Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 6
From CommunityData
- the cut() function: cut(airquality$Temp, quantile(airquality$temp))
- tabular data: many ways to create it
- input it directly (using the matrix command, which I used in week 2 or using rbind):
genpol <- as.matrix(rbind(c(762, 327, 468), c(484, 239, 477))) dimnames(gen.pol) <- list(gender = c("F", "M") party = c("Democrat","Independent", "Republican"))
- more ways to create tabular data:
- with tapply: tapply(warpbreaks$breaks, list(warpbreaks$wool, warpbreaks$tension), sum)
- creating it with table(): table(cut(airquality$Temp, quantile(airquality$Temp)), airquality$Month)
- once we have tables, we can look at them: margin.table() is fast; prop.table() is super useful
- chisq tests: chisq.test()
- looking into the chisq.test() object; i often use TAB
- debugging code
- print line debugging
- running the inside of functions