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 when set to *TRUE*, remove the NAs:

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

```

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?