data <- matrix(scan(),ncol=3,byrow=TRUE)
  1 1 7.75   1 1 8.25
  1 2 8.25   1 2 8.75
  1 3 7.75   1 3 8.25
  2 1 6.75   2 1 7.25
  2 2 7.25   2 2 7.75
  2 3 6.75   2 3 7.25
  3 1 6.25   3 1 6.75
  3 2 8.25   3 2 7.75
  3 3 8.75   3 3 9.25


data

HotDogs1 <- data.frame(data)
colnames(HotDogs1) <- c("meat","salt","y")
HotDogs1$meat <- as.factor(HotDogs1$meat)    # to make sure that meat is a factor, not numeric
HotDogs1$salt <- as.factor(HotDogs1$salt)    # to make sure that salt is a factor, not numeric

levels(HotDogs1$meat) <- c("beef","pork","chic")     # names for the 3 levels of meat


rm(data)
HotDogs1

summary(lm(y ~ meat + salt + meat:salt, data = HotDogs1))
anova(lm(y ~ meat + salt + meat:salt, data = HotDogs1))

interaction.plot(HotDogs1$meat,HotDogs1$salt,HotDogs1$y,xlab="Types of meat",
                 ylab="Taste score",main="Interaction of Meat and Salt")     
      # nice plot showing the interaction


HotDogs1$meatsalt <- as.numeric(HotDogs1$meat)*10 + as.numeric(HotDogs1$salt)  
      # a way to obtain all factor combinations


tapply(HotDogs1$y,HotDogs1$meatsalt,mean)    # means for all factor combinations