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(This post originally appeared on my R blog)

## Writing functions

This post outlines the writing of a basic function. Writing functions in R (R Core Team 2017) is fairly simple, and the usefulness of function writing cannot be conveyed in a single post. I have included “Part one” in the title, and I will add follow-up posts in time.

The basic code to write a function looks like this:

function_name <- function(){}

The code for the task you want your function to perform goes inside the curly brackets {}, and the object you wish the function to work on goes inside the parenthesis().

### The problem

I have often found myself using a number of different functions together for multiple variables. For each variable, I need re-type each function. For example, when looking at a variable, I would often run the functions mean(), sd(), min(), max(), and length() together. Each time I wanted to inspect a new variable, I had to type all five functions for the variable in question. For example, looking at the Temp variable, from the airquality dataset in the datasets package, would require typing the following: mean(airquality$Temp), sd(airquality$Temp), min(airquality$Temp), max(airquality$Temp), length(airquality$Temp). This can get very tedious and repetitive. ### The solution In response to repeatedly typing these functions together, I created the descriptives() function which combines these frequently used functions into a single function. #### The descriptives() function The descriptives() function combines the functions mean(), sd(), min(), max(), and length() to return a table displaying the mean, standard deviation, minimum, maximum, and length of a vector.1 The code for creating this function is below, each line of code within the function is explained in the comment above (denoted with the # symbol). The code below can be copied and pasted into your R session to create the descriptives() function. descriptives <- function(x){ # create an object "mean" which contains the mean of x mean <- mean(x, na.rm = TRUE) # create an object "sd" which contains the sd of x sd <- sd(x, na.rm = TRUE) # create an object "min" which contains the min of x min <- min(x, na.rm = TRUE) # create an object "max" which contains the max of x max <- max(x, na.rm = TRUE) # create an object "len" which contains the length of x len <- length(x) # combine the objects created into a table data.frame(mean, sd, min, max, len) } When you pass a vector x through the function descriptives(), it creates 5 objects which are then combined into a table. Running the function returns the table: descriptives(airquality$Temp)
##       mean      sd min max len
## 1 77.88235 9.46527  56  97 153

### Things to bear in mind when writing functions

1. Try to give your function a name that is short and easy to remember.
2. If you are writing a longer more complex function, it may be useful to test it line-by-line, before seeing if it “works”; this will help to identify any errors before they cause your function to fail.
3. If the function returns an error, testing the code line by line will help you find the source of the error.
4. The final line of code in a function will be the “output” of the function.
5. Objects created within the function are not saved in the global environment: in the descriptives() function, all that is returned is a table containing the variables specified. The individual objects that were created disappear when the function has finished running.
6. The disappearing of objects created within a function described above can be very useful for keeping a tidy working environment.

### Conclusion

I find myself writing functions regularly, for various tasks. Often a function may be specific to a particular task, or even to a particular dataset. One example of such a function builds on the previous post, in which I described how to create a dataframe from multiple files. In practice, I rarely create data frames exactly as described. I usually nest the “read.csv” function within a larger function that also sorts the data, creating a more manageable dataframe, better suited to my purposes; e.g., removing variables that are of no interest or computing/recoding variables. I can then run this function to build my dataframe at the start of a session.

### References

R Core Team. 2017. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

1. Most of what descriptives() does can also be achieved by the summary() function, however sd() and length() are missing.