#load brfss
library(car)

## Loading required package: carData

library(stargazer)

## 
## Please cite as:

##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.

##  R package version 5.2.2. https://CRAN.R-project.org/package=stargazer

library(survey)

## Loading required package: grid

## Loading required package: Matrix

## Loading required package: survival

## 
## Attaching package: 'survey'

## The following object is masked from 'package:graphics':
## 
##     dotchart

library(questionr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:car':
## 
##     recode

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tableone)

load(url("https://github.com/coreysparks/data/blob/master/brfss_2017.Rdata?raw=true"))
View(brfss_17)

##Fix variables

nams<-names(brfss_17)
head(nams, n=10)

##  [1] "dispcode" "statere1" "safetime" "hhadult"  "genhlth"  "physhlth"
##  [7] "menthlth" "poorhlth" "hlthpln1" "persdoc2"

newnames<-tolower(gsub(pattern = "_",replacement =  "",x =  nams))
names(brfss_17)<-newnames
##Filter Texas
brfss_17$tx<-NA
brfss_17$tx[grep(pattern = "TX", brfss_17$mmsaname)]<-1

##Remove Non-Responses 
brfss_17<-brfss_17%>%
  filter(tx==1, is.na(mmsawt)==F, is.na(hlthpln1)==F)

Recoding of variables

Be sure to always check your codebooks!

#I will be using e-cigarette smoking(ECIGARET)
#recode 
brfss_17$ecigaret<-Recode(brfss_17$ecigaret, recodes ="7:9=NA; 1=1;2=0")

Define a binary outcome variable of your choosing and define how you recode the original variable.

##The binary outcome i chose was ECIGARET
##THE VARIABLE NAME CHANGED TO CIG AND 7:9 ARE NOW NA, WHILE 1 IS RECODED AS 1 TO MEAN YES THEY ARE E-CIGERATE SMOKERS WHILE 2=0 MEANS NO THEY ARE NOT SMOKERS

State a research question about what factors you believe will affect your outcome variable.

##How do exercise and e-cigaretteS affect ones health?

Define at least 2 predictor variables, based on your research question. For this assignment, it’s best if these are categorical variables.

##The predictor variables are exercise and sex
##I would like to see if males or females that exercise are more prone to use e-cigarettes

Perform a descriptive analysis of the outcome variable by each of the variables you defined in part b. (e.g. 2 x 2 table, 2 x k table). Follow a similar approach to presenting your statistics as presented in Sparks 2009 (in the Google drive). This can be done easily using the tableone package!

##Recode
##Exercise
brfss_17$exerany2<-Recode(brfss_17$exerany2, recodes ="7:9=NA; 1=1;2=0")
na.omit(brfss_17$exerany2)

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## attr(,"na.action")
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## attr(,"class")
## [1] "omit"

##Sex code 1
brfss_17$sex<-as.factor(ifelse(brfss_17$sex==1, "Male", "Female"))

##choose only TX
brfss_17$tx<-NA
brfss_17$tx[grep(pattern = "TX", brfss_17$mmsaname)]<-1

##Raw frequencies;un weighted data

##Exercise
table(brfss_17$ecigaret, brfss_17$exerany2)

##    
##        0    1
##   0 1959 4750
##   1  340  801

##column percentages
prop.table(table(brfss_17$ecigaret, brfss_17$exerany2), margin=2)

##    
##             0         1
##   0 0.8521096 0.8557017
##   1 0.1478904 0.1442983

##basic chi square test of independence 
chisq.test(table(brfss_17$ecigaret, brfss_17$exerany2))

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table(brfss_17$ecigaret, brfss_17$exerany2)
## X-squared = 0.14118, df = 1, p-value = 0.7071

##Sex
table(brfss_17$ecigaret, brfss_17$sex)

##    
##     Female Male
##   0   4154 2827
##   1    613  584

##column percentages
prop.table(table(brfss_17$ecigaret, brfss_17$sex), margin=2)

##    
##        Female      Male
##   0 0.8714076 0.8287892
##   1 0.1285924 0.1712108

##basic chi square test of independence 
chisq.test(table(brfss_17$ecigaret, brfss_17$sex))

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table(brfss_17$ecigaret, brfss_17$sex)
## X-squared = 28.564, df = 1, p-value = 9.066e-08

##Using table one
t1<-CreateTableOne(vars = c("exerany2", "sex"), strata = "ecigaret", test = T, data = brfss_17)
#t1<-print(t1, format="p")
print(t1,format="p")

##                       Stratified by ecigaret
##                        0           1           p      test
##   n                    6981        1197                   
##   exerany2 (mean (SD)) 0.71 (0.45) 0.70 (0.46)  0.681     
##   sex = Male (%)       40.5        48.8        <0.001

Calculate descriptive statistics (mean or percentages) for each variable using no weights or survey design, as well as with full survey design and weights.

#survey design
des<-svydesign(ids=~1, strata=~ststr, weights=~mmsawt, data = brfss_17 )
##Exercise
#counts
cat<-wtd.table(brfss_17$ecigaret, brfss_17$exerany2, weights = brfss_17$mmsawt)

#proportions
prop.table(wtd.table(brfss_17$ecigaret, brfss_17$exerany2, weights = brfss_17$mmsawt), margin=2)

##           0         1
## 0 0.8151963 0.7934441
## 1 0.1848037 0.2065559

#compare that with the original, unweighted proportions
prop.table(table(brfss_17$ecigaret, brfss_17$exerany2), margin=2)

##    
##             0         1
##   0 0.8521096 0.8557017
##   1 0.1478904 0.1442983

##Sex
#counts
cat<-wtd.table(brfss_17$ecigaret, brfss_17$sex, weights = brfss_17$mmsawt)

#proportions
prop.table(wtd.table(brfss_17$ecigaret, brfss_17$sex, weights = brfss_17$mmsawt), margin=2)

##      Female      Male
## 0 0.8258119 0.7765149
## 1 0.1741881 0.2234851

#compare that with the original, unweighted proportions
prop.table(table(brfss_17$ecigaret, brfss_17$sex), margin=2)

##    
##        Female      Male
##   0 0.8714076 0.8287892
##   1 0.1285924 0.1712108

Calculate percentages, or means, for each of your independent variables for each level of your outcome variable and present this in a table, with appropriate survey-corrected test statistics. (tableone package helps)

library(tableone)
#survey design
des<-svydesign(ids=~1, strata=~ststr, weights=~mmsawt, data = brfss_17)
#not using survey design
options(survey.lonely.psu = "adjust")
st1<-svyCreateTableOne(vars = c("exerany2", "sex"), strata = "ecigaret", test = T, data = des)
st1<-print(st1, format="p")

##                       Stratified by ecigaret
##                        0                  1                 p      test
##   n                    11542277.85        2851551.68                   
##   exerany2 (mean (SD))        0.70 (0.46)       0.73 (0.45)  0.297     
##   sex = Male (%)              47.0              54.7         0.010

Are there substantive differences in the descriptive results between the analysis using survey design and that not using survey design?

##Yes there are substantial differences when using survey design and not using survey design as seen above

Homework 3

For this homework, you will perform a logistic regression (or probit regression) of a binary outcome, using the dataset of your choice. I ask you specify a research question for your analysis and generate appropriate predictors in order to examine your question.

fit.logit<-svyglm(ecigaret ~ exerany2 + sex,
                  design = des,
                  family = binomial)

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

library(broom)
fit.logit%>%
  tidy()%>%
  knitr::kable(digits = 3)

term	estimate	std.error	statistic	p.value
(Intercept)	-1.630	0.128	-12.687	0.000
exerany2	0.126	0.136	0.928	0.353
sexMale	0.304	0.125	2.437	0.015

Present results from a model with sample weights and design effects, if your data allow for this. Present the results in tabular form, with Parameter estimates, odds ratios (if using the logit model) and confidence intervals for the odds ratios.

##logit model
fit.logit%>%
  tidy()%>%
  mutate(OR = exp(estimate))%>%
  knitr::kable(digits = 3)

term	estimate	std.error	statistic	p.value	OR
(Intercept)	-1.630	0.128	-12.687	0.000	0.196
exerany2	0.126	0.136	0.928	0.353	1.134
sexMale	0.304	0.125	2.437	0.015	1.356

##confidence intervals
fit.logit%>%
  tidy()%>%
  mutate(OR = exp(estimate),
         LowerOR_Ci = exp(estimate - 1.96*std.error),
         UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
  knitr::kable(digits = 3)

term	estimate	std.error	statistic	p.value	OR	LowerOR_Ci	UpperOR_Ci
(Intercept)	-1.630	0.128	-12.687	0.000	0.196	0.152	0.252
exerany2	0.126	0.136	0.928	0.353	1.134	0.870	1.479
sexMale	0.304	0.125	2.437	0.015	1.356	1.061	1.731

If you need help figuring out what your PSU, weight and sample strata variable are called in YOUR data, please ask.

Generate predicted probabilities for some “interesting” cases from your analysis, to highlight the effects from the model and your stated research question.

library(emmeans)
rg<-ref_grid(fit.logit)

marg_logit<-emmeans(object = rg,
              specs = c( "exerany2",  "sex"),
              type="response" )

knitr::kable(marg_logit,  digits = 4)

exerany2	sex	prob	SE	df	asymp.LCL	asymp.UCL
0	Female	0.1638	0.0176	Inf	0.1322	0.2013
1	Female	0.1818	0.0151	Inf	0.1540	0.2133
0	Male	0.2098	0.0216	Inf	0.1707	0.2552
1	Male	0.2315	0.0164	Inf	0.2009	0.2651

Homework 3

Lydia Okabe

2/4/2021

Recoding of variables

Homework 3