QOL baseline analysis
Jacqueline Burgara
5/14/2021
```
Firstly we want to load in the files we will be using as well as combine the data as needed.
pedsql<- read_excel("pesql data.xlsx",
sheet="Output") #read pedsql data baseline sheet## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...7
## * `` -> ...11fs2r <- read_excel("C:/Users/burga/Downloads/FS2R Scoring Doc Equity Exploration 2018-2019.xlsx",
sheet="Calculations") #read fs2r data baseline sheet
demographic.en <- read_csv("C:/Users/burga/Downloads/OAPEnglishNov2017Oct-EquityExplorationDem_DATA_2021-05-14_0705.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## gender_other = col_character(),
## other_related = col_character(),
## guard_gender_other = col_logical()
## )
## i Use `spec()` for the full column specifications.demographic.sp <-read_csv("C:/Users/burga/Downloads/OAPSpanishNov2017Oct-EquityExplorationDem_DATA_2021-05-14_0704.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## gender_other = col_logical(),
## other_related = col_logical(),
## guard_gender_other = col_logical()
## )
## i Use `spec()` for the full column specifications.#select only the need columns
pedsql.clean<- pedsql[ -c(2:13) ]
fs2r.clean <- fs2r[-c(2:10)]#data clean up
#we didn't collect unit every time so we are going to ignore those columns
dem.clean.en <- demographic.en[ -c(2) ]
dem.clean.sp <-demographic.sp[ -c(5) ]
dem.merge <-rbind(dem.clean.en,dem.clean.sp)
# childgender
dem.merge$childgender <- as.factor(dem.merge$childgender)
levels (dem.merge$childgender) <-gsub("0","Male",levels(dem.merge$childgender))
levels (dem.merge$childgender) <-gsub("1","Female",levels(dem.merge$childgender))
levels (dem.merge$childgender) <-gsub("2","Other",levels(dem.merge$childgender))
levels (dem.merge$childgender) <-gsub("3","Prefer not to answer",levels(dem.merge$childgender))
#interviewlang
dem.merge$interviewlang <- as.factor(dem.merge$interviewlang)
levels (dem.merge$interviewlang) <-gsub("1","English",levels(dem.merge$interviewlang))
levels (dem.merge$interviewlang) <-gsub("2","Spanish",levels(dem.merge$interviewlang))
levels (dem.merge$interviewlang) <-gsub("3","Vietnamese",levels(dem.merge$interviewlang))
levels (dem.merge$interviewlang) <-gsub("4","Russian",levels(dem.merge$interviewlang))
levels (dem.merge$interviewlang) <-gsub("5","Chinese",levels(dem.merge$interviewlang))
levels (dem.merge$interviewlang) <-gsub("6","Somali",levels(dem.merge$interviewlang))
#interp_needed
dem.merge$interp_needed<- as.factor(dem.merge$interp_needed)
levels (dem.merge$interp_needed) <-gsub("0","No",levels(dem.merge$interp_needed))
levels (dem.merge$interp_needed) <-gsub("1","Yes",levels(dem.merge$interp_needed))
#pq_method
dem.merge$pq_method<- as.factor(dem.merge$pq_method)
levels (dem.merge$pq_method) <-gsub("0","Self administered",levels(dem.merge$pq_method))
levels (dem.merge$pq_method) <-gsub("1","Via telephone interview",levels(dem.merge$pq_method))
#interviewlang_change
dem.merge$interviewlang_change <- as.factor(dem.merge$interviewlang_change )
levels (dem.merge$interviewlang_change ) <-gsub("1","English",levels(dem.merge$interviewlang_change ))
levels (dem.merge$interviewlang_change ) <-gsub("2","Spanish",levels(dem.merge$interviewlang_change ))
levels (dem.merge$interviewlang_change ) <-gsub("3","Vietnamese",levels(dem.merge$interviewlang_change ))
levels (dem.merge$interviewlang_change ) <-gsub("4","Russian",levels(dem.merge$interviewlang_change ))
levels (dem.merge$interviewlang_change ) <-gsub("5","Chinese",levels(dem.merge$interviewlang_change ))
levels (dem.merge$interviewlang_change ) <-gsub("6","Somali",levels(dem.merge$interviewlang_change ))
#related
dem.merge$related<- as.factor(dem.merge$related)
levels (dem.merge$related) <-gsub("1","Mother",levels(dem.merge$related))
levels (dem.merge$related) <-gsub("2","Father",levels(dem.merge$related))
levels (dem.merge$related) <-gsub("3","Stepmother",levels(dem.merge$related))
levels (dem.merge$related) <-gsub("4","Stepfather",levels(dem.merge$related))
levels (dem.merge$related) <-gsub("5","Grandmother",levels(dem.merge$related))
levels (dem.merge$related) <-gsub("6","Grandfather",levels(dem.merge$related))
levels (dem.merge$related) <-gsub("8","Aunt or Uncle",levels(dem.merge$related))
levels (dem.merge$related) <-gsub("9","Older brother or sister",levels(dem.merge$related))
levels (dem.merge$related) <-gsub("10","Other relative",levels(dem.merge$related))
levels (dem.merge$related) <-gsub("7","Other relative",levels(dem.merge$related))
#other_related
#guard_gender
dem.merge$guard_gender<- as.factor(dem.merge$guard_gender)
levels (dem.merge$guard_gender) <-gsub("0","Male",levels(dem.merge$guard_gender))
levels (dem.merge$guard_gender) <-gsub("1","Female",levels(dem.merge$guard_gender))
levels (dem.merge$guard_gender) <-gsub("2","Other",levels(dem.merge$guard_gender))
levels (dem.merge$guard_gender) <-gsub("3","Prefer not to answer",levels(dem.merge$guard_gender))
#guard_gender_other
#parentage
#dem.merge$parentage<- as.numeric(dem.merge$parentage)
#levels (dem.merge$parentage) <-gsub("1","18-24",levels(dem.merge$parentage))
#levels (dem.merge$parentage) <-gsub("2","25-34",levels(dem.merge$parentage))
#levels (dem.merge$parentage) <-gsub("3","35-44",levels(dem.merge$parentage))
#levels (dem.merge$parentage) <-gsub("4","45-54",levels(dem.merge$parentage))
#levels (dem.merge$parentage) <-gsub("5","55-64",levels(dem.merge$parentage))
#levels (dem.merge$parentage) <-gsub("6","65-74",levels(dem.merge$parentage))
#levels (dem.merge$parentage) <-gsub("7","75+",levels(dem.merge$parentage))
dem.merge$parentage<- as.factor(dem.merge$parentage)
#education
dem.merge$education<- as.factor(dem.merge$education)
levels (dem.merge$education) <-gsub("1","8th grade or less",levels(dem.merge$education))
levels (dem.merge$education) <-gsub("2","Some high school, but did not graduate",levels(dem.merge$education))
levels (dem.merge$education) <-gsub("3","high school grad or GED",levels(dem.merge$education))
levels (dem.merge$education) <-gsub("4","Some college or 2 year degree (or trade or technical school)",levels(dem.merge$education))
levels (dem.merge$education) <-gsub("5","4-year college graduate",levels(dem.merge$education))
levels (dem.merge$education) <-gsub("6","More than 4-year college degree",levels(dem.merge$education))
#race___1 race___2 race___3 race___4 race___5 race___6 race___7
dem.merge$race___1<- as.factor(dem.merge$race___1)
levels (dem.merge$race___1) <-gsub("0","",levels(dem.merge$race___1))
levels (dem.merge$race___1) <-gsub("1","White",levels(dem.merge$race___1))
dem.merge$race___2<- as.factor(dem.merge$race___2)
levels (dem.merge$race___2) <-gsub("0","",levels(dem.merge$race___2))
levels (dem.merge$race___2) <-gsub("1","Black or African American",levels(dem.merge$race___2))
dem.merge$race___3<- as.factor(dem.merge$race___3)
levels (dem.merge$race___3) <-gsub("0","",levels(dem.merge$race___3))
levels (dem.merge$race___3) <-gsub("1","Latino or Hispanic",levels(dem.merge$race___3))
dem.merge$race___4<- as.factor(dem.merge$race___4)
levels (dem.merge$race___4) <-gsub("0","",levels(dem.merge$race___4))
levels (dem.merge$race___4) <-gsub("1","Asian",levels(dem.merge$race___4))
dem.merge$race___5<- as.factor(dem.merge$race___5)
levels (dem.merge$race___5) <-gsub("0","",levels(dem.merge$race___5))
levels (dem.merge$race___5) <-gsub("1","Native Hawaiian or other Pacific Islander",levels(dem.merge$race___5))
dem.merge$race___6<- as.factor(dem.merge$race___6)
levels (dem.merge$race___6) <-gsub("0","",levels(dem.merge$race___6))
levels (dem.merge$race___6) <-gsub("1","American Indian, or Alaskan Indian or Alaskan Native",levels(dem.merge$race___6))
dem.merge$race___7<- as.factor(dem.merge$race___7)
levels (dem.merge$race___7) <-gsub("0","",levels(dem.merge$race___7))
levels (dem.merge$race___7) <-gsub("1","Other or multiracial",levels(dem.merge$race___7))
dem.merge$race.ethnicity <- paste(dem.merge$race___1, dem.merge$race___2, dem.merge$race___3, dem.merge$race___4, dem.merge$race___5, dem.merge$race___6, dem.merge$race___7,sep= "-")
#childrace___1 childrace___2 childrace___3 childrace___4 childrace___5 childrace___6 childrace___7
dem.merge$childrace___1<- as.factor(dem.merge$childrace___1)
levels (dem.merge$childrace___1) <-gsub("0","",levels(dem.merge$childrace___1))
levels (dem.merge$childrace___1) <-gsub("1","White",levels(dem.merge$childrace___1))
dem.merge$childrace___2<- as.factor(dem.merge$childrace___2)
levels (dem.merge$childrace___2) <-gsub("0","",levels(dem.merge$childrace___2))
levels (dem.merge$childrace___2) <-gsub("1","Black or African American",levels(dem.merge$childrace___2))
dem.merge$childrace___3<- as.factor(dem.merge$childrace___3)
levels (dem.merge$childrace___3) <-gsub("0","",levels(dem.merge$childrace___3))
levels (dem.merge$childrace___3) <-gsub("1","Latino or Hispanic",levels(dem.merge$childrace___3))
dem.merge$childrace___4<- as.factor(dem.merge$childrace___4)
levels (dem.merge$childrace___4) <-gsub("0","",levels(dem.merge$childrace___4))
levels (dem.merge$childrace___4) <-gsub("1","Asian",levels(dem.merge$childrace___4))
dem.merge$childrace___5<- as.factor(dem.merge$childrace___5)
levels (dem.merge$childrace___5) <-gsub("0","",levels(dem.merge$childrace___5))
levels (dem.merge$childrace___5) <-gsub("1","Native Hawaiian or other Pacific Islander",levels(dem.merge$childrace___5))
dem.merge$childrace___6<- as.factor(dem.merge$childrace___6)
levels (dem.merge$childrace___6) <-gsub("0","",levels(dem.merge$childrace___6))
levels (dem.merge$childrace___6) <-gsub("1","American Indian, or Alaskan Indian or Alaskan Native",levels(dem.merge$childrace___6))
dem.merge$childrace___7<- as.factor(dem.merge$childrace___7)
levels (dem.merge$childrace___7) <-gsub("0","",levels(dem.merge$childrace___7))
levels (dem.merge$childrace___7) <-gsub("1","Other or multiracial",levels(dem.merge$childrace___7))
dem.merge$race.ethnicity.child <- paste(dem.merge$childrace___1, dem.merge$childrace___2, dem.merge$childrace___3, dem.merge$childrace___4, dem.merge$childrace___5, dem.merge$childrace___6, dem.merge$childrace___7,sep= "-")
dem.final <-dem.merge[ -c(4,10,12,15:28) ]Next we want to get rid of any blanks and merge our data.
#get rid of blanks
pedsql.final <- pedsql.clean %>%
mutate_all(~ifelse(. %in% c("N/A", "null", ""), NA, .)) %>%
na.omit()
fs2r.final <- fs2r.clean %>%
mutate_all(~ifelse(. %in% c("N/A", "null", ""), NA, .)) %>%
na.omit()
#join data sets
join1 <- rbind(pedsql.final,fs2r.final) #join the scores of both data sets
join2 <- inner_join(join1,dem.final, by= "record_id") #join the scores and demographic data
QOL.Equity.Exploraton <- select(join2,-c()) #deleate any possiBLE doubles
write.csv(QOL.Equity.Exploraton, "QOL_data.csv")
qol_final <- read_csv("QOL_data_edits.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## record_id = col_double(),
## `Back to Baseline` = col_double(),
## childage = col_double(),
## childgender = col_character(),
## interviewlang = col_character(),
## interp_needed = col_character(),
## interviewlang_change = col_character(),
## pq_method = col_character(),
## related = col_character(),
## guard_gender = col_character(),
## parentage = col_character(),
## education = col_character(),
## race.ethnicity = col_character(),
## r.e.parent = col_character(),
## race.ethnicity.child = col_character(),
## r.e.child = col_character(),
## r.e.mismatch = col_character()
## )qol_final$`Back to Baseline` <- as.factor(qol_final$`Back to Baseline`)
levels (qol_final$`Back to Baseline`) <-gsub("0","did not return to baseline",levels(qol_final$`Back to Baseline`))
levels (qol_final$`Back to Baseline`) <-gsub("1","returned to baseline",levels(qol_final$`Back to Baseline`))
#change American Indian, or Alaskan Indian or Alaskan Native to indiginous so that graphs dont look weird when factoring in r&e
qol_final$r.e.parent <- as.factor(qol_final$r.e.parent)
levels (qol_final$r.e.parent) <-gsub("American Indian, or Alaskan Indian or Alaskan Native","Indigenous",levels(qol_final$r.e.parent))
qol_final$r.e.child <- as.factor(qol_final$r.e.child)
levels (qol_final$r.e.child) <-gsub("American Indian, or Alaskan Indian or Alaskan Native","Indigenous",levels(qol_final$r.e.child))Our final data set is qol_final, now we just need to make the graphs and make some counts left NAs as they mean something here
#parent education and parent/child race and ethnicity
ggplot(qol_final)+
geom_jitter(aes(x=r.e.child, y=education,color=`Back to Baseline`))+
labs(x = "Child race and ethnicity", y= "Parent education", title = 'Parent Education and Child Race & Ethnicity effects on baseline',
caption = "")+
theme_classic()
ggplot(qol_final)+
geom_jitter(aes( x=r.e.parent, y=education,color=`Back to Baseline`))+
labs(x = "Parent race and ethnicity", y= "Parent education", title = 'Parent Education and Race & Ethicity effects on baseline',
caption = "")+
theme_classic()
table_1<-qol_final %>% count(education,r.e.parent,`Back to Baseline`, sort = TRUE)
#parent race and child race
ggplot(qol_final)+
geom_jitter(aes(x=r.e.child, y=r.e.parent,color=`Back to Baseline`))+
labs(x = "Child race and ethnicity", y= "Parent race and ethnicity", title = 'Parent and Child Race and Ethnicity effects on baseline',
caption = "")+
theme_classic()
table_2<-qol_final %>% count(r.e.child,r.e.parent,`Back to Baseline`, sort = TRUE)
#View(table_2)
#child gender and race
ggplot(qol_final)+
geom_jitter(aes(x=r.e.child, y=childgender, color=`Back to Baseline`))+
labs(x = "Child race and ethnicity", y= "Child Gender", title = 'Child Race and Ethnicity and Gender effects on baseline',
caption = "")+
theme_classic()
table_3<-qol_final %>% count(r.e.child,childgender,`Back to Baseline`, sort = TRUE)
#baseline and language
ggplot(qol_final)+
geom_jitter(aes(x=interp_needed, y=interviewlang_change,color=`Back to Baseline`))+
labs(x = "Interpretor needed", y= "Survey Language", title = 'Interpretor need and language effects on baseline',
caption = "")+
theme_classic()
table_4<-qol_final %>% count(interp_needed,interviewlang_change,`Back to Baseline`, sort = TRUE)multiple linear regression, failing to knit so these were the results
significance in parent (0.423427) or child being latinx (-0.408379) with base being Indiginous familes high school, but did not graduate: change in slope 0.179474 with base being 4-year college degree 8th grade or less: change in slope 0.381653 base being 4-year college degree
notable effects from
intreview in Sp change in slope -0.247186 with base being english high school grad/ged change in slope 0.097220 with base being 4-year college degree There has to be bais in this data b/c the main survey language was English and the big other varible was latinx families
#``{r} #str(qol_final) #what type of variables #qol_final$b.to.baseline <-as.numeric(qol_final$Back to Baseline`) #str(qol_final)
#lm_test <- lm (qol_final$b.to.baseline ~ childgender + interviewlang_change + education + r.e.child + r.e.parent + r.e.mismatch,data= qol_final) #summary(lm_test) ``` # Tables
- Organized by race/ethnicity, education, language
tinytex::reinstall_tinytex()## If reinstallation fails, try install_tinytex() again. Then install the following packages:
##
## tinytex::tlmgr_install(c("amsfonts", "amsmath", "atbegshi", "atveryend", "auxhook", "babel", "bibtex", "bigintcalc", "bitset", "booktabs", "cm", "colortbl", "dehyph", "dvipdfmx", "dvips", "ec", "environ", "epstopdf-pkg", "etex", "etexcmds", "etoolbox", "euenc", "everyshi", "fancyvrb", "filehook", "firstaid", "float", "fontspec", "framed", "geometry", "gettitlestring", "glyphlist", "graphics", "graphics-cfg", "graphics-def", "grffile", "helvetic", "hycolor", "hyperref", "hyph-utf8", "hyphen-base", "iftex", "inconsolata", "infwarerr", "intcalc", "knuth-lib", "kpathsea", "kvdefinekeys", "kvoptions", "kvsetkeys", "l3backend", "l3kernel", "l3packages", "latex", "latex-amsmath-dev", "latex-bin", "latex-fonts", "latex-tools-dev", "latexconfig", "latexmk", "letltxmacro", "lm", "lm-math", "ltxcmds", "lua-alt-getopt", "luahbtex", "lualatex-math", "lualibs", "luaotfload", "luatex", "makecell", "mdwtools", "metafont", "mfware", "modes", "multirow", "natbib", "pdfescape", "pdflscape", "pdftex", "pdftexcmds", "plain", "psnfss", "refcount", "rerunfilecheck", "scheme-infraonly", "stringenc", "symbol", "tabu", "tex", "tex-ini-files", "texlive-scripts", "texlive.infra", "threeparttable", "threeparttablex", "times", "tipa", "tlgs", "tlperl", "tools", "trimspaces", "ulem", "unicode-data", "unicode-math", "uniquecounter", "url", "varwidth", "wrapfig", "xcolor", "xetex", "xetexconfig", "xkeyval", "xunicode", "zapfding"))## The directory C:\Users\burga\AppData\Roaming\TinyTeX/texmf-local is not empty. It will be backed up to C:\Users\burga\AppData\Local\Temp\RtmpcTIn9N\file497839a05d2c and restored later.## tlmgr install colortbl environ makecell multirow pdflscape tabu threeparttable threeparttablex trimspaces ulem varwidth wrapfigkable(table_1,booktabs = T, col.names = c("Parent Education","Parent Race & Ethnicity","Baseline Status", "n"), caption = " Table 1: Baseline Staus based on Parent education and race/ethnicity")%>%
kableExtra::kable_styling(latex_options = c("striped","scale_down"))| Parent Education | Parent Race & Ethnicity | Baseline Status | n |
|---|---|---|---|
| Some college or 2 year degree (or trade or technical school) | White | returned to baseline | 139 |
| More than 4-year college degree | White | returned to baseline | 129 |
| 4-year college graduate | White | returned to baseline | 110 |
| Some college or 2 year degree (or trade or technical school) | White | did not return to baseline | 89 |
| More than 4-year college degree | White | did not return to baseline | 85 |
| 4-year college graduate | White | did not return to baseline | 83 |
| high school grad or GED | White | returned to baseline | 69 |
| high school grad or GED | Latino or Hispanic | returned to baseline | 27 |
| high school grad or GED | White | did not return to baseline | 27 |
| Some college or 2 year degree (or trade or technical school) | Other or multiracial | returned to baseline | 26 |
| 4-year college graduate | Asian | returned to baseline | 25 |
| Some college or 2 year degree (or trade or technical school) | Latino or Hispanic | returned to baseline | 20 |
| high school grad or GED | Latino or Hispanic | did not return to baseline | 15 |
| high school grad or GED | Other or multiracial | returned to baseline | 15 |
| More than 4-year college degree | Asian | returned to baseline | 15 |
| 4-year college graduate | Asian | did not return to baseline | 14 |
| More than 4-year college degree | Asian | did not return to baseline | 13 |
| Some high school, but did not graduate | Latino or Hispanic | returned to baseline | 13 |
| Some college or 2 year degree (or trade or technical school) | Asian | returned to baseline | 12 |
| 4-year college graduate | Other or multiracial | returned to baseline | 10 |
| high school grad or GED | Other or multiracial | did not return to baseline | 10 |
| Some college or 2 year degree (or trade or technical school) | Other or multiracial | did not return to baseline | 9 |
| Some high school, but did not graduate | White | returned to baseline | 9 |
| More than 4-year college degree | Other or multiracial | returned to baseline | 8 |
| 8th grade or less | Latino or Hispanic | returned to baseline | 7 |
| Some college or 2 year degree (or trade or technical school) | Asian | did not return to baseline | 7 |
| Some college or 2 year degree (or trade or technical school) | Black or African American | returned to baseline | 7 |
| Some high school, but did not graduate | Latino or Hispanic | did not return to baseline | 7 |
| 4-year college graduate | Black or African American | returned to baseline | 6 |
| Some college or 2 year degree (or trade or technical school) | Latino or Hispanic | did not return to baseline | 6 |
| 4-year college graduate | Latino or Hispanic | returned to baseline | 5 |
| high school grad or GED | Indigenous | returned to baseline | 5 |
| More than 4-year college degree | Other or multiracial | did not return to baseline | 5 |
| Some college or 2 year degree (or trade or technical school) | Black or African American | did not return to baseline | 5 |
| 4-year college graduate | Other or multiracial | did not return to baseline | 4 |
| More than 4-year college degree | Latino or Hispanic | returned to baseline | 4 |
| Some college or 2 year degree (or trade or technical school) | Indigenous | returned to baseline | 4 |
| Some college or 2 year degree (or trade or technical school) | NA | did not return to baseline | 4 |
| Some high school, but did not graduate | Other or multiracial | returned to baseline | 4 |
| 4-year college graduate | Latino or Hispanic | did not return to baseline | 3 |
| high school grad or GED | Asian | did not return to baseline | 3 |
| high school grad or GED | Asian | returned to baseline | 3 |
| More than 4-year college degree | Black or African American | returned to baseline | 3 |
| More than 4-year college degree | NA | did not return to baseline | 3 |
| Some college or 2 year degree (or trade or technical school) | Indigenous | did not return to baseline | 3 |
| Some high school, but did not graduate | Black or African American | returned to baseline | 3 |
| NA | Other or multiracial | returned to baseline | 3 |
| 4-year college graduate | Indigenous | did not return to baseline | 2 |
| 4-year college graduate | Black or African American | did not return to baseline | 2 |
| 8th grade or less | Latino or Hispanic | did not return to baseline | 2 |
| high school grad or GED | Indigenous | did not return to baseline | 2 |
| high school grad or GED | Black or African American | did not return to baseline | 2 |
| More than 4-year college degree | Black or African American | did not return to baseline | 2 |
| More than 4-year college degree | Latino or Hispanic | did not return to baseline | 2 |
| Some high school, but did not graduate | Other or multiracial | did not return to baseline | 2 |
| Some high school, but did not graduate | White | did not return to baseline | 2 |
| NA | White | returned to baseline | 2 |
| 4-year college graduate | NA | returned to baseline | 1 |
| 8th grade or less | Black or African American | returned to baseline | 1 |
| 8th grade or less | White | returned to baseline | 1 |
| 8th grade or less | NA | returned to baseline | 1 |
| high school grad or GED | Black or African American | returned to baseline | 1 |
| More than 4-year college degree | NA | returned to baseline | 1 |
| Some college or 2 year degree (or trade or technical school) | NA | returned to baseline | 1 |
| Some high school, but did not graduate | Asian | did not return to baseline | 1 |
| Some high school, but did not graduate | Asian | returned to baseline | 1 |
| Some high school, but did not graduate | Black or African American | did not return to baseline | 1 |
| NA | Asian | returned to baseline | 1 |
| NA | Black or African American | did not return to baseline | 1 |
| NA | NA | did not return to baseline | 1 |
| NA | NA | returned to baseline | 1 |
kable(table_2, col.names = c("Child Race & Ethnicity","Parent Race & Ethnicity","Baseline Status", "n"), caption = "Table 2: Baseline Staus based on Parent and Child race/ethnicity")%>%
kableExtra::kable_styling(latex_options = c("striped","scale_down"))| Child Race & Ethnicity | Parent Race & Ethnicity | Baseline Status | n |
|---|---|---|---|
| White | White | returned to baseline | 376 |
| White | White | did not return to baseline | 239 |
| Other or multiracial | Other or multiracial | returned to baseline | 58 |
| Latino or Hispanic | Latino or Hispanic | returned to baseline | 54 |
| Other or multiracial | White | returned to baseline | 54 |
| Asian | Asian | returned to baseline | 40 |
| Latino or Hispanic | Latino or Hispanic | did not return to baseline | 33 |
| Asian | Asian | did not return to baseline | 28 |
| Other or multiracial | White | did not return to baseline | 28 |
| Other or multiracial | Other or multiracial | did not return to baseline | 20 |
| Black or African American | Black or African American | returned to baseline | 17 |
| Other or multiracial | Asian | returned to baseline | 15 |
| NA | White | returned to baseline | 13 |
| Black or African American | Black or African American | did not return to baseline | 11 |
| NA | White | did not return to baseline | 9 |
| Other or multiracial | Asian | did not return to baseline | 8 |
| Other or multiracial | Latino or Hispanic | returned to baseline | 8 |
| Asian | White | returned to baseline | 7 |
| White | Latino or Hispanic | returned to baseline | 7 |
| Indigenous | Indigenous | returned to baseline | 6 |
| Black or African American | White | returned to baseline | 6 |
| Asian | White | did not return to baseline | 5 |
| White | Other or multiracial | did not return to baseline | 5 |
| NA | Latino or Hispanic | returned to baseline | 5 |
| Indigenous | Indigenous | did not return to baseline | 4 |
| White | NA | did not return to baseline | 4 |
| NA | Other or multiracial | did not return to baseline | 4 |
| Indigenous | White | returned to baseline | 3 |
| Other or multiracial | Indigenous | returned to baseline | 3 |
| Other or multiracial | Black or African American | returned to baseline | 3 |
| White | Other or multiracial | returned to baseline | 3 |
| NA | NA | returned to baseline | 3 |
| Black or African American | Latino or Hispanic | returned to baseline | 2 |
| Black or African American | Other or multiracial | returned to baseline | 2 |
| Black or African American | White | did not return to baseline | 2 |
| Latino or Hispanic | White | did not return to baseline | 2 |
| Latino or Hispanic | NA | returned to baseline | 2 |
| NA | NA | did not return to baseline | 2 |
| Indigenous | Asian | did not return to baseline | 1 |
| Indigenous | Other or multiracial | returned to baseline | 1 |
| Indigenous | White | did not return to baseline | 1 |
| Latino or Hispanic | Indigenous | did not return to baseline | 1 |
| Latino or Hispanic | Other or multiracial | did not return to baseline | 1 |
| Latino or Hispanic | Other or multiracial | returned to baseline | 1 |
| Latino or Hispanic | NA | did not return to baseline | 1 |
| Other or multiracial | Indigenous | did not return to baseline | 1 |
| Other or multiracial | Black or African American | did not return to baseline | 1 |
| Other or multiracial | NA | did not return to baseline | 1 |
| White | Indigenous | did not return to baseline | 1 |
| White | Asian | returned to baseline | 1 |
| White | Latino or Hispanic | did not return to baseline | 1 |
| NA | Asian | did not return to baseline | 1 |
| NA | Asian | returned to baseline | 1 |
| NA | Black or African American | did not return to baseline | 1 |
| NA | Black or African American | returned to baseline | 1 |
| NA | Latino or Hispanic | did not return to baseline | 1 |
| NA | Other or multiracial | returned to baseline | 1 |
kable(table_3, col.names = c("Child Race & Ethnicity","Child Gender","Baseline Status","n"),caption = "Table 3: Baseline Staus based on Child race/ethnicity and Gender")%>%
kableExtra::kable_styling(latex_options = c("striped", "scale_down"))| Child Race & Ethnicity | Child Gender | Baseline Status | n |
|---|---|---|---|
| White | Male | returned to baseline | 201 |
| White | Female | returned to baseline | 186 |
| White | Male | did not return to baseline | 130 |
| White | Female | did not return to baseline | 118 |
| Other or multiracial | Male | returned to baseline | 72 |
| Other or multiracial | Female | returned to baseline | 69 |
| Latino or Hispanic | Female | returned to baseline | 34 |
| Other or multiracial | Male | did not return to baseline | 32 |
| Other or multiracial | Female | did not return to baseline | 27 |
| Asian | Female | returned to baseline | 24 |
| Asian | Male | returned to baseline | 23 |
| Latino or Hispanic | Male | returned to baseline | 23 |
| Latino or Hispanic | Male | did not return to baseline | 22 |
| Asian | Female | did not return to baseline | 18 |
| Latino or Hispanic | Female | did not return to baseline | 16 |
| NA | Male | returned to baseline | 16 |
| Asian | Male | did not return to baseline | 15 |
| Black or African American | Female | returned to baseline | 14 |
| Black or African American | Male | returned to baseline | 13 |
| NA | Female | did not return to baseline | 11 |
| NA | Female | returned to baseline | 8 |
| Indigenous | Female | returned to baseline | 7 |
| Black or African American | Male | did not return to baseline | 7 |
| NA | Male | did not return to baseline | 7 |
| Black or African American | Female | did not return to baseline | 6 |
| Indigenous | Female | did not return to baseline | 3 |
| Indigenous | Male | did not return to baseline | 3 |
| Indigenous | Male | returned to baseline | 3 |
| White | Other | did not return to baseline | 1 |
| White | Prefer not to answer | did not return to baseline | 1 |
kable(table_4,col.names = c("Interpretor Need","Language","Baseline Status","n"), caption = " Table 4:Baseline Staus based on Interpretor need and Language")%>%
kableExtra::kable_styling(latex_options = c("striped", "scale_down"))| Interpretor Need | Language | Baseline Status | n |
|---|---|---|---|
| No | English | returned to baseline | 678 |
| No | English | did not return to baseline | 406 |
| Yes | Spanish | returned to baseline | 6 |
| No | Spanish | did not return to baseline | 5 |
| No | Spanish | returned to baseline | 5 |
| Yes | Spanish | did not return to baseline | 4 |
| NA | English | returned to baseline | 3 |
| Yes | English | did not return to baseline | 2 |
| Yes | English | returned to baseline | 1 |
Tables as instructed
Language
table.lan <-qol_final %>%
tabyl(interviewlang_change, `Back to Baseline`) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 0) %>%
adorn_ns()
knitr::kable(table.lan,col.names = c("Language","No return to baseline","Return to baseline","Totals"), caption = " Table 5:Language and Baseline")%>%
kableExtra::kable_styling(latex_options = c("striped", "scale_down"))| Language | No return to baseline | Return to baseline | Totals |
|---|---|---|---|
| English | 37% (408) | 63% (682) | 100% (1090) |
| Spanish | 45% (9) | 55% (11) | 100% (20) |
| Total | 38% (417) | 62% (693) | 100% (1110) |
Interpretor used
table.int <-qol_final %>%
tabyl(interp_needed, `Back to Baseline`) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 0) %>%
adorn_ns()
knitr::kable(table.int,col.names = c("Interpreter","No return to baseline","Return to baseline","Totals"), caption = " Table 6:Interpreter need and Baseline")%>%
kableExtra::kable_styling(latex_options = c("striped", "scale_down"))| Interpreter | No return to baseline | Return to baseline | Totals |
|---|---|---|---|
| No | 38% (411) | 62% (683) | 100% (1094) |
| Yes | 46% (6) | 54% (7) | 100% (13) |
| NA | 0% (0) | 100% (3) | 100% (3) |
| Total | 38% (417) | 62% (693) | 100% (1110) |
Parent Education
table.edu <-qol_final %>%
tabyl(education, `Back to Baseline`) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 0) %>%
adorn_ns()
knitr::kable(table.edu,col.names = c("Education level","No return to baseline","Return to baseline","Totals"), caption = " Table 7: Education and Baseline")%>%
kableExtra::kable_styling(latex_options = c("striped", "scale_down"))| Education level | No return to baseline | Return to baseline | Totals |
|---|---|---|---|
| 4-year college graduate | 41% (108) | 59% (157) | 100% (265) |
| 8th grade or less | 17% (2) | 83% (10) | 100% (12) |
| high school grad or GED | 33% (59) | 67% (120) | 100% (179) |
| More than 4-year college degree | 41% (110) | 59% (160) | 100% (270) |
| Some college or 2 year degree (or trade or technical school) | 37% (123) | 63% (209) | 100% (332) |
| Some high school, but did not graduate | 30% (13) | 70% (30) | 100% (43) |
| NA | 22% (2) | 78% (7) | 100% (9) |
| Total | 38% (417) | 62% (693) | 100% (1110) |
Parent R&E
table.pre <-qol_final %>%
tabyl(r.e.parent, `Back to Baseline`) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 0) %>%
adorn_ns()
knitr::kable(table.pre,col.names = c("Race and or Ethnicity","No return to baseline","Return to baseline","Totals"), caption = " Table 8:Parent Race and or Ethnicity and Baseline")%>%
kableExtra::kable_styling(latex_options = c("striped", "scale_down"))| Race and or Ethnicity | No return to baseline | Return to baseline | Totals |
|---|---|---|---|
| Indigenous | 44% (7) | 56% (9) | 100% (16) |
| Asian | 40% (38) | 60% (57) | 100% (95) |
| Black or African American | 38% (13) | 62% (21) | 100% (34) |
| Latino or Hispanic | 32% (35) | 68% (76) | 100% (111) |
| Other or multiracial | 31% (30) | 69% (66) | 100% (96) |
| White | 38% (286) | 62% (459) | 100% (745) |
| NA | 62% (8) | 38% (5) | 100% (13) |
| Total | 38% (417) | 62% (693) | 100% (1110) |
Child R&E
table.cre <-qol_final %>%
tabyl(r.e.child, `Back to Baseline`) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 0) %>%
adorn_ns()
knitr::kable(table.cre,col.names = c("Race and or Ethnicity","No return to baseline","Return to baseline","Totals"), caption = " Table 9:Child Race and or Ethnicity and Baseline")%>%
kableExtra::kable_styling(latex_options = c("striped", "scale_down"))| Race and or Ethnicity | No return to baseline | Return to baseline | Totals |
|---|---|---|---|
| Indigenous | 38% (6) | 63% (10) | 100% (16) |
| Asian | 41% (33) | 59% (47) | 100% (80) |
| Black or African American | 33% (13) | 68% (27) | 100% (40) |
| Latino or Hispanic | 40% (38) | 60% (57) | 100% (95) |
| Other or multiracial | 30% (59) | 71% (141) | 100% (200) |
| White | 39% (250) | 61% (387) | 100% (637) |
| NA | 43% (18) | 57% (24) | 100% (42) |
| Total | 38% (417) | 62% (693) | 100% (1110) |
Child Gender
table.cge<-qol_final %>%
tabyl(childgender, `Back to Baseline`) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 0) %>%
adorn_ns()
knitr::kable(table.cge,col.names = c("Gender Identity","No return to baseline","Return to baseline","Totals"), caption = " Table 10: Child Gender Identity and Baseline")%>%
kableExtra::kable_styling(latex_options = c("striped", "scale_down"))| Gender Identity | No return to baseline | Return to baseline | Totals |
|---|---|---|---|
| Female | 37% (199) | 63% (342) | 100% (541) |
| Male | 38% (216) | 62% (351) | 100% (567) |
| Other | 100% (1) | 0% (0) | 100% (1) |
| Prefer not to answer | 100% (1) | 0% (0) | 100% (1) |
| Total | 38% (417) | 62% (693) | 100% (1110) |