data-raw/BRFSS.R
In bencapistrant/brfssR: Behavioral Risk Factor Surveillance Survey (BRFSS) Data
library(tidyverse)
library(lubridate)
library(foreign)
library(usethis)
library(devtools)
## BRING IN DATA FROM 2014 - 2017
### 2014
VAR_2014_0<-read.xport("data-raw/LLCP2014.XPT")
data_2014 <- VAR_2014_0 %>%
mutate(YEAR=2014,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV4,X_BMI5CAT,X_BMI5,
FLUSHOT = FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2014_0)
#----------------------------------------------------------------------------------#
### 2015
VAR_2015_0<-read.xport("data-raw/LLCP2015.XPT")
data_2015 <- VAR_2015_0 %>%
mutate(YEAR=2015,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character)%>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1, SEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV5,X_BMI5CAT,X_BMI5,
FLUSHOT =FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2015_0)
## 2015 Notes:
### No Imputed Race variable (X_IMPRACE)
### No Dentist Variable (LASTDEN3)
### No HIV risk variable - doesn't start until 2016 (HIVRISK4)
### No Kidney disease variable (CHCKDNY1)
#----------------------------------------------------------------------------------#
### 2016
VAR_2016_0<-read.xport("data-raw/LLCP2016.XPT")
data_2016 <- VAR_2016_0 %>%
mutate(YEAR=2016,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1, SEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV5,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK4,FLUSHOT = FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,LASTDEN3,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2016_0)
## 2016 Notes:
### No Imputed Race variable (X_IMPRACE)
### No HIV risk variable - doesn't start until 2016 (HIVRISK4)
### No Kidney disease variable (CHCKDNY1)
#----------------------------------------------------------------------------------#
### 2017
VAR_2017_0<-read.xport("data-raw/LLCP2017.XPT")
data_2017 <- VAR_2017_0 %>%
mutate(YEAR=2017,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV5,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK5,FLUSHOT = FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2017_0)
## 2017 Notes:
### No Imputed Race variable (X_IMPRACE)
### No HIV risk variable - doesn't start until 2016 (HIVRISK4)
### No Kidney disease variable (CHCKDNY1)
#----------------------------------------------------------------------------------#
### 2018
VAR_2018_0<-read.xport("data-raw/LLCP2018.XPT")
data_2018 <- VAR_2018_0 %>%
mutate(YEAR=2018,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX=SEX1,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV6,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK5,FLUSHOT = FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,CHCKDNY1,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,X_METSTAT,X_URBSTAT,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2018_0)
#table(data_2018$HVYDRNK,useNA = c("always"))
## 2018 Notes:
### No Imputed Race variable (X_IMPRACE)
### Sex renamed from SEX1 (now includes "Sex assigned at birth")
### Heavy Drinking Changed and renamed
#----------------------------------------------------------------------------------#
### 2019
VAR_2019_0<-read.xport("data-raw/LLCP2019.XPT")
data_2019 <- VAR_2019_0 %>%
mutate(YEAR=2019,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX=SEXVAR,
BIRTHSEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV7,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK5,FLUSHOT = FLUSHOT7,FLSHTMY=FLSHTMY3,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3=DIABETE4,ASTHMA3,HAVARTH3=HAVARTH4,CHCCOPD1=CHCCOPD2,CHCOCNCR,CHCKDNY1 = CHCKDNY2,
ADDEPEV2=ADDEPEV3,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,X_METSTAT,X_URBSTAT,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2019_0)
#table(data_2019$HVYDRNK,useNA = c("always"))
#----------------------------------------------------------------------------------#
### 2020
VAR_2020_0<-read.xport("data-raw/LLCP2020.XPT")
data_2020 <- VAR_2020_0 %>%
mutate(YEAR=2020,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX=SEXVAR,
BIRTHSEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV7,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK5,FLUSHOT = FLUSHOT7,FLSHTMY=FLSHTMY3,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3=DIABETE4,ASTHMA3,HAVARTH3=HAVARTH4,CHCCOPD1=CHCCOPD2,CHCOCNCR,CHCKDNY1 = CHCKDNY2,
ADDEPEV2=ADDEPEV3,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,X_METSTAT,X_URBSTAT,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2020_0)
#table(data_2020$HVYDRNK,useNA = c("always"))
#----------------------------------------------------------------------------------#
# table(data_2014$SEX,useNA = c("always"))
# table(data_2015$SEX,useNA = c("always"))
# table(data_2016$SEX,useNA = c("always"))
# table(data_2017$SEX,useNA = c("always"))
# table(data_2018$SEX,useNA = c("always"))
# table(data_2019$SEX,useNA = c("always"))
# table(data_2020$SEX,useNA = c("always"))
### COMBINING 2014-2018
brfss_VAR<-bind_rows(data_2020,data_2019,data_2018,data_2017,data_2016,data_2015,data_2014)
#table(brfss_VAR$SEX,useNA = c("always"))
#table(data_2018$HVYDRNK,useNA = c("always"))
varstates<-c(1,2,4,5,6,8,9,10,
11,12,13,15,16,17,18,19,20,
21,22,23,24,25,26,27,28,29,30,
31,32,33,34,35,36,37,38,39,40,
41,42,44,45,46,47,48,49,50,
51,53,54,55,56,66,72,78)
varstatelabel<-c("AL","AK","AZ","AR","CA","CO","CT","DE",
"DC","FL","GA","HI","ID","IL","IN","IA","KS",
"KY","LA","ME","MD","MA","MI","MN","MS","MO","MT",
"NE","NV","NH","NJ","NM","NY","NC","ND","OH","OK",
"OR","PA","RI","SC","SD","TN","TX","UT","VT",
"VA","WA","WV","WI","WY","GU","PR","VI")
brfss_VAR$state<- factor(brfss_VAR$X_STATE, levels=varstates, labels=varstatelabel)
brfss_VAR1<-brfss_VAR%>%
mutate(fips=X_STATE) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,SEX,BIRTHSEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK,X_BMI5CAT,X_BMI5,
HIVRISK,FLUSHOT,FLSHTMY,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,LASTDEN3,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,CHCKDNY1,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,X_METSTAT,X_URBSTAT,
fips,X_STATE,state,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(data_2020,data_2019,data_2018,data_2017,data_2016,data_2015,data_2014,brfss_VAR)
#---------------------------------------#
# FUNCTIONS
#---------------------------------------#
binary_d_num<-function(x) {
ifelse(x==2,0,
ifelse(x==1,1,NA_real_))
}
binary_d_fct<-function(x) {
factor(
ifelse(x==2,"No",
ifelse(x==1,"Yes",NA_character_))
)
}
rawnames = c("CVDCRHD4","CVDSTRK3","DIABETE3","ASTHMA3","CHCOCNCR","CHCCOPD1","HAVARTH3")
newnames = c("cvd","strk","diab","asth","cncr","copd","arth")
lastnames = c("cvd_d_num","strk_d_num","diab_d_num","asth_d_num","cncr_d_num","copd_d_num","arth_d_num")
brfss_covariates<-brfss_VAR1 %>%
#---------------------------------------#
# Demographics
# Age, Sex, Race, Marital Status,
# Number of Children, Veterans Status,
# Metropolitan Service Area
#---------------------------------------#
# Age
## Quantitative / Numeric
mutate(age_num=X_AGE80) %>%
## Categorical - five year categories
mutate(age_cat = as.factor(if_else(X_AGE80 >= 18 & X_AGE80 < 25, "18-24",
if_else(X_AGE80 >= 25 & X_AGE80 < 30, "25-29",
if_else(X_AGE80 >= 30 & X_AGE80 < 35, "30-34",
if_else(X_AGE80 >= 35 & X_AGE80 < 40, "35-39",
if_else(X_AGE80 >= 40 & X_AGE80 < 45, "40-44",
if_else(X_AGE80 >= 45 & X_AGE80 < 50, "45-49",
if_else(X_AGE80 >= 50 & X_AGE80 < 55, "50-54",
if_else(X_AGE80 >= 55 & X_AGE80 < 60, "55-59",
if_else(X_AGE80 >= 60 & X_AGE80 < 65, "60-64",
if_else(X_AGE80 >= 65 & X_AGE80 < 70, "65-69",
if_else(X_AGE80 >= 70 & X_AGE80 < 75, "70-74",
if_else(X_AGE80 >= 75 & X_AGE80 < 80, "75-79",
if_else(X_AGE80 >= 80 & X_AGE80 < 85, "80+", NA_character_))))))))))))))
) %>%
## Categorical - 10 year categories
mutate(age_10ycat_fct=as.factor(if_else(age_num %in% 18:24, "18-24",
if_else(age_num %in% 25:34, "25-34",
if_else(age_num %in% 35:44, "35-44",
if_else(age_num %in% 45:54, "45-54",
if_else(age_num %in% 55:64, "55-64",
if_else(age_num >=65, "65+",NA_character_)))))))) %>%
## Dichotomous/Binary: Millennial (18-35 vs. 36+); 45+ vs 18-44; 65+ vs 18-64
mutate(millennial_d_num=as.numeric(if_else(YEAR==2013, if_else(age_num %in% 18:32,1,0),
if_else(YEAR==2014, if_else(age_num %in% 18:33,1,0),
if_else(YEAR==2015, if_else(age_num %in% 18:34,1,0),
if_else(YEAR==2016, if_else(age_num %in% 19:35,1,0),
if_else(YEAR==2017, if_else(age_num %in% 20:36,1,0), NA_real_)))))),
millennial_d_fct=as.factor(if_else(millennial_d_num==1, "Yes",
if_else(millennial_d_num==0, "No", NA_character_))),
agege45_d_num=as.numeric(if_else(age_num %in% 18:44, 0,
if_else(age_num>=45,1, NA_real_))),
agege45_d_fct=as.factor(if_else(age_num %in% 18:44, "No",
if_else(age_num>=45,"Yes", NA_character_))),
agege65_d_num=as.numeric(if_else(age_num %in% 18:64, 0,
if_else(age_num>=65,1, NA_real_))),
agege65_d_fct=as.factor(if_else(age_num %in% 18:64, "No",
if_else(age_num>=65,"Yes", NA_character_)))
) %>%
# Sex
mutate(sex_d_fct = as.factor(if_else(SEX == 1, "Male",
if_else(SEX == 2, "Female", NA_character_))),
fem_d_num = as.numeric(if_else(SEX == 1, 0,
if_else(SEX == 2, 1, NA_real_))),
sexab_cat_fct = as.factor(if_else(BIRTHSEX == 1, "Male",
if_else(BIRTHSEX == 2, "Female",
if_else(BIRTHSEX %in% c(7,9), "Other", NA_character_)))),
) %>%
# Race/Ethnicity (X_RACEGR3, X_MRACE1 & X_HISPANC)
mutate(race_cat_fct=as.factor(if_else(X_MRACE1==1, "White only",
if_else(X_MRACE1==2, "Black or African American only",
if_else(X_MRACE1==3, "American Indian or Alaskan Native only",
if_else(X_MRACE1==4, "Asian only",
if_else(X_MRACE1==5, "Native Hawaiian or other Pacific Islander only",
if_else(X_MRACE1==6, "Other race only",
if_else(X_MRACE1==7, "Multiracial", NA_character_))))))))
) %>%
mutate(raceth_cat_fct = as.factor(ifelse(X_RACEGR3 == 1, "White, Non-Hispanic",
ifelse(X_RACEGR3 == 2, "Black, Non-Hispanic",
ifelse(X_RACEGR3 == 3, "Other, Non-Hispanic",
ifelse(X_RACEGR3 == 4, "Multiple, Non-Hispanic",
ifelse(X_RACEGR3 == 5, "Hispanic",
ifelse(X_RACEGR3 == 9, "DK/Refused", NA_character_)))))))) %>%
mutate(ethn_cat_fct = as.factor(if_else(X_HISPANC == 1, "Hispanic",
if_else(X_HISPANC == 2, "Non-Hispanic", NA_character_)))
) %>%
# Marital Status (MARITAL)
mutate(mstat_cat_fct=as.factor(if_else(MARITAL==1, "Married",
if_else(MARITAL==2, "Divorced",
if_else(MARITAL==3, "Widowed",
if_else(MARITAL==4, "Separated",
if_else(MARITAL==5, "Never Married",
if_else(MARITAL==6, "Coupled",NA_character_))))))),
mstat_d_fct=as.factor(if_else(MARITAL==1, "Married",
if_else(MARITAL %in% 2:6, "Non-Married",NA_character_))) ) %>%
mutate(married_d_fct=as.factor(if_else(mstat_cat_fct %in% c("Married", "Coupled"), "Married",
if_else(mstat_cat_fct %in% c("Divorced", "Never Married", "Separated", "Widowed"), "Not Married", NA_character_))),
) %>%
# Number of Children (CHILDREN)
mutate(chld_num = as.numeric(ifelse(CHILDREN %in% 1:87, CHILDREN,
ifelse(CHILDREN == 88, 0, NA_real_))),
chld_cat_fct = as.factor(if_else(X_CHLDCNT==1, "None",
if_else(X_CHLDCNT==2, "One",
if_else(X_CHLDCNT %in% 3:6, "Two or More", NA_character_ ))))) %>%
mutate(kids_d_fct = as.factor(if_else(X_CHLDCNT==1, "No",
if_else(X_CHLDCNT %in% 2:6, "Yes", NA_character_ )))
) %>%
# Veteran Status (VETERAN3)
mutate(vtrn_d_num=if_else(VETERAN3==2,0,
if_else(VETERAN3==1,1,NA_real_)),
vtrn_d_fct=as.factor(if_else(VETERAN3==2,"Non-Veteran",
if_else(VETERAN3==1,"Veteran",NA_character_)))
) %>%
# Metropolitan Service Area (MSA)
mutate(msa_cat_fct=as.factor(if_else(MSCODE==1, "City_Center",
if_else(MSCODE==2, "City_County",
if_else(MSCODE==3, "Suburb",
if_else(MSCODE==5, "Outside MSA",NA_character_))))),
msa_d_fct=as.factor(if_else(MSCODE %in% 1:3, "Inside MSA",
if_else(MSCODE==5, "Outside MSA",NA_character_)))
) %>%
# Urban and Metropolitcan Counties (only in 2018 data)
mutate(metro_d_fct=as.factor(if_else(X_METSTAT==1, "Yes",
if_else(X_METSTAT==2, "No", NA_character_))),
urban_d_fct=as.factor(if_else(X_URBSTAT==1, "Yes",
if_else(X_URBSTAT==2, "No", NA_character_)))
) %>%
#---------------------------------------#
# SOCIOECONOMIC STATUS
# Employment Status, Income
# Education
#---------------------------------------#
# Employment
mutate(empl_cat_fct = as.factor(
ifelse(EMPLOY1==1, "Employed for wages",
ifelse(EMPLOY1==2, "Self-Employed",
ifelse(EMPLOY1 %in% 3:4, "Out of work",
ifelse(EMPLOY1==5, "Homemaker",
ifelse(EMPLOY1==6, "Student",
ifelse(EMPLOY1==7, "Retired",
ifelse(EMPLOY1== 8, "Unable", NA_character_ ))))))))) %>%
mutate(employed_d_fct=as.factor(if_else(empl_cat_fct %in% c("Employed for wages","Self-employed"), "Yes", "No/Missing"))
) %>%
# Income
mutate(INCOME3 = ifelse(INCOME2 %in% 77:99, 9, INCOME2)) %>%
mutate(inc_cat_fct = factor(INCOME3, levels = c(1:9),
labels = c("<$10,000", "$10,000-$14,999",
"$15,000-19,999", "$20,000-$24,999",
"$25,000-34,999", "$35,000-$49,999",
"$50,000-74,999", "$75,000+",
"Don't know or refused")) ) %>%
mutate(income_4cats_fct=as.factor(if_else(inc_cat_fct %in% c("<$10,000","$10,000-$14,999","$15,000-19,999"), "<$20,000",
if_else(inc_cat_fct %in% c("$20,000-$24,999","$25,000-34,999"), "$20,000-34,999",
if_else(inc_cat_fct %in% c("$35,000-$49,999","$50,000-74,999"), "$35,000-74,999",
if_else(inc_cat_fct=="$75,000+", "$75,000+",
if_else(inc_cat_fct=="Don't know or refused", "Missing", NA_character_))))))
) %>%
# Education
mutate(educ_cat_fct = as.factor(if_else(X_EDUCAG==1, "<High School",
if_else(X_EDUCAG==2, "High School",
if_else(X_EDUCAG==3, "Some College",
if_else(X_EDUCAG==4, "College or More",NA_character_)))))) %>%
mutate(college_d_fct=as.factor(if_else(educ_cat_fct=="College or More", "College",
if_else(educ_cat_fct %in% c("<High School", "High School", "Some College"), "<College", NA_character_)))
) %>%
#---------------------------------------#
# HEALTH BEHAVIORS
# Alcohol, BMI, Fruit & Vegetable Intake
# HIV Risk, Influenza Vaccine,
# Physical Activity, Smoking Status
#---------------------------------------#
#Alcohol
##Heavy Drinking / Binge Drinking
mutate(drnkhvy_d_fct = as.factor(ifelse(HVYDRNK == 1, "No",
ifelse(HVYDRNK == 2, "Yes", NA_character_))),
drnkbng_d_fct = as.factor(if_else(X_RFBING5 == 1,"No",
if_else(X_RFBING5 == 2,"Yes",NA_character_)))
) %>%
#BMI
## Numeric
mutate(bmi_num = as.numeric(X_BMI5/100) ) %>%
## Categories
mutate(bmi_cat_fct = as.factor(if_else(X_BMI5CAT == 1, "Underweight",
if_else(X_BMI5CAT == 2, "Normal Weight",
if_else(X_BMI5CAT == 3, "Overweight",
if_else(X_BMI5CAT == 4, "Obese",
if_else(is.na(X_BMI5CAT), "Unknown", NA_character_)))))),
bmi_40d_fct = as.factor(if_else(bmi_num %in% 0:40, "<40",
if_else(bmi_num>=40, ">=40", NA_character_)))) %>%
mutate(obese_d_fct=as.factor(if_else(X_BMI5CAT == 4, "Yes","No"))) %>%
#Smoking
mutate(smk_cat_fct = as.factor(if_else(X_SMOKER3 %in% 1:2, "Current smoker",
if_else(X_SMOKER3==3, "Former smoker",
if_else(X_SMOKER3==4, "Never smoker",
if_else(X_SMOKER3==9, "DK/Refused",NA_character_ ))))),
smoker_d_fct=as.factor(if_else(X_SMOKER3 %in% 1:2, "Smoker",
if_else(X_SMOKER3 %in% 3:9, "Non-Smoker", NA_character_)))
) %>%
#High HIV risk binary variable
mutate(hiv_d_num = as.numeric(ifelse(HIVRISK == 1, 1,
ifelse(HIVRISK == 2, 0, NA_real_))),
hiv_d_fct = as.factor(ifelse(HIVRISK == 1, "Yes",
ifelse(HIVRISK == 2, "No", NA_character_)))
) %>%
#Leisure-time physical activity
mutate(ltpa_d_fct = as.factor(if_else(X_TOTINDA==1, "Yes",
if_else(X_TOTINDA==2, "No",NA_character_))),
ltpa_cat_fct = as.factor(if_else(X_TOTINDA==1, "Yes",
if_else(X_TOTINDA==2, "No",
if_else(X_TOTINDA %in% 7:9, "DK/Refused",NA_character_))))
) %>%
# Influenza Vaccine: Any Vaccine, When, and Where the vaccine happened
mutate(fluvac_cat_fct = as.factor(if_else(FLUSHOT==1, "Yes",
if_else(FLUSHOT==2, "No", "DK/Refused"))),
fluvac_d_fct = as.factor(if_else(FLUSHOT==1, "Yes",
if_else(FLUSHOT==2, "No",NA_character_))),
fluvac_d_num = as.numeric(if_else(FLUSHOT==1, 1,
if_else(FLUSHOT==2, 0, NA_real_)))
#FLSHTMY3=if_else(FLSHTMY2 %in% 777777:999999, NA_real_, FLSHTMY2),
#fluvac_date_num = mdy(FLSHTMY3)
# IMFVPLAC2=if_else(IMFVPLAC %in% 77:99, NA_real_, IMFVPLAC),
# fluplc_cat_fct=factor(IMFVPLAC2, levels=1:11,
# labels=c("Doctor's office", "Health dept", "Health center",
# "Community center", "Store", "Hospital",
# "Emergency room", "Workplace", "Other",
# "Canada/Mexico", "School" ))
) %>%
#---------------------------------------#
# HEALTH CARE
# Doctor Cost, Health Care Coverage,
# Time since last checkup,
# Time since last dentist visit
#---------------------------------------#
# Cost prevented needed doctor visit
mutate(drcost_d_fct = as.factor(if_else(MEDCOST == 1, "Yes",
if_else(MEDCOST == 2, "No", NA_character_)))
) %>%
# Health Care Coverage
mutate(hcplan_cat_fct = as.factor(if_else(HLTHPLN1 == 1, "Yes",
if_else(HLTHPLN1 == 2, "No",
if_else(HLTHPLN1 %in% 7:9, "DK/Refused",NA_character_)))),
hcplan_d_fct = as.factor(if_else(HLTHPLN1 == 1, "Yes",
if_else(HLTHPLN1 == 2, "No", NA_character_)))
) %>%
# Time since last checkup
mutate(chckup_cat_fct = as.factor(if_else(CHECKUP1 == 1, "<1 year",
if_else(CHECKUP1 == 2, "1-2 years",
if_else(CHECKUP1 == 3, "2-5 years",
if_else(CHECKUP1 == 4, "5+ years",
if_else(CHECKUP1 == 8, "Never",
if_else(CHECKUP1 == 7 | CHECKUP1 == 9, "DK/Refused",NA_character_))))))),
drlastyr_d_fct=as.factor(if_else(CHECKUP1 == 1, "<1 year",
if_else(CHECKUP1 != 1, "No", NA_character_)))
) %>%
#---------------------------------------#
# HEALTH STATUS
# Chronic Diseases,
# Depressive Disorder,
# Mental & Physcial Health HRQOL,
# Self-Rated Health
# Pregnant
#---------------------------------------#
# Chronic Disease (CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,CHCOCNCR,CHCCOPD1,HAVARTH3)
### These lists - rawnames, newnames, and lastnames - were defined at the top of the file under "functions"
### This code is to: 1. change the raw BRFSS names into brief, conceptual names; 2. revise the name to include that it's a dichotomous, numeric variable
rename_at(.vars=vars(rawnames), ~ newnames) %>%
rename_at(.vars=vars(newnames),
function(z) paste0(z,"_d_num")) %>%
mutate_at(.vars=vars(lastnames),
.funs = binary_d_num) %>%
## Count of Conditions
mutate(chron_num = rowSums(.[lastnames], na.rm = TRUE)) %>%
## Any
mutate(chron_d_fct = ifelse(chron_num>=1,"Yes","No")) %>%
#Kidney Disease
mutate(kidn_d_fct = as.factor(if_else(CHCKDNY1 == 1, "Yes",
if_else(CHCKDNY1 == 2, "No", NA_character_))),
kidn_d_num = as.numeric(if_else(CHCKDNY1 == 1, 1,
if_else(CHCKDNY1 == 2, 0, NA_real_)))
) %>%
# Depressive Disorder
mutate(dep_d_fct = as.factor(if_else(ADDEPEV2 == 1, "Yes",
if_else(ADDEPEV2 == 2, "No", NA_character_))),
dep_d_num = as.numeric(if_else(ADDEPEV2 == 1, 1,
if_else(ADDEPEV2 == 2, 0, NA_real_)))
) %>%
# Mental Health QOL
mutate(mentqol_num = MENTHLTH,
mentqol_num = replace(mentqol_num, mentqol_num==88, 0),
mentqol_num = replace(mentqol_num, mentqol_num %in% c(77,99), NA) )%>%
mutate(mentqol14_d_num = as.numeric(if_else(mentqol_num %in% 0:14, 0,
if_else(mentqol_num %in% 14:30, 1, NA_real_))),
mentqol14_d_fct = as.factor(if_else(mentqol_num %in% 0:14 , "No",
if_else(mentqol_num %in% 14:30, "Yes", NA_character_)))
) %>%
# Physical Health QOL
mutate(physqol_num = PHYSHLTH,
physqol_num = replace(physqol_num, physqol_num==88, 0),
physqol_num = replace(physqol_num, physqol_num %in% c(77,99), NA) ) %>%
mutate(physqol14_d_num = as.numeric(if_else(physqol_num %in% 0:14, 0,
if_else(physqol_num %in% 14:30, 1, NA_real_))),
physqol14_d_fct = as.factor(if_else(physqol_num %in% 0:14, "No",
if_else(physqol_num %in% 14:30, "Yes", NA_character_)))
) %>%
# Self-Rated Health
mutate(srh_d_fct=as.factor(if_else(GENHLTH %in% 1:3, "Good+",
if_else(GENHLTH %in% 4:5, "Fair/Poor", NA_character_))),
srh_cat_fct=as.factor(if_else(GENHLTH==1, "Excellent",
if_else(GENHLTH==2, "Very Good",
if_else(GENHLTH==3, "Good",
if_else(GENHLTH==4, "Fair",
if_else(GENHLTH==5, "Poor", NA_character_))))))
) %>%
# Pregnant
mutate(preg_d_fct=as.factor(if_else(PREGNANT==1, "Yes",
if_else(PREGNANT==2, "No", NA_character_)))
) %>%
rename_all(tolower)
#table(brfss_covariates$sex,useNA = c("always"))
#table(brfss_covariates$fem_d_num,useNA = c("always"))
#table(brfss_covariates$sex_d_fct,useNA = c("always"))
brfss_core<-brfss_covariates %>%
select(
age_num,age_cat,age_10ycat_fct,
millennial_d_num,millennial_d_fct,
agege45_d_num,agege45_d_fct,
agege65_d_num,agege65_d_fct,
mstat_cat_fct,mstat_cat_fct,married_d_fct,
chld_num,chld_cat_fct,kids_d_fct,
race_cat_fct,ethn_cat_fct,raceth_cat_fct,
sex_d_fct,fem_d_num,sexab_cat_fct,
vtrn_d_num,vtrn_d_fct,
msa_cat_fct,msa_d_fct,
metro_d_fct,urban_d_fct,
drnkbng_d_fct,drnkhvy_d_fct,
bmi_cat_fct, bmi_40d_fct, bmi_num,obese_d_fct,
hiv_d_num,hiv_d_fct,
fluvac_cat_fct,fluvac_d_fct,fluvac_d_num,
ltpa_d_fct,ltpa_cat_fct,
smk_cat_fct,smoker_d_fct,
drcost_d_fct,
hcplan_d_fct,hcplan_cat_fct,
chckup_cat_fct,drlastyr_d_fct,
chron_num,chron_d_fct,cvd_d_num,strk_d_num,diab_d_num,asth_d_num,arth_d_num,copd_d_num,cncr_d_num,kidn_d_num,
dep_d_fct,
mentqol_num,mentqol14_d_num,mentqol14_d_fct,
physqol_num,physqol14_d_num,physqol14_d_fct,
srh_d_fct,srh_cat_fct,
preg_d_fct,
empl_cat_fct,employed_d_fct,
inc_cat_fct,income_4cats_fct,
educ_cat_fct,college_d_fct,
x_state,state,fips,year,seqno,x_psu,x_ststr,
var_wt_raw,version_var
)
rm(brfss_covariates,brfss_VAR1)
rm(brfss_covariates2)
table(brfss_core$employed_d_fct,useNA = c("always"))
#save(brfss_core, file = "data/brfss_core.rda", compress = "bzip2", version=2)
use_data(brfss_core, overwrite = TRUE,compress = "bzip2")
bencapistrant/brfssR documentation built on Sept. 24, 2021, 2:10 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(tidyverse)
library(lubridate)
library(foreign)
library(usethis)
library(devtools)
## BRING IN DATA FROM 2014 - 2017
### 2014
VAR_2014_0<-read.xport("data-raw/LLCP2014.XPT")
data_2014 <- VAR_2014_0 %>%
mutate(YEAR=2014,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV4,X_BMI5CAT,X_BMI5,
FLUSHOT = FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2014_0)
#----------------------------------------------------------------------------------#
### 2015
VAR_2015_0<-read.xport("data-raw/LLCP2015.XPT")
data_2015 <- VAR_2015_0 %>%
mutate(YEAR=2015,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character)%>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1, SEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV5,X_BMI5CAT,X_BMI5,
FLUSHOT =FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2015_0)
## 2015 Notes:
### No Imputed Race variable (X_IMPRACE)
### No Dentist Variable (LASTDEN3)
### No HIV risk variable - doesn't start until 2016 (HIVRISK4)
### No Kidney disease variable (CHCKDNY1)
#----------------------------------------------------------------------------------#
### 2016
VAR_2016_0<-read.xport("data-raw/LLCP2016.XPT")
data_2016 <- VAR_2016_0 %>%
mutate(YEAR=2016,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1, SEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV5,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK4,FLUSHOT = FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,LASTDEN3,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2016_0)
## 2016 Notes:
### No Imputed Race variable (X_IMPRACE)
### No HIV risk variable - doesn't start until 2016 (HIVRISK4)
### No Kidney disease variable (CHCKDNY1)
#----------------------------------------------------------------------------------#
### 2017
VAR_2017_0<-read.xport("data-raw/LLCP2017.XPT")
data_2017 <- VAR_2017_0 %>%
mutate(YEAR=2017,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV5,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK5,FLUSHOT = FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2017_0)
## 2017 Notes:
### No Imputed Race variable (X_IMPRACE)
### No HIV risk variable - doesn't start until 2016 (HIVRISK4)
### No Kidney disease variable (CHCKDNY1)
#----------------------------------------------------------------------------------#
### 2018
VAR_2018_0<-read.xport("data-raw/LLCP2018.XPT")
data_2018 <- VAR_2018_0 %>%
mutate(YEAR=2018,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX=SEX1,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV6,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK5,FLUSHOT = FLUSHOT6,FLSHTMY=FLSHTMY2,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,CHCKDNY1,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,X_METSTAT,X_URBSTAT,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2018_0)
#table(data_2018$HVYDRNK,useNA = c("always"))
## 2018 Notes:
### No Imputed Race variable (X_IMPRACE)
### Sex renamed from SEX1 (now includes "Sex assigned at birth")
### Heavy Drinking Changed and renamed
#----------------------------------------------------------------------------------#
### 2019
VAR_2019_0<-read.xport("data-raw/LLCP2019.XPT")
data_2019 <- VAR_2019_0 %>%
mutate(YEAR=2019,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX=SEXVAR,
BIRTHSEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV7,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK5,FLUSHOT = FLUSHOT7,FLSHTMY=FLSHTMY3,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3=DIABETE4,ASTHMA3,HAVARTH3=HAVARTH4,CHCCOPD1=CHCCOPD2,CHCOCNCR,CHCKDNY1 = CHCKDNY2,
ADDEPEV2=ADDEPEV3,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,X_METSTAT,X_URBSTAT,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2019_0)
#table(data_2019$HVYDRNK,useNA = c("always"))
#----------------------------------------------------------------------------------#
### 2020
VAR_2020_0<-read.xport("data-raw/LLCP2020.XPT")
data_2020 <- VAR_2020_0 %>%
mutate(YEAR=2020,
VERSION_VAR="X_LLCPWT",
VAR_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO)) %>%
mutate_if(is.double, as.integer) %>%
mutate_if(is.factor, as.character) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,X_IMPRACE, SEX=SEXVAR,
BIRTHSEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK=X_RFDRHV7,X_BMI5CAT,X_BMI5,
HIVRISK=HIVRISK5,FLUSHOT = FLUSHOT7,FLSHTMY=FLSHTMY3,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,
CVDCRHD4,CVDSTRK3,DIABETE3=DIABETE4,ASTHMA3,HAVARTH3=HAVARTH4,CHCCOPD1=CHCCOPD2,CHCOCNCR,CHCKDNY1 = CHCKDNY2,
ADDEPEV2=ADDEPEV3,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,X_METSTAT,X_URBSTAT,
X_STATE,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(VAR_2020_0)
#table(data_2020$HVYDRNK,useNA = c("always"))
#----------------------------------------------------------------------------------#
# table(data_2014$SEX,useNA = c("always"))
# table(data_2015$SEX,useNA = c("always"))
# table(data_2016$SEX,useNA = c("always"))
# table(data_2017$SEX,useNA = c("always"))
# table(data_2018$SEX,useNA = c("always"))
# table(data_2019$SEX,useNA = c("always"))
# table(data_2020$SEX,useNA = c("always"))
### COMBINING 2014-2018
brfss_VAR<-bind_rows(data_2020,data_2019,data_2018,data_2017,data_2016,data_2015,data_2014)
#table(brfss_VAR$SEX,useNA = c("always"))
#table(data_2018$HVYDRNK,useNA = c("always"))
varstates<-c(1,2,4,5,6,8,9,10,
11,12,13,15,16,17,18,19,20,
21,22,23,24,25,26,27,28,29,30,
31,32,33,34,35,36,37,38,39,40,
41,42,44,45,46,47,48,49,50,
51,53,54,55,56,66,72,78)
varstatelabel<-c("AL","AK","AZ","AR","CA","CO","CT","DE",
"DC","FL","GA","HI","ID","IL","IN","IA","KS",
"KY","LA","ME","MD","MA","MI","MN","MS","MO","MT",
"NE","NV","NH","NJ","NM","NY","NC","ND","OH","OK",
"OR","PA","RI","SC","SD","TN","TX","UT","VT",
"VA","WA","WV","WI","WY","GU","PR","VI")
brfss_VAR$state<- factor(brfss_VAR$X_STATE, levels=varstates, labels=varstatelabel)
brfss_VAR1<-brfss_VAR%>%
mutate(fips=X_STATE) %>%
select(X_AGE80,MARITAL,CHILDREN,X_CHLDCNT,X_RACEGR3,X_HISPANC,X_MRACE1,SEX,BIRTHSEX,VETERAN3,MSCODE,X_RFBING5,HVYDRNK,X_BMI5CAT,X_BMI5,
HIVRISK,FLUSHOT,FLSHTMY,X_TOTINDA,X_SMOKER3,MEDCOST,HLTHPLN1,CHECKUP1,LASTDEN3,
CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,HAVARTH3,CHCCOPD1,CHCOCNCR,CHCKDNY1,
ADDEPEV2,MENTHLTH,PHYSHLTH,PREGNANT,GENHLTH,EMPLOY1,INCOME2,X_EDUCAG,X_METSTAT,X_URBSTAT,
fips,X_STATE,state,YEAR,SEQNO,VAR_wt_raw,VERSION_VAR,X_PSU, X_STSTR)
rm(data_2020,data_2019,data_2018,data_2017,data_2016,data_2015,data_2014,brfss_VAR)
#---------------------------------------#
# FUNCTIONS
#---------------------------------------#
binary_d_num<-function(x) {
ifelse(x==2,0,
ifelse(x==1,1,NA_real_))
}
binary_d_fct<-function(x) {
factor(
ifelse(x==2,"No",
ifelse(x==1,"Yes",NA_character_))
)
}
rawnames = c("CVDCRHD4","CVDSTRK3","DIABETE3","ASTHMA3","CHCOCNCR","CHCCOPD1","HAVARTH3")
newnames = c("cvd","strk","diab","asth","cncr","copd","arth")
lastnames = c("cvd_d_num","strk_d_num","diab_d_num","asth_d_num","cncr_d_num","copd_d_num","arth_d_num")
brfss_covariates<-brfss_VAR1 %>%
#---------------------------------------#
# Demographics
# Age, Sex, Race, Marital Status,
# Number of Children, Veterans Status,
# Metropolitan Service Area
#---------------------------------------#
# Age
## Quantitative / Numeric
mutate(age_num=X_AGE80) %>%
## Categorical - five year categories
mutate(age_cat = as.factor(if_else(X_AGE80 >= 18 & X_AGE80 < 25, "18-24",
if_else(X_AGE80 >= 25 & X_AGE80 < 30, "25-29",
if_else(X_AGE80 >= 30 & X_AGE80 < 35, "30-34",
if_else(X_AGE80 >= 35 & X_AGE80 < 40, "35-39",
if_else(X_AGE80 >= 40 & X_AGE80 < 45, "40-44",
if_else(X_AGE80 >= 45 & X_AGE80 < 50, "45-49",
if_else(X_AGE80 >= 50 & X_AGE80 < 55, "50-54",
if_else(X_AGE80 >= 55 & X_AGE80 < 60, "55-59",
if_else(X_AGE80 >= 60 & X_AGE80 < 65, "60-64",
if_else(X_AGE80 >= 65 & X_AGE80 < 70, "65-69",
if_else(X_AGE80 >= 70 & X_AGE80 < 75, "70-74",
if_else(X_AGE80 >= 75 & X_AGE80 < 80, "75-79",
if_else(X_AGE80 >= 80 & X_AGE80 < 85, "80+", NA_character_))))))))))))))
) %>%
## Categorical - 10 year categories
mutate(age_10ycat_fct=as.factor(if_else(age_num %in% 18:24, "18-24",
if_else(age_num %in% 25:34, "25-34",
if_else(age_num %in% 35:44, "35-44",
if_else(age_num %in% 45:54, "45-54",
if_else(age_num %in% 55:64, "55-64",
if_else(age_num >=65, "65+",NA_character_)))))))) %>%
## Dichotomous/Binary: Millennial (18-35 vs. 36+); 45+ vs 18-44; 65+ vs 18-64
mutate(millennial_d_num=as.numeric(if_else(YEAR==2013, if_else(age_num %in% 18:32,1,0),
if_else(YEAR==2014, if_else(age_num %in% 18:33,1,0),
if_else(YEAR==2015, if_else(age_num %in% 18:34,1,0),
if_else(YEAR==2016, if_else(age_num %in% 19:35,1,0),
if_else(YEAR==2017, if_else(age_num %in% 20:36,1,0), NA_real_)))))),
millennial_d_fct=as.factor(if_else(millennial_d_num==1, "Yes",
if_else(millennial_d_num==0, "No", NA_character_))),
agege45_d_num=as.numeric(if_else(age_num %in% 18:44, 0,
if_else(age_num>=45,1, NA_real_))),
agege45_d_fct=as.factor(if_else(age_num %in% 18:44, "No",
if_else(age_num>=45,"Yes", NA_character_))),
agege65_d_num=as.numeric(if_else(age_num %in% 18:64, 0,
if_else(age_num>=65,1, NA_real_))),
agege65_d_fct=as.factor(if_else(age_num %in% 18:64, "No",
if_else(age_num>=65,"Yes", NA_character_)))
) %>%
# Sex
mutate(sex_d_fct = as.factor(if_else(SEX == 1, "Male",
if_else(SEX == 2, "Female", NA_character_))),
fem_d_num = as.numeric(if_else(SEX == 1, 0,
if_else(SEX == 2, 1, NA_real_))),
sexab_cat_fct = as.factor(if_else(BIRTHSEX == 1, "Male",
if_else(BIRTHSEX == 2, "Female",
if_else(BIRTHSEX %in% c(7,9), "Other", NA_character_)))),
) %>%
# Race/Ethnicity (X_RACEGR3, X_MRACE1 & X_HISPANC)
mutate(race_cat_fct=as.factor(if_else(X_MRACE1==1, "White only",
if_else(X_MRACE1==2, "Black or African American only",
if_else(X_MRACE1==3, "American Indian or Alaskan Native only",
if_else(X_MRACE1==4, "Asian only",
if_else(X_MRACE1==5, "Native Hawaiian or other Pacific Islander only",
if_else(X_MRACE1==6, "Other race only",
if_else(X_MRACE1==7, "Multiracial", NA_character_))))))))
) %>%
mutate(raceth_cat_fct = as.factor(ifelse(X_RACEGR3 == 1, "White, Non-Hispanic",
ifelse(X_RACEGR3 == 2, "Black, Non-Hispanic",
ifelse(X_RACEGR3 == 3, "Other, Non-Hispanic",
ifelse(X_RACEGR3 == 4, "Multiple, Non-Hispanic",
ifelse(X_RACEGR3 == 5, "Hispanic",
ifelse(X_RACEGR3 == 9, "DK/Refused", NA_character_)))))))) %>%
mutate(ethn_cat_fct = as.factor(if_else(X_HISPANC == 1, "Hispanic",
if_else(X_HISPANC == 2, "Non-Hispanic", NA_character_)))
) %>%
# Marital Status (MARITAL)
mutate(mstat_cat_fct=as.factor(if_else(MARITAL==1, "Married",
if_else(MARITAL==2, "Divorced",
if_else(MARITAL==3, "Widowed",
if_else(MARITAL==4, "Separated",
if_else(MARITAL==5, "Never Married",
if_else(MARITAL==6, "Coupled",NA_character_))))))),
mstat_d_fct=as.factor(if_else(MARITAL==1, "Married",
if_else(MARITAL %in% 2:6, "Non-Married",NA_character_))) ) %>%
mutate(married_d_fct=as.factor(if_else(mstat_cat_fct %in% c("Married", "Coupled"), "Married",
if_else(mstat_cat_fct %in% c("Divorced", "Never Married", "Separated", "Widowed"), "Not Married", NA_character_))),
) %>%
# Number of Children (CHILDREN)
mutate(chld_num = as.numeric(ifelse(CHILDREN %in% 1:87, CHILDREN,
ifelse(CHILDREN == 88, 0, NA_real_))),
chld_cat_fct = as.factor(if_else(X_CHLDCNT==1, "None",
if_else(X_CHLDCNT==2, "One",
if_else(X_CHLDCNT %in% 3:6, "Two or More", NA_character_ ))))) %>%
mutate(kids_d_fct = as.factor(if_else(X_CHLDCNT==1, "No",
if_else(X_CHLDCNT %in% 2:6, "Yes", NA_character_ )))
) %>%
# Veteran Status (VETERAN3)
mutate(vtrn_d_num=if_else(VETERAN3==2,0,
if_else(VETERAN3==1,1,NA_real_)),
vtrn_d_fct=as.factor(if_else(VETERAN3==2,"Non-Veteran",
if_else(VETERAN3==1,"Veteran",NA_character_)))
) %>%
# Metropolitan Service Area (MSA)
mutate(msa_cat_fct=as.factor(if_else(MSCODE==1, "City_Center",
if_else(MSCODE==2, "City_County",
if_else(MSCODE==3, "Suburb",
if_else(MSCODE==5, "Outside MSA",NA_character_))))),
msa_d_fct=as.factor(if_else(MSCODE %in% 1:3, "Inside MSA",
if_else(MSCODE==5, "Outside MSA",NA_character_)))
) %>%
# Urban and Metropolitcan Counties (only in 2018 data)
mutate(metro_d_fct=as.factor(if_else(X_METSTAT==1, "Yes",
if_else(X_METSTAT==2, "No", NA_character_))),
urban_d_fct=as.factor(if_else(X_URBSTAT==1, "Yes",
if_else(X_URBSTAT==2, "No", NA_character_)))
) %>%
#---------------------------------------#
# SOCIOECONOMIC STATUS
# Employment Status, Income
# Education
#---------------------------------------#
# Employment
mutate(empl_cat_fct = as.factor(
ifelse(EMPLOY1==1, "Employed for wages",
ifelse(EMPLOY1==2, "Self-Employed",
ifelse(EMPLOY1 %in% 3:4, "Out of work",
ifelse(EMPLOY1==5, "Homemaker",
ifelse(EMPLOY1==6, "Student",
ifelse(EMPLOY1==7, "Retired",
ifelse(EMPLOY1== 8, "Unable", NA_character_ ))))))))) %>%
mutate(employed_d_fct=as.factor(if_else(empl_cat_fct %in% c("Employed for wages","Self-employed"), "Yes", "No/Missing"))
) %>%
# Income
mutate(INCOME3 = ifelse(INCOME2 %in% 77:99, 9, INCOME2)) %>%
mutate(inc_cat_fct = factor(INCOME3, levels = c(1:9),
labels = c("<$10,000", "$10,000-$14,999",
"$15,000-19,999", "$20,000-$24,999",
"$25,000-34,999", "$35,000-$49,999",
"$50,000-74,999", "$75,000+",
"Don't know or refused")) ) %>%
mutate(income_4cats_fct=as.factor(if_else(inc_cat_fct %in% c("<$10,000","$10,000-$14,999","$15,000-19,999"), "<$20,000",
if_else(inc_cat_fct %in% c("$20,000-$24,999","$25,000-34,999"), "$20,000-34,999",
if_else(inc_cat_fct %in% c("$35,000-$49,999","$50,000-74,999"), "$35,000-74,999",
if_else(inc_cat_fct=="$75,000+", "$75,000+",
if_else(inc_cat_fct=="Don't know or refused", "Missing", NA_character_))))))
) %>%
# Education
mutate(educ_cat_fct = as.factor(if_else(X_EDUCAG==1, "<High School",
if_else(X_EDUCAG==2, "High School",
if_else(X_EDUCAG==3, "Some College",
if_else(X_EDUCAG==4, "College or More",NA_character_)))))) %>%
mutate(college_d_fct=as.factor(if_else(educ_cat_fct=="College or More", "College",
if_else(educ_cat_fct %in% c("<High School", "High School", "Some College"), "<College", NA_character_)))
) %>%
#---------------------------------------#
# HEALTH BEHAVIORS
# Alcohol, BMI, Fruit & Vegetable Intake
# HIV Risk, Influenza Vaccine,
# Physical Activity, Smoking Status
#---------------------------------------#
#Alcohol
##Heavy Drinking / Binge Drinking
mutate(drnkhvy_d_fct = as.factor(ifelse(HVYDRNK == 1, "No",
ifelse(HVYDRNK == 2, "Yes", NA_character_))),
drnkbng_d_fct = as.factor(if_else(X_RFBING5 == 1,"No",
if_else(X_RFBING5 == 2,"Yes",NA_character_)))
) %>%
#BMI
## Numeric
mutate(bmi_num = as.numeric(X_BMI5/100) ) %>%
## Categories
mutate(bmi_cat_fct = as.factor(if_else(X_BMI5CAT == 1, "Underweight",
if_else(X_BMI5CAT == 2, "Normal Weight",
if_else(X_BMI5CAT == 3, "Overweight",
if_else(X_BMI5CAT == 4, "Obese",
if_else(is.na(X_BMI5CAT), "Unknown", NA_character_)))))),
bmi_40d_fct = as.factor(if_else(bmi_num %in% 0:40, "<40",
if_else(bmi_num>=40, ">=40", NA_character_)))) %>%
mutate(obese_d_fct=as.factor(if_else(X_BMI5CAT == 4, "Yes","No"))) %>%
#Smoking
mutate(smk_cat_fct = as.factor(if_else(X_SMOKER3 %in% 1:2, "Current smoker",
if_else(X_SMOKER3==3, "Former smoker",
if_else(X_SMOKER3==4, "Never smoker",
if_else(X_SMOKER3==9, "DK/Refused",NA_character_ ))))),
smoker_d_fct=as.factor(if_else(X_SMOKER3 %in% 1:2, "Smoker",
if_else(X_SMOKER3 %in% 3:9, "Non-Smoker", NA_character_)))
) %>%
#High HIV risk binary variable
mutate(hiv_d_num = as.numeric(ifelse(HIVRISK == 1, 1,
ifelse(HIVRISK == 2, 0, NA_real_))),
hiv_d_fct = as.factor(ifelse(HIVRISK == 1, "Yes",
ifelse(HIVRISK == 2, "No", NA_character_)))
) %>%
#Leisure-time physical activity
mutate(ltpa_d_fct = as.factor(if_else(X_TOTINDA==1, "Yes",
if_else(X_TOTINDA==2, "No",NA_character_))),
ltpa_cat_fct = as.factor(if_else(X_TOTINDA==1, "Yes",
if_else(X_TOTINDA==2, "No",
if_else(X_TOTINDA %in% 7:9, "DK/Refused",NA_character_))))
) %>%
# Influenza Vaccine: Any Vaccine, When, and Where the vaccine happened
mutate(fluvac_cat_fct = as.factor(if_else(FLUSHOT==1, "Yes",
if_else(FLUSHOT==2, "No", "DK/Refused"))),
fluvac_d_fct = as.factor(if_else(FLUSHOT==1, "Yes",
if_else(FLUSHOT==2, "No",NA_character_))),
fluvac_d_num = as.numeric(if_else(FLUSHOT==1, 1,
if_else(FLUSHOT==2, 0, NA_real_)))
#FLSHTMY3=if_else(FLSHTMY2 %in% 777777:999999, NA_real_, FLSHTMY2),
#fluvac_date_num = mdy(FLSHTMY3)
# IMFVPLAC2=if_else(IMFVPLAC %in% 77:99, NA_real_, IMFVPLAC),
# fluplc_cat_fct=factor(IMFVPLAC2, levels=1:11,
# labels=c("Doctor's office", "Health dept", "Health center",
# "Community center", "Store", "Hospital",
# "Emergency room", "Workplace", "Other",
# "Canada/Mexico", "School" ))
) %>%
#---------------------------------------#
# HEALTH CARE
# Doctor Cost, Health Care Coverage,
# Time since last checkup,
# Time since last dentist visit
#---------------------------------------#
# Cost prevented needed doctor visit
mutate(drcost_d_fct = as.factor(if_else(MEDCOST == 1, "Yes",
if_else(MEDCOST == 2, "No", NA_character_)))
) %>%
# Health Care Coverage
mutate(hcplan_cat_fct = as.factor(if_else(HLTHPLN1 == 1, "Yes",
if_else(HLTHPLN1 == 2, "No",
if_else(HLTHPLN1 %in% 7:9, "DK/Refused",NA_character_)))),
hcplan_d_fct = as.factor(if_else(HLTHPLN1 == 1, "Yes",
if_else(HLTHPLN1 == 2, "No", NA_character_)))
) %>%
# Time since last checkup
mutate(chckup_cat_fct = as.factor(if_else(CHECKUP1 == 1, "<1 year",
if_else(CHECKUP1 == 2, "1-2 years",
if_else(CHECKUP1 == 3, "2-5 years",
if_else(CHECKUP1 == 4, "5+ years",
if_else(CHECKUP1 == 8, "Never",
if_else(CHECKUP1 == 7 | CHECKUP1 == 9, "DK/Refused",NA_character_))))))),
drlastyr_d_fct=as.factor(if_else(CHECKUP1 == 1, "<1 year",
if_else(CHECKUP1 != 1, "No", NA_character_)))
) %>%
#---------------------------------------#
# HEALTH STATUS
# Chronic Diseases,
# Depressive Disorder,
# Mental & Physcial Health HRQOL,
# Self-Rated Health
# Pregnant
#---------------------------------------#
# Chronic Disease (CVDCRHD4,CVDSTRK3,DIABETE3,ASTHMA3,CHCOCNCR,CHCCOPD1,HAVARTH3)
### These lists - rawnames, newnames, and lastnames - were defined at the top of the file under "functions"
### This code is to: 1. change the raw BRFSS names into brief, conceptual names; 2. revise the name to include that it's a dichotomous, numeric variable
rename_at(.vars=vars(rawnames), ~ newnames) %>%
rename_at(.vars=vars(newnames),
function(z) paste0(z,"_d_num")) %>%
mutate_at(.vars=vars(lastnames),
.funs = binary_d_num) %>%
## Count of Conditions
mutate(chron_num = rowSums(.[lastnames], na.rm = TRUE)) %>%
## Any
mutate(chron_d_fct = ifelse(chron_num>=1,"Yes","No")) %>%
#Kidney Disease
mutate(kidn_d_fct = as.factor(if_else(CHCKDNY1 == 1, "Yes",
if_else(CHCKDNY1 == 2, "No", NA_character_))),
kidn_d_num = as.numeric(if_else(CHCKDNY1 == 1, 1,
if_else(CHCKDNY1 == 2, 0, NA_real_)))
) %>%
# Depressive Disorder
mutate(dep_d_fct = as.factor(if_else(ADDEPEV2 == 1, "Yes",
if_else(ADDEPEV2 == 2, "No", NA_character_))),
dep_d_num = as.numeric(if_else(ADDEPEV2 == 1, 1,
if_else(ADDEPEV2 == 2, 0, NA_real_)))
) %>%
# Mental Health QOL
mutate(mentqol_num = MENTHLTH,
mentqol_num = replace(mentqol_num, mentqol_num==88, 0),
mentqol_num = replace(mentqol_num, mentqol_num %in% c(77,99), NA) )%>%
mutate(mentqol14_d_num = as.numeric(if_else(mentqol_num %in% 0:14, 0,
if_else(mentqol_num %in% 14:30, 1, NA_real_))),
mentqol14_d_fct = as.factor(if_else(mentqol_num %in% 0:14 , "No",
if_else(mentqol_num %in% 14:30, "Yes", NA_character_)))
) %>%
# Physical Health QOL
mutate(physqol_num = PHYSHLTH,
physqol_num = replace(physqol_num, physqol_num==88, 0),
physqol_num = replace(physqol_num, physqol_num %in% c(77,99), NA) ) %>%
mutate(physqol14_d_num = as.numeric(if_else(physqol_num %in% 0:14, 0,
if_else(physqol_num %in% 14:30, 1, NA_real_))),
physqol14_d_fct = as.factor(if_else(physqol_num %in% 0:14, "No",
if_else(physqol_num %in% 14:30, "Yes", NA_character_)))
) %>%
# Self-Rated Health
mutate(srh_d_fct=as.factor(if_else(GENHLTH %in% 1:3, "Good+",
if_else(GENHLTH %in% 4:5, "Fair/Poor", NA_character_))),
srh_cat_fct=as.factor(if_else(GENHLTH==1, "Excellent",
if_else(GENHLTH==2, "Very Good",
if_else(GENHLTH==3, "Good",
if_else(GENHLTH==4, "Fair",
if_else(GENHLTH==5, "Poor", NA_character_))))))
) %>%
# Pregnant
mutate(preg_d_fct=as.factor(if_else(PREGNANT==1, "Yes",
if_else(PREGNANT==2, "No", NA_character_)))
) %>%
rename_all(tolower)
#table(brfss_covariates$sex,useNA = c("always"))
#table(brfss_covariates$fem_d_num,useNA = c("always"))
#table(brfss_covariates$sex_d_fct,useNA = c("always"))
brfss_core<-brfss_covariates %>%
select(
age_num,age_cat,age_10ycat_fct,
millennial_d_num,millennial_d_fct,
agege45_d_num,agege45_d_fct,
agege65_d_num,agege65_d_fct,
mstat_cat_fct,mstat_cat_fct,married_d_fct,
chld_num,chld_cat_fct,kids_d_fct,
race_cat_fct,ethn_cat_fct,raceth_cat_fct,
sex_d_fct,fem_d_num,sexab_cat_fct,
vtrn_d_num,vtrn_d_fct,
msa_cat_fct,msa_d_fct,
metro_d_fct,urban_d_fct,
drnkbng_d_fct,drnkhvy_d_fct,
bmi_cat_fct, bmi_40d_fct, bmi_num,obese_d_fct,
hiv_d_num,hiv_d_fct,
fluvac_cat_fct,fluvac_d_fct,fluvac_d_num,
ltpa_d_fct,ltpa_cat_fct,
smk_cat_fct,smoker_d_fct,
drcost_d_fct,
hcplan_d_fct,hcplan_cat_fct,
chckup_cat_fct,drlastyr_d_fct,
chron_num,chron_d_fct,cvd_d_num,strk_d_num,diab_d_num,asth_d_num,arth_d_num,copd_d_num,cncr_d_num,kidn_d_num,
dep_d_fct,
mentqol_num,mentqol14_d_num,mentqol14_d_fct,
physqol_num,physqol14_d_num,physqol14_d_fct,
srh_d_fct,srh_cat_fct,
preg_d_fct,
empl_cat_fct,employed_d_fct,
inc_cat_fct,income_4cats_fct,
educ_cat_fct,college_d_fct,
x_state,state,fips,year,seqno,x_psu,x_ststr,
var_wt_raw,version_var
)
rm(brfss_covariates,brfss_VAR1)
rm(brfss_covariates2)
table(brfss_core$employed_d_fct,useNA = c("always"))
#save(brfss_core, file = "data/brfss_core.rda", compress = "bzip2", version=2)
use_data(brfss_core, overwrite = TRUE,compress = "bzip2")
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