Handling missing/incomplete data in R--is there function to mask but not remove NAs?

Question

As you would expect from a DSL aimed at data analysis, R handles missing/incomplete data very well, for instance:

Many R functions have an 'na.rm' flag that you can set to 'T' to remove the NAs:

mean( c(5,6,12,87,9,NA,43,67), na.rm=T)

But if you want to deal with NAs before the function call, you need to do something like this:

to remove each 'NA' from a vector:

vx = vx[!is.na(a)]

to remove each 'NA' from a vector and replace it w/ a '0':

ifelse(is.na(vx), 0, vx)

to remove entire each row that contains 'NA' from a data frame:

dfx = dfx[complete.cases(dfx),]

All of these functions permanently remove 'NA' or rows with an 'NA' in them.

Sometimes this isn't quite what you want though--making an 'NA'-excised copy of the data frame might be necessary for the next step in the workflow but in subsequent steps you often want those rows back (e.g., to calculate a column-wise statistic for a column that has missing rows caused by a prior call to 'complete cases' yet that column has no 'NA' values in it).

to be as clear as possible about what i'm looking for: python/numpy has a class, 'masked array', with a 'mask' method, which lets you conceal--but not remove--NAs during a function call. Is there an analogous function in R?

Answer 1

Exactly what to do with missing data -- which may be flagged as NA if we know it is missing -- may well differ from domain to domain.

To take an example related to time series, where you may want to skip, or fill, or interpolate, or interpolate differently, ... is that just the (very useful and popular) zoo has all these functions related to NA handling:

zoo::na.approx  zoo::na.locf    
zoo::na.spline  zoo::na.trim    

allowing to approximate (using different algorithms), carry-forward or backward, use spline interpolation or trim.

Another example would be the numerous missing imputation packages on CRAN -- often providing domain-specific solutions. [ So if you call R a DSL, what is this? "Sub-domain specific solutions for domain specific languages" or SDSSFDSL? Quite a mouthful :) ]

But for your specific question: no, I am not aware of a bit-level flag in base R that allows you to mark observations as 'to be excluded'. I presume most R users would resort to functions like na.omit() et al or use the na.rm=TRUE option you mentioned.

Answer 2

It's a good practice to look at the data, hence infer about the type of missing values: is it MCAR (missing complete and random), MAR (missing at random) or MNAR (missing not at random)? Based on these three types, you can study the underlying structure of missing values and conclude whether imputation is at all applicable (you're lucky if it's not MNAR, 'cause, in that case, missing values are considered non-ignorable, and are related to some unknown underlying influence, factor, process, variable... whatever).

Chapter 3. in "Interactive and Dynamic Graphics for Data Analyst with R and GGobi" by Di Cook and Deborah Swayne is great reference regarding this topic.

You'll see norm package in action in this chapter, but Hmisc package has data imputation routines. See also Amelia, cat (for categorical missings imputation), mi, mitools, VIM, vmv (for missing data visualisation).

Honestly, I still don't quite understand is your question about statistics, or about R missing data imputation capabilities? I reckon that I've provided good references on second one, and about the first one: you can replace your NA's either with central tendency (mean, median, or similar), hence reduce the variability, or with random constant "pulled out" of observed (recorded) cases, or you can apply regression analysis with variable that contains NA's as criteria, and other variables as predictors, then assign residuals to NA's... it's an elegant way to deal with NA's, but quite often it would not go easy on your CPU (I have Celeron on 1.1GHz, so I have to be gentle).

This is an optimization problem... there's no definite answer, you should decide what/why are you sticking with some method. But it's always good practice to look at the data! =) Be sure to check Cook & Swayne - it's an excellent, skilfully written guide. "Linear Models with R" by Faraway also contains a chapter about missing values.

So there.

Good luck! =)