Homework 2

#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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## [7179] 0 0 1 0 1 1 1 0 1 1 0 0 0 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1
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## [7253] 0 0 0 1 1 1 0 1 0 1 0 0 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 0 0 1 1 0 0 1 1 1 1
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## [7327] 1 1 1 0 0 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 0 1 1
## [7364] 0 0 1 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [7401] 1 0 0 1 1 1 1 1 0 0 1 1 0 1 1 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1
## [7438] 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 1 0 0 1 1
## [7475] 1 0 1 0 1 0 0 1 1 1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0 0 0 0 0 1 1 0 0 0 1 0 1
## [7512] 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 1 1 1 0 0 1 1 1 0 1 0 1 1 0 1 1 1 1 0 0 0 1
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## [7586] 0 1 1 1 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1 0 1 0 0 1 0
## [7623] 0 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 0
## [7660] 1 0 0 1 0 1 1 1 1 0 1 1 0 0 1 1 1 0 1 0 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1
## [7697] 1 0 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0
## [7734] 1 1 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 1
## [7771] 0 1 1 1 0 0 0 0 1 1 1 1 0 1 1 0 0 0 0 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 1
## [7808] 1 1 1 1 0 1 0 1 1 0 0 0 0 1 1 1 1 0 1 1 1 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1
## [7845] 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1
## attr(,"na.action")
##   [1]   36   38   88  180  182  191  202  230  252  256  257  300  306  319  327
##  [16]  331  332  337  347  360  403  405  416  421  427  434  442  448  490  494
##  [31]  501  504  526  547  561  564  572  579  598  628  660  686  715  733  754
##  [46]  758  770  776  785  790  811  813  825  864  881  889  900  903  904  916
##  [61]  918  925  927  950  952  965  980  997 1010 1012 1029 1033 1034 1039 1044
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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