, .data$CCAPS_66)), na.rm = T)
data$Eating34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_06, .data$CCAPS_16, .data$CCAPS_29)), na.rm = T)
data$Hostility34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_36, .data$CCAPS_40, .data$CCAPS_48, .data$CCAPS_58, .data$CCAPS_64, .data$CCAPS_68)), na.rm = T)
data$Alcohol34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_30, .data$CCAPS_33, .data$CCAPS_54, .data$CCAPS_63)), na.rm = T)
data$CCAPS34_Tmiss <- rowSums(dplyr::select(data, .data$Depression34_MISS, .data$Anxiety34_MISS, .data$Social_Anxiety34_MISS, .data$Academics34_MISS, .data$Eating34_MISS, .data$Hostility34_MISS, .data$Alcohol34_MISS), na.rm = T)
# Variance
data$variance <- matrixStats::rowVars(as.matrix(dplyr::select(data,.data$CCAPS_01:.data$CCAPS_70)), na.rm = T)
# Indicators of valid/invalid administrations to score
# Administration is invalid if any subscale has 50% or more missing items, or entire administration has 50% or more missing, or variance is 0
data$Is_ValidCCAPS <- 0
data$Is_ValidCCAPS[which(data$Has_CCAPS == 1 & data$Depression34_MISS < 3 &
data$Anxiety34_MISS < 3 & data$Social_Anxiety34_MISS < 2 &
data$Academics34_MISS < 2 & data$Eating34_MISS < 2 &
data$Hostility34_MISS < 3 & data$Alcohol34_MISS < 2 &
data$CCAPS34_Tmiss <= 17 & data$variance >0)] <- 1
data$Is_ValidCCAPS[which(data$Is_CCAPS62 == 1 & (data$Depression62_MISS > 4 |
data$Anxiety62_MISS > 3 |
data$Social_Anxiety62_MISS > 2 |
data$Academics62_MISS > 1 |
data$Eating62_MISS > 3 |
data$Hostility62_MISS > 2 |
data$Substance62_MISS > 2 |
data$Family62_MISS > 2 |
data$CCAPS62_Tmiss >= 32))] <- 0
data$Is_ValidCCAPS62 <- 0
data$Is_ValidCCAPS62[which(data$Is_CCAPS62 == 1 & data$Depression62_MISS < 5 &
data$Anxiety62_MISS < 4 & data$Social_Anxiety62_MISS < 3 &
data$Academics62_MISS < 2 & data$Eating62_MISS < 4 &
data$Hostility62_MISS < 3 & data$Substance62_MISS < 3 &
data$Family62_MISS < 3 & data$CCAPS62_Tmiss <= 31 &
data$variance > 0)] <- 1
data$Is_ValidCCAPS62[which(data$Is_ValidCCAPS == 0 )] <- 0
data$Is_ValidCCAPS34 <- 0
data$Is_ValidCCAPS34[which(data$Is_ValidCCAPS == 1 & data$Is_ValidCCAPS62 == 0)] <- 1
# Reverse scoring
data$CCAPS_18r <- abs(data$CCAPS_18-4)
data$CCAPS_08r <- abs(data$CCAPS_08-4)
data$CCAPS_61r <- abs(data$CCAPS_61-4)
data$CCAPS_32r <- abs(data$CCAPS_32-4)
data$CCAPS_23r <- abs(data$CCAPS_23-4)
data$CCAPS_25r <- abs(data$CCAPS_25-4)
data$CCAPS_09r <- abs(data$CCAPS_09-4)
data$CCAPS_39r <- abs(data$CCAPS_39-4)
data$CCAPS_60r <- abs(data$CCAPS_60-4)
# Scoring subscales
data$Depression34 <- apply(dplyr::select(data, c(.data$CCAPS_11, .data$CCAPS_13,.data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_45, .data$CCAPS_51)), 1, mean, na.rm=T)
data$Depression34[which(data$Is_ValidCCAPS==0)] <- NA
data$Anxiety34 <- apply(dplyr::select(data, c(.data$CCAPS_05, .data$CCAPS_17,.data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_31, .data$CCAPS_34)), 1, mean, na.rm=T)
data$Anxiety34[which(data$Is_ValidCCAPS==0)]=NA
data$Social_Anxiety34 <- apply(dplyr::select(data, c(.data$CCAPS_03,.data$CCAPS_39r, .data$CCAPS_46, .data$CCAPS_49, .data$CCAPS_52)), 1, mean, na.rm=T)
data$Social_Anxiety34[which(data$Is_ValidCCAPS==0)]=NA
data$Academics34 <- apply(dplyr::select(data, c(.data$CCAPS_18r, .data$CCAPS_57, .data$CCAPS_59, .data$CCAPS_66)), 1, mean, na.rm=T)
data$Academics34[which(data$Is_ValidCCAPS==0)]=NA
data$Eating34 <- apply(dplyr::select(data, c(.data$CCAPS_06, .data$CCAPS_16, .data$CCAPS_29)), 1, mean, na.rm=T)
data$Eating34[which(data$Is_ValidCCAPS==0)]=NA
data$Hostility34 <- apply(dplyr::select(data, c(.data$CCAPS_36, .data$CCAPS_40, .data$CCAPS_48, .data$CCAPS_58, .data$CCAPS_64, .data$CCAPS_68)), 1, mean, na.rm=T)
data$Hostility34[which(data$Is_ValidCCAPS==0)]=NA
data$Alcohol34 <- apply(dplyr::select(data, c(.data$CCAPS_30, .data$CCAPS_33,.data$CCAPS_54, .data$CCAPS_63)), 1, mean, na.rm=T)
data$Alcohol34[which(data$Is_ValidCCAPS==0)]=NA
data$DI <- apply(dplyr::select(data, c(.data$CCAPS_05, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_24,.data$CCAPS_27, .data$CCAPS_31, .data$CCAPS_34, .data$CCAPS_40, .data$CCAPS_45,.data$CCAPS_46, .data$CCAPS_48, .data$CCAPS_51, .data$CCAPS_52, .data$CCAPS_57, .data$CCAPS_58, .data$CCAPS_59, .data$CCAPS_66)), 1, mean, na.rm=T)
data$DI[which(data$Is_ValidCCAPS==0)]=NA
data$Depression62 <- apply(dplyr::select(data, c(.data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_45, .data$CCAPS_27, .data$CCAPS_51, .data$CCAPS_10, .data$CCAPS_61r,.data$CCAPS_70, .data$CCAPS_32r, .data$CCAPS_11, .data$CCAPS_15, .data$CCAPS_41, .data$CCAPS_65)), 1, mean, na.rm=T)
data$Depression62[which(data$Is_ValidCCAPS62==0)]=NA
data$Eating62 <- apply(dplyr::select(data, c(.data$CCAPS_29, .data$CCAPS_16, .data$CCAPS_35, .data$CCAPS_06, .data$CCAPS_26, .data$CCAPS_23r, .data$CCAPS_69, .data$CCAPS_38, .data$CCAPS_53)), 1, mean, na.rm=T)
data$Eating62[which(data$Is_ValidCCAPS62==0)]=NA
data$Substance62 <- apply(dplyr::select(data, c(.data$CCAPS_54, .data$CCAPS_30, .data$CCAPS_56, .data$CCAPS_33, .data$CCAPS_63, .data$CCAPS_28)), 1, mean, na.rm=T)
data$Substance62[which(data$Is_ValidCCAPS62==0)]=NA
data$Anxiety62 <- apply(dplyr::select (data, c(.data$CCAPS_31, .data$CCAPS_17, .data$CCAPS_05, .data$CCAPS_22, .data$CCAPS_34, .data$CCAPS_37, .data$CCAPS_04, .data$CCAPS_21, .data$CCAPS_44)), 1, mean, na.rm=T)
data$Anxiety62[which(data$Is_ValidCCAPS62==0)]=NA
data$Hostility62 <- apply(dplyr::select(data, c(.data$CCAPS_36, .data$CCAPS_48, .data$CCAPS_64, .data$CCAPS_58, .data$CCAPS_40, .data$CCAPS_68, .data$CCAPS_50)), 1, mean, na.rm=T)
data$Hostility62[which(data$Is_ValidCCAPS62==0)]=NA
data$Social_Anxiety62 <- apply(dplyr::select (data, c(.data$CCAPS_03, .data$CCAPS_49, .data$CCAPS_39r, .data$CCAPS_60r, .data$CCAPS_52, .data$CCAPS_19, .data$CCAPS_46)), 1, mean, na.rm=T)
data$Social_Anxiety62[which(data$Is_ValidCCAPS62==0)]=NA
data$Family62 <- apply(dplyr::select(data, c(.data$CCAPS_25r, .data$CCAPS_47, .data$CCAPS_09r, .data$CCAPS_01, .data$CCAPS_14, .data$CCAPS_43)), 1, mean, na.rm=T)
data$Family62[which(data$Is_ValidCCAPS62==0)]=NA
data$Academics62 <- apply(dplyr::select(data, c(.data$CCAPS_59, .data$CCAPS_66, .data$CCAPS_18r, .data$CCAPS_08r, .data$CCAPS_57)), 1, mean, na.rm=T)
data$Academics62[which(data$Is_ValidCCAPS62==0)]=NA
# Delete created vars after creating subscales
data <- dplyr::select(data, -c(.data$Depression62_MISS:.data$variance, .data$CCAPS_18r:.data$CCAPS_60r))
return(data)
} else if (CCAPS62 == F) {
if(!all(c("CCAPS_03", "CCAPS_05", "CCAPS_06", "CCAPS_11", "CCAPS_13", "CCAPS_16", "CCAPS_17", "CCAPS_18", "CCAPS_21", "CCAPS_22", "CCAPS_24", "CCAPS_27", "CCAPS_29", "CCAPS_30", "CCAPS_31", "CCAPS_33", "CCAPS_34", "CCAPS_36", "CCAPS_39", "CCAPS_40", "CCAPS_45", "CCAPS_46", "CCAPS_48", "CCAPS_49", "CCAPS_51", "CCAPS_52", "CCAPS_54", "CCAPS_57", "CCAPS_58", "CCAPS_59", "CCAPS_63", "CCAPS_64", "CCAPS_66", "CCAPS_68") %in% colnames(data))) stop('CCAPS items not present in data or not properly named. CCAPS items should be named CCAPS_01 through CCAPS_70.')
# Has_CCAPS
data$Has_CCAPS <- rowSums(!is.na(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_05, .data$CCAPS_06, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_16, .data$CCAPS_17, .data$CCAPS_18, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_29, .data$CCAPS_30, .data$CCAPS_31, .data$CCAPS_33, .data$CCAPS_34, .data$CCAPS_36, .data$CCAPS_39, .data$CCAPS_40, .data$CCAPS_45, .data$CCAPS_46, .data$CCAPS_48, .data$CCAPS_49, .data$CCAPS_51, .data$CCAPS_52, .data$CCAPS_54, .data$CCAPS_57, .data$CCAPS_58, .data$CCAPS_59, .data$CCAPS_63, .data$CCAPS_64, .data$CCAPS_66, .data$CCAPS_68)), na.rm = T)
data$Has_CCAPS[which(data$Has_CCAPS > 0)] <- 1
#Number missing per subscale
data$Depression34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_45, .data$CCAPS_51)), na.rm = T)
data$Anxiety34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_05, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_31, .data$CCAPS_34)), na.rm = T)
data$Social_Anxiety34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_39, .data$CCAPS_46, .data$CCAPS_49, .data$CCAPS_52)), na.rm = T)
data$Academics34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_18, .data$CCAPS_57, .data$CCAPS_59, .data$CCAPS_66)), na.rm = T)
data$Eating34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_06, .data$CCAPS_16, .data$CCAPS_29)), na.rm = T)
data$Hostility34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_36, .data$CCAPS_40, .data$CCAPS_48, .data$CCAPS_58, .data$CCAPS_64, .data$CCAPS_68)), na.rm = T)
data$Alcohol34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_30, .data$CCAPS_33, .data$CCAPS_54, .data$CCAPS_63)), na.rm = T)
data$CCAPS34_Tmiss <- rowSums(dplyr::select(data, .data$Depression34_MISS, .data$Anxiety34_MISS, .data$Social_Anxiety34_MISS, .data$Academics34_MISS, .data$Eating34_MISS, .data$Hostility34_MISS, .data$Alcohol34_MISS), na.rm = T)
# Variance
data$variance <- matrixStats::rowVars(as.matrix(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_05, .data$CCAPS_06, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_16, .data$CCAPS_17, .data$CCAPS_18, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_29, .data$CCAPS_30, .data$CCAPS_31, .data$CCAPS_33, .data$CCAPS_34, .data$CCAPS_36, .data$CCAPS_39, .data$CCAPS_40, .data$CCAPS_45, .data$CCAPS_46, .data$CCAPS_48, .data$CCAPS_49, .data$CCAPS_51, .data$CCAPS_52, .data$CCAPS_54, .data$CCAPS_57, .data$CCAPS_58, .data$CCAPS_59, .data$CCAPS_63, .data$CCAPS_64, .data$CCAPS_66, .data$CCAPS_68)), na.rm=T)
# Indicators of valid/invalid administrations to score
data$Is_ValidCCAPS <- 0
data$Is_ValidCCAPS[which(data$Has_CCAPS == 1 & data$Depression34_MISS < 3 & data$Anxiety34_MISS < 3 & data$Social_Anxiety34_MISS < 2 & data$Academics34_MISS < 2 & data$Eating34_MISS < 2 & data$Hostility34_MISS < 3 & data$Alcohol34_MISS < 2 & data$CCAPS34_Tmiss <= 17 & data$variance >0)] <- 1
# Reverse scoring
data$CCAPS_18r <- abs(data$CCAPS_18-4)
data$CCAPS_39r <- abs(data$CCAPS_39-4)
# Scoring subscales
data$Depression34 <- apply(dplyr::select(data, c(.data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_45, .data$CCAPS_51)), 1, mean, na.rm=T)
data$Depression34[which(data$Is_ValidCCAPS==0)] <- NA
data$Anxiety34 <- apply(dplyr::select(data, c(.data$CCAPS_05, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_31, .data$CCAPS_34)), 1, mean, na.rm=T)
data$Anxiety34[which(data$Is_ValidCCAPS==0)]=NA
data$Social_Anxiety34 <- apply(dplyr::select(data, c(.data$CCAPS_03, .data$CCAPS_39r, .data$CCAPS_46, .data$CCAPS_49, .data$CCAPS_52)), 1, mean, na.rm=T)
data$Social_Anxiety34[which(data$Is_ValidCCAPS==0)]=NA
data$Academics34 <- apply(dplyr::select(data, c(.data$CCAPS_18r, .data$CCAPS_57, .data$CCAPS_59, .data$CCAPS_66)), 1, mean, na.rm=T)
data$Academics34[which(data$Is_ValidCCAPS==0)]=NA
data$Eating34 <- apply(dplyr::select(data, c(.data$CCAPS_06, .data$CCAPS_16, .data$CCAPS_29)), 1, mean, na.rm=T)
data$Eating34[which(data$Is_ValidCCAPS==0)]=NA
data$Hostility34 <- apply(dplyr::select(data, c(.data$CCAPS_36, .data$CCAPS_40, .data$CCAPS_48, .data$CCAPS_58, .data$CCAPS_64, .data$CCAPS_68)), 1, mean, na.rm=T)
data$Hostility34[which(data$Is_ValidCCAPS==0)]=NA
data$Alcohol34 <- apply(dplyr::select(data, c(.data$CCAPS_30, .data$CCAPS_33, .data$CCAPS_54, .data$CCAPS_63)), 1, mean, na.rm=T)
data$Alcohol34[which(data$Is_ValidCCAPS==0)]=NA
data$DI <- apply(dplyr::select(data, c(.data$CCAPS_05, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_31, .data$CCAPS_34, .data$CCAPS_40, .data$CCAPS_45, .data$CCAPS_46, .data$CCAPS_48, .data$CCAPS_51, .data$CCAPS_52, .data$CCAPS_57, .data$CCAPS_58, .data$CCAPS_59, .data$CCAPS_66)), 1, mean, na.rm=T)
data$DI[which(data$Is_ValidCCAPS==0)]=NA
# Delete created vars after creating subscales
data <- dplyr::select(data, -c(.data$Depression34_MISS:.data$variance, .data$CCAPS_18r:.data$CCAPS_39r))
return(data)
} else {
print("Please indicate whether to score the CCAPS62 or not by setting the CCAPS62 argument to TRUE or FALSE.")
}
}
CCMH-PSU/CCMHr documentation built on Jan. 31, 2024, 1:08 a.m.
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Defines functions score_CCAPS
Documented in score_CCAPS
#' Score the CCAPS subscales from CCAPS items
#'
#' @description Scores the CCAPS subscales for valid CCAPS administrations.
#' @param data A data file containing CCAPS item data
#' @param CCAPS62 A logical value indicating whether the CCAPS62 should be scored. If FALSE, only the CCAPS34 will be scored.
#' @note `score_CCAPS` calls on CCAPS items by name, so they must be named properly in
#' `data`. Variable naming convention is `CCAPS_01`... `CCAPS_70`.
#' If 'data does not contain the proper CCAPS variable names
#' (or the naming convention was changed in the new year's data), the
#' function will return an error.
#' Administrations are considered valid and will be scored if 1/3 or less of the items on any subscale are missing, and if less than
#' 1/2 of the data overall is missing, and if the variance of the items is > 0, indicating that the items were not all marked
#' as the same number.
#' Function does not overwrite CCAPS items to reverse score them. It creates new reverse scored version, which it then deletes.
#' Note that this takes several minutes to run.
#' @return A data frame with all the original data in `data`, and several additional columns: `Has_CCAPS`, `Is_CCAPS62`, `Is_ValidCCAPS`, `Is_ValidCCAPS62`, `Is_CCAPS34`, `Is_ValidCCAPS34`, `Depression34`, `Anxiety34`, `Social_Anxiety34`, `Academics34`, `Eating34`, `Hostility34`, `Alcohol34`, `DI`, `Depression62`, `Eating62`, `Substance62`, `Anxiety62`, `Hostility62`, `Social_Anxiety62`, `Family62`, `Academics62`
#' @export
score_CCAPS <- function(data, CCAPS62 = T) {
if(CCAPS62 == T) {
if(!all(c("CCAPS_01", "CCAPS_03", "CCAPS_04", "CCAPS_05", "CCAPS_06", "CCAPS_08", "CCAPS_09", "CCAPS_10", "CCAPS_11", "CCAPS_13", "CCAPS_14", "CCAPS_15", "CCAPS_16", "CCAPS_17", "CCAPS_18", "CCAPS_19", "CCAPS_21", "CCAPS_22", "CCAPS_23", "CCAPS_24", "CCAPS_25", "CCAPS_26", "CCAPS_27", "CCAPS_28", "CCAPS_29", "CCAPS_30", "CCAPS_31", "CCAPS_32", "CCAPS_33", "CCAPS_34", "CCAPS_35", "CCAPS_36", "CCAPS_37", "CCAPS_38", "CCAPS_39", "CCAPS_40", "CCAPS_41", "CCAPS_43", "CCAPS_44", "CCAPS_45", "CCAPS_46", "CCAPS_47", "CCAPS_48", "CCAPS_49", "CCAPS_50", "CCAPS_51", "CCAPS_52", "CCAPS_53", "CCAPS_54", "CCAPS_56", "CCAPS_57", "CCAPS_58", "CCAPS_59", "CCAPS_60", "CCAPS_61", "CCAPS_63", "CCAPS_64", "CCAPS_65", "CCAPS_66", "CCAPS_68", "CCAPS_69", "CCAPS_70") %in% colnames(data))) stop('CCAPS items not present in data or not properly named. CCAPS items should be named CCAPS_01 through CCAPS_70.')
# Has_CCAPS
data$Has_CCAPS <- rowSums(!is.na(dplyr::select(data, .data$CCAPS_01:.data$CCAPS_70)), na.rm = T)
data$Has_CCAPS[which(data$Has_CCAPS > 0)] <- 1
# Is_CCAPS62 or Is_CCAPS34
data$Is_CCAPS62 <- rowSums(!is.na(dplyr::select(data, .data$CCAPS_01, .data$CCAPS_04, .data$CCAPS_08, .data$CCAPS_09, .data$CCAPS_10, .data$CCAPS_14, .data$CCAPS_15, .data$CCAPS_19, .data$CCAPS_23,.data$CCAPS_25, .data$CCAPS_26, .data$CCAPS_28, .data$CCAPS_32, .data$CCAPS_35, .data$CCAPS_37, .data$CCAPS_38, .data$CCAPS_41, .data$CCAPS_43, .data$CCAPS_44, .data$CCAPS_47, .data$CCAPS_50, .data$CCAPS_53, .data$CCAPS_56, .data$CCAPS_60, .data$CCAPS_61, .data$CCAPS_65, .data$CCAPS_69, .data$CCAPS_70)), na.rm = T)
data$Is_CCAPS62[which(data$Is_CCAPS62 > 0)] <- 1
data$Is_CCAPS34 <- 0
data$Is_CCAPS34[which(data$Has_CCAPS ==1 & data$Is_CCAPS62 == 0)] <- 1
#Number missing per subscale and overall for 34 and 62
data$Depression62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_45, .data$CCAPS_27, .data$CCAPS_51, .data$CCAPS_10, .data$CCAPS_61, .data$CCAPS_70, .data$CCAPS_32, .data$CCAPS_11, .data$CCAPS_15, .data$CCAPS_41, .data$CCAPS_65)), na.rm = T)
data$Eating62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_29, .data$CCAPS_16, .data$CCAPS_35, .data$CCAPS_06, .data$CCAPS_26, .data$CCAPS_23, .data$CCAPS_69, .data$CCAPS_38, .data$CCAPS_53)), na.rm = T)
data$Substance62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_54, .data$CCAPS_30, .data$CCAPS_56, .data$CCAPS_33, .data$CCAPS_63, .data$CCAPS_28)), na.rm = T)
data$Anxiety62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_31, .data$CCAPS_17, .data$CCAPS_05, .data$CCAPS_22, .data$CCAPS_34, .data$CCAPS_37, .data$CCAPS_04, .data$CCAPS_21, .data$CCAPS_44)), na.rm = T)
data$Hostility62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_36, .data$CCAPS_48, .data$CCAPS_64, .data$CCAPS_58, .data$CCAPS_40, .data$CCAPS_68, .data$CCAPS_50)), na.rm = T)
data$Social_Anxiety62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_49, .data$CCAPS_39, .data$CCAPS_60, .data$CCAPS_52, .data$CCAPS_19, .data$CCAPS_46)), na.rm = T)
data$Family62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_25, .data$CCAPS_47, .data$CCAPS_09, .data$CCAPS_01, .data$CCAPS_14, .data$CCAPS_43)), na.rm = T)
data$Academics62_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_59, .data$CCAPS_66, .data$CCAPS_18, .data$CCAPS_08, .data$CCAPS_57)), na.rm = T)
data$CCAPS62_Tmiss <- rowSums(is.na(dplyr::select(data, .data$CCAPS_01:.data$CCAPS_70)), na.rm = T)
data$Depression34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_45, .data$CCAPS_51)), na.rm = T)
data$Anxiety34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_05, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_31, .data$CCAPS_34)), na.rm = T)
data$Social_Anxiety34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_39, .data$CCAPS_46, .data$CCAPS_49, .data$CCAPS_52)), na.rm = T)
data$Academics34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_18, .data$CCAPS_57, .data$CCAPS_59, .data$CCAPS_66)), na.rm = T)
data$Eating34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_06, .data$CCAPS_16, .data$CCAPS_29)), na.rm = T)
data$Hostility34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_36, .data$CCAPS_40, .data$CCAPS_48, .data$CCAPS_58, .data$CCAPS_64, .data$CCAPS_68)), na.rm = T)
data$Alcohol34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_30, .data$CCAPS_33, .data$CCAPS_54, .data$CCAPS_63)), na.rm = T)
data$CCAPS34_Tmiss <- rowSums(dplyr::select(data, .data$Depression34_MISS, .data$Anxiety34_MISS, .data$Social_Anxiety34_MISS, .data$Academics34_MISS, .data$Eating34_MISS, .data$Hostility34_MISS, .data$Alcohol34_MISS), na.rm = T)
# Variance
data$variance <- matrixStats::rowVars(as.matrix(dplyr::select(data,.data$CCAPS_01:.data$CCAPS_70)), na.rm = T)
# Indicators of valid/invalid administrations to score
# Administration is invalid if any subscale has 50% or more missing items, or entire administration has 50% or more missing, or variance is 0
data$Is_ValidCCAPS <- 0
data$Is_ValidCCAPS[which(data$Has_CCAPS == 1 & data$Depression34_MISS < 3 &
data$Anxiety34_MISS < 3 & data$Social_Anxiety34_MISS < 2 &
data$Academics34_MISS < 2 & data$Eating34_MISS < 2 &
data$Hostility34_MISS < 3 & data$Alcohol34_MISS < 2 &
data$CCAPS34_Tmiss <= 17 & data$variance >0)] <- 1
data$Is_ValidCCAPS[which(data$Is_CCAPS62 == 1 & (data$Depression62_MISS > 4 |
data$Anxiety62_MISS > 3 |
data$Social_Anxiety62_MISS > 2 |
data$Academics62_MISS > 1 |
data$Eating62_MISS > 3 |
data$Hostility62_MISS > 2 |
data$Substance62_MISS > 2 |
data$Family62_MISS > 2 |
data$CCAPS62_Tmiss >= 32))] <- 0
data$Is_ValidCCAPS62 <- 0
data$Is_ValidCCAPS62[which(data$Is_CCAPS62 == 1 & data$Depression62_MISS < 5 &
data$Anxiety62_MISS < 4 & data$Social_Anxiety62_MISS < 3 &
data$Academics62_MISS < 2 & data$Eating62_MISS < 4 &
data$Hostility62_MISS < 3 & data$Substance62_MISS < 3 &
data$Family62_MISS < 3 & data$CCAPS62_Tmiss <= 31 &
data$variance > 0)] <- 1
data$Is_ValidCCAPS62[which(data$Is_ValidCCAPS == 0 )] <- 0
data$Is_ValidCCAPS34 <- 0
data$Is_ValidCCAPS34[which(data$Is_ValidCCAPS == 1 & data$Is_ValidCCAPS62 == 0)] <- 1
# Reverse scoring
data$CCAPS_18r <- abs(data$CCAPS_18-4)
data$CCAPS_08r <- abs(data$CCAPS_08-4)
data$CCAPS_61r <- abs(data$CCAPS_61-4)
data$CCAPS_32r <- abs(data$CCAPS_32-4)
data$CCAPS_23r <- abs(data$CCAPS_23-4)
data$CCAPS_25r <- abs(data$CCAPS_25-4)
data$CCAPS_09r <- abs(data$CCAPS_09-4)
data$CCAPS_39r <- abs(data$CCAPS_39-4)
data$CCAPS_60r <- abs(data$CCAPS_60-4)
# Scoring subscales
data$Depression34 <- apply(dplyr::select(data, c(.data$CCAPS_11, .data$CCAPS_13,.data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_45, .data$CCAPS_51)), 1, mean, na.rm=T)
data$Depression34[which(data$Is_ValidCCAPS==0)] <- NA
data$Anxiety34 <- apply(dplyr::select(data, c(.data$CCAPS_05, .data$CCAPS_17,.data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_31, .data$CCAPS_34)), 1, mean, na.rm=T)
data$Anxiety34[which(data$Is_ValidCCAPS==0)]=NA
data$Social_Anxiety34 <- apply(dplyr::select(data, c(.data$CCAPS_03,.data$CCAPS_39r, .data$CCAPS_46, .data$CCAPS_49, .data$CCAPS_52)), 1, mean, na.rm=T)
data$Social_Anxiety34[which(data$Is_ValidCCAPS==0)]=NA
data$Academics34 <- apply(dplyr::select(data, c(.data$CCAPS_18r, .data$CCAPS_57, .data$CCAPS_59, .data$CCAPS_66)), 1, mean, na.rm=T)
data$Academics34[which(data$Is_ValidCCAPS==0)]=NA
data$Eating34 <- apply(dplyr::select(data, c(.data$CCAPS_06, .data$CCAPS_16, .data$CCAPS_29)), 1, mean, na.rm=T)
data$Eating34[which(data$Is_ValidCCAPS==0)]=NA
data$Hostility34 <- apply(dplyr::select(data, c(.data$CCAPS_36, .data$CCAPS_40, .data$CCAPS_48, .data$CCAPS_58, .data$CCAPS_64, .data$CCAPS_68)), 1, mean, na.rm=T)
data$Hostility34[which(data$Is_ValidCCAPS==0)]=NA
data$Alcohol34 <- apply(dplyr::select(data, c(.data$CCAPS_30, .data$CCAPS_33,.data$CCAPS_54, .data$CCAPS_63)), 1, mean, na.rm=T)
data$Alcohol34[which(data$Is_ValidCCAPS==0)]=NA
data$DI <- apply(dplyr::select(data, c(.data$CCAPS_05, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_24,.data$CCAPS_27, .data$CCAPS_31, .data$CCAPS_34, .data$CCAPS_40, .data$CCAPS_45,.data$CCAPS_46, .data$CCAPS_48, .data$CCAPS_51, .data$CCAPS_52, .data$CCAPS_57, .data$CCAPS_58, .data$CCAPS_59, .data$CCAPS_66)), 1, mean, na.rm=T)
data$DI[which(data$Is_ValidCCAPS==0)]=NA
data$Depression62 <- apply(dplyr::select(data, c(.data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_45, .data$CCAPS_27, .data$CCAPS_51, .data$CCAPS_10, .data$CCAPS_61r,.data$CCAPS_70, .data$CCAPS_32r, .data$CCAPS_11, .data$CCAPS_15, .data$CCAPS_41, .data$CCAPS_65)), 1, mean, na.rm=T)
data$Depression62[which(data$Is_ValidCCAPS62==0)]=NA
data$Eating62 <- apply(dplyr::select(data, c(.data$CCAPS_29, .data$CCAPS_16, .data$CCAPS_35, .data$CCAPS_06, .data$CCAPS_26, .data$CCAPS_23r, .data$CCAPS_69, .data$CCAPS_38, .data$CCAPS_53)), 1, mean, na.rm=T)
data$Eating62[which(data$Is_ValidCCAPS62==0)]=NA
data$Substance62 <- apply(dplyr::select(data, c(.data$CCAPS_54, .data$CCAPS_30, .data$CCAPS_56, .data$CCAPS_33, .data$CCAPS_63, .data$CCAPS_28)), 1, mean, na.rm=T)
data$Substance62[which(data$Is_ValidCCAPS62==0)]=NA
data$Anxiety62 <- apply(dplyr::select (data, c(.data$CCAPS_31, .data$CCAPS_17, .data$CCAPS_05, .data$CCAPS_22, .data$CCAPS_34, .data$CCAPS_37, .data$CCAPS_04, .data$CCAPS_21, .data$CCAPS_44)), 1, mean, na.rm=T)
data$Anxiety62[which(data$Is_ValidCCAPS62==0)]=NA
data$Hostility62 <- apply(dplyr::select(data, c(.data$CCAPS_36, .data$CCAPS_48, .data$CCAPS_64, .data$CCAPS_58, .data$CCAPS_40, .data$CCAPS_68, .data$CCAPS_50)), 1, mean, na.rm=T)
data$Hostility62[which(data$Is_ValidCCAPS62==0)]=NA
data$Social_Anxiety62 <- apply(dplyr::select (data, c(.data$CCAPS_03, .data$CCAPS_49, .data$CCAPS_39r, .data$CCAPS_60r, .data$CCAPS_52, .data$CCAPS_19, .data$CCAPS_46)), 1, mean, na.rm=T)
data$Social_Anxiety62[which(data$Is_ValidCCAPS62==0)]=NA
data$Family62 <- apply(dplyr::select(data, c(.data$CCAPS_25r, .data$CCAPS_47, .data$CCAPS_09r, .data$CCAPS_01, .data$CCAPS_14, .data$CCAPS_43)), 1, mean, na.rm=T)
data$Family62[which(data$Is_ValidCCAPS62==0)]=NA
data$Academics62 <- apply(dplyr::select(data, c(.data$CCAPS_59, .data$CCAPS_66, .data$CCAPS_18r, .data$CCAPS_08r, .data$CCAPS_57)), 1, mean, na.rm=T)
data$Academics62[which(data$Is_ValidCCAPS62==0)]=NA
# Delete created vars after creating subscales
data <- dplyr::select(data, -c(.data$Depression62_MISS:.data$variance, .data$CCAPS_18r:.data$CCAPS_60r))
return(data)
} else if (CCAPS62 == F) {
if(!all(c("CCAPS_03", "CCAPS_05", "CCAPS_06", "CCAPS_11", "CCAPS_13", "CCAPS_16", "CCAPS_17", "CCAPS_18", "CCAPS_21", "CCAPS_22", "CCAPS_24", "CCAPS_27", "CCAPS_29", "CCAPS_30", "CCAPS_31", "CCAPS_33", "CCAPS_34", "CCAPS_36", "CCAPS_39", "CCAPS_40", "CCAPS_45", "CCAPS_46", "CCAPS_48", "CCAPS_49", "CCAPS_51", "CCAPS_52", "CCAPS_54", "CCAPS_57", "CCAPS_58", "CCAPS_59", "CCAPS_63", "CCAPS_64", "CCAPS_66", "CCAPS_68") %in% colnames(data))) stop('CCAPS items not present in data or not properly named. CCAPS items should be named CCAPS_01 through CCAPS_70.')
# Has_CCAPS
data$Has_CCAPS <- rowSums(!is.na(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_05, .data$CCAPS_06, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_16, .data$CCAPS_17, .data$CCAPS_18, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_29, .data$CCAPS_30, .data$CCAPS_31, .data$CCAPS_33, .data$CCAPS_34, .data$CCAPS_36, .data$CCAPS_39, .data$CCAPS_40, .data$CCAPS_45, .data$CCAPS_46, .data$CCAPS_48, .data$CCAPS_49, .data$CCAPS_51, .data$CCAPS_52, .data$CCAPS_54, .data$CCAPS_57, .data$CCAPS_58, .data$CCAPS_59, .data$CCAPS_63, .data$CCAPS_64, .data$CCAPS_66, .data$CCAPS_68)), na.rm = T)
data$Has_CCAPS[which(data$Has_CCAPS > 0)] <- 1
#Number missing per subscale
data$Depression34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_45, .data$CCAPS_51)), na.rm = T)
data$Anxiety34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_05, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_31, .data$CCAPS_34)), na.rm = T)
data$Social_Anxiety34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_39, .data$CCAPS_46, .data$CCAPS_49, .data$CCAPS_52)), na.rm = T)
data$Academics34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_18, .data$CCAPS_57, .data$CCAPS_59, .data$CCAPS_66)), na.rm = T)
data$Eating34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_06, .data$CCAPS_16, .data$CCAPS_29)), na.rm = T)
data$Hostility34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_36, .data$CCAPS_40, .data$CCAPS_48, .data$CCAPS_58, .data$CCAPS_64, .data$CCAPS_68)), na.rm = T)
data$Alcohol34_MISS <- rowSums(is.na(dplyr::select(data, .data$CCAPS_30, .data$CCAPS_33, .data$CCAPS_54, .data$CCAPS_63)), na.rm = T)
data$CCAPS34_Tmiss <- rowSums(dplyr::select(data, .data$Depression34_MISS, .data$Anxiety34_MISS, .data$Social_Anxiety34_MISS, .data$Academics34_MISS, .data$Eating34_MISS, .data$Hostility34_MISS, .data$Alcohol34_MISS), na.rm = T)
# Variance
data$variance <- matrixStats::rowVars(as.matrix(dplyr::select(data, .data$CCAPS_03, .data$CCAPS_05, .data$CCAPS_06, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_16, .data$CCAPS_17, .data$CCAPS_18, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_29, .data$CCAPS_30, .data$CCAPS_31, .data$CCAPS_33, .data$CCAPS_34, .data$CCAPS_36, .data$CCAPS_39, .data$CCAPS_40, .data$CCAPS_45, .data$CCAPS_46, .data$CCAPS_48, .data$CCAPS_49, .data$CCAPS_51, .data$CCAPS_52, .data$CCAPS_54, .data$CCAPS_57, .data$CCAPS_58, .data$CCAPS_59, .data$CCAPS_63, .data$CCAPS_64, .data$CCAPS_66, .data$CCAPS_68)), na.rm=T)
# Indicators of valid/invalid administrations to score
data$Is_ValidCCAPS <- 0
data$Is_ValidCCAPS[which(data$Has_CCAPS == 1 & data$Depression34_MISS < 3 & data$Anxiety34_MISS < 3 & data$Social_Anxiety34_MISS < 2 & data$Academics34_MISS < 2 & data$Eating34_MISS < 2 & data$Hostility34_MISS < 3 & data$Alcohol34_MISS < 2 & data$CCAPS34_Tmiss <= 17 & data$variance >0)] <- 1
# Reverse scoring
data$CCAPS_18r <- abs(data$CCAPS_18-4)
data$CCAPS_39r <- abs(data$CCAPS_39-4)
# Scoring subscales
data$Depression34 <- apply(dplyr::select(data, c(.data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_45, .data$CCAPS_51)), 1, mean, na.rm=T)
data$Depression34[which(data$Is_ValidCCAPS==0)] <- NA
data$Anxiety34 <- apply(dplyr::select(data, c(.data$CCAPS_05, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_31, .data$CCAPS_34)), 1, mean, na.rm=T)
data$Anxiety34[which(data$Is_ValidCCAPS==0)]=NA
data$Social_Anxiety34 <- apply(dplyr::select(data, c(.data$CCAPS_03, .data$CCAPS_39r, .data$CCAPS_46, .data$CCAPS_49, .data$CCAPS_52)), 1, mean, na.rm=T)
data$Social_Anxiety34[which(data$Is_ValidCCAPS==0)]=NA
data$Academics34 <- apply(dplyr::select(data, c(.data$CCAPS_18r, .data$CCAPS_57, .data$CCAPS_59, .data$CCAPS_66)), 1, mean, na.rm=T)
data$Academics34[which(data$Is_ValidCCAPS==0)]=NA
data$Eating34 <- apply(dplyr::select(data, c(.data$CCAPS_06, .data$CCAPS_16, .data$CCAPS_29)), 1, mean, na.rm=T)
data$Eating34[which(data$Is_ValidCCAPS==0)]=NA
data$Hostility34 <- apply(dplyr::select(data, c(.data$CCAPS_36, .data$CCAPS_40, .data$CCAPS_48, .data$CCAPS_58, .data$CCAPS_64, .data$CCAPS_68)), 1, mean, na.rm=T)
data$Hostility34[which(data$Is_ValidCCAPS==0)]=NA
data$Alcohol34 <- apply(dplyr::select(data, c(.data$CCAPS_30, .data$CCAPS_33, .data$CCAPS_54, .data$CCAPS_63)), 1, mean, na.rm=T)
data$Alcohol34[which(data$Is_ValidCCAPS==0)]=NA
data$DI <- apply(dplyr::select(data, c(.data$CCAPS_05, .data$CCAPS_11, .data$CCAPS_13, .data$CCAPS_17, .data$CCAPS_21, .data$CCAPS_22, .data$CCAPS_24, .data$CCAPS_27, .data$CCAPS_31, .data$CCAPS_34, .data$CCAPS_40, .data$CCAPS_45, .data$CCAPS_46, .data$CCAPS_48, .data$CCAPS_51, .data$CCAPS_52, .data$CCAPS_57, .data$CCAPS_58, .data$CCAPS_59, .data$CCAPS_66)), 1, mean, na.rm=T)
data$DI[which(data$Is_ValidCCAPS==0)]=NA
# Delete created vars after creating subscales
data <- dplyr::select(data, -c(.data$Depression34_MISS:.data$variance, .data$CCAPS_18r:.data$CCAPS_39r))
return(data)
} else {
print("Please indicate whether to score the CCAPS62 or not by setting the CCAPS62 argument to TRUE or FALSE.")
}
}
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