data-raw/COG.R
In bencapistrant/brfssR: Behavioral Risk Factor Surveillance Survey (BRFSS) Data
library(tidyverse)
library(lubridate)
library(foreign)
library(usethis)
library(devtools)
### 2015
#LLCP2015: ALABAMA, ARIZONA, ARKANSAS, DISTRICT OF COLUMBIA, GEORGIA, HAWAII, ILLINOIS, IOWA, LOUISIANA, MINNESOTA, MISSISSIPPI,
###NEVADA, NEW JERSEY, NORTH DAKOTA, OHIO, OREGON, RHODE ISLAND, SOUTH CAROLINA, SOUTH DAKOTA, TENNESSEE, VIRGINIA, WEST VIRGINIA, WISCONSIN, WYOMING, PUERTO RICO
# LLCP15V1: MARYLAND, MICHIGAN, NEBRASKA, OKLAHOMA
# LLCP15V2: CALIFORNIA, COLORADO, FLORIDA, NEW YORK, TEXAS, UTAH
COG2015_0<-c(1,4,5,11,13,15,17,19,22,27,28,
32,34,38,39,41,44,45,46,47,51,54,55,56,72)
COG2015_1<-c(24,26,31,40)
COG2015_2<-c(6,8,12,36,48,49)
COG_2015_0<-read.xport("data-raw/LLCP2015.XPT")
COG_2015_0 <- COG_2015_0 %>%
filter(X_STATE %in% COG2015_0) %>%
mutate(YEAR=2015,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2015_1<-read.xport("data-raw/LLCP15V1.XPT")
COG_2015_1 <- COG_2015_1 %>%
filter(X_STATE %in% COG2015_1) %>%
mutate(YEAR=2015,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2015_2<-read.xport("data-raw/LLCP15V2.XPT")
COG_2015_2 <- COG_2015_2 %>%
filter(X_STATE %in% COG2015_2) %>%
mutate(YEAR=2015,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
list2015<-list(COG_2015_0, COG_2015_1,COG_2015_2)
data_2015<-bind_rows(list2015)
rm(COG_2015_0, COG_2015_1,COG_2015_2, list2015)
### 2016
# LLCP2016 Alaska, Delaware, Idaho, Indiana, Kentucky, Massachusetts, Missouri, Montana,
## New Hampshire, New Jersey, New Mexico, North Carolina, Oregon, Tennessee, Vermont, Washington
# LLCP16V1 Connecticut, Maine, Utah
# LLCP16V2 Kansas
#LLCP16V3 New York
COG2016_0<-c(2,10,16,18,21,25,29,30,33,34,35,37,41,47,50,53)
COG2016_1<-c(9,23,49)
COG2016_2<-c(20)
COG2016_3<-c(36)
COG_2016_0<-read.xport("data-raw/LLCP2016.XPT")
COG_2016_0 <- COG_2016_0 %>%
filter(X_STATE %in% COG2016_0) %>%
mutate(YEAR=2016,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2016_1<-read.xport("data-raw/LLCP16V1.XPT")
COG_2016_1 <- COG_2016_1 %>%
filter(X_STATE %in% COG2016_1) %>%
mutate(YEAR=2016,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2016_2<-read.xport("data-raw/LLCP16V2.XPT")
COG_2016_2 <- COG_2016_2 %>%
filter(X_STATE %in% COG2016_2) %>%
mutate(YEAR=2016,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
COG_2016_3<-read.xport("data-raw/LLCP16V3.XPT")
COG_2016_3 <- COG_2016_3 %>%
filter(X_STATE %in% COG2016_3) %>%
mutate(YEAR=2016,
version_cog="X_LCPWTV3",
COG_wt_raw=X_LCPWTV3,
SEQNO=as.integer(SEQNO))
list2016<-list(COG_2016_0, COG_2016_1,COG_2016_2,COG_2016_3)
data_2016<-bind_rows(list2016)
rm(COG_2016_0, COG_2016_1,COG_2016_2,COG_2016_3, list2016)
### 2017
# LLCP2017 _LLCPWT Georgia, Hawaii, Mississippi, Oregon, Puerto Rico
# LLCP17V2 _LCPWTV2 Michigan, New Jersey, New York, Utah
# LLCP17V3 _LCPWTV3 Maryland
COG2017_0<-c(13,15,28,41,72)
COG2017_2<-c(26,34,36,49)
COG2017_3<-c(24)
COG_2017_0<-read.xport("data-raw/LLCP2017.XPT")
COG_2017_0 <- COG_2017_0 %>%
filter(X_STATE %in% COG2017_0) %>%
mutate(YEAR=2017,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2017_2<-read.xport("data-raw/LLCP17V2.XPT")
COG_2017_2 <- COG_2017_2 %>%
filter(X_STATE %in% COG2017_2) %>%
mutate(YEAR=2017,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
COG_2017_3<-read.xport("data-raw/LLCP17V3.XPT")
COG_2017_3 <- COG_2017_3 %>%
filter(X_STATE %in% COG2017_3) %>%
mutate(YEAR=2017,
version_cog="X_LCPWTV3",
COG_wt_raw=X_LCPWTV3,
SEQNO=as.integer(SEQNO))
list2017<-list(COG_2017_0, COG_2017_2,COG_2017_3)
data_2017<-bind_rows(list2017)
rm(COG_2017_0,COG_2017_2,COG_2017_3, list2017)
# LLCP2018 _LLCPWT New Jersey, Oregon, Pennsylvania
# LLCP18V1 _LCPWTV1 Maine
# LLCP18V2 _LCPWTV2 Michigan, New York
COG2018_0<-c(34,41,42)
COG2018_1<-c(23)
COG2018_2<-c(26,36)
COG_2018_0<-read.xport("data-raw/LLCP2018.XPT")
COG_2018_0 <- COG_2018_0 %>%
filter(X_STATE %in% COG2018_0) %>%
mutate(YEAR=2018,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2018_1<-read.xport("data-raw/LLCP18V1.XPT")
COG_2018_1 <- COG_2018_1 %>%
filter(X_STATE %in% COG2018_1) %>%
mutate(YEAR=2018,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2018_2<-read.xport("data-raw/LLCP18V2.XPT")
COG_2018_2 <- COG_2018_2 %>%
filter(X_STATE %in% COG2018_2) %>%
mutate(YEAR=2018,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
list2018<-list(COG_2018_0, COG_2018_1,COG_2018_2)
data_2018<-bind_rows(list2018)
rm(COG_2018_0,COG_2018_1,COG_2018_2, list2018)
# LLCP2019 _LLCPWT New Jersey, Oregon, Pennsylvania
# LLCP19V1 _LCPWTV1 Maine
# LLCP19V2 _LCPWTV2 Michigan, New York
COG2019_0<-c(1,9,11,12,13,18,19,22,27,28,29,32,35,38,41,42,44,45,46,47,48,51,54,55)
COG2019_1<-c(26,32,39,49)
COG2019_2<-c(20,36,40)
COG2019_3<-c(24)
COG_2019_0<-read.xport("data-raw/LLCP2019.XPT")
COG_2019_0 <- COG_2019_0 %>%
filter(X_STATE %in% COG2019_0) %>%
mutate(YEAR=2019,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2019_1<-read.xport("data-raw/LLCP19V1.XPT")
COG_2019_1 <- COG_2019_1 %>%
filter(X_STATE %in% COG2019_1) %>%
mutate(YEAR=2019,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2019_2<-read.xport("data-raw/LLCP19V2.XPT")
COG_2019_2 <- COG_2019_2 %>%
filter(X_STATE %in% COG2019_2) %>%
mutate(YEAR=2019,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
COG_2019_3<-read.xport("data-raw/LLCP19V3.XPT")
COG_2019_3 <- COG_2019_3 %>%
filter(X_STATE %in% COG2019_3) %>%
mutate(YEAR=2019,
version_cog="X_LCPWTV3",
COG_wt_raw=X_LCPWTV3,
SEQNO=as.integer(SEQNO))
list2019<-list(COG_2019_0, COG_2019_1,COG_2019_2, COG_2019_3)
data_2019<-bind_rows(list2019)
rm(COG_2019_0,COG_2019_1,COG_2019_2, COG_2019_3, list2019)
# LLCP2020 _LLCPWT Alaska, Arizona, Arkansas, Delaware, District of Columbia,
# Hawaii, Idaho, Illinois, Kentucky, Maine, Nevada, New Hampshire,
# North Carolina, Oregon, Puerto Rico, Vermont, Washington, Wyoming
# LLCP20V1 _LCPWTV1 California, Michigan, New York, Ohio
# LLCP20V2 _LCPWTV2 Utah
COG2020_0<-c(2,4,5,10,
11,15,16,17,
21,23,24,25,26,27,28,29,
30,31,42,44,47,49,
50,53,56,72)
COG2020_1<-c(6,26,36,39)
COG2020_2<-c(49)
COG_2020_0<-read.xport("data-raw/LLCP2020.XPT")
COG_2020_0 <- COG_2020_0 %>%
filter(X_STATE %in% COG2020_0) %>%
mutate(YEAR=2020,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2020_1<-read.xport("data-raw/LLCP17V1.XPT")
COG_2020_1 <- COG_2020_1 %>%
filter(X_STATE %in% COG2020_1) %>%
mutate(YEAR=2020,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2020_2<-read.xport("data-raw/LLCP17V2.XPT")
COG_2020_2 <- COG_2020_2 %>%
filter(X_STATE %in% COG2020_2) %>%
mutate(YEAR=2020,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
list2020<-list(COG_2020_0, COG_2020_1,COG_2020_2)
data_2020<-bind_rows(list2020)
rm(COG_2020_0,COG_2020_1,COG_2020_2, list2020)
### COMBINING 2015-2020
brfss_COG<-bind_rows(data_2020,data_2019,data_2018, data_2017,data_2016,data_2015)
rm(data_2020,data_2019,data_2018,data_2017,data_2016,data_2015)
### Labeling State indicators
COGstates<- 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)
COGstatelabel<-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_COG$state<- factor(brfss_COG$X_STATE, levels=COGstates, labels=COGstatelabel)
#------------------------------------------------------------------------------------#
# Cleaning Variables
#------------------------------------------------------------------------------------#
## Sexual / Gender Minority Status
brfss_cog <- brfss_COG %>%
mutate(cog_mem_d_fct=as.factor(if_else(CIMEMLOS==1, "Yes",
if_else(CIMEMLOS==2, "No", NA_character_))),
cog_mem_d_num=as.factor(if_else(CIMEMLOS==1, 1,
if_else(CIMEMLOS==2, 0, NA_real_))),
cog_hous_cat_fct=as.factor(if_else(CDHOUSE==1, "Always",
if_else(CDHOUSE==2, "Usually",
if_else(CDHOUSE==3, "Sometimes",
if_else(CDHOUSE==4, "Rarely",
if_else(CDHOUSE==5, "Never", NA_character_)))))),
cog_hous_d_fct=as.factor(if_else(CDHOUSE %in% 1:3, "Sometimes+",
if_else(CDHOUSE %in% 4:5, "Rarely_Never", NA_character_))),
cog_asst_cat_fct=as.factor(if_else(CDASSIST==1, "Always",
if_else(CDASSIST==2, "Usually",
if_else(CDASSIST==3, "Sometimes",
if_else(CDASSIST==4, "Rarely",
if_else(CDASSIST==5, "Never", NA_character_)))))),
cog_asst_d_fct=as.factor(if_else(CDASSIST %in% 1:3, "Sometimes+",
if_else(CDASSIST %in% 4:5, "Rarely_Never", NA_character_))),
cog_help_cat_fct=as.factor(if_else(CDHELP==1, "Always",
if_else(CDHELP==2, "Usually",
if_else(CDHELP==3, "Sometimes",
if_else(CDHELP==4, "Rarely",
if_else(CDHELP==5, "Never", NA_character_)))))),
cog_help_cat_fct2=as.factor(if_else(CDHELP==1, "Always",
if_else(CDHELP==2, "Usually",
if_else(CDHELP==3, "Sometimes",
if_else(CDHELP==4, "Rarely",
if_else(CDHELP==5, "Never",
if_else(CDASSIST %in% 4:5, "Rarely/Never Needs Assistance", NA_character_))))))),
cog_help_d_fct=as.factor(if_else(CDHELP %in% 1:3, "Sometimes+",
if_else(CDHELP %in% 4:5, "Rarely_Never", NA_character_))),
cog_social_cat_fct=as.factor(if_else(CDSOCIAL==1, "Always",
if_else(CDSOCIAL==2, "Usually",
if_else(CDSOCIAL==3, "Sometimes",
if_else(CDSOCIAL==4, "Rarely",
if_else(CDSOCIAL==5, "Never", NA_character_)))))),
cog_social_cat_fct2=as.factor(if_else(CDSOCIAL==1, "Always",
if_else(CDSOCIAL==2, "Usually",
if_else(CDSOCIAL==3, "Sometimes",
if_else(CDSOCIAL==4, "Rarely",
if_else(CDSOCIAL==5, "Never",
if_else(CDASSIST %in% 4:5, "Rarely/Never Needs Assistance", NA_character_))))))),
cog_social_d_fct=as.factor(if_else(CDSOCIAL %in% 1:3, "Sometimes+",
if_else(CDSOCIAL %in% 4:5, "Rarely_Never", NA_character_))),
cog_dscs_d_fct=as.factor(if_else(CDDISCUS==1, "Yes",
if_else(CDDISCUS==2, "No", NA_character_))),
cog_dscs_d_num=as.factor(if_else(CDDISCUS==1, 1,
if_else(CDDISCUS==2, 0, NA_real_)))
) %>%
# Need to select the variables
mutate(incog=1) %>%
rename_all(tolower) %>%
select(cog_mem_d_fct,cog_mem_d_num,
cog_hous_cat_fct,cog_hous_d_fct,
cog_asst_cat_fct,cog_asst_d_fct,
cog_help_cat_fct,cog_help_cat_fct2,cog_help_d_fct,
cog_social_cat_fct,cog_social_cat_fct2,cog_social_d_fct,
cog_dscs_d_fct,cog_dscs_d_num,
x_state,state,year,seqno,x_psu,x_ststr,
cog_wt_raw,version_cog,incog
)
rm(brfss_COG)
#save(brfss_cog, file = "data/brfss_cog.rda", compress = "bzip2", version=2)
use_data(brfss_cog, 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(usethis)
library(devtools)
### 2015
#LLCP2015: ALABAMA, ARIZONA, ARKANSAS, DISTRICT OF COLUMBIA, GEORGIA, HAWAII, ILLINOIS, IOWA, LOUISIANA, MINNESOTA, MISSISSIPPI,
###NEVADA, NEW JERSEY, NORTH DAKOTA, OHIO, OREGON, RHODE ISLAND, SOUTH CAROLINA, SOUTH DAKOTA, TENNESSEE, VIRGINIA, WEST VIRGINIA, WISCONSIN, WYOMING, PUERTO RICO
# LLCP15V1: MARYLAND, MICHIGAN, NEBRASKA, OKLAHOMA
# LLCP15V2: CALIFORNIA, COLORADO, FLORIDA, NEW YORK, TEXAS, UTAH
COG2015_0<-c(1,4,5,11,13,15,17,19,22,27,28,
32,34,38,39,41,44,45,46,47,51,54,55,56,72)
COG2015_1<-c(24,26,31,40)
COG2015_2<-c(6,8,12,36,48,49)
COG_2015_0<-read.xport("data-raw/LLCP2015.XPT")
COG_2015_0 <- COG_2015_0 %>%
filter(X_STATE %in% COG2015_0) %>%
mutate(YEAR=2015,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2015_1<-read.xport("data-raw/LLCP15V1.XPT")
COG_2015_1 <- COG_2015_1 %>%
filter(X_STATE %in% COG2015_1) %>%
mutate(YEAR=2015,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2015_2<-read.xport("data-raw/LLCP15V2.XPT")
COG_2015_2 <- COG_2015_2 %>%
filter(X_STATE %in% COG2015_2) %>%
mutate(YEAR=2015,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
list2015<-list(COG_2015_0, COG_2015_1,COG_2015_2)
data_2015<-bind_rows(list2015)
rm(COG_2015_0, COG_2015_1,COG_2015_2, list2015)
### 2016
# LLCP2016 Alaska, Delaware, Idaho, Indiana, Kentucky, Massachusetts, Missouri, Montana,
## New Hampshire, New Jersey, New Mexico, North Carolina, Oregon, Tennessee, Vermont, Washington
# LLCP16V1 Connecticut, Maine, Utah
# LLCP16V2 Kansas
#LLCP16V3 New York
COG2016_0<-c(2,10,16,18,21,25,29,30,33,34,35,37,41,47,50,53)
COG2016_1<-c(9,23,49)
COG2016_2<-c(20)
COG2016_3<-c(36)
COG_2016_0<-read.xport("data-raw/LLCP2016.XPT")
COG_2016_0 <- COG_2016_0 %>%
filter(X_STATE %in% COG2016_0) %>%
mutate(YEAR=2016,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2016_1<-read.xport("data-raw/LLCP16V1.XPT")
COG_2016_1 <- COG_2016_1 %>%
filter(X_STATE %in% COG2016_1) %>%
mutate(YEAR=2016,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2016_2<-read.xport("data-raw/LLCP16V2.XPT")
COG_2016_2 <- COG_2016_2 %>%
filter(X_STATE %in% COG2016_2) %>%
mutate(YEAR=2016,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
COG_2016_3<-read.xport("data-raw/LLCP16V3.XPT")
COG_2016_3 <- COG_2016_3 %>%
filter(X_STATE %in% COG2016_3) %>%
mutate(YEAR=2016,
version_cog="X_LCPWTV3",
COG_wt_raw=X_LCPWTV3,
SEQNO=as.integer(SEQNO))
list2016<-list(COG_2016_0, COG_2016_1,COG_2016_2,COG_2016_3)
data_2016<-bind_rows(list2016)
rm(COG_2016_0, COG_2016_1,COG_2016_2,COG_2016_3, list2016)
### 2017
# LLCP2017 _LLCPWT Georgia, Hawaii, Mississippi, Oregon, Puerto Rico
# LLCP17V2 _LCPWTV2 Michigan, New Jersey, New York, Utah
# LLCP17V3 _LCPWTV3 Maryland
COG2017_0<-c(13,15,28,41,72)
COG2017_2<-c(26,34,36,49)
COG2017_3<-c(24)
COG_2017_0<-read.xport("data-raw/LLCP2017.XPT")
COG_2017_0 <- COG_2017_0 %>%
filter(X_STATE %in% COG2017_0) %>%
mutate(YEAR=2017,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2017_2<-read.xport("data-raw/LLCP17V2.XPT")
COG_2017_2 <- COG_2017_2 %>%
filter(X_STATE %in% COG2017_2) %>%
mutate(YEAR=2017,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
COG_2017_3<-read.xport("data-raw/LLCP17V3.XPT")
COG_2017_3 <- COG_2017_3 %>%
filter(X_STATE %in% COG2017_3) %>%
mutate(YEAR=2017,
version_cog="X_LCPWTV3",
COG_wt_raw=X_LCPWTV3,
SEQNO=as.integer(SEQNO))
list2017<-list(COG_2017_0, COG_2017_2,COG_2017_3)
data_2017<-bind_rows(list2017)
rm(COG_2017_0,COG_2017_2,COG_2017_3, list2017)
# LLCP2018 _LLCPWT New Jersey, Oregon, Pennsylvania
# LLCP18V1 _LCPWTV1 Maine
# LLCP18V2 _LCPWTV2 Michigan, New York
COG2018_0<-c(34,41,42)
COG2018_1<-c(23)
COG2018_2<-c(26,36)
COG_2018_0<-read.xport("data-raw/LLCP2018.XPT")
COG_2018_0 <- COG_2018_0 %>%
filter(X_STATE %in% COG2018_0) %>%
mutate(YEAR=2018,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2018_1<-read.xport("data-raw/LLCP18V1.XPT")
COG_2018_1 <- COG_2018_1 %>%
filter(X_STATE %in% COG2018_1) %>%
mutate(YEAR=2018,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2018_2<-read.xport("data-raw/LLCP18V2.XPT")
COG_2018_2 <- COG_2018_2 %>%
filter(X_STATE %in% COG2018_2) %>%
mutate(YEAR=2018,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
list2018<-list(COG_2018_0, COG_2018_1,COG_2018_2)
data_2018<-bind_rows(list2018)
rm(COG_2018_0,COG_2018_1,COG_2018_2, list2018)
# LLCP2019 _LLCPWT New Jersey, Oregon, Pennsylvania
# LLCP19V1 _LCPWTV1 Maine
# LLCP19V2 _LCPWTV2 Michigan, New York
COG2019_0<-c(1,9,11,12,13,18,19,22,27,28,29,32,35,38,41,42,44,45,46,47,48,51,54,55)
COG2019_1<-c(26,32,39,49)
COG2019_2<-c(20,36,40)
COG2019_3<-c(24)
COG_2019_0<-read.xport("data-raw/LLCP2019.XPT")
COG_2019_0 <- COG_2019_0 %>%
filter(X_STATE %in% COG2019_0) %>%
mutate(YEAR=2019,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2019_1<-read.xport("data-raw/LLCP19V1.XPT")
COG_2019_1 <- COG_2019_1 %>%
filter(X_STATE %in% COG2019_1) %>%
mutate(YEAR=2019,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2019_2<-read.xport("data-raw/LLCP19V2.XPT")
COG_2019_2 <- COG_2019_2 %>%
filter(X_STATE %in% COG2019_2) %>%
mutate(YEAR=2019,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
COG_2019_3<-read.xport("data-raw/LLCP19V3.XPT")
COG_2019_3 <- COG_2019_3 %>%
filter(X_STATE %in% COG2019_3) %>%
mutate(YEAR=2019,
version_cog="X_LCPWTV3",
COG_wt_raw=X_LCPWTV3,
SEQNO=as.integer(SEQNO))
list2019<-list(COG_2019_0, COG_2019_1,COG_2019_2, COG_2019_3)
data_2019<-bind_rows(list2019)
rm(COG_2019_0,COG_2019_1,COG_2019_2, COG_2019_3, list2019)
# LLCP2020 _LLCPWT Alaska, Arizona, Arkansas, Delaware, District of Columbia,
# Hawaii, Idaho, Illinois, Kentucky, Maine, Nevada, New Hampshire,
# North Carolina, Oregon, Puerto Rico, Vermont, Washington, Wyoming
# LLCP20V1 _LCPWTV1 California, Michigan, New York, Ohio
# LLCP20V2 _LCPWTV2 Utah
COG2020_0<-c(2,4,5,10,
11,15,16,17,
21,23,24,25,26,27,28,29,
30,31,42,44,47,49,
50,53,56,72)
COG2020_1<-c(6,26,36,39)
COG2020_2<-c(49)
COG_2020_0<-read.xport("data-raw/LLCP2020.XPT")
COG_2020_0 <- COG_2020_0 %>%
filter(X_STATE %in% COG2020_0) %>%
mutate(YEAR=2020,
version_cog="X_LLCPWT",
COG_wt_raw=X_LLCPWT,
SEQNO=as.integer(SEQNO))
COG_2020_1<-read.xport("data-raw/LLCP17V1.XPT")
COG_2020_1 <- COG_2020_1 %>%
filter(X_STATE %in% COG2020_1) %>%
mutate(YEAR=2020,
version_cog="X_LCPWTV1",
COG_wt_raw=X_LCPWTV1,
SEQNO=as.integer(SEQNO))
COG_2020_2<-read.xport("data-raw/LLCP17V2.XPT")
COG_2020_2 <- COG_2020_2 %>%
filter(X_STATE %in% COG2020_2) %>%
mutate(YEAR=2020,
version_cog="X_LCPWTV2",
COG_wt_raw=X_LCPWTV2,
SEQNO=as.integer(SEQNO))
list2020<-list(COG_2020_0, COG_2020_1,COG_2020_2)
data_2020<-bind_rows(list2020)
rm(COG_2020_0,COG_2020_1,COG_2020_2, list2020)
### COMBINING 2015-2020
brfss_COG<-bind_rows(data_2020,data_2019,data_2018, data_2017,data_2016,data_2015)
rm(data_2020,data_2019,data_2018,data_2017,data_2016,data_2015)
### Labeling State indicators
COGstates<- 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)
COGstatelabel<-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_COG$state<- factor(brfss_COG$X_STATE, levels=COGstates, labels=COGstatelabel)
#------------------------------------------------------------------------------------#
# Cleaning Variables
#------------------------------------------------------------------------------------#
## Sexual / Gender Minority Status
brfss_cog <- brfss_COG %>%
mutate(cog_mem_d_fct=as.factor(if_else(CIMEMLOS==1, "Yes",
if_else(CIMEMLOS==2, "No", NA_character_))),
cog_mem_d_num=as.factor(if_else(CIMEMLOS==1, 1,
if_else(CIMEMLOS==2, 0, NA_real_))),
cog_hous_cat_fct=as.factor(if_else(CDHOUSE==1, "Always",
if_else(CDHOUSE==2, "Usually",
if_else(CDHOUSE==3, "Sometimes",
if_else(CDHOUSE==4, "Rarely",
if_else(CDHOUSE==5, "Never", NA_character_)))))),
cog_hous_d_fct=as.factor(if_else(CDHOUSE %in% 1:3, "Sometimes+",
if_else(CDHOUSE %in% 4:5, "Rarely_Never", NA_character_))),
cog_asst_cat_fct=as.factor(if_else(CDASSIST==1, "Always",
if_else(CDASSIST==2, "Usually",
if_else(CDASSIST==3, "Sometimes",
if_else(CDASSIST==4, "Rarely",
if_else(CDASSIST==5, "Never", NA_character_)))))),
cog_asst_d_fct=as.factor(if_else(CDASSIST %in% 1:3, "Sometimes+",
if_else(CDASSIST %in% 4:5, "Rarely_Never", NA_character_))),
cog_help_cat_fct=as.factor(if_else(CDHELP==1, "Always",
if_else(CDHELP==2, "Usually",
if_else(CDHELP==3, "Sometimes",
if_else(CDHELP==4, "Rarely",
if_else(CDHELP==5, "Never", NA_character_)))))),
cog_help_cat_fct2=as.factor(if_else(CDHELP==1, "Always",
if_else(CDHELP==2, "Usually",
if_else(CDHELP==3, "Sometimes",
if_else(CDHELP==4, "Rarely",
if_else(CDHELP==5, "Never",
if_else(CDASSIST %in% 4:5, "Rarely/Never Needs Assistance", NA_character_))))))),
cog_help_d_fct=as.factor(if_else(CDHELP %in% 1:3, "Sometimes+",
if_else(CDHELP %in% 4:5, "Rarely_Never", NA_character_))),
cog_social_cat_fct=as.factor(if_else(CDSOCIAL==1, "Always",
if_else(CDSOCIAL==2, "Usually",
if_else(CDSOCIAL==3, "Sometimes",
if_else(CDSOCIAL==4, "Rarely",
if_else(CDSOCIAL==5, "Never", NA_character_)))))),
cog_social_cat_fct2=as.factor(if_else(CDSOCIAL==1, "Always",
if_else(CDSOCIAL==2, "Usually",
if_else(CDSOCIAL==3, "Sometimes",
if_else(CDSOCIAL==4, "Rarely",
if_else(CDSOCIAL==5, "Never",
if_else(CDASSIST %in% 4:5, "Rarely/Never Needs Assistance", NA_character_))))))),
cog_social_d_fct=as.factor(if_else(CDSOCIAL %in% 1:3, "Sometimes+",
if_else(CDSOCIAL %in% 4:5, "Rarely_Never", NA_character_))),
cog_dscs_d_fct=as.factor(if_else(CDDISCUS==1, "Yes",
if_else(CDDISCUS==2, "No", NA_character_))),
cog_dscs_d_num=as.factor(if_else(CDDISCUS==1, 1,
if_else(CDDISCUS==2, 0, NA_real_)))
) %>%
# Need to select the variables
mutate(incog=1) %>%
rename_all(tolower) %>%
select(cog_mem_d_fct,cog_mem_d_num,
cog_hous_cat_fct,cog_hous_d_fct,
cog_asst_cat_fct,cog_asst_d_fct,
cog_help_cat_fct,cog_help_cat_fct2,cog_help_d_fct,
cog_social_cat_fct,cog_social_cat_fct2,cog_social_d_fct,
cog_dscs_d_fct,cog_dscs_d_num,
x_state,state,year,seqno,x_psu,x_ststr,
cog_wt_raw,version_cog,incog
)
rm(brfss_COG)
#save(brfss_cog, file = "data/brfss_cog.rda", compress = "bzip2", version=2)
use_data(brfss_cog, overwrite = TRUE,compress = "bzip2")
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