The `while`

loop is a sometimes controversial programming construct since it is often not clear that the loop will ever end. Here’s one situation where I think it is appropriate to use. In this example I would like to generate a small dataset with a high correlation coefficient (\(r > 0.9\)) and a significant p-value for the correlation (\(p < 0.05\)). In order to do this I just keep generating random datasets until I find one that satisfies my criteria. This is certainly a non-elegant brute-force approach, but it gets the job done. I use this approach when I’m trying to generate a “true positive” plot to illustrate the concept of using a line up for graphical inference.

```
library(dplyr)
set.seed(2018-02-01)
n <- 10
data <- data_frame(x = runif(n),
y = runif(n))
test <- cor.test(data$x, data$y)
while(!(test$p.value < 0.05 && abs(test$estimate) > 0.9)) {
data$x <- runif(n)
data$y <- runif(n)
test <- cor.test(data$x, data$y)
}
plot(data, xlim = c(0, 1), ylim = c(0, 1), asp = 1)
```