Preparing for data submission
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(dplyr)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionPID <- read.csv("~//Downloads/PIDinfo_long_2021-2022_Sam_autoentry_Special_Characters_Changed.csv")
GUID = read.csv("~/Downloads/Pseudo_GUIDs_6_22.csv")
GUID = GUID %>%
rename(PID = ID)
# Replace blank entries for gender with "NR" (Not reported)
GUID$SEX <- sub("^$", "NR", GUID$SEX)
# We need the raw data from qualtrics which includes participant survey completion dates
# Fall 2021 Prefrosh
prefrosh_raw = read.csv("~/Downloads/Stanford Communities Project - Prefrosh 2021-2022_June 20, 2022_15.30.csv")
prefrosh = read.csv("~/Google Drive/Shared drives/Stanford Communities Project/2021-2022/MASTER/Prefrosh_survey/Fall 2021/df.prefrosh2021.csv")
# Fall 2021 Networks
networks_raw = rawDataChar <- read.csv("~/Downloads/Stanford Communities Project - Network+Trait Fall 2021_June 27, 2022_18.55.csv")
networks = read.csv("~/Google Drive/Shared drives/Stanford Communities Project/2021-2022/MASTER/Networks_survey/Fall 2021/df.trait_fa21.csv")# De-ID the raw prefrosh data and add startdate
PID = PID %>%
distinct(email, .keep_all = TRUE)
prefrosh_raw = prefrosh_raw %>%
rename(email = RecipientEmail) %>%
left_join(PID, by = "email") %>%
select(-email) %>%
select(PID, StartDate) %>%
distinct(PID, .keep_all = TRUE)
prefrosh = prefrosh %>%
left_join(prefrosh_raw, by = "PID")
# Putting our start dates in the correct format requested by the NIH (m/d/y)
prefrosh$StartDate = as.POSIXct(prefrosh$StartDate, format = "%Y-%m-%d %H:%M:%OS")
prefrosh$StartDate = format(prefrosh$StartDate, "%m/%d/%Y")
# Removing any NA's dates
prefrosh = prefrosh[!is.na(prefrosh$StartDate), ]
# Joining with our GUID list
prefrosh = prefrosh %>%
left_join(GUID, by = "PID") %>%
mutate(Age = "18+")
# Removing rows with NA'sPID = PID %>%
distinct(email, .keep_all = TRUE)
networks_raw = networks_raw %>%
rename(email = RecipientEmail) %>%
left_join(PID, by = "email") %>%
select(-email) %>%
select(PID, StartDate) %>%
distinct(PID, .keep_all = TRUE)
networks = networks %>%
left_join(networks_raw, by = "PID")
# Putting our start dates in the correct format requested by the NIH (m/d/y)
networks$StartDate <- as.POSIXct(networks$StartDate, format = "%Y-%m-%d %H:%M:%OS")
networks$StartDate <- format(networks$StartDate, "%m/%d/%Y")
networks = networks[!is.na(networks$StartDate), ]
networks = networks %>%
left_join(GUID, by = "PID")
networks = networks %>%
mutate(Age = Age*12) %>%
filter(Age < 1000)#Brief Fear of Negative Evaluation
bfnes = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, NegativeWellBeing_8, NegativeWellBeing_7)
bfnes = bfnes %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX) %>%
rename(bfnes_2 = NegativeWellBeing_8) %>%
rename(bfnes_4 = NegativeWellBeing_7) %>%
mutate(bfnes_score = 999)
bfnes = bfnes %>%
drop_na()pss = networks %>%
mutate(respondent = "Self") %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, respondent, PSS_1, PSS_2R, PSS_3R, PSS_4)
pss = pss %>%
rename(pss2 = PSS_1) %>%
mutate(PSS_2 = 6 - PSS_2R,
PSS_3 = 6 - PSS_3R) %>%
select(-(c("PSS_2R", "PSS_3R"))) %>%
rename(pss4 = PSS_2) %>%
rename(pss5 = PSS_3) %>%
rename(pss10 = PSS_4) %>%
select(PSEUDO.GUID:pss2, pss4, pss5, pss10) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age)
pss = pss %>%
drop_na()IRI_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, IRI_1, IRI_2, IRI_3, IRI_4, IRI_5, IRI_6, IRI_7, Empathy_8, Empathy_13, Empathy_7, Empathy_11, Empathy_9)
IRI_networks = IRI_networks %>%
rename(emergency_apprehension = IRI_1) %>%
rename(tense_emotional = IRI_2) %>%
rename(unfairly_pity = IRI_3) %>%
rename(describe_soft_hearted = IRI_4) %>%
rename(emergencies_lose_control = IRI_5) %>%
rename(upset_try_shoes = IRI_6) %>%
rename(help_go_to_pieces = IRI_7) %>%
rename(tender_feelings = Empathy_8) %>%
rename(all_sides_disagreement = Empathy_13) %>%
rename(protective = Empathy_7) %>%
rename(before_criticizing = Empathy_11) %>%
rename(other_perspective = Empathy_9) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Prefrosh
IRI_prefrosh = prefrosh %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, IRI_1, IRI_2, Empathy_7, Empathy_6.1, IRI_3, IRI_4, IRI_5, IRI_6, IRI_7, IRI_8, IRI_9, Empathy_4)
IRI_prefrosh = IRI_prefrosh %>%
rename(emergency_apprehension = IRI_2) %>%
rename(tense_emotional = IRI_4) %>%
rename(unfairly_pity = IRI_5) %>%
rename(describe_soft_hearted = IRI_6) %>%
rename(emergencies_lose_control = IRI_7) %>%
rename(upset_try_shoes = IRI_8) %>%
rename(help_go_to_pieces = IRI_9) %>%
rename(tender_feelings = IRI_1) %>%
rename(all_sides_disagreement = Empathy_7) %>%
rename(protective = Empathy_6.1) %>%
rename(before_criticizing = Empathy_4) %>%
rename(other_perspective = IRI_3) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Binding
IRI_prefrosh = IRI_prefrosh %>%
mutate(duplicate = ifelse((src_subject_id %in% IRI_networks$src_subject_id), "yes", "no"))
IRI_networks = IRI_networks %>%
mutate(duplicate = "no")
IRI = rbind(IRI_networks, IRI_prefrosh)
IRI = IRI %>%
filter(duplicate == "no") %>%
select(-(duplicate))
IRI = IRI %>%
drop_na()CESD_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, CESD_1, CESD_2, NegativeWellBeing_1, CESD_3, CESD_4R, NegativeWellBeing_10, CESD_6R, CESD_7, CESD_8)
CESD_networks = CESD_networks %>%
rename(bothered_by_things = CESD_1) %>%
rename(trouble_keeping_mind = CESD_2) %>%
rename(felt_depressed = NegativeWellBeing_1) %>%
rename(everything_effort = CESD_3) %>%
rename(hopeful_future = CESD_4R) %>%
rename(fearful = NegativeWellBeing_10) %>%
#rename(sleep_restless = CESD_5) %>%
rename(happy = CESD_6R) %>%
rename(felt_lonely = CESD_7) %>%
rename(couldnt_get_going = CESD_8) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Prefrosh
CESD_prefrosh = prefrosh %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, CESD_1, CESD_2, NegativeWellBeing_1, CESD_3, CESD_4R, NegativeWellBeing_10, CESD_6R, CESD_7, CESD_8)
CESD_prefrosh = CESD_prefrosh %>%
rename(bothered_by_things = CESD_1) %>%
rename(trouble_keeping_mind = CESD_2) %>%
rename(felt_depressed = NegativeWellBeing_1) %>%
rename(everything_effort = CESD_3) %>%
rename(hopeful_future = CESD_4R) %>%
rename(fearful = NegativeWellBeing_10) %>%
rename(happy = CESD_6R) %>%
rename(felt_lonely = CESD_7) %>%
rename(couldnt_get_going = CESD_8) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX) %>%
# mutate(sleep_restless = "") %>%
select(subjectkey:fearful, happy:couldnt_get_going)
# Binding
CESD_prefrosh = CESD_prefrosh %>%
mutate(duplicate = ifelse((src_subject_id %in% CESD_networks$src_subject_id), "yes", "no"))
CESD_networks = CESD_networks %>%
mutate(duplicate = "no")
CESD = rbind(CESD_networks, CESD_prefrosh)
CESD = CESD %>%
filter(duplicate == "no") %>%
select(-(duplicate))
CESD = CESD %>%
drop_na()# Networks
TAI_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, TAI_1R, TAI_2, TAI_3R, TAI_4, NegativeWellBeing_3, TAI_5R, TAI_6R, TAI_7, TAI_8, TAI_9R, TAI_10, TAI_11, TAI_12R, TAI_13R, NegativeWellBeing_3.1, TAI_14R, TAI_15, TAI_16, TAI_17R, TAI_18)
TAI_networks = TAI_networks %>%
mutate(TAI_1 = 5 - TAI_1R) %>%
select(-(TAI_1R)) %>%
rename(stait1 = TAI_1) %>%
rename(stait2 = TAI_2) %>%
mutate(TAI_3 = 5 - TAI_3R) %>%
select(-(TAI_3R)) %>%
rename(stait3 = TAI_3) %>%
rename(stait4 = TAI_4) %>%
rename(stait5 = NegativeWellBeing_3) %>%
mutate(TAI_5 = 5 - TAI_5R) %>%
select(-(TAI_5R)) %>%
rename(stait6 = TAI_5) %>%
mutate(TAI_6 = 5 - TAI_6R) %>%
select(-(TAI_6R)) %>%
rename(stait7 = TAI_6) %>%
rename(stait8 = TAI_7) %>%
rename(stait9 = TAI_8) %>%
mutate(TAI_9 = 5 - TAI_9R) %>%
select(-(TAI_9R)) %>%
rename(stait10 = TAI_9) %>%
rename(stait11 = TAI_10) %>%
rename(stait12 = TAI_11) %>%
mutate(TAI_12 = 5 - TAI_12R) %>%
select(-(TAI_12R)) %>%
rename(stait13 = TAI_12) %>%
mutate(TAI_13 = 5 - TAI_13R) %>%
select(-(TAI_13R)) %>%
rename(stait14 = TAI_13) %>%
rename(stait15 = NegativeWellBeing_3.1) %>%
mutate(TAI_14 = 5 - TAI_14R) %>%
select(-(TAI_14R)) %>%
rename(stait16 = TAI_14) %>%
rename(stait17 = TAI_15) %>%
rename(stait18 = TAI_16) %>%
mutate(TAI_17 = 5 - TAI_17R) %>%
select(-(TAI_17R)) %>%
rename(stait19 = TAI_17) %>%
rename(stait20 = TAI_18) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Prefrosh
TAI_prefrosh = prefrosh %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, TAI_1R, TAI_2, TAI_3R, TAI_4, NegativeWellBeing_3, TAI_5R, TAI_6R, TAI_7, TAI_8, TAI_9R, TAI_10, TAI_11, TAI_12R, TAI_13R, NegativeWellBeing_3.1, TAI_14R, TAI_15, TAI_16, TAI_17R, TAI_18)
TAI_prefrosh = TAI_prefrosh %>%
mutate(TAI_1 = 5 - TAI_1R) %>%
select(-(TAI_1R)) %>%
rename(stait1 = TAI_1) %>%
rename(stait2 = TAI_2) %>%
mutate(TAI_3 = 5 - TAI_3R) %>%
select(-(TAI_3R)) %>%
rename(stait3 = TAI_3) %>%
rename(stait4 = TAI_4) %>%
rename(stait5 = NegativeWellBeing_3) %>%
mutate(TAI_5 = 5 - TAI_5R) %>%
select(-(TAI_5R)) %>%
rename(stait6 = TAI_5) %>%
mutate(TAI_6 = 5 - TAI_6R) %>%
select(-(TAI_6R)) %>%
rename(stait7 = TAI_6) %>%
rename(stait8 = TAI_7) %>%
rename(stait9 = TAI_8) %>%
mutate(TAI_9 = 5 - TAI_9R) %>%
select(-(TAI_9R)) %>%
rename(stait10 = TAI_9) %>%
rename(stait11 = TAI_10) %>%
rename(stait12 = TAI_11) %>%
mutate(TAI_12 = 5 - TAI_12R) %>%
select(-(TAI_12R)) %>%
rename(stait13 = TAI_12) %>%
mutate(TAI_13 = 5 - TAI_13R) %>%
select(-(TAI_13R)) %>%
rename(stait14 = TAI_13) %>%
rename(stait15 = NegativeWellBeing_3.1) %>%
mutate(TAI_14 = 5 - TAI_14R) %>%
select(-(TAI_14R)) %>%
rename(stait16 = TAI_14) %>%
rename(stait17 = TAI_15) %>%
rename(stait18 = TAI_16) %>%
mutate(TAI_17 = 5 - TAI_17R) %>%
select(-(TAI_17R)) %>%
rename(stait19 = TAI_17) %>%
rename(stait20 = TAI_18) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Binding
TAI_prefrosh = TAI_prefrosh %>%
mutate(duplicate = ifelse((src_subject_id %in% CESD_networks$src_subject_id), "yes", "no"))
TAI_networks = TAI_networks %>%
mutate(duplicate = "no")
TAI = rbind(TAI_networks, TAI_prefrosh)
TAI = TAI %>%
filter(duplicate == "no") %>%
select(-(duplicate))
TAI = TAI %>%
drop_na()UCLA_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, loneliness_1, loneliness_2, loneliness_3R, loneliness_4, loneliness_5, loneliness_6R, loneliness_8, loneliness_7)
UCLA_networks = UCLA_networks %>%
rename(uclals4 = loneliness_1) %>%
rename(uclals7 = loneliness_2) %>%
mutate(outgoing = 5 - loneliness_3R) %>%
select(-(loneliness_3R)) %>%
rename(uclals10 = loneliness_4) %>%
rename(uclals16 = loneliness_5) %>%
mutate(uls15 = 5 - loneliness_6R) %>%
select(-(loneliness_6R)) %>%
rename(uclals20 = loneliness_8) %>%
rename(uclals17 = loneliness_7) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Prefrosh
UCLA_prefrosh = prefrosh %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, loneliness_1, loneliness_2, loneliness_3R, loneliness_4, loneliness_5, loneliness_6R, loneliness_8, loneliness_7)
UCLA_prefrosh = UCLA_prefrosh %>%
rename(uclals4 = loneliness_1) %>%
rename(uclals7 = loneliness_2) %>%
mutate(outgoing = 5 - loneliness_3R) %>%
select(-(loneliness_3R)) %>%
rename(uclals10 = loneliness_4) %>%
rename(uclals16 = loneliness_5) %>%
mutate(uls15 = 5 - loneliness_6R) %>%
select(-(loneliness_6R)) %>%
rename(uclals20 = loneliness_8) %>%
rename(uclals17 = loneliness_7) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Binding
UCLA_prefrosh = UCLA_prefrosh %>%
mutate(duplicate = ifelse((src_subject_id %in% UCLA_networks$src_subject_id), "yes", "no"))
UCLA_networks = UCLA_networks %>%
mutate(duplicate = "no")
UCLA = rbind(UCLA_networks, UCLA_prefrosh)
UCLA = UCLA %>%
filter(duplicate == "no") %>%
select(-(duplicate))
UCLA = UCLA %>%
drop_na()# Networks
SWL_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, LifeSatisfaction_3, LifeSatisfaction_4, LifeSatisfaction_1, LifeSatisfaction_6, LifeSatisfaction_9)
SWL_networks = SWL_networks %>%
rename(swl1 = LifeSatisfaction_3) %>%
rename(swl2 = LifeSatisfaction_4) %>%
rename(swl3 = LifeSatisfaction_1) %>%
rename(swl4 = LifeSatisfaction_6) %>%
rename(swl5 = LifeSatisfaction_9) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Prefrosh
SWL_prefrosh = prefrosh %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, LifeSatisfaction_3, LifeSatisfaction_4, LifeSatisfaction_1, LifeSatisfaction_6, LifeSatisfaction_9)
SWL_prefrosh = SWL_prefrosh %>%
rename(swl1 = LifeSatisfaction_3) %>%
rename(swl2 = LifeSatisfaction_4) %>%
rename(swl3 = LifeSatisfaction_1) %>%
rename(swl4 = LifeSatisfaction_6) %>%
rename(swl5 = LifeSatisfaction_9) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Binding
SWL_prefrosh = SWL_prefrosh %>%
mutate(duplicate = ifelse((src_subject_id %in% SWL_networks$src_subject_id), "yes", "no"))
SWL_networks = SWL_networks %>%
mutate(duplicate = "no")
SWL = rbind(SWL_networks, SWL_prefrosh)
SWL = SWL %>%
filter(duplicate == "no") %>%
select(-(duplicate))
SWL = SWL %>%
drop_na()# Networks
CDRS_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, CD.RISC_1, CD.RISC_2)
CDRS = CDRS_networks %>%
rename(cdrs1 = CD.RISC_1) %>%
rename(cdrs2 = CD.RISC_2) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
CDRS = CDRS %>%
drop_na()# Networks
NTB_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, NTB_single)
NTB_networks = NTB_networks %>%
rename(ntbs8 = NTB_single) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Prefrosh
NTB_prefrosh = prefrosh %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, NTB_single)
NTB_prefrosh = NTB_prefrosh %>%
rename(ntbs8 = NTB_single) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Binding
NTB_prefrosh = NTB_prefrosh %>%
mutate(duplicate = ifelse((src_subject_id %in% NTB_networks$src_subject_id), "yes", "no"))
NTB_networks = NTB_networks %>%
mutate(duplicate = "no")
NTB = rbind(NTB_networks, NTB_prefrosh)
NTB = NTB %>%
filter(duplicate == "no") %>%
select(-(duplicate))
NTB = NTB %>%
drop_na()# Networks
GAD_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, GAD_1, GAD_2, GAD_3, GAD_4, GAD_5, GAD_6, GAD_7)
GAD = GAD_networks %>%
rename(gad7_1 = GAD_1) %>%
rename(gad7_2 = GAD_2) %>%
rename(gad7_3 = GAD_3) %>%
rename(gad7_4 = GAD_4) %>%
rename(gad7_5 = GAD_5) %>%
rename(gad7_6 = GAD_6) %>%
rename(gad7_7 = GAD_7) %>%
mutate(gad7_8 = 1) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
GAD = GAD %>%
drop_na()# Networks
PHQ_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, PHQ_1, PHQ_2, PHQ_3, PHQ_4, PHQ_5, PHQ_6, PHQ_7, PHQ_8, PHQ_9)
PHQ = PHQ_networks %>%
rename(phq9_1 = PHQ_1) %>%
rename(phq9_2 = PHQ_2) %>%
rename(phq9_3 = PHQ_3) %>%
rename(phq9_4 = PHQ_4) %>%
rename(phq9_5 = PHQ_5) %>%
rename(phq9_6 = PHQ_6) %>%
rename(phq9_7 = PHQ_7) %>%
rename(phq9_8 = PHQ_8) %>%
rename(phq9_9 = PHQ_9) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
PHQ = PHQ %>%
drop_na()# Networks
TIPI_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, TIPI_Extra, TIPI_Criti_R, TIPI_Depen, TIPI_Anx_R, TIPI_Open, TIPI_Reserv_R, TIPI_Symp, TIPI_Disorg_R, TIPI_EmoSta, TIPI_Conven_R)
TIPI_networks = TIPI_networks %>%
rename(extravertenthusiastic = TIPI_Extra) %>%
rename(quarrelsome = TIPI_Criti_R) %>%
rename(dependable = TIPI_Depen) %>%
rename(anxiouseasyupset = TIPI_Anx_R) %>%
rename(opentonewexperiences = TIPI_Open) %>%
rename(reservedquiet = TIPI_Reserv_R) %>%
rename(sympatheticwarm = TIPI_Symp) %>%
rename(disorganizedcareless = TIPI_Disorg_R) %>%
rename(calmemostable = TIPI_EmoSta) %>%
rename(uncreative = TIPI_Conven_R) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Prefrosh
TIPI_prefrosh = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, TIPI_Extra, TIPI_Criti_R, TIPI_Depen, TIPI_Anx_R, TIPI_Open, TIPI_Reserv_R, TIPI_Symp, TIPI_Disorg_R, TIPI_EmoSta, TIPI_Conven_R)
TIPI_prefrosh = TIPI_prefrosh %>%
rename(extravertenthusiastic = TIPI_Extra) %>%
rename(quarrelsome = TIPI_Criti_R) %>%
rename(dependable = TIPI_Depen) %>%
rename(anxiouseasyupset = TIPI_Anx_R) %>%
rename(opentonewexperiences = TIPI_Open) %>%
rename(reservedquiet = TIPI_Reserv_R) %>%
rename(sympatheticwarm = TIPI_Symp) %>%
rename(disorganizedcareless = TIPI_Disorg_R) %>%
rename(calmemostable = TIPI_EmoSta) %>%
rename(uncreative = TIPI_Conven_R) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
# Binding
TIPI_prefrosh = TIPI_prefrosh %>%
mutate(duplicate = ifelse((src_subject_id %in% TIPI_networks$src_subject_id), "yes", "no"))
TIPI_networks = TIPI_networks %>%
mutate(duplicate = "no")
TIPI = rbind(TIPI_networks, TIPI_prefrosh)
TIPI = TIPI %>%
filter(duplicate == "no") %>%
select(-(duplicate))
TIPI = TIPI %>%
drop_na()# Networks
scomp_networks = networks %>%
select(PSEUDO.GUID, PID, StartDate, Age, SEX, selfComp_1, selfComp_2, selfComp_3R, selfComp_4R, selfComp_5, selfComp_6R)
selfcom01 = scomp_networks %>%
rename(scs_q14_bls = selfComp_1) %>%
rename(scs_q12_bls = selfComp_2) %>%
rename(scs_q25_bls = selfComp_3R) %>%
rename(scs_q2_bls = selfComp_4R) %>%
rename(scs_q10_bls = selfComp_5) %>%
rename(scs_q1_bls = selfComp_6R) %>%
rename(subjectkey = PSEUDO.GUID) %>%
rename(src_subject_id = PID) %>%
rename(interview_date = StartDate) %>%
rename(interview_age = Age) %>%
rename(sex = SEX)
selfcom01 = selfcom01 %>%
drop_na()
Social Phobia