data-raw/SGM.R
In bencapistrant/brfssR: Behavioral Risk Factor Surveillance Survey (BRFSS) Data
library(tidyverse)
library(lubridate)
library(foreign)
library(labelled)
library(usethis)
library(devtools)
#------------------------------------------------------------------------------------#
# Bringing in Data
#------------------------------------------------------------------------------------#
### 2014
sgm2014_0<-c(10,15,16,18,19,
20,21,22,24,27,
30,32,36,39,42,
50,51,55,56,66)
SGM_2014_0<-read.xport("data-raw/LLCP2014.XPT")
data_2014 <- SGM_2014_0 %>%
filter(X_STATE %in% sgm2014_0) %>%
mutate(YEAR=2014,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2014_0)
### 2015
sgm2015_0<-c(8,9,10,13,15,16,17,
18,19,20,24,27,29,32,
36,39,42,48,50,54,55)
sgm2015_1<-19
SGM_2015_0<-read.xport("data-raw/LLCP2015.XPT")
SGM_2015_0 <- SGM_2015_0 %>%
filter(X_STATE %in% sgm2015_0) %>%
mutate(YEAR=2015,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
SGM_2015_1<-read.xport("data-raw/LLCP15V1.XPT")
SGM_2015_1 <- SGM_2015_1 %>%
filter(X_STATE %in% sgm2015_1) %>%
mutate(YEAR=2015,
VERSION_SGM="X_LCPWTV1",
sgm_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
list2015<-list(SGM_2015_0, SGM_2015_1)
data_2015<-bind_rows(list2015)
rm(SGM_2015_0, SGM_2015_1, list2015)
### 2016
sgm2016_0<-c(6,9,10,13,15,
16,17,18,19,21,
22,25,27,28,29,
32,36,39,42,44,
48,50,51,53,55,66)
SGM_2016_0<-read.xport("data-raw/LLCP2016.XPT")
data_2016 <- SGM_2016_0 %>%
filter(X_STATE %in% sgm2016_0) %>%
mutate(YEAR=2016,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2016_0)
### 2017
sgm2017_0<-c(6,9,10,12,13,15,17,
18,19,22,25,27,28,30,
32,36,37,39,40,42,44,
45,48,50,51,53,55,66)
SGM_2017_0<-read.xport("data-raw/LLCP2017.XPT")
data_2017<- SGM_2017_0 %>%
filter(X_STATE %in% sgm2017_0) %>%
mutate(YEAR=2017,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2017_0)
### 2018
# states administered SOGI Module in 2018: Arizona(V2), Connecticut, Delaware, Florida, Guam, Hawaii,
### Idaho, Illinois, Kansas, Louisiana, Maryland, Minnesota, Mississippi, Missouri, Montana,
### Nevada, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina,
### Tennessee, Texas, Vermont, Virginia, Washington, West Virginia, Wisconsin
sgm2018_0 <- c(9, 10, 12, 66, 15, 16,
17, 20, 22, 24, 27, 28,
29, 30, 32, 36, 37, 39,
40, 42, 44, 45, 47, 48,
50, 51, 53, 54, 55)
sgm2018_2 <- c(4)
SGM_2018_0 <- read.xport("data-raw/LLCP2018.XPT")
SGM_2018_2 <- read.xport("data-raw/LLCP18V2.XPT")
data_2018_0<- SGM_2018_0 %>%
filter(X_STATE %in% sgm2018_0) %>%
mutate(YEAR=2018,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2018_0)
data_2018_2<- SGM_2018_2 %>%
filter(X_STATE %in% sgm2018_2) %>%
mutate(YEAR=2018,
VERSION_SGM="X_LLCPWT2",
sgm_wt_raw=X_LLCPWT2,
SEQNO=as.integer(SEQNO))
rm(SGM_2018_2)
data_2018 <- bind_rows(data_2018_0, data_2018_2)
rm(data_2018_0, data_2018_2)
# recode 2018 sexual orientation vars (delivered seperately for men and women)
## NOTE: coded to match 2017 levels
### in 2018, SXORIENT is two variables, SOMALE and SOFEMALE
### in 2017, 1 is straight and 2 is gay, opposite in 2018
data_2018 <- data_2018 %>%
mutate(SXORIENT = ifelse(is.na(SOMALE) == FALSE, SOMALE, SOFEMALE)) %>%
mutate(SXORIENT = ifelse(SXORIENT == 1, 2,
ifelse(SXORIENT == 2, 1, SXORIENT)))
### 2019
# states administered SOGI Module in 2019: Alaska, Arizona, Colorado,
### Connecticut, Delaware, Florida, Georgia, Guam, Hawaii, Idaho, Iowa,
### Kansas, Louisiana, Maryland, Minnesota, Mississippi, Montana, New York,
### North Carolina, Ohio, Oklahoma, Rhode Island, South Carolina, Tennessee,
### Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin
sgm2019_0 <- c(2,4,8,9,10,
12,13,15,16,19,
20,22,24,27,28,
30,36,37,39,
40,44,45,47,48,49,
50,51,53,54,55,66)
SGM_2019_0 <- read.xport("data-raw/LLCP2019.XPT")
data_2019<- SGM_2019_0 %>%
filter(X_STATE %in% sgm2019_0) %>%
mutate(YEAR=2019,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2019_0)
#data_2019 <- bind_rows(data_2019_0, data_2019_2)
# recode 2019 sexual orientation vars (delivered seperately for men and women)
## NOTE: coded to match 2017 levels
### in 2019, SXORIENT is two variables, SOMALE and SOFEMALE
### in 2017, 1 is straight and 2 is gay, opposite in 2019
data_2019 <- data_2019 %>%
mutate(SXORIENT = ifelse(is.na(SOMALE) == FALSE, SOMALE, SOFEMALE)) %>%
mutate(SXORIENT = ifelse(SXORIENT == 1, 2,
ifelse(SXORIENT == 2, 1, SXORIENT)))
#---------------------------------------------------------------------------#
### 2020
# states administered SOGI Module in 2020: Alaska, Arkansas, California,
### Colorado, Connecticut, Georgia, Guam, Hawaii, Idaho, Illinois, Indiana,
### Iowa, Kansas, Louisiana, Massachusetts, Michigan, Minnesota, Montana,
### New Jersey, New Mexico, New York, North Carolina, Ohio, Oklahoma,
### Rhode Island, South Carolina, Texas, Utah, Vermont, Virginia, Washington,
### West Virginia, Wisconsin
sgm2020_0 <- c(2,5,6,8,9,
13,15,16,17,18,19,
20,22,25,26,27,
30,34,35,36,37,39,
40,44,45,48,49,
50,51,53,54,55,66)
SGM_2020_0 <- read.xport("data-raw/LLCP2020.XPT")
data_2020<- SGM_2020_0 %>%
filter(X_STATE %in% sgm2020_0) %>%
mutate(YEAR=2020,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2020_0)
#data_2020 <- bind_rows(data_2020_0, data_2020_2)
# recode 2020 sexual orientation vars (delivered seperately for men and women)
## NOTE: coded to match 2017 levels
### in 2020, SXORIENT is two variables, SOMALE and SOFEMALE
### in 2017, 1 is straight and 2 is gay, opposite in 2020
data_2020 <- data_2020 %>%
mutate(SXORIENT = ifelse(is.na(SOMALE) == FALSE, SOMALE, SOFEMALE)) %>%
mutate(SXORIENT = ifelse(SXORIENT == 1, 2,
ifelse(SXORIENT == 2, 1, SXORIENT)))
### COMBINING 2014-2020
brfss_sgm_A<-bind_rows(data_2020,data_2019,data_2018)
rm(data_2020,data_2019,data_2018)
brfss_sgm_A <- brfss_sgm_A %>%
select(SXORIENT, TRNSGNDR, X_STATE, YEAR, SEQNO, X_PSU, X_STSTR, sgm_wt_raw,VERSION_SGM)
brfss_sgm_B<-bind_rows(data_2017,data_2016,data_2015,data_2014)
rm(data_2017,data_2016,data_2015,data_2014)
brfss_sgm_B <- brfss_sgm_B %>%
select(SXORIENT, TRNSGNDR, X_STATE, YEAR, SEQNO, X_PSU, X_STSTR, sgm_wt_raw,VERSION_SGM)
brfss_sgm<-bind_rows(brfss_sgm_A,brfss_sgm_B)
rm(brfss_sgm_A,brfss_sgm_B)
### Labeling State indicators
sgmstates<- 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)
sgmstatelabel<-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")
brfss_sgm$state<-factor(brfss_sgm$X_STATE, levels=sgmstates, labels=sgmstatelabel)
#------------------------------------------------------------------------------------#
# Cleaning Variables
#------------------------------------------------------------------------------------#
## Sexual / Gender Minority Status
brfss_sgm <- brfss_sgm %>%
mutate(
#Sexual and Gender Minority Status
#Sexual Orientation - Lesbian, Gay, Bisexual vs. Heterosexual
lgb_d=if_else(SXORIENT==1, 0,
if_else(SXORIENT %in% 2:3, 1, NA_real_)),
lgb_het_d=if_else(SXORIENT==1, 1,
if_else(SXORIENT %in% 2:3, 0, NA_real_)),
lgb_gayles_d=if_else(SXORIENT==2, 1,
if_else(SXORIENT %in% c(1,3), 0, NA_real_)),
lgb_bi_d=if_else(SXORIENT==3, 1,
if_else(SXORIENT %in% 1:2, 0, NA_real_)),
lgb_cat=as.factor(if_else(SXORIENT==1, "Heterosexual",
if_else(SXORIENT==2, "Gay or Lesbian",
if_else(SXORIENT==3, "Bisexual", NA_character_)))),
#Gender Minority Identity - Trans* or Gender-non-Conforming vs. Cis-gendered
genmin_d=if_else(TRNSGNDR %in% 1:3, 1,
if_else(TRNSGNDR==4, 0, NA_real_)),
genmin_fem_d=if_else(TRNSGNDR==1, 1,
if_else(TRNSGNDR %in% 2:4, 0, NA_real_)),
genmin_men_d=if_else(TRNSGNDR==2, 1,
if_else(TRNSGNDR %in% c(1,3,4), 0, NA_real_)),
genmin_gnc_d=if_else(TRNSGNDR==3, 1,
if_else(TRNSGNDR %in% c(1,2,4), 0, NA_real_)),
genmin_cis_d=if_else(TRNSGNDR==4, 1,
if_else(TRNSGNDR %in% 1:3, 0, NA_real_)),
genmin_only_cat=as.factor(if_else(TRNSGNDR==1, "Transgender, Female",
if_else(TRNSGNDR==2, "Transgender, Male",
if_else(TRNSGNDR==3, "Gender Non Conforming",NA_character_)))),
genmin_cat=as.factor(if_else(TRNSGNDR==1, "Transgender, Female",
if_else(TRNSGNDR==2, "Transgender, Male",
if_else(TRNSGNDR==3, "Gender Non Conforming",
if_else(TRNSGNDR==4, "Cis-Gender", NA_character_))))),
#Sexual / Gender Minority - Either LGB or Trans/GNC vs. Cis-gendered Heterosexuals
sgm_d=if_else(lgb_d==1 | genmin_d==1, 1,
if_else(lgb_d==0 & genmin_d==0, 0, NA_real_)),
sgm_sex_cat=as.factor(if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==1, "Cis_Het_Male",
if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==2, "Cis_Gay_Male",
if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==3, "Cis_Bi_Male",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==1, "Cis_Het_Female",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==2, "Cis_Gay_Female",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==3, "Cis_Bi_Female",
if_else(TRNSGNDR==1, "Trans_Female",
if_else(TRNSGNDR==2, "Trans_Male",
if_else(TRNSGNDR==3, "Trans_GNC",NA_character_)))))))))),
sgm_sex_cat2=as.factor(if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==1, "Cis_Het_Male",
if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==2, "Cis_Gay_Male",
if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==3, "Cis_Bi_Male",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==1, "Cis_Het_Female",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==2, "Cis_Gay_Female",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==3, "Cis_Bi_Female",
if_else(TRNSGNDR %in% 1:3, "Transgender",NA_character_))))))))
) %>%
rename_all(tolower) %>%
select(sgm_d,sgm_sex_cat,sgm_sex_cat2,
lgb_d, lgb_het_d, lgb_gayles_d,lgb_bi_d, lgb_cat,
genmin_d, genmin_fem_d, genmin_men_d,genmin_cis_d,genmin_gnc_d,genmin_only_cat,genmin_cat,
x_state,state,year,seqno,x_psu,x_ststr,sgm_wt_raw,version_sgm)
# Setting Reference Groups: LGB vs. (reference) Heterosexual
brfss_sgm$lgb_cat<-relevel(factor(brfss_sgm$lgb_cat), ref="Heterosexual")
brfss_sgm$genmin_cat<-relevel(factor(brfss_sgm$genmin_cat), ref="Cis-Gender")
#save(brfss_sgm, file = "data/brfss_sgm.rda", compress = "bzip2", version=2)
use_data(brfss_sgm, overwrite = TRUE,compress = "bzip2")
bencapistrant/brfssR documentation built on Sept. 24, 2021, 2:10 p.m.
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library(tidyverse)
library(lubridate)
library(foreign)
library(labelled)
library(usethis)
library(devtools)
#------------------------------------------------------------------------------------#
# Bringing in Data
#------------------------------------------------------------------------------------#
### 2014
sgm2014_0<-c(10,15,16,18,19,
20,21,22,24,27,
30,32,36,39,42,
50,51,55,56,66)
SGM_2014_0<-read.xport("data-raw/LLCP2014.XPT")
data_2014 <- SGM_2014_0 %>%
filter(X_STATE %in% sgm2014_0) %>%
mutate(YEAR=2014,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2014_0)
### 2015
sgm2015_0<-c(8,9,10,13,15,16,17,
18,19,20,24,27,29,32,
36,39,42,48,50,54,55)
sgm2015_1<-19
SGM_2015_0<-read.xport("data-raw/LLCP2015.XPT")
SGM_2015_0 <- SGM_2015_0 %>%
filter(X_STATE %in% sgm2015_0) %>%
mutate(YEAR=2015,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
SGM_2015_1<-read.xport("data-raw/LLCP15V1.XPT")
SGM_2015_1 <- SGM_2015_1 %>%
filter(X_STATE %in% sgm2015_1) %>%
mutate(YEAR=2015,
VERSION_SGM="X_LCPWTV1",
sgm_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
list2015<-list(SGM_2015_0, SGM_2015_1)
data_2015<-bind_rows(list2015)
rm(SGM_2015_0, SGM_2015_1, list2015)
### 2016
sgm2016_0<-c(6,9,10,13,15,
16,17,18,19,21,
22,25,27,28,29,
32,36,39,42,44,
48,50,51,53,55,66)
SGM_2016_0<-read.xport("data-raw/LLCP2016.XPT")
data_2016 <- SGM_2016_0 %>%
filter(X_STATE %in% sgm2016_0) %>%
mutate(YEAR=2016,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2016_0)
### 2017
sgm2017_0<-c(6,9,10,12,13,15,17,
18,19,22,25,27,28,30,
32,36,37,39,40,42,44,
45,48,50,51,53,55,66)
SGM_2017_0<-read.xport("data-raw/LLCP2017.XPT")
data_2017<- SGM_2017_0 %>%
filter(X_STATE %in% sgm2017_0) %>%
mutate(YEAR=2017,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2017_0)
### 2018
# states administered SOGI Module in 2018: Arizona(V2), Connecticut, Delaware, Florida, Guam, Hawaii,
### Idaho, Illinois, Kansas, Louisiana, Maryland, Minnesota, Mississippi, Missouri, Montana,
### Nevada, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina,
### Tennessee, Texas, Vermont, Virginia, Washington, West Virginia, Wisconsin
sgm2018_0 <- c(9, 10, 12, 66, 15, 16,
17, 20, 22, 24, 27, 28,
29, 30, 32, 36, 37, 39,
40, 42, 44, 45, 47, 48,
50, 51, 53, 54, 55)
sgm2018_2 <- c(4)
SGM_2018_0 <- read.xport("data-raw/LLCP2018.XPT")
SGM_2018_2 <- read.xport("data-raw/LLCP18V2.XPT")
data_2018_0<- SGM_2018_0 %>%
filter(X_STATE %in% sgm2018_0) %>%
mutate(YEAR=2018,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2018_0)
data_2018_2<- SGM_2018_2 %>%
filter(X_STATE %in% sgm2018_2) %>%
mutate(YEAR=2018,
VERSION_SGM="X_LLCPWT2",
sgm_wt_raw=X_LLCPWT2,
SEQNO=as.integer(SEQNO))
rm(SGM_2018_2)
data_2018 <- bind_rows(data_2018_0, data_2018_2)
rm(data_2018_0, data_2018_2)
# recode 2018 sexual orientation vars (delivered seperately for men and women)
## NOTE: coded to match 2017 levels
### in 2018, SXORIENT is two variables, SOMALE and SOFEMALE
### in 2017, 1 is straight and 2 is gay, opposite in 2018
data_2018 <- data_2018 %>%
mutate(SXORIENT = ifelse(is.na(SOMALE) == FALSE, SOMALE, SOFEMALE)) %>%
mutate(SXORIENT = ifelse(SXORIENT == 1, 2,
ifelse(SXORIENT == 2, 1, SXORIENT)))
### 2019
# states administered SOGI Module in 2019: Alaska, Arizona, Colorado,
### Connecticut, Delaware, Florida, Georgia, Guam, Hawaii, Idaho, Iowa,
### Kansas, Louisiana, Maryland, Minnesota, Mississippi, Montana, New York,
### North Carolina, Ohio, Oklahoma, Rhode Island, South Carolina, Tennessee,
### Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin
sgm2019_0 <- c(2,4,8,9,10,
12,13,15,16,19,
20,22,24,27,28,
30,36,37,39,
40,44,45,47,48,49,
50,51,53,54,55,66)
SGM_2019_0 <- read.xport("data-raw/LLCP2019.XPT")
data_2019<- SGM_2019_0 %>%
filter(X_STATE %in% sgm2019_0) %>%
mutate(YEAR=2019,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2019_0)
#data_2019 <- bind_rows(data_2019_0, data_2019_2)
# recode 2019 sexual orientation vars (delivered seperately for men and women)
## NOTE: coded to match 2017 levels
### in 2019, SXORIENT is two variables, SOMALE and SOFEMALE
### in 2017, 1 is straight and 2 is gay, opposite in 2019
data_2019 <- data_2019 %>%
mutate(SXORIENT = ifelse(is.na(SOMALE) == FALSE, SOMALE, SOFEMALE)) %>%
mutate(SXORIENT = ifelse(SXORIENT == 1, 2,
ifelse(SXORIENT == 2, 1, SXORIENT)))
#---------------------------------------------------------------------------#
### 2020
# states administered SOGI Module in 2020: Alaska, Arkansas, California,
### Colorado, Connecticut, Georgia, Guam, Hawaii, Idaho, Illinois, Indiana,
### Iowa, Kansas, Louisiana, Massachusetts, Michigan, Minnesota, Montana,
### New Jersey, New Mexico, New York, North Carolina, Ohio, Oklahoma,
### Rhode Island, South Carolina, Texas, Utah, Vermont, Virginia, Washington,
### West Virginia, Wisconsin
sgm2020_0 <- c(2,5,6,8,9,
13,15,16,17,18,19,
20,22,25,26,27,
30,34,35,36,37,39,
40,44,45,48,49,
50,51,53,54,55,66)
SGM_2020_0 <- read.xport("data-raw/LLCP2020.XPT")
data_2020<- SGM_2020_0 %>%
filter(X_STATE %in% sgm2020_0) %>%
mutate(YEAR=2020,
VERSION_SGM="X_LLCPWT",
sgm_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
rm(SGM_2020_0)
#data_2020 <- bind_rows(data_2020_0, data_2020_2)
# recode 2020 sexual orientation vars (delivered seperately for men and women)
## NOTE: coded to match 2017 levels
### in 2020, SXORIENT is two variables, SOMALE and SOFEMALE
### in 2017, 1 is straight and 2 is gay, opposite in 2020
data_2020 <- data_2020 %>%
mutate(SXORIENT = ifelse(is.na(SOMALE) == FALSE, SOMALE, SOFEMALE)) %>%
mutate(SXORIENT = ifelse(SXORIENT == 1, 2,
ifelse(SXORIENT == 2, 1, SXORIENT)))
### COMBINING 2014-2020
brfss_sgm_A<-bind_rows(data_2020,data_2019,data_2018)
rm(data_2020,data_2019,data_2018)
brfss_sgm_A <- brfss_sgm_A %>%
select(SXORIENT, TRNSGNDR, X_STATE, YEAR, SEQNO, X_PSU, X_STSTR, sgm_wt_raw,VERSION_SGM)
brfss_sgm_B<-bind_rows(data_2017,data_2016,data_2015,data_2014)
rm(data_2017,data_2016,data_2015,data_2014)
brfss_sgm_B <- brfss_sgm_B %>%
select(SXORIENT, TRNSGNDR, X_STATE, YEAR, SEQNO, X_PSU, X_STSTR, sgm_wt_raw,VERSION_SGM)
brfss_sgm<-bind_rows(brfss_sgm_A,brfss_sgm_B)
rm(brfss_sgm_A,brfss_sgm_B)
### Labeling State indicators
sgmstates<- 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)
sgmstatelabel<-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")
brfss_sgm$state<-factor(brfss_sgm$X_STATE, levels=sgmstates, labels=sgmstatelabel)
#------------------------------------------------------------------------------------#
# Cleaning Variables
#------------------------------------------------------------------------------------#
## Sexual / Gender Minority Status
brfss_sgm <- brfss_sgm %>%
mutate(
#Sexual and Gender Minority Status
#Sexual Orientation - Lesbian, Gay, Bisexual vs. Heterosexual
lgb_d=if_else(SXORIENT==1, 0,
if_else(SXORIENT %in% 2:3, 1, NA_real_)),
lgb_het_d=if_else(SXORIENT==1, 1,
if_else(SXORIENT %in% 2:3, 0, NA_real_)),
lgb_gayles_d=if_else(SXORIENT==2, 1,
if_else(SXORIENT %in% c(1,3), 0, NA_real_)),
lgb_bi_d=if_else(SXORIENT==3, 1,
if_else(SXORIENT %in% 1:2, 0, NA_real_)),
lgb_cat=as.factor(if_else(SXORIENT==1, "Heterosexual",
if_else(SXORIENT==2, "Gay or Lesbian",
if_else(SXORIENT==3, "Bisexual", NA_character_)))),
#Gender Minority Identity - Trans* or Gender-non-Conforming vs. Cis-gendered
genmin_d=if_else(TRNSGNDR %in% 1:3, 1,
if_else(TRNSGNDR==4, 0, NA_real_)),
genmin_fem_d=if_else(TRNSGNDR==1, 1,
if_else(TRNSGNDR %in% 2:4, 0, NA_real_)),
genmin_men_d=if_else(TRNSGNDR==2, 1,
if_else(TRNSGNDR %in% c(1,3,4), 0, NA_real_)),
genmin_gnc_d=if_else(TRNSGNDR==3, 1,
if_else(TRNSGNDR %in% c(1,2,4), 0, NA_real_)),
genmin_cis_d=if_else(TRNSGNDR==4, 1,
if_else(TRNSGNDR %in% 1:3, 0, NA_real_)),
genmin_only_cat=as.factor(if_else(TRNSGNDR==1, "Transgender, Female",
if_else(TRNSGNDR==2, "Transgender, Male",
if_else(TRNSGNDR==3, "Gender Non Conforming",NA_character_)))),
genmin_cat=as.factor(if_else(TRNSGNDR==1, "Transgender, Female",
if_else(TRNSGNDR==2, "Transgender, Male",
if_else(TRNSGNDR==3, "Gender Non Conforming",
if_else(TRNSGNDR==4, "Cis-Gender", NA_character_))))),
#Sexual / Gender Minority - Either LGB or Trans/GNC vs. Cis-gendered Heterosexuals
sgm_d=if_else(lgb_d==1 | genmin_d==1, 1,
if_else(lgb_d==0 & genmin_d==0, 0, NA_real_)),
sgm_sex_cat=as.factor(if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==1, "Cis_Het_Male",
if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==2, "Cis_Gay_Male",
if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==3, "Cis_Bi_Male",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==1, "Cis_Het_Female",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==2, "Cis_Gay_Female",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==3, "Cis_Bi_Female",
if_else(TRNSGNDR==1, "Trans_Female",
if_else(TRNSGNDR==2, "Trans_Male",
if_else(TRNSGNDR==3, "Trans_GNC",NA_character_)))))))))),
sgm_sex_cat2=as.factor(if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==1, "Cis_Het_Male",
if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==2, "Cis_Gay_Male",
if_else(TRNSGNDR==4 & SEX == 1 & SXORIENT==3, "Cis_Bi_Male",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==1, "Cis_Het_Female",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==2, "Cis_Gay_Female",
if_else(TRNSGNDR==4 & SEX == 2 & SXORIENT==3, "Cis_Bi_Female",
if_else(TRNSGNDR %in% 1:3, "Transgender",NA_character_))))))))
) %>%
rename_all(tolower) %>%
select(sgm_d,sgm_sex_cat,sgm_sex_cat2,
lgb_d, lgb_het_d, lgb_gayles_d,lgb_bi_d, lgb_cat,
genmin_d, genmin_fem_d, genmin_men_d,genmin_cis_d,genmin_gnc_d,genmin_only_cat,genmin_cat,
x_state,state,year,seqno,x_psu,x_ststr,sgm_wt_raw,version_sgm)
# Setting Reference Groups: LGB vs. (reference) Heterosexual
brfss_sgm$lgb_cat<-relevel(factor(brfss_sgm$lgb_cat), ref="Heterosexual")
brfss_sgm$genmin_cat<-relevel(factor(brfss_sgm$genmin_cat), ref="Cis-Gender")
#save(brfss_sgm, file = "data/brfss_sgm.rda", compress = "bzip2", version=2)
use_data(brfss_sgm, overwrite = TRUE,compress = "bzip2")
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