We can write a for()loop- stddev = vector('double', ncol(airquality)) for(i in seq_along(airquality)) )) This is however impractical for large data sets and breaks the rule of thumb of not running the same operation more than twice. You could copy-paste the same code for each column- sd(airquality$Ozone) 33.27597 sd(airquality$Solar.R) 91.1523 sd(airquality$Wind) 3.557713 sd(airquality$Temp) 9.529969 sd(airquality$Month) 1.473434 sd(airquality$Day) 8.707194 Problem 1: You want to find the standard deviation for each variable in the dataset. Let’s pick the inbuilt R dataset- air quality. Now that you’re convinced to iterate - let’s jump right in!

It might take some time to wrap your head around the idea of iterating, but trust me, it’s worth the investment. Due to its concise nature, you’re likely to encounter fewer bugs.

It draws attention to the part of code that is different, thus making it easier to spot the intent of the operation.

As a general rule of thumb, if we need to run a block of code more than two times, it is a good idea to iterate! Here are two reasons why this will make your code richer. But it is important to recognise that this is a sub-optimal route and frankly impractical. Expanding a similar analogy to web speak, there are about a hundred ways to iterate code! To add to this, the syntax can be confusing and some of us also run the risk of just lazily resorting to copy-pasting the code 4 times. Turns out that’s part myth and part misunderstanding. Inuits do not actually have a hundred names for snow. A step-by-step guide to perform clean and efficient iterations in R.

Excel iterative function