```{r}
library(aws.s3)
library('lehmansociology')
library(ggplot2)
library(dplyr)
library(lsr)
s3load('gss100.Rdata', bucket = 'lehmansociologydata')
gss100<-droplevels(gss100)
```

#GRASS
#create a dichotomized variable where TRUE or 1 = supports legalization of grass and
#FALSE or 0 = does not support legalization of grass
```{r}
frequency(gss100$grass)
frequency(as.numeric(gss100$grass))
gss100$favorgrass<-as.numeric(gss100$grass)==1
frequency(gss100$favorgrass)
```
#HEALTH
#create a dichotomized variable where TRUE or 1=excellent or good
#health and FALSE or 0=fair or poor health
```{r}
frequency(gss100$health)
frequency(as.numeric(gss100$health))
gss100$goodhealth<-as.numeric(gss100$health)<3
frequency(gss100$goodhealth)
```
```{r}
mean(gss100$goodhealth, na.rm = TRUE)
ciMean(as.numeric(gss100$goodhealth), na.rm=TRUE, conf =0.95)
ciMean(as.numeric(gss100$goodhealth), na.rm=TRUE, conf =0.99)
```
```{r}
mean(gss100$favorgrass, na.rm = TRUE)
ciMean(as.numeric(gss100$favorgrass), na.rm=TRUE, conf =0.95)
ciMean(as.numeric(gss100$favorgrass), na.rm=TRUE, conf =0.99)
```
#HEALTH INDEPENDENT, GRASS DEPENDENT
#REMEMBER TO USE YOUR DICHOTOMIZED VARIABLES
#SO THIS WILL BE GOODHEALTH INDEPENDENT, FAVORGRASS DEPENDENT
```{r}
goodhealth<-dplyr::filter(gss100, goodhealth=="TRUE")
ciMean(as.numeric(goodhealth$favorgrass), na.rm=TRUE, conf =0.95)
ciMean(as.numeric(goodhealth$favorgrass), na.rm=TRUE, conf =0.99)
badhealth<-dplyr::filter(gss100, goodhealth=="FALSE")
ciMean(as.numeric(badhealth$favorgrass), na.rm=TRUE, conf =0.95)
ciMean(as.numeric(badhealth$favorgrass), na.rm=TRUE, conf =0.99)
```

#GRASS INDEPENDENT, HEALTH DEPENDENT
#REMEMBER TO USE YOUR DICHOTOMIZED VARIABLES
#SO THIS WILL BE FAVORGRASS INDEPENDENT, GOODHEALTH DEPENDENT
```{r}
yesgrass<-dplyr::filter(gss100, favorgrass=="TRUE")
ciMean(as.numeric(yesgrass$goodhealth), na.rm=TRUE, conf =0.95)
ciMean(as.numeric(yesgrass$goodhealth), na.rm=TRUE, conf =0.99)
nograss<-dplyr::filter(gss100, favorgrass=="FALSE")
ciMean(as.numeric(nograss$goodhealth), na.rm=TRUE, conf =0.95)
ciMean(as.numeric(nograss$goodhealth), na.rm=TRUE, conf =0.99)
```