Add variables whilst ignoring NA`s using transform function

r

Solution

My first instinct was to suggest to use `sum()` since then you can use the `na.rm` argument. However, this doesn't work, since `sum()` reduces it arguments to a single scalar value, not a vector.

This means you need to write a `parallel sum` function. Let's call this `psum()`, similar to the base R function `pmin()` or `pmax()`:

psum <- function(..., na.rm=FALSE) { 
  x <- list(...)
  rowSums(matrix(unlist(x), ncol=length(x)), na.rm=na.rm)
}

Now set up some data and use `psum()` to get the desired vector:

dat <- data.frame(
  x = c(1,2,3, NA),
  y = c(NA, 4, 5, NA))

transform(dat, new=psum(x, y, na.rm=TRUE))
   x  y new
1  1 NA   1
2  2  4   6
3  3  5   8
4 NA NA   0

Similarly, you can define a `parallel product`, or `pprod()` like this:

pprod <- function(..., na.rm=FALSE) { 
  x <- list(...)
  m <- matrix(unlist(x), ncol=length(x))
  apply(m, 1, prod, na.rm=TRUE)
} 

transform(dat, new=pprod(x, y, na.rm=TRUE))
   x  y new
1  1 NA   1
2  2  4   8
3  3  5  15
4 NA NA   1

This example of `pprod` provides a general template for what you want to do: Create a function that uses `apply()` to summarize a matrix of input into the desired vector.

Problem

I have a data frame with a large number of variables. I am creating new variables by adding together some of the old ones. The code I am using to do so is: ``` name_of_data_frame<- transform(name_of_data_frame, new_variable=var1+var2 +....) ``` When transform comes across a NA in one of the observations, it returns "NA" in the new variable, even if some of the other variables it was adding were not NA. e.g. if `var1= 4`, `var2=3`, `var3=NA`, then using `transform`, if I did `var1+var2+var3` it would give out `NA`, whereas I would like it to give me 7. I don't want to recode my `NA`s to zero within the data frame, as I may need to refer back to the `NA`s later, so don't want to confuse the `NA`s with the observations which were genuinely `0`. Any help on how to get around R treating `NA`s in the way described above with the transform function would be great (or if there are alternative functions to use, that would be great also). Please note that I am not always just summing variables that are next to each other, I am also often dividing variables, multiplying, subtracting etc.

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