data("DaytonSurvey", package = "vcdExtra")
str(DaytonSurvey)
## 'data.frame': 32 obs. of 6 variables:
## $ cigarette: Factor w/ 2 levels "Yes","No": 1 2 1 2 1 2 1 2 1 2 ...
## $ alcohol : Factor w/ 2 levels "Yes","No": 1 1 2 2 1 1 2 2 1 1 ...
## $ marijuana: Factor w/ 2 levels "Yes","No": 1 1 1 1 2 2 2 2 1 1 ...
## $ sex : Factor w/ 2 levels "female","male": 1 1 1 1 1 1 1 1 2 2 ...
## $ race : Factor w/ 2 levels "white","other": 1 1 1 1 1 1 1 1 1 1 ...
## $ Freq : num 405 13 1 1 268 218 17 117 453 28 ...
head(DaytonSurvey)
## cigarette alcohol marijuana sex race Freq
## 1 Yes Yes Yes female white 405
## 2 No Yes Yes female white 13
## 3 Yes No Yes female white 1
## 4 No No Yes female white 1
## 5 Yes Yes No female white 268
## 6 No Yes No female white 218
Dayton_ACM_df <- aggregate(Freq ~ cigarette + alcohol + marijuana,
data = DaytonSurvey, FUN = sum)
Dayton_ACM_df
## cigarette alcohol marijuana Freq
## 1 Yes Yes Yes 911
## 2 No Yes Yes 44
## 3 Yes No Yes 3
## 4 No No Yes 2
## 5 Yes Yes No 538
## 6 No Yes No 456
## 7 Yes No No 43
## 8 No No No 279
dayton1 <- DaytonSurvey
library(vcd)
## Loading required package: grid
mosaic(data=DaytonSurvey, ~cigarette+alcohol+marijuana, shade=TRUE, cex=2.5)
mosaic(data=DaytonSurvey, ~cigarette+marijuana, shade = TRUE, cex = 2.5)
mosaic(data=DaytonSurvey, ~alcohol+marijuana, shade = TRUE, cex = 2.5)
mosaic(data=DaytonSurvey, ~sex+marijuana+alcohol, shade = TRUE, cex = 2.5)
##Observations from this exercise: 1. Regardless of sex, there is a higher chance that people who smoke cigarettes, drink alcohol 2. Amongst cigarette smokers, there is a bigger set of people who smoke marijuana. 3. Close to half the people who consume alcohol smoke marijuna in this sampled data set. 4. There are more males who smokes marujuana and consume alcohol in this senior high school dataset than females.