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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?

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