Advanced programming

In this session, we will introduce methods to adjust p-values to account for multiple testing, learn advanced programming techniques including for loops and writing custom functions, and cover basic statistical tests to conduct hypothesis testing for different combinations of continuous, categorical, and paired data.

Custom functions

We have previously discussed the role of functions in R, and have seen examples of built-in R functions, such as mean() and p.adjust().

But sometimes we’ll want to do something that isn’t included in a built-in R function, or that simplifies use of existing functions.

User-defined functions are created using the function() function.

Basic usage is:

function(arguments) expression

Where arguments are arguments you supply to the function and expression is the expression you want to evaluate.

For more complicated procedures, you can wrap multiple expressions in curly brackets, and can also specify what value to return using the return() function:

function(arguments) {
  expression1
  expression2
  return(value)
  }

For example, I always want to show NA values when I look at a contingency table, which means I have to type in the useNA = "ifany" arguement every time I use the table() function, since the default in that function is to exclude missing values.

To streamline things, I can create a custom function that includes this option:

tabna <- function(x) table(x, useNA = 'ifany')

Now instead of typing:

library(gtsummary)

table(trial$response, useNA = 'ifany')


   0    1 <NA> 
 132   61    7

I can type:

tabna(trial$response)

x
   0    1 <NA> 
 132   61    7

This gets particularly useful for long or complex procedures, but is also really useful for short procedures that will be repeated many times - I often use this function 10+ times in a day.

Try writing a custom function based on the mean() function but including the option to remove NAs from the calculation.

Loops

Often we will want to repeat a set of operations several times, and we can do so using a loop.

There are three main types of loops in R:

for loop
while loop
repeat loop

We will focus on the for loop today.

Here is a basic example using the print() function to repeatedly print a value:

for (i in 1:5) {
  print(i)
}

[1] 1
[1] 2
[1] 3
[1] 4
[1] 5

Here are the steps of the execution:

The value of i is set to 1
The value of i is printed to the console (first iteration complete)
The value of i is set to 2 (the for loop loops back to the beginning)
The value of i is printed to the console

And so on until we reach the last value of i, and the process is complete.

Say you have a biomarker in your dataset but you know the machine that generated the data has a lower limit of detection of 0.2. You could choose to impute half the detection limit for any values that fall below 0.2 as follows:

trial$marker_corrected <- trial$marker

for(i in 1:nrow(trial)) {
  if (is.na(trial$marker[i])) {
    trial$marker_corrected[i] <- NA
  } else if (trial$marker[i] < 0.2) {
    trial$marker_corrected[i] <- 0.1
  }
}

And we can see that our new variable has the value 0.1 for all cases where marker was <0.2:

trial[trial$marker < 0.2, c("marker", "marker_corrected")]

# A tibble: 54 × 2
   marker marker_corrected
    <dbl>            <dbl>
 1  0.16               0.1
 2  0.144              0.1
 3  0.06               0.1
 4  0.128              0.1
 5  0.157              0.1
 6  0.066              0.1
 7  0.096              0.1
 8  0.105              0.1
 9  0.043              0.1
10  0.105              0.1
# ℹ 44 more rows