setwd("C:/Work Files/Lab/Data Sets/SIF16 Data")SPR 2025 Data
Packages:
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.2 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(haven)
library(psych)
Attaching package: 'psych'
The following objects are masked from 'package:ggplot2':
%+%, alphalibrary(purrr)
library(forcats)
library(haven)Required Individual Data Sets
Demographics <- read.csv("demographics.csv")
Chris_Data <- read.csv("ChrisData.csv")
ASSM <- read.csv("ASSM.csv")
IBQ <- read.csv("IBQ.csv")
ECBQ <-read.csv("ECBQ.csv")
CBQ <-read.csv("CBQ.csv")
ASQ_Communication <-read.csv("ASQ Communication.csv")
ASQ_Fine_Motor <-read.csv("ASQ Fine Motor.csv")
ASQ_Gross_Motor <-read.csv("ASQ Gross Motor.csv")
ASQ_Personal_Social <-read.csv("ASQ Personal and Social.csv")
ASQ_Prob_Solving <-read.csv("ASQ Problem Solving.csv")Demographics Data Wrangling
names(Demographics) [1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2" "PROGRAMNAME"
[5] "INTAKEDATE" "AGEINTK" "FEMALE" "AGEFOCUSINTAKE"
[9] "FOCUSFEMALE" "AMINDR" "ASIANR" "BLACKR"
[13] "HISPR" "EASTR" "HAWAIIR" "WHITER"
[17] "OTHERR" "PRIMEENG" "PRIMESPAN" "PRIMEARAB"
[21] "PRIMEOTH" "LIVSPOUCE" "RECEIVEPA" "NUMHOUSE"
[25] "EDUCATE" ##Participant Type Restructure##
str(Demographics$PARTTYPE2) chr [1:992] "A_Intervention" "A_Intervention" "A_Intervention" ...table(Demographics$PARTTYPE2)
A_Intervention B_Comparison
518 474 Demographics$PARTTYPE2 <- recode(Demographics$PARTTYPE2,
"A_Intervention" = 1,
"B_Comparison" = 0)
Demographics$PARTTYPE2 <-factor(Demographics$PARTTYPE2,
levels = c(0,1),
labels = c("Control", "Intervention"),
ordered = TRUE)
## Program Where Participant Recieved Services Restructure##
table(Demographics$PROGRAMNAME)
ACCESS CARE for Southeastern Michigan
171 201
Leaps and Bounds NKFM
169 237
Oakland Family Services
214 Demographics$PROGRAMNAME <- recode(Demographics$PROGRAMNAME,
"ACCESS" = 1,
"CARE for Southeastern Michigan" = 2,
"Leaps and Bounds" = 3,
"NKFM" = 4,
"Oakland Family Services" = 5)
Demographics$PROGRAMNAME <- factor(Demographics$PROGRAMNAME,
levels = c(1,2,3,4,5),
labels = c("ACCESS", "CARE", "L_N_B", "NKFM", "OFS"),
ordered = FALSE)
##Caregiver Gender##
table(Demographics$FEMALE)
0 1
29 956 str(Demographics$FEMALE) int [1:992] 1 1 1 1 1 1 1 1 1 1 ...Demographics$FEMALE <- factor(Demographics$FEMALE,
levels = c(0,1),
labels = c("Male","Female"))
##Focus Child Gender##
table(Demographics$FOCUSFEMALE)
0 1
499 492 Demographics$FOCUSFEMALE <-factor(Demographics$FOCUSFEMALE,
levels = c(0,1),
labels = c("Male","Female"))
##Age of Caregiver##
str(Demographics$CG_AGE_Intake) NULLDemographics <- Demographics %>%
rename(CG_AGE_Intake = AGEINTK)
##Age of Focus Child##
str(Demographics$AGEFOCUSINTAKE) num [1:992] 3.41 1.68 4.76 4.7 3.52 ...##Race of Caregiver##
str(Demographics$BLACKR) int [1:992] 1 0 0 0 0 0 1 0 0 0 ...Demographics$AMINDR <-factor(Demographics$AMINDR,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$ASIANR <-factor(Demographics$ASIANR,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$BLACKR <-factor(Demographics$BLACKR,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$HISPR <-factor(Demographics$HISPR,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$EASTR <-factor(Demographics$EASTR,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$HAWAIIR <-factor(Demographics$HAWAIIR,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$WHITER <-factor(Demographics$WHITER,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$OTHERR <-factor(Demographics$OTHERR,
levels = c(0,1),
labels = c("No","Yes"))
table(Demographics$BLACKR)
No Yes
521 464 ##Race of Focus Child - NOT INCLUDED IN DEMOGRAPHIC DATA FRAME##
##Language Spoken At Home##
str(Demographics$PRIMEENG) int [1:992] 1 1 1 0 0 0 0 0 0 0 ...Demographics$PRIMEARAB <-factor(Demographics$PRIMEARAB,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$PRIMEENG <-factor(Demographics$PRIMEENG,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$PRIMESPAN <-factor(Demographics$PRIMESPAN,
levels = c(0,1),
labels = c("No","Yes"))
Demographics$PRIMEOTH <-factor(Demographics$PRIMEOTH,
levels = c(0,1),
labels = c("No","Yes"))
table(Demographics$PRIMEARAB)
No Yes
789 195 Data Wrangling Chris Data
names(Chris_Data) [1] "Program" "ParticipantType"
[3] "SiteID" "FamilyID"
[5] "ResponseID" "DOB"
[7] "Gender" "FamilyNeed"
[9] "NeedCat" "PARTTYPE2"
[11] "HEALTHCARE" "LEGAL"
[13] "PARENTINGSKILLS" "INCOME"
[15] "SOCIAL" "EMPLOYMENT"
[17] "CHILDCARE" "EDUCATE"
[19] "ADULTED" "FOOD"
[21] "HOUSING" "TRANSPORTATION"
[23] "MENTAL" "SUBABUSE"
[25] "DISABILITIES" "COMMUNINVOLV"
[27] "SAFETY" "LIFESKILLS"
[29] "BASENEEDS2" "PARENTS2"
[31] "SOCIAL2" "BEHAVE2"
[33] "ReferralID" "Refer_for_Goal"
[35] "ASSMCategory" "ReferredTo"
[37] "GoalID" "GoalDomainCategory"
[39] "DomainOther" "Goal"
[41] "PossibleResources" "GoalStatus"
[43] "SiteName" "ProgramName"
[45] "DateofEnrollment" "DateofBirthmmddyyyy"
[47] "PrimaryLanguage" "PrimaryLanguageAtHome"
[49] "AmericanIndianorAlaskaNative" "Asian"
[51] "BlackorAfricanAmerican" "HispanicLatinoorSpanish"
[53] "MiddleEasternorNorthAfrican" "NativeHawaiianorPacificIslander"
[55] "White" "SomeOtherRaceEthnicity"
[57] "BornInUnitedStates" "HowLongInUSIfNotBorn"
[59] "PrimaryFirst" table(Chris_Data$HowLongInUSIfNotBorn)
0 4 5 7 15 18 20 21 22 27 28 35 1993 1999 2002 2003
1 1 2 1 1 1 2 1 1 1 1 1 1 3 1 1
2005 2007 2009 2010 2011 2012 2013 2014 2015 2016 2017
1 3 1 1 1 1 3 1 4 5 3 table(Chris_Data$BornInUnitedStates)
0 1
551 251 Chris_Data <- Chris_Data %>%
rename(Immigrant_Status = BornInUnitedStates)
table(Chris_Data$Immigrant_Status)
0 1
551 251 Chris_Data$Immigrant_Status <-factor(Chris_Data$Immigrant_Status,
levels = c(0,1),
labels = c("No","Yes"))
str(Chris_Data$HowLongInUSIfNotBorn) int [1:13393] NA NA NA NA NA NA NA NA NA NA ...Chris_Data$HowLongInUSIfNotBorn <- ifelse(Chris_Data$HowLongInUSIfNotBorn >= 1900, 2017 - Chris_Data$HowLongInUSIfNotBorn, Chris_Data$HowLongInUSIfNotBorn)
sum(!is.na(Chris_Data$HowLongInUSIfNotBorn))[1] 44##There are only 44 cases where the participant indicated how long they were in the US.##Data Wrangling ASSM scores
names(ASSM) [1] "FAMILYID" "PARTICIPANTID"
[3] "PARTTYPE2" "PROGRAMNAME"
[5] "DATETAKEN_200" "ASSESSMENTTIMEFRAME_16255"
[7] "BASENEEDS2" "ASSM_HOUSING"
[9] "ASSM_INCOME" "ASSM_FOOD"
[11] "ASSM_ADULTED" "ASSM_EMPLOYMENT"
[13] "ASSM_TRANSPORTATION" "ASSM_HEALTHCARE"
[15] "ASSM_SAFETY" "ASSM_LIFESKILLS"
[17] "ASSM_PARENTS2" "ASSM_CHILDCARE"
[19] "ASSM_PARENTINGSKILLS" "ASSM_EDUCATE"
[21] "ASSM_SOCIAL2" "ASSM_SOCIAL"
[23] "ASSM_COMMUNINVOLV" "ASSM_BEHAVE2"
[25] "ASSM_MENTAL" "ASSM_SUBABUSE"
[27] "ASSM_DISABILITIES" "ASSM_LEGAL" str(ASSM)'data.frame': 2311 obs. of 28 variables:
$ FAMILYID : int 572 572 573 574 574 574 579 579 579 580 ...
$ PARTICIPANTID : int 105899 105899 105901 105903 105903 105903 106524 106524 106524 106529 ...
$ PARTTYPE2 : chr "A_Intervention" "A_Intervention" "A_Intervention" "A_Intervention" ...
$ PROGRAMNAME : chr "Oakland Family Services" "Oakland Family Services" "Oakland Family Services" "Oakland Family Services" ...
$ DATETAKEN_200 : chr "4/6/2020" "10/17/2019" "4/22/2019" "2/13/2019" ...
$ ASSESSMENTTIMEFRAME_16255: chr "6 Months" "Baseline" "Baseline" "12 Months" ...
$ BASENEEDS2 : num 4.12 3.67 4 2.5 2.12 ...
$ ASSM_HOUSING : int 4 3 3 2 3 2 5 5 5 4 ...
$ ASSM_INCOME : int 3 2 3 2 2 2 4 5 5 3 ...
$ ASSM_FOOD : int 1 2 1 3 1 1 1 5 5 1 ...
$ ASSM_ADULTED : int 5 5 5 3 3 3 3 5 5 2 ...
$ ASSM_EMPLOYMENT : int NA 1 NA NA NA NA NA NA NA NA ...
$ ASSM_TRANSPORTATION : int 5 5 5 3 1 1 4 4 4 4 ...
$ ASSM_HEALTHCARE : int 5 5 5 5 5 5 5 5 5 5 ...
$ ASSM_SAFETY : int 5 5 5 1 1 1 5 5 1 5 ...
$ ASSM_LIFESKILLS : int 5 5 5 1 1 NA 5 5 5 5 ...
$ ASSM_PARENTS2 : num 5 5 5 2.33 3 ...
$ ASSM_CHILDCARE : int NA NA NA 1 NA 1 NA 5 5 NA ...
$ ASSM_PARENTINGSKILLS : int 5 5 5 1 1 4 5 3 3 5 ...
$ ASSM_EDUCATE : int 5 5 5 5 5 NA 5 5 5 5 ...
$ ASSM_SOCIAL2 : num 4.5 3.5 3.5 1 1.5 2 4.5 5 5 4.5 ...
$ ASSM_SOCIAL : int 5 5 5 1 2 2 5 5 5 5 ...
$ ASSM_COMMUNINVOLV : int 4 2 2 1 1 2 4 5 5 4 ...
$ ASSM_BEHAVE2 : num 5 4 5 3.5 4 3 5 5 5 5 ...
$ ASSM_MENTAL : int 5 5 5 3 3 1 5 5 5 5 ...
$ ASSM_SUBABUSE : int 5 5 5 5 5 5 5 5 5 5 ...
$ ASSM_DISABILITIES : int 5 5 5 5 3 5 5 5 5 5 ...
$ ASSM_LEGAL : int 5 1 5 1 5 1 5 5 5 5 ...table(ASSM$ASSESSMENTTIMEFRAME_16255)
12 Months 6 Months Baseline
582 763 966 ASSM <- ASSM %>%
rename(Wave = ASSESSMENTTIMEFRAME_16255)
table(ASSM$Wave)
12 Months 6 Months Baseline
582 763 966 str(ASSM$Wave) chr [1:2311] "6 Months" "Baseline" "Baseline" "12 Months" "6 Months" ...ASSM$Wave <- recode(ASSM$Wave,
"Baseline" = 1,
"6 Months" = 2,
"12 Months" = 3)
ASSM$Wave <-factor(ASSM$Wave,
levels = c(1,2,3),
labels = c("Baseline", "6 Months", "12 Months"), ordered = FALSE)
ASSM <- ASSM %>%
mutate(across(c("ASSM_HOUSING", "ASSM_INCOME","ASSM_FOOD","ASSM_ADULTED","ASSM_EMPLOYMENT","ASSM_TRANSPORTATION","ASSM_HEALTHCARE","ASSM_SAFETY","ASSM_LIFESKILLS","ASSM_CHILDCARE","ASSM_PARENTINGSKILLS","ASSM_EDUCATE","ASSM_SOCIAL","ASSM_COMMUNINVOLV","ASSM_MENTAL","ASSM_SUBABUSE","ASSM_DISABILITIES","ASSM_LEGAL"), as.numeric)) Data Wrangling ASQ
names(ASQ_Personal_Social) [1] "FAMILYID" "DATE_TAKE" "PARTTYPE2" "WAVE"
[5] "AGERANGE" "DATETAKEN_201" "PS2" "PS4"
[9] "PS6" "PS8" "PS9" "PS10"
[13] "PS12" "PS14" "PS16" "PS18"
[17] "PS20" "PS22" "PS24" "PS27"
[21] "PS30" "PS33" "PS36" "PS42"
[25] "PS48" "PS54" "PS60" head(ASQ_Personal_Social) FAMILYID DATE_TAKE PARTTYPE2 WAVE AGERANGE DATETAKEN_201 PS2 PS4
1 572 4/6/2020 A_Intervention 6 Months 48 months 10/17/2019 NA NA
2 572 11/6/2019 A_Intervention Baseline 42 months 10/17/2019 NA NA
3 574 8/27/2018 A_Intervention 6 Months 60 months 2/22/2018 NA NA
4 574 2/27/2018 A_Intervention Baseline 60 months 2/22/2018 NA NA
5 579 6/18/2019 B_Comparison 6 Months 60 months 1/16/2019 NA NA
6 579 1/16/2019 B_Comparison Baseline 54 months 1/16/2019 NA NA
PS6 PS8 PS9 PS10 PS12 PS14 PS16 PS18 PS20 PS22 PS24 PS27 PS30 PS33 PS36 PS42
1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 60
3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
PS48 PS54 PS60
1 55 NA NA
2 NA NA NA
3 NA NA 45
4 NA NA 60
5 NA NA 50
6 NA 50 NAtable(ASQ_Personal_Social$WAVE)
12 Months 6 Months Baseline
517 712 889 str(ASQ_Personal_Social$WAVE) chr [1:2118] "6 Months" "Baseline" "6 Months" "Baseline" "6 Months" ...ASQ_Personal_Social$WAVE <- recode(ASQ_Personal_Social$WAVE,
"Baseline" = 1,
"6 Months" = 2,
"12 Months" = 3)
ASQ_Personal_Social$WAVE <-factor(ASQ_Personal_Social$WAVE,
levels = c(1,2,3),
labels = c("Baseline", "6 Months", "12 Months"), ordered = FALSE)
ASQ_Personal_Social$PS_Score <- rowSums(ASQ_Personal_Social[, c("PS2", "PS4", "PS6","PS8","PS9","PS10","PS12","PS14","PS16","PS18","PS20","PS22","PS24","PS27","PS30","PS33","PS36","PS42","PS48","PS54","PS60")], na.rm = TRUE)
str(ASQ_Personal_Social$PS_Score) num [1:2118] 55 60 45 60 50 50 50 50 50 60 ...describeBy(ASQ_Personal_Social$PS_Score,ASQ_Personal_Social$WAVE)
Descriptive statistics by group
group: Baseline
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 889 50.08 11.47 55 52.1 7.41 0 60 60 -1.52 2.45 0.38
------------------------------------------------------------
group: 6 Months
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 712 51.51 10.36 55 53.41 7.41 0 60 60 -1.68 3.4 0.39
------------------------------------------------------------
group: 12 Months
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 517 51.36 11.28 55 53.51 7.41 0 60 60 -1.75 3.49 0.5Data Wrangling IBQ
names(IBQ)[1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2"
[4] "PROGRAMNAME" "WAVE" "DATE"
[7] "IBQ_NEGATIVESCORE" "IBQ_EFFORTFULSCORE"str(IBQ)'data.frame': 398 obs. of 8 variables:
$ FAMILYID : int 585 585 586 586 592 592 594 595 601 610 ...
$ PARTICIPANTID : int 111516 111516 106590 106590 106666 106665 106674 106783 107035 107281 ...
$ PARTTYPE2 : chr "B_Comparison" "B_Comparison" "A_Intervention" "A_Intervention" ...
$ PROGRAMNAME : chr "ACCESS" "ACCESS" "ACCESS" "ACCESS" ...
$ WAVE : chr "12 Months" "6 Months" "6 Months" "Baseline" ...
$ DATE : chr "4/25/2019" "11/15/2018" "8/14/2019" "4/12/2018" ...
$ IBQ_NEGATIVESCORE : num 4.58 3.56 2.83 2.58 2.08 ...
$ IBQ_EFFORTFULSCORE: num 6.08 6.08 4.17 4.91 6.17 ...table(IBQ$WAVE)
12 Months 6 Months Baseline
16 141 241 IBQ$WAVE <- recode(IBQ$WAVE,
"Baseline" = 1,
"6 Months" = 2,
"12 Months" = 3)
IBQ$WAVE <-factor(IBQ$WAVE,
levels = c(1,2,3),
labels = c("Baseline", "6 Months", "12 Months"), ordered = FALSE)Data Wrangling ECBQ
names(ECBQ)[1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2"
[4] "PROGRAMNAME" "WAVE" "DATE"
[7] "ECBQ_NEGATIVESCORE" "ECBQ_EFFORTFULSCORE"str(ECBQ)'data.frame': 918 obs. of 8 variables:
$ FAMILYID : int 582 582 583 584 586 587 587 587 589 589 ...
$ PARTICIPANTID : int 106556 106556 106574 106578 106590 106634 106634 106634 106659 106659 ...
$ PARTTYPE2 : chr "B_Comparison" "B_Comparison" "A_Intervention" "A_Intervention" ...
$ PROGRAMNAME : chr "ACCESS" "ACCESS" "ACCESS" "ACCESS" ...
$ WAVE : chr "6 Months" "Baseline" "Baseline" "Baseline" ...
$ DATE : chr "11/19/2018" "5/22/2018" "5/31/2018" "3/29/2018" ...
$ ECBQ_NEGATIVESCORE : num 2.1 3 4.17 3.17 4.08 ...
$ ECBQ_EFFORTFULSCORE: num 4 4.5 5.33 5.33 5.58 ...table(ECBQ$WAVE)
12 Months 6 Months Baseline
272 309 337 ECBQ$WAVE <- recode(ECBQ$WAVE,
"Baseline" = 1,
"6 Months" = 2,
"12 Months" = 3)
ECBQ$WAVE <-factor(ECBQ$WAVE,
levels = c(1,2,3),
labels = c("Baseline", "6 Months", "12 Months"), ordered = FALSE)Data Wrangling CBQ
names(CBQ)[1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2"
[4] "PROGRAMNAME" "WAVE" "DATE"
[7] "CBQ_NEGATIVESCORE" "CBQ_EFFORTFULSCORE"str(CBQ)'data.frame': 863 obs. of 8 variables:
$ FAMILYID : int 572 572 574 574 574 579 579 579 580 580 ...
$ PARTICIPANTID : int 105900 105900 105904 105904 105904 106525 106525 106525 106530 106530 ...
$ PARTTYPE2 : chr "A_Intervention" "A_Intervention" "A_Intervention" "A_Intervention" ...
$ PROGRAMNAME : chr "Oakland Family Services" "Oakland Family Services" "Oakland Family Services" "Oakland Family Services" ...
$ WAVE : chr "6 Months" "Baseline" "12 Months" "6 Months" ...
$ DATE : chr "4/6/2020" "11/6/2019" "2/13/2019" "8/27/2018" ...
$ CBQ_NEGATIVESCORE : num 1.67 4 4.33 3.42 3.08 ...
$ CBQ_EFFORTFULSCORE: num 7 6.42 2.92 3.17 2.67 ...table(CBQ$WAVE)
12 Months 6 Months Baseline
291 298 274 CBQ$WAVE <- recode(CBQ$WAVE,
"Baseline" = 1,
"6 Months" = 2,
"12 Months" = 3)
CBQ$WAVE <-factor(CBQ$WAVE,
levels = c(1,2,3),
labels = c("Baseline", "6 Months", "12 Months"), ordered = FALSE)Creating Data Subsets specific to the SPR analyses
names(Demographics) [1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2" "PROGRAMNAME"
[5] "INTAKEDATE" "CG_AGE_Intake" "FEMALE" "AGEFOCUSINTAKE"
[9] "FOCUSFEMALE" "AMINDR" "ASIANR" "BLACKR"
[13] "HISPR" "EASTR" "HAWAIIR" "WHITER"
[17] "OTHERR" "PRIMEENG" "PRIMESPAN" "PRIMEARAB"
[21] "PRIMEOTH" "LIVSPOUCE" "RECEIVEPA" "NUMHOUSE"
[25] "EDUCATE" SPR_Demographics <-Demographics[,c(1,2,3,4,6,7,8,9:21)]
names(SPR_Demographics) [1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2" "PROGRAMNAME"
[5] "CG_AGE_Intake" "FEMALE" "AGEFOCUSINTAKE" "FOCUSFEMALE"
[9] "AMINDR" "ASIANR" "BLACKR" "HISPR"
[13] "EASTR" "HAWAIIR" "WHITER" "OTHERR"
[17] "PRIMEENG" "PRIMESPAN" "PRIMEARAB" "PRIMEOTH" names(ASSM) [1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2"
[4] "PROGRAMNAME" "DATETAKEN_200" "Wave"
[7] "BASENEEDS2" "ASSM_HOUSING" "ASSM_INCOME"
[10] "ASSM_FOOD" "ASSM_ADULTED" "ASSM_EMPLOYMENT"
[13] "ASSM_TRANSPORTATION" "ASSM_HEALTHCARE" "ASSM_SAFETY"
[16] "ASSM_LIFESKILLS" "ASSM_PARENTS2" "ASSM_CHILDCARE"
[19] "ASSM_PARENTINGSKILLS" "ASSM_EDUCATE" "ASSM_SOCIAL2"
[22] "ASSM_SOCIAL" "ASSM_COMMUNINVOLV" "ASSM_BEHAVE2"
[25] "ASSM_MENTAL" "ASSM_SUBABUSE" "ASSM_DISABILITIES"
[28] "ASSM_LEGAL" SPR_ASSM <-ASSM[,c(1,2,6:28)]
names(SPR_ASSM) [1] "FAMILYID" "PARTICIPANTID" "Wave"
[4] "BASENEEDS2" "ASSM_HOUSING" "ASSM_INCOME"
[7] "ASSM_FOOD" "ASSM_ADULTED" "ASSM_EMPLOYMENT"
[10] "ASSM_TRANSPORTATION" "ASSM_HEALTHCARE" "ASSM_SAFETY"
[13] "ASSM_LIFESKILLS" "ASSM_PARENTS2" "ASSM_CHILDCARE"
[16] "ASSM_PARENTINGSKILLS" "ASSM_EDUCATE" "ASSM_SOCIAL2"
[19] "ASSM_SOCIAL" "ASSM_COMMUNINVOLV" "ASSM_BEHAVE2"
[22] "ASSM_MENTAL" "ASSM_SUBABUSE" "ASSM_DISABILITIES"
[25] "ASSM_LEGAL" SPR_ASSM <- SPR_ASSM %>%
rename(WAVE = Wave)
names(Chris_Data) [1] "Program" "ParticipantType"
[3] "SiteID" "FamilyID"
[5] "ResponseID" "DOB"
[7] "Gender" "FamilyNeed"
[9] "NeedCat" "PARTTYPE2"
[11] "HEALTHCARE" "LEGAL"
[13] "PARENTINGSKILLS" "INCOME"
[15] "SOCIAL" "EMPLOYMENT"
[17] "CHILDCARE" "EDUCATE"
[19] "ADULTED" "FOOD"
[21] "HOUSING" "TRANSPORTATION"
[23] "MENTAL" "SUBABUSE"
[25] "DISABILITIES" "COMMUNINVOLV"
[27] "SAFETY" "LIFESKILLS"
[29] "BASENEEDS2" "PARENTS2"
[31] "SOCIAL2" "BEHAVE2"
[33] "ReferralID" "Refer_for_Goal"
[35] "ASSMCategory" "ReferredTo"
[37] "GoalID" "GoalDomainCategory"
[39] "DomainOther" "Goal"
[41] "PossibleResources" "GoalStatus"
[43] "SiteName" "ProgramName"
[45] "DateofEnrollment" "DateofBirthmmddyyyy"
[47] "PrimaryLanguage" "PrimaryLanguageAtHome"
[49] "AmericanIndianorAlaskaNative" "Asian"
[51] "BlackorAfricanAmerican" "HispanicLatinoorSpanish"
[53] "MiddleEasternorNorthAfrican" "NativeHawaiianorPacificIslander"
[55] "White" "SomeOtherRaceEthnicity"
[57] "Immigrant_Status" "HowLongInUSIfNotBorn"
[59] "PrimaryFirst" SPR_Immigration <-Chris_Data[,c(4,57,58)]
names(SPR_Immigration)[1] "FamilyID" "Immigrant_Status" "HowLongInUSIfNotBorn"SPR_Immigration <- SPR_Immigration %>%
rename(FAMILYID = FamilyID)
str(SPR_Immigration$FAMILYID) chr [1:13393] "495" "495" "506" "Leaps and Bounds" "506" "506" " " "506" ...SPR_Immigration %>%
filter(!grepl("^[0-9]+$", FAMILYID)) %>%
select(FAMILYID) FAMILYID
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49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310 Leaps and Bounds
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362 NKFM
363
364
365
366 NKFM
367
368
369
370
371
372
373 ACCESS
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391 Goal Dropped
392
393
394
395 Goal Met
396 Goal Dropped
397 Goal Met
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446 ACCESS
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468 ACCESS
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563 NKFM
564 Bib to Backpack
565
566
567
568
569
570 Bib to Backpack
571
572 Oakland Family Services
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592 Bib to Backpack
593
594 CARE for Southeastern Michigan
595 Oakland Family Services
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659 ACCESS
660 NKFM
661
662
663
664 ACCESS
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680 Bib to Backpack
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742 Bib to Backpack
743 Bib to Backpack
744
745
746
747
748
749
750
751 Oakland Family Services
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806 Bib to Backpack
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864 NKFM
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950 Bib to Backpack
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994 ACCESS
995
996
997 ACCESS
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031 NKFM
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042 Bib to Backpack
1043
1044
1045 Bib to Backpack
1046
1047
1048
1049 NKFM
1050
1051
1052
1053
1054 CARE for Southeastern Michigan
1055
1056
1057
1058 Bib to Backpack
1059
1060
1061
1062
1063 NKFM
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077 NKFM
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089 Bib to Backpack
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116 Bib to Backpack
1117
1118 NKFM
1119
1120
1121
1122 NKFM
1123
1124
1125
1126
1127
1128
1129
1130 ACCESS
1131 Goal Met
1132
1133 Goal Met
1134
1135
1136
1137
1138
1139
1140 Bib to Backpack
1141
1142
1143
1144
1145 Bib to Backpack
1146
1147
1148
1149 Bib to Backpack
1150
1151
1152
1153
1154
1155
1156
1157 Bib to Backpack
1158
1159
1160
1161
1162
1163
1164
1165
1166 CARE for Southeastern Michigan
1167
1168
1169
1170
1171
1172
1173
1174
1175 ACCESS
1176
1177 CARE for Southeastern Michigan
1178
1179
1180
1181
1182 Bib to Backpack
1183 ACCESS
1184
1185
1186
1187 CARE for Southeastern Michigan
1188
1189
1190
1191
1192 Bib to Backpack
1193
1194
1195
1196
1197 Bib to Backpack
1198
1199
1200
1201
1202
1203
1204 Bib to Backpack
1205
1206
1207
1208 Bib to Backpack
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224 Bib to Backpack
1225
1226
1227
1228 Bib to Backpack
1229
1230
1231 Bib to Backpack
1232
1233
1234
1235
1236 Bib to Backpack
1237
1238
1239
1240
1241
1242 Oakland Family Services
1243
1244
1245
1246
1247 Oakland Family Services
1248 SPR_Immigration <- SPR_Immigration %>%
filter(grepl("^[0-9]+$", FAMILYID))
unique(SPR_Immigration$FAMILYID) [1] "495" "506" "508" "509" "510" "515" "519" "520" "521" "523"
[11] "525" "526" "527" "530" "531" "532" "533" "534" "535" "536"
[21] "538" "539" "540" "546" "547" "550" "552" "554" "556" "558"
[31] "559" "560" "561" "562" "563" "564" "565" "566" "567" "570"
[41] "572" "573" "574" "575" "579" "580" "581" "582" "583" "584"
[51] "585" "586" "587" "588" "589" "590" "591" "592" "593" "594"
[61] "595" "596" "597" "598" "600" "601" "602" "603" "604" "605"
[71] "606" "607" "608" "609" "610" "611" "612" "613" "614" "615"
[81] "616" "617" "618" "619" "621" "622" "623" "624" "625" "626"
[91] "627" "628" "629" "630" "631" "633" "634" "635" "636" "637"
[101] "638" "639" "640" "641" "642" "643" "644" "645" "646" "647"
[111] "648" "649" "650" "651" "652" "655" "658" "659" "661" "662"
[121] "663" "664" "666" "668" "669" "674" "675" "677" "678" "681"
[131] "682" "685" "687" "690" "691" "693" "694" "695" "698" "699"
[141] "700" "701" "702" "704" "705" "706" "708" "709" "710" "711"
[151] "713" "714" "715" "716" "717" "719" "720" "721" "722" "723"
[161] "724" "725" "727" "728" "729" "730" "732" "734" "736" "737"
[171] "738" "739" "740" "741" "742" "744" "748" "749" "750" "751"
[181] "752" "753" "754" "755" "756" "757" "759" "760" "761" "762"
[191] "763" "764" "765" "766" "767" "768" "769" "770" "771" "772"
[201] "773" "774" "776" "777" "778" "779" "780" "781" "782" "783"
[211] "784" "785" "786" "787" "788" "789" "790" "791" "792" "793"
[221] "796" "797" "798" "799" "800" "801" "802" "803" "804" "805"
[231] "806" "807" "808" "809" "811" "812" "813" "814" "815" "816"
[241] "817" "818" "830" "833" "837" "838" "845" "846" "847" "848"
[251] "849" "850" "851" "852" "853" "855" "858" "859" "860" "861"
[261] "862" "863" "864" "865" "866" "867" "868" "870" "871" "872"
[271] "873" "874" "875" "876" "877" "878" "881" "882" "885" "886"
[281] "888" "889" "890" "891" "892" "893" "894" "895" "896" "897"
[291] "898" "899" "900" "901" "904" "905" "906" "907" "908" "909"
[301] "910" "911" "912" "913" "915" "916" "917" "919" "920" "921"
[311] "922" "923" "924" "925" "926" "927" "928" "931" "933" "934"
[321] "937" "938" "939" "940" "941" "942" "943" "945" "946" "947"
[331] "949" "950" "951" "952" "953" "954" "955" "956" "957" "958"
[341] "959" "960" "961" "962" "963" "964" "965" "966" "967" "968"
[351] "969" "970" "971" "972" "973" "974" "975" "976" "977" "978"
[361] "982" "983" "984" "985" "986" "987" "988" "989" "990" "991"
[371] "992" "993" "994" "995" "996" "999" "1000" "1008" "1009" "1010"
[381] "1011" "1012" "1013" "1014" "1015" "1018" "1019" "1020" "1021" "1022"
[391] "1023" "1024" "1025" "1026" "1027" "1028" "1031" "1032" "1034" "1035"
[401] "1036" "1037" "1038" "1039" "1040" "1041" "1042" "1043" "1044" "1045"
[411] "1046" "1047" "1048" "1050" "1051" "1052" "1053" "1054" "1055" "1056"
[421] "1057" "1058" "1059" "1060" "1061" "1062" "1063" "1064" "1065" "1066"
[431] "1067" "1068" "1069" "1070" "1071" "1073" "1075" "1076" "1077" "1086"
[441] "1094" "1095" "1096" "1097" "1098" "1099" "1100" "1101" "1102" "1103"
[451] "1107" "1108" "1109" "1110" "1111" "1112" "1113" "1114" "1115" "1116"
[461] "1117" "1118" "1120" "1121" "1123" "1124" "1125" "1126" "1127" "1128"
[471] "1129" "1130" "1131" "1132" "1133" "1134" "1135" "1136" "1137" "1138"
[481] "1139" "1142" "1152" "1156" "1158" "1159" "1161" "1162" "1163" "1164"
[491] "1165" "1167" "1169" "1170" "1176" "1177" "1178" "1180" "1182" "1183"
[501] "1184" "1185" "1186" "1188" "1189" "1190" "1191" "1192" "1193" "1194"
[511] "1195" "1196" "1197" "1198" "1205" "1206" "1207" "1208" "1209" "1215"
[521] "1217" "1219" "1221" "1222" "1223" "1224" "1226" "1227" "1228" "1229"
[531] "1230" "1231" "1232" "1233" "1234" "1235" "1236" "1237" "1238" "1239"
[541] "1240" "1241" "1242" "1243" "1244" "1245" "1246" "1247" "1248" "1249"
[551] "1250" "1251" "1252" "1253" "1254" "1255" "1256" "1257" "1258" "1259"
[561] "1260" "1261" "1262" "1263" "1264" "1265" "1266" "1267" "1268" "1270"
[571] "1275" "1276" "1277" "1278" "1282" "1284" "1285" "1286" "1287" "1288"
[581] "1289" "1290" "1291" "1292" "1293" "1294" "1295" "1296" "1297" "1298"
[591] "1300" "1301" "1302" "1304" "1305" "1306" "1307" "1308" "1309" "1310"
[601] "1311" "1314" "1315" "1316" "1317" "1318" "1319" "1320" "1322" "1323"
[611] "1324" "1325" "1326" "1327" "1328" "1329" "1330" "1331" "1332" "1333"
[621] "1335" "1336" "1337" "1338" "1339" "1340" "1341" "1342" "1343" "1346"
[631] "1347" "1348" "1349" "1350" "1351" "1352" "1353" "1354" "1357" "1358"
[641] "1359" "1360" "1361" "1362" "1363" "1364" "1365" "1368" "1369" "1370"
[651] "1371" "1372" "1373" "1374" "1375" "1376" "1377" "1378" "1379" "1380"
[661] "1381" "1382" "1383" "1384" "1385" "1387" "1389" "1390" "1391" "1392"
[671] "1393" "1394" "1395" "1396" "1397" "1399" "1400" "1401" "1402" "1403"
[681] "1404" "1406" "1407" "1408" "1409" "1410" "1414" "1419" "1420" "1422"
[691] "1423" "1424" "1425" "1426" "1427" "1428" "1429" "1430" "1431" "1432"
[701] "1433" "1434" "1435" "1436" "1437" "1438" "1439" "1440" "1441" "1442"
[711] "1443" "1444" "1445" "1446" "1447" "1448" "1449" "1450" "1451" "1452"
[721] "1453" "1454" "1455" "1456" "1457" "1458" "1459" "1461" "1462" "1463"
[731] "1464" "1465" "1466" "1467" "1468" "1469" "1470" "1471" "1472" "1473"
[741] "1474" "1475" "1476" "1477" "1478" "1479" "1480" "1481" "1482" "1483"
[751] "1484" "1485" "1486" "1487" "1488" "1489" "1490" "1491" "1492" "1493"
[761] "1494" "1495" "1496" "1497" "1498" "1499" "1500" "1501" "1502" "1504"
[771] "1505" "1506" "1507" "1508" "1509" "1510" "1511" "1512" "1513" "1514"
[781] "1515" "1516" "1517" "1518" "1519" "1520" "1521" "1522" "1523" "1524"
[791] "1525" "1526" "1527" "1528" "1529" "1530" "1531" "1532" "1533" "1534"
[801] "1535" "1536" "1537" "1538" "1539" "1540" "1541" "1542" "1543" "1544"
[811] "1545" "1546" "1548" "1549" "1550" "1551" "1552" "1553" "1554" "1555"
[821] "1556" "1557" "1558" "1559" "1560" "1561" "1562" "1563" "1564" "1565"
[831] "1566" "1567" "1568" "1569" "1570" "1573" "1574" "1575" "1576" "1577"
[841] "1578" "1579" "1580" "1581" "1582" "1583" "1584" "1585" "1586" "1587"
[851] "1588" "1589" "1590" "1591" "1592" "1593" "1594" "1595" "1596" "1597"
[861] "1598" "1599" "1600" "1601" "1602" "1603" "1604" "1605" "1606" "1607"
[871] "1608" "1609" "1610" "1611" "1612" "1613" "1614" "1615" "1616" "1618"
[881] "1620" "1621" "1622" "1623" "1625" "1626" "1627" "1628" "1629" "1630"
[891] "1631" "1632" "1633" "1634" "1635" "1636" "1638" "1639" "1640" "1641"
[901] "1642" "1643" "1644" "1645" "1646" "1647" "1648" "1649" "1650" "1651"
[911] "1653" "1654" "1655" "1656" "1657" "1658" "1659" "1660" "1661" "1662"
[921] "1663" "1664" "1665" "1666" "1667" "1668" "1669" "1670" "1671" "1672"
[931] "1673" "1674" "1675" "1676" "1677" "1678" "1679" "1680" "1681" "1682"
[941] "1683" "1684" "1685" "1686" "1687" "1688" "1689" "1690" "1691" "1692"
[951] "1693" "1694" "1695" "1696" "1697" "1698" "1699" "1700" "1701" "1702"
[961] "1703" "1704" "1705" "1706" "1709" "1710" "1711" "1712" "1713" "1714"
[971] "1715" "1716" "1717" "1718" "1719" "1720" "1721" "1722" "1723" "1724"
[981] "1725" "1726" "1728" "1729" "1730" "1731" "1732" "1733" "1734" "1735"
[991] "1736" "1737" "1738" "1739" "1740" "1741" "1742" "1744" "1745" "1746"
[1001] "1747" "1748" "1749" "1750" "1751" "1752" "1753" "1754" "1755" "1756"
[1011] "1757" "1758" "1759" "1760" "1761" "1762" "1763" "1764" "1765" "1766"
[1021] "1767" "1768" "1769" "1770" "1771" "1772" "1773" "1774" "1775" "1776"
[1031] "1777" "1778" "1779" "1780" "1781" "1783" "1784" "1786" "1787" "1788"
[1041] "1789" "1790" "1791" "1792" "1793" "1794" "1795" "1796" "1797" "1798"
[1051] "1799" "1800" "1801" "1802" "1803" "1804" "1805" "1806" "1807" "1808"
[1061] "1809" "1810" "1811" "1812" "1813" "1814" "1815" "1816" "1817" "1818"
[1071] "1819" "1820" "1821" "1822" "1823" "1824" "1825" "1826" "1827" "1828"
[1081] "1829" "1830" "1831" "1832" "1833" "1834" "1835" "1836" "1837" "1838"
[1091] "1839" "1840" "1841" "1842" "1843" "1844" "1845" "1846" "1848" "1849"
[1101] "1850" "1851" "1852" "1853" "1854" "1855" "1856" "1857" "1858" "1859"
[1111] "1860" "1861" "1862" "1863" "1864" "1865" "1866" "1867" "1868" "1869"
[1121] "1870" "1871" "1872" "1873" "1874" "1875" "1876" "1877" "1878" "1879"
[1131] "1880" "1881" "1882" "1883" "1884" "1885" "1886" "1887" "1888" "1889"
[1141] "1890" "1891" "1893" "1894" "1895" "1896" "1897" "1898" "1899" "1900"
[1151] "1901" "1902" "1903" "1904" "1905" "1906" "1907" "1908" "1909" "1910"
[1161] "1911" "1912" "1913" "1914" "1915" "1916" "1917" "1918" "1919" "1920"
[1171] "1921" "1922" "1923" "1924" "1925" "1926" "1927" "1928" "1929" "1930"
[1181] "1931" "1932" "1933" "1934" "1935" "1936" "1938" "1939" "1940" "1941"
[1191] "1942" "1943" "1944" "1945" "1946" "1947" "1948" "1949" "1950" "1951"
[1201] "1952" "1953" "1954" "1955" "1956" "1957" "1958" "1959" "1960" "1961"
[1211] "1962" "1963" "1964" "1965" "1966" "1967" "1968" "1969" "1970" "1971"
[1221] "1972" "1973" "1974" "1975" "1976" "1977" "1978" "1979" "1980" "1981"
[1231] "1982" "1983" "1984" "1985" "1986" "1987" "1988" "1989" "1990" "1991"
[1241] "1992" "1993" "1994" "1995" "1996" "1997" "1998" "1999" "2000" "2001"
[1251] "2002" "2003" "2004" "2005" "2006" "2007" "2008" "2009" "2010" "2011"
[1261] "2012" "2013" "2014" "2015" "2016" "2017" "2018" "2019" "2020" "2021"
[1271] "2022" "2023" "2024" "2025" "2026" "2027" "2028" "2029" "2030" "2031"
[1281] "2033" "2034" "2035" "2036" "2038" "2039" "2040" "2041" "2042" "2043"
[1291] "2044" "2045" "2046" "2047" "2048" "2049" "2050" "2051" "2052" "2053"
[1301] "2054" "2055" "2056" "2057" "2058" "2059" "2060" "2061" "2062" "2063"
[1311] "2064" "2065" "2066" "2067" "2068" "2069" "2070" "2071" "2072" "2074"
[1321] "2075" "2076" "2077" "2078" "2079" "2081" "2082" "2083" "2084" "2085"
[1331] "2086" "2087" "2088" "2089" "2090" "2091" "2092" "2093" "2094" "2095"
[1341] "2096" "2097" "2098" "2099" "2100" "2101" "2102" "2103" "2104" "2105"
[1351] "2106" "2107" "2108" "2109" "2110" "2111" "2112" "2113" "2114" "2115"
[1361] "2116" "2117" "2118" "2119" "2120" "2121" "2122" "2123"table(SPR_Immigration$FAMILYID)
1000 1008 1009 1010 1011 1012 1013 1014 1015 1018 1019 1020 1021 1022 1023 1024
2 1 2 2 2 2 3 5 5 3 2 2 216 2 3 4
1025 1026 1027 1028 1031 1032 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043
2 2 18 2 3 2 13 2 10 3 5 2 3 5 8 3
1044 1045 1046 1047 1048 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060
3 54 2 2 4 3 4 2 4 2 3 6 9 2 5 2
1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1073 1075 1076 1077 1086
5 5 3 3 2 6 3 3 21 3 3 2 135 3 135 1
1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1107 1108 1109 1110 1111 1112
3 4 3 3 2 1 4 3 6 2 7 2 6 2 6 3
1113 1114 1115 1116 1117 1118 1120 1121 1123 1124 1125 1126 1127 1128 1129 1130
7 2 4 2 4 4 5 2 2 2 9 4 81 1 6 4
1131 1132 1133 1134 1135 1136 1137 1138 1139 1142 1152 1156 1158 1159 1161 1162
2 4 3 3 4 2 9 2 10 14 2 2 198 1 27 5
1163 1164 1165 1167 1169 1170 1176 1177 1178 1180 1182 1183 1184 1185 1186 1188
10 3 3 2 4 2 4 153 3 2 6 5 2 3 3 7
1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1205 1206 1207 1208 1209 1215
3 3 4 3 6 2 81 5 3 81 3 6 5 2 4 6
1217 1219 1221 1222 1223 1224 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235
2 4 4 5 3 3 1 2 2 7 4 3 4 5 8 2
1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251
4 2 4 3 3 2 4 3 6 2 2 2 3 2 6 2
1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267
2 3 2 2 3 4 2 7 3 2 2 3 3 2 2 2
1268 1270 1275 1276 1277 1278 1282 1284 1285 1286 1287 1288 1289 1290 1291 1292
2 6 4 4 10 3 4 2 135 2 2 2 1 1 1 1
1293 1294 1295 1296 1297 1298 1300 1301 1302 1304 1305 1306 1307 1308 1309 1310
1 1 3 90 2 108 2 3 2 2 3 3 2 2 2 1
1311 1314 1315 1316 1317 1318 1319 1320 1322 1323 1324 1325 1326 1327 1328 1329
5 6 2 2 3 1 2 2 3 2 2 3 2 2 3 6
1330 1331 1332 1333 1335 1336 1337 1338 1339 1340 1341 1342 1343 1346 1347 1348
5 2 2 2 2 2 2 2 9 3 2 2 4 2 2 2
1349 1350 1351 1352 1353 1354 1357 1358 1359 1360 1361 1362 1363 1364 1365 1368
3 4 5 3 4 7 2 108 5 90 3 2 2 2 4 2
1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384
2 27 4 1 2 4 3 30 4 24 6 3 54 30 2 2
1385 1387 1389 1390 1391 1392 1393 1394 1395 1396 1397 1399 1400 1401 1402 1403
3 2 2 4 2 3 3 2 2 18 3 30 2 2 2 3
1404 1406 1407 1408 1409 1410 1414 1419 1420 1422 1423 1424 1425 1426 1427 1428
1 1 2 3 4 3 3 4 2 1 1 20 15 20 4 3
1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444
4 2 4 4 4 2 4 5 5 7 4 5 4 2 6 4
1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1461
4 9 2 1 6 1 1 1 4 2 3 2 3 4 6 2
1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477
3 2 2 2 2 2 14 4 2 2 1 2 1 1 2 6
1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493
12 18 3 3 3 1 2 7 2 3 2 6 2 3 2 5
1494 1495 1496 1497 1498 1499 1500 1501 1502 1504 1505 1506 1507 1508 1509 1510
5 8 3 5 2 6 3 54 3 3 3 2 6 3 2 1
1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526
3 3 3 3 2 3 2 2 2 2 3 2 2 3 6 6
1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542
5 18 4 3 4 1 4 72 4 3 3 5 3 3 3 2
1543 1544 1545 1546 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559
4 11 3 16 4 3 3 3 3 6 2 5 4 2 3 2
1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1573 1574 1575 1576 1577
2 2 6 6 4 3 2 2 2 18 7 2 2 2 2 12
1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593
3 2 2 2 3 4 18 2 54 3 3 2 3 15 5 20
1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609
2 24 1 2 3 4 4 2 2 2 3 6 2 2 18 2
1610 1611 1612 1613 1614 1615 1616 1618 1620 1621 1622 1623 1625 1626 1627 1628
36 3 2 3 2 2 2 2 2 8 2 4 1 2 3 27
1629 1630 1631 1632 1633 1634 1635 1636 1638 1639 1640 1641 1642 1643 1644 1645
2 2 2 2 9 2 2 18 3 6 4 5 2 2 2 4
1646 1647 1648 1649 1650 1651 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662
3 2 2 3 2 2 2 2 2 4 4 18 4 2 2 2
1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678
2 2 4 5 3 4 5 2 12 8 2 4 2 4 2 12
1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694
4 2 2 2 3 2 2 2 15 7 5 2 3 2 8 4
1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1709 1710 1711 1712
18 3 2 3 5 9 2 8 3 2 2 3 5 2 3 3
1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1728 1729
3 2 2 20 4 12 4 3 3 6 2 12 1 4 2 2
1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1744 1745 1746
2 2 2 2 2 2 3 2 2 2 1 2 2 2 16 1
1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762
2 12 1 2 3 2 2 2 2 2 5 2 11 2 10 2
1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778
3 2 3 3 5 3 15 5 18 6 20 8 2 2 3 2
1779 1780 1781 1783 1784 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796
12 6 4 10 2 12 2 4 2 2 2 2 2 4 3 4
1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812
2 5 2 3 6 3 7 4 5 5 6 8 5 2 4 12
1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828
4 2 3 2 4 5 2 2 4 3 14 2 6 6 2 4
1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844
3 2 3 3 2 6 2 3 4 5 2 2 2 2 2 2
1845 1846 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861
5 3 3 2 3 9 2 13 3 9 3 3 2 2 2 2
1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877
3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3
1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1893 1894
2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2
1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910
4 1 2 2 2 2 2 2 2 2 2 2 2 2 4 2
1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926
2 2 2 2 2 2 5 2 3 2 3 3 2 2 2 2
1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1938 1939 1940 1941 1942 1943
5 2 4 2 2 3 8 4 2 2 3 2 9 2 2 2
1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959
2 5 5 3 10 2 3 2 3 2 2 2 2 2 2 4
1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975
3 5 4 4 7 6 2 5 6 2 7 2 4 2 2 2
1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991
16 3 2 5 3 2 7 4 2 2 2 2 2 2 2 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
2 2 4 4 3 2 2 2 2 2 2 2 2 2 2 2
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
2 2 2 2 2 2 2 2 1 3 6 2 2 3 2 3
2024 2025 2026 2027 2028 2029 2030 2031 2033 2034 2035 2036 2038 2039 2040 2041
2 2 1 2 2 2 2 2 3 2 5 4 16 2 2 2
2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057
2 2 2 4 3 5 5 2 2 2 2 2 2 2 2 2
2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2074
2 2 3 4 2 2 3 4 4 2 2 2 2 2 3 3
2075 2076 2077 2078 2079 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091
2 3 4 4 3 3 4 2 2 2 2 2 2 2 3 3
2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107
5 2 3 2 3 2 2 2 2 2 2 2 3 4 2 2
2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123
2 3 3 3 2 2 2 2 5 2 4 3 2 1 1 1
495 506 508 509 510 515 519 520 521 523 525 526 527 530 531 532
2 8 3 258 298 345 7 15 8 36 32 171 17 243 240 33
533 534 535 536 538 539 540 546 547 550 552 554 556 558 559 560
18 13 11 66 20 72 6 1 8 1 7 3 2 2 12 8
561 562 563 564 565 566 567 570 572 573 574 575 579 580 581 582
2 36 2 2 2 4 2 2 2 2 162 9 4 5 5 3
583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598
20 20 7 18 18 6 2 49 2 3 6 2 162 6 2 3
600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615
4 6 4 9 2 28 2 27 3 4 4 7 2 5 3 4
616 617 618 619 621 622 623 624 625 626 627 628 629 630 631 633
11 7 2 5 3 3 6 2 3 3 3 40 4 2 6 4
634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649
6 6 4 4 35 2 3 4 2 40 5 4 2 3 2 8
650 651 652 655 658 659 661 662 663 664 666 668 669 674 675 677
8 4 8 16 12 6 3 17 6 3 3 135 2 2 3 6
678 681 682 685 687 690 691 693 694 695 698 699 700 701 702 704
5 18 2 2 7 2 4 2 72 2 2 2 5 3 8 6
705 706 708 709 710 711 713 714 715 716 717 719 720 721 722 723
3 2 2 36 6 4 2 2 72 4 72 19 3 189 4 2
724 725 727 728 729 730 732 734 736 737 738 739 740 741 742 744
4 5 4 2 2 2 2 3 3 54 4 4 4 36 2 4
748 749 750 751 752 753 754 755 756 757 759 760 761 762 763 764
36 2 2 7 2 2 2 4 2 4 2 126 2 3 3 3
765 766 767 768 769 770 771 772 773 774 776 777 778 779 780 781
4 180 192 2 23 2 2 3 3 3 5 27 4 10 108 4
782 783 784 785 786 787 788 789 790 791 792 793 796 797 798 799
2 2 90 2 2 2 4 3 2 9 2 5 2 3 2 5
800 801 802 803 804 805 806 807 808 809 811 812 813 814 815 816
3 4 3 108 2 2 2 2 2 2 2 18 2 3 3 2
817 818 830 833 837 838 845 846 847 848 849 850 851 852 853 855
2 3 7 2 3 108 3 2 5 2 2 2 2 2 2 6
858 859 860 861 862 863 864 865 866 867 868 870 871 872 873 874
108 3 5 1 6 10 3 3 4 2 2 18 2 2 2 18
875 876 877 878 881 882 885 886 888 889 890 891 892 893 894 895
2 8 2 7 1 1 2 1 9 1 12 3 27 2 1 162
896 897 898 899 900 901 904 905 906 907 908 909 910 911 912 913
1 17 1 2 1 2 4 2 6 3 2 3 4 3 7 2
915 916 917 919 920 921 922 923 924 925 926 927 928 931 933 934
3 9 2 6 4 2 12 2 2 2 2 5 2 18 1 6
937 938 939 940 941 942 943 945 946 947 949 950 951 952 953 954
2 108 2 2 3 5 2 5 2 2 2 10 3 2 3 2
955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970
2 3 2 5 4 3 4 7 3 11 3 1 120 2 135 2
971 972 973 974 975 976 977 978 982 983 984 985 986 987 988 989
168 2 2 2 6 4 2 3 135 2 2 6 3 3 2 40
990 991 992 993 994 995 996 999
3 2 24 5 48 1 2 2 SPR_Immigration <- SPR_Immigration %>%
mutate(FAMILYID = as.integer(FAMILYID))
SPR_Immigration.1 <- SPR_Immigration %>%
distinct(FAMILYID, .keep_all = TRUE)
names(ASQ_Personal_Social) [1] "FAMILYID" "DATE_TAKE" "PARTTYPE2" "WAVE"
[5] "AGERANGE" "DATETAKEN_201" "PS2" "PS4"
[9] "PS6" "PS8" "PS9" "PS10"
[13] "PS12" "PS14" "PS16" "PS18"
[17] "PS20" "PS22" "PS24" "PS27"
[21] "PS30" "PS33" "PS36" "PS42"
[25] "PS48" "PS54" "PS60" "PS_Score" SPR_ASQ_P_S <-ASQ_Personal_Social[,c(1,4,28)]
names(SPR_ASQ_P_S)[1] "FAMILYID" "WAVE" "PS_Score"str(SPR_ASQ_P_S)'data.frame': 2118 obs. of 3 variables:
$ FAMILYID: int 572 572 574 574 579 579 580 580 580 581 ...
$ WAVE : Factor w/ 3 levels "Baseline","6 Months",..: 2 1 2 1 2 1 3 2 1 3 ...
$ PS_Score: num 55 60 45 60 50 50 50 50 50 60 ...names(IBQ)[1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2"
[4] "PROGRAMNAME" "WAVE" "DATE"
[7] "IBQ_NEGATIVESCORE" "IBQ_EFFORTFULSCORE"SPR_IBQ <-IBQ[,c(1,2,5,7,8)]
names(SPR_IBQ)[1] "FAMILYID" "PARTICIPANTID" "WAVE"
[4] "IBQ_NEGATIVESCORE" "IBQ_EFFORTFULSCORE"names(ECBQ)[1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2"
[4] "PROGRAMNAME" "WAVE" "DATE"
[7] "ECBQ_NEGATIVESCORE" "ECBQ_EFFORTFULSCORE"SPR_ECBQ<-ECBQ[,c(1,2,5,7,8)]
names(SPR_ECBQ)[1] "FAMILYID" "PARTICIPANTID" "WAVE"
[4] "ECBQ_NEGATIVESCORE" "ECBQ_EFFORTFULSCORE"names(CBQ)[1] "FAMILYID" "PARTICIPANTID" "PARTTYPE2"
[4] "PROGRAMNAME" "WAVE" "DATE"
[7] "CBQ_NEGATIVESCORE" "CBQ_EFFORTFULSCORE"SPR_CBQ <-CBQ[,c(1,2,5,7,8)]
names(SPR_CBQ)[1] "FAMILYID" "PARTICIPANTID" "WAVE"
[4] "CBQ_NEGATIVESCORE" "CBQ_EFFORTFULSCORE"Define data structure for all 7 SPR datasets
list(
str(SPR_Demographics),
str(SPR_Immigration.1),
str(SPR_ASSM),
str(SPR_ASQ_P_S),
str(SPR_IBQ),
str(SPR_ECBQ),
str(SPR_CBQ)
)'data.frame': 992 obs. of 20 variables:
$ FAMILYID : int 572 573 574 579 580 581 582 583 584 585 ...
$ PARTICIPANTID : int 105899 105901 105903 106524 106529 106551 106555 106573 106577 106580 ...
$ PARTTYPE2 : Ord.factor w/ 2 levels "Control"<"Intervention": 2 2 2 1 2 2 1 2 2 1 ...
$ PROGRAMNAME : Factor w/ 5 levels "ACCESS","CARE",..: 5 5 5 1 1 1 1 1 1 1 ...
$ CG_AGE_Intake : num 29.7 28.4 47.7 32.4 28.8 ...
$ FEMALE : Factor w/ 2 levels "Male","Female": 2 2 2 2 2 2 2 2 2 2 ...
$ AGEFOCUSINTAKE: num 3.41 1.68 4.76 4.7 3.52 ...
$ FOCUSFEMALE : Factor w/ 2 levels "Male","Female": 1 2 1 1 1 2 1 2 2 2 ...
$ AMINDR : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 1 1 1 1 ...
$ ASIANR : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ BLACKR : Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 2 1 1 1 ...
$ HISPR : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 1 1 1 1 ...
$ EASTR : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 1 1 2 1 1 ...
$ HAWAIIR : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ WHITER : Factor w/ 2 levels "No","Yes": 1 2 2 1 2 1 1 1 2 2 ...
$ OTHERR : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 2 ...
$ PRIMEENG : Factor w/ 2 levels "No","Yes": 2 2 2 1 1 1 1 1 1 1 ...
$ PRIMESPAN : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 2 1 1 1 1 ...
$ PRIMEARAB : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 1 2 2 2 2 ...
$ PRIMEOTH : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
'data.frame': 1368 obs. of 3 variables:
$ FAMILYID : int 495 506 508 509 510 515 519 520 521 523 ...
$ Immigrant_Status : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
$ HowLongInUSIfNotBorn: num NA NA NA NA NA NA NA NA NA NA ...
'data.frame': 2311 obs. of 25 variables:
$ FAMILYID : int 572 572 573 574 574 574 579 579 579 580 ...
$ PARTICIPANTID : int 105899 105899 105901 105903 105903 105903 106524 106524 106524 106529 ...
$ WAVE : Factor w/ 3 levels "Baseline","6 Months",..: 2 1 1 3 2 1 3 2 1 3 ...
$ BASENEEDS2 : num 4.12 3.67 4 2.5 2.12 ...
$ ASSM_HOUSING : num 4 3 3 2 3 2 5 5 5 4 ...
$ ASSM_INCOME : num 3 2 3 2 2 2 4 5 5 3 ...
$ ASSM_FOOD : num 1 2 1 3 1 1 1 5 5 1 ...
$ ASSM_ADULTED : num 5 5 5 3 3 3 3 5 5 2 ...
$ ASSM_EMPLOYMENT : num NA 1 NA NA NA NA NA NA NA NA ...
$ ASSM_TRANSPORTATION : num 5 5 5 3 1 1 4 4 4 4 ...
$ ASSM_HEALTHCARE : num 5 5 5 5 5 5 5 5 5 5 ...
$ ASSM_SAFETY : num 5 5 5 1 1 1 5 5 1 5 ...
$ ASSM_LIFESKILLS : num 5 5 5 1 1 NA 5 5 5 5 ...
$ ASSM_PARENTS2 : num 5 5 5 2.33 3 ...
$ ASSM_CHILDCARE : num NA NA NA 1 NA 1 NA 5 5 NA ...
$ ASSM_PARENTINGSKILLS: num 5 5 5 1 1 4 5 3 3 5 ...
$ ASSM_EDUCATE : num 5 5 5 5 5 NA 5 5 5 5 ...
$ ASSM_SOCIAL2 : num 4.5 3.5 3.5 1 1.5 2 4.5 5 5 4.5 ...
$ ASSM_SOCIAL : num 5 5 5 1 2 2 5 5 5 5 ...
$ ASSM_COMMUNINVOLV : num 4 2 2 1 1 2 4 5 5 4 ...
$ ASSM_BEHAVE2 : num 5 4 5 3.5 4 3 5 5 5 5 ...
$ ASSM_MENTAL : num 5 5 5 3 3 1 5 5 5 5 ...
$ ASSM_SUBABUSE : num 5 5 5 5 5 5 5 5 5 5 ...
$ ASSM_DISABILITIES : num 5 5 5 5 3 5 5 5 5 5 ...
$ ASSM_LEGAL : num 5 1 5 1 5 1 5 5 5 5 ...
'data.frame': 2118 obs. of 3 variables:
$ FAMILYID: int 572 572 574 574 579 579 580 580 580 581 ...
$ WAVE : Factor w/ 3 levels "Baseline","6 Months",..: 2 1 2 1 2 1 3 2 1 3 ...
$ PS_Score: num 55 60 45 60 50 50 50 50 50 60 ...
'data.frame': 398 obs. of 5 variables:
$ FAMILYID : int 585 585 586 586 592 592 594 595 601 610 ...
$ PARTICIPANTID : int 111516 111516 106590 106590 106666 106665 106674 106783 107035 107281 ...
$ WAVE : Factor w/ 3 levels "Baseline","6 Months",..: 3 2 2 1 2 1 1 1 1 1 ...
$ IBQ_NEGATIVESCORE : num 4.58 3.56 2.83 2.58 2.08 ...
$ IBQ_EFFORTFULSCORE: num 6.08 6.08 4.17 4.91 6.17 ...
'data.frame': 918 obs. of 5 variables:
$ FAMILYID : int 582 582 583 584 586 587 587 587 589 589 ...
$ PARTICIPANTID : int 106556 106556 106574 106578 106590 106634 106634 106634 106659 106659 ...
$ WAVE : Factor w/ 3 levels "Baseline","6 Months",..: 2 1 1 1 3 3 2 1 3 2 ...
$ ECBQ_NEGATIVESCORE : num 2.1 3 4.17 3.17 4.08 ...
$ ECBQ_EFFORTFULSCORE: num 4 4.5 5.33 5.33 5.58 ...
'data.frame': 863 obs. of 5 variables:
$ FAMILYID : int 572 572 574 574 574 579 579 579 580 580 ...
$ PARTICIPANTID : int 105900 105900 105904 105904 105904 106525 106525 106525 106530 106530 ...
$ WAVE : Factor w/ 3 levels "Baseline","6 Months",..: 2 1 3 2 1 3 2 1 3 2 ...
$ CBQ_NEGATIVESCORE : num 1.67 4 4.33 3.42 3.08 ...
$ CBQ_EFFORTFULSCORE: num 7 6.42 2.92 3.17 2.67 ...[[1]]
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NULLMerge Across all 7 data sets
wave_levels <- c("Baseline", "6 Months", "12 Months")
wave_labels <- c("Baseline", "6 Months", "12 Months")
format_wave <- function(df) {
df %>%
mutate(WAVE = as.factor(WAVE)) %>%
mutate(WAVE = fct_relevel(WAVE, wave_levels))
}
SPR_ASSM <- format_wave(SPR_ASSM)
SPR_ASQ_P_S <- format_wave(SPR_ASQ_P_S)
SPR_IBQ <- SPR_IBQ %>%
mutate(WAVE = factor(WAVE, levels = wave_levels))
SPR_ECBQ <- format_wave(SPR_ECBQ)
SPR_CBQ <- SPR_CBQ %>%
mutate(WAVE = factor(WAVE, levels = wave_levels))
SPR_Demographics_full <- SPR_Demographics %>%
left_join(SPR_Immigration.1, by = "FAMILYID")
stopifnot(nrow(SPR_Demographics_full) <= 992)
SPR_Long <- SPR_ASSM %>%
left_join(SPR_ASQ_P_S, by = c("FAMILYID", "WAVE")) %>%
left_join(SPR_IBQ, by = c("FAMILYID", "PARTICIPANTID", "WAVE")) %>%
left_join(SPR_ECBQ, by = c("FAMILYID", "PARTICIPANTID", "WAVE")) %>%
left_join(SPR_CBQ, by = c("FAMILYID", "PARTICIPANTID", "WAVE")) %>%
left_join(SPR_Demographics_full, by = c("FAMILYID", "PARTICIPANTID"))
num_ids <- SPR_Long %>%
distinct(FAMILYID, PARTICIPANTID) %>%
nrow()
total_rows <- nrow(SPR_Long)
stopifnot(num_ids <= 992)
stopifnot(total_rows <= 2976)
SPR_Long <- SPR_Long %>%
mutate(WAVE = factor(WAVE, levels = wave_levels, labels = wave_labels, ordered = TRUE))
cat("Unique IDs:", num_ids, "\n")Unique IDs: 969 cat("Total rows:", total_rows, "\n")Total rows: 2311 write_sav(SPR_Long,"SPR_Long.sav")