Gender<-c("Female","Female","Male","Male")
Smoke<-c("No","Yes","No","Yes")
count<-c(314,44,334,33)
tab<-matrix(c(314,44,334,33),nrow = 2,byrow = T)
colnames(tab)<-c("No","Yes")
rownames(tab)<-c("Female","Male")
tab
##         No Yes
## Female 314  44
## Male   334  33

visualize

barplot(tab,beside=T,legend=T)

ANALYSIS

library(epiR)
## Loading required package: survival
## Package epiR 2.0.63 is loaded
## Type help(epi.about) for summary information
## Type browseVignettes(package = 'epiR') to learn how to use epiR for applied epidemiological analyses
## 
epi.2by2(tab,method="cohort.count",conf.level=0.95)
##              Outcome +    Outcome -      Total                 Inc risk *
## Exposed +          314           44        358     87.71 (83.85 to 90.93)
## Exposed -          334           33        367     91.01 (87.60 to 93.73)
## Total              648           77        725     89.38 (86.91 to 91.53)
## 
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio                                 0.96 (0.92, 1.01)
## Inc odds ratio                                 0.71 (0.44, 1.14)
## Attrib risk in the exposed *                   -3.30 (-7.79, 1.19)
## Attrib fraction in the exposed (%)            -3.76 (-9.12, 1.34)
## Attrib risk in the population *                -1.63 (-5.32, 2.06)
## Attrib fraction in the population (%)         -1.82 (-4.34, 0.64)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 2.077 Pr>chi2 = 0.150
## Fisher exact test that OR = 1: Pr>chi2 = 0.185
##  Wald confidence limits
##  CI: confidence interval
##  * Outcomes per 100 population units

the odds of a female not smoking are 0.71 times the odds of a male not smoking .the inverse of 0.71=1/0.71=1.4 interpreted as the odds of a male not smoking are 1.4 times the odds of a female not smoking.

tab1

tab1<-matrix(c(44,314,33,334),nrow = 2,byrow = T)
colnames(tab1)<-c("Yes","No")
rownames(tab1)<-c("Female","Male")
tab1
##        Yes  No
## Female  44 314
## Male    33 334

COMBINE tab1 and tab

tab3<-cbind(tab[,2],tab1[,2])
tab3