SPSS contains a very useful set of functions that R lacks. If you’re lucky enough to have access to SPSS, you can use SPSS and R very well together. If not, it’s easy to add these functions to R. The functions perform calculations across values within each observation. Rather than limit you to removing missing values or not, they let you specify how many valid values you want before setting the result to missing. For example in SPSS,
MEAN.5(Q1 TO Q10) asks for the mean only if at least five of the ten variables have valid values. Otherwise the result will be a missing value. This “.n” extension is also available for SPSS’ SUM, SD, VARIANCE, MIN and MAX functions.

Let’s now take a look at how to do this in R. First we’ll create some data with different numbers of missing values for each observations.

> q1 <- c(1, 1, 1)
> q2 <- c(2, 2, NA)
> q3 <- c(3, NA, NA)
> df <- data.frame(q1, q2, q3)
> df
q1 q2  q3
1 1 2   3
2 1 2  NA
3 1 NA NA

R already has a mean function, but it lacks a function to count the number of valid values. A common way to do this in R is to use the is.na() function to generate a vector of TRUE/FALSE values for missing or not, respectively, then sum them. As with many software packages, R views TRUE as having the value 1 and FALSE as having a value of 0, so this approach gets us the number of missing values. The “!” symbol means “not” in R so !is.na() will find the number non-missing values. Here’s a function that does this:

> nvalid <- function(x) sum(!is.na(x))
> nvalid(q2)
[1] 2

So it has found that there are two valid values for q2. This nvalid() function obviously works on vectors, but we need to apply it to the rows of our data frame. We can select the first three variables using df[1:3] and then pass the result into as.matrix() to make the rows easily accessible by R’s apply() function. The apply() function’s second argument is 1 indicating that we would like to compute the mean across rows (the value 2 would indicate columns). The final arguments are the functions to apply and any arguments they need.

> means  <- apply(as.matrix(df[1:3]), 1, mean, na.rm = TRUE)
> counts <- apply(as.matrix(df[1:3]), 1, nvalid)
> means
[1] 2.0 1.5 1.0
> counts
[1] 3 2 1

We have our means and the counts of valid values, so all that remains is to choose our desired value of counts and accept the mean if the data have that value or greater, but return a missing value (NA) if not. This can be done using the ifelse() function, whose first argument is the logical condition, followed by the value desired when TRUE, then the value when false.

> means <- ifelse(counts >= 2, means, NA)
> means
[1] 2.0 1.5 NA

We’ve seen all the parts work, so all that remains is to put them together into a single function that has two arguments, one for the data frame and one for the n required.

mean.n   <- function(df, n) {
  means <- apply(as.matrix(df), 1, mean, na.rm = TRUE)
  nvalid <- apply(as.matrix(df), 1, function(df) sum(!is.na(df)))
  ifelse(nvalid >= n, means, NA)
}

Let’s test our function requiring 1, 2 and 3 valid values.

> df$mean1 <- mean.n(df[1:3], 1)
> df$mean2 <- mean.n(df[1:3], 2)
> df$mean3 <- mean.n(df[1:3], 3)
> df
q1 q2  q3 mean1 mean2 mean3
1 1 2   3   2.0   2.0     2
2 1 2  NA   1.5   1.5    NA
3 1 NA NA   1.0   NA     NA

That looks good. You could apply this same idea to various other R functions such as sd() or var(). You could also apply it to sum() as SPSS does, but I rarely do that. If you were creating a scale score from a set of survey Likert items measuring agreement and a person replied “strongly agree” (a value of 5), to only half the items but skipped the others, would you want the resulting score to be a neutral value as the sum would imply, or “strongly agree” as the mean would indicate? The mean makes much more sense in most situations. Be careful though as there are standardized tests that require use of the sum.

If you’re an SPSS user looking to learn just enough R to use the two together, you might want to read this, or to learn more you could take one of my workshops. If you really want to dive into the details, you might consider reading my book, R for SAS and SPSS Users.