Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 6

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"))
 * 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):


 * 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; names is also good
 * debugging code
 * print line debugging
 * running the inside of functions