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28 Ways to Be Missing in SAS	A SAS numeric missing is not a lone wolf - there are 28 of them, and Data Controller has handled all of them since v4. How they work, and what the validation rules do with them.	2026-09-22 09:00:00	Allan Bowe	https://www.linkedin.com/in/allanbowe/	Special Missings Data Quality	./cover.jpeg

28 Ways to Be Missing in SAS

A numeric missing value in SAS is not a lone wolf. The ordinary missing (.) is one of 28 distinct missing values available for a numeric variable - the other 27 are written with a single character, the letters A to Z or an underscore (._).

They exist because "missing" is usually not the whole story. A survey question that was never reached, a reading that was illegible, a value the respondent refused to give - in a well run process those are different facts, and a lone . throws the difference away. Special missings record why the value is missing.

They are numbers, not text

• Arithmetic on them yields missing - .A + 1 is .
• proc means, proc summary and friends exclude them, exactly as they exclude .
• They sort below every non-missing number, in the order ._, then ., then .A to .Z
• The NMISS() and CMISS() functions count them as missing
• Converting one to text drops the period - cats(.A) is the string A

A display gotcha worth knowing: a regular missing prints as ., unless options MISSING= changes that character - set it to blank and a regular missing renders as an empty cell. The option affects only the regular missing; ._ and .A-.Z always print as their own letter. So a blank cell in a SAS listing is still unambiguously a regular missing, and a lone letter is still a special one. Data Controller itself is unaffected either way - it sends a regular missing to the browser as null and a special missing as its letter.

In a SAS dataset they are written with a leading period (.A, .B ... ._). In Data Controller you type just the letter or the underscore - no period - and the letter is not case sensitive. Two letters, or a letter mixed with a number, are refused rather than guessed.

Carrying them between the browser and SAS

The Data Controller frontend and the SAS backend exchange data as JSON, and JSON has no way to express a letter as a numeric value - A is a string. The conversion is handled in the open source SASjs Adapter:

• The adapter infers each column's SAS type from the values it is given. All numeric values means numeric; all strings means character; and a column holding a single character (a-z, _ or .) alongside numeric values is numeric, with the lone characters written to SAS as special missings.
• null becomes . or an empty string, according to the type derived for that column.
• Two cases cannot be inferred from the values alone: a numeric column containing only special missings looks like a single character column, and a character column containing only nulls looks numeric. For those, the adapter accepts an explicit format for the column - and Data Controller sends one automatically, because it already knows each column's SAS format from the metadata returned by the backend.
• A value that is neither a number nor a single valid character is refused rather than guessed (for example aaaa, or ! in a numeric column), and a literal . is refused in favour of null for a regular missing.

There is nothing to configure. Special missings are available by default, for numeric cells.

Once in SAS they are ordinary values, so they are what the approval DIFF screen compares, and the DIFF's formatted / unformatted switch shows either the formatted representation or the raw value - useful for confirming exactly which missing was set.

What the Data Controller validation rules do with them

These are Data Controller's own rules, configured per column in the MPE_VALIDATIONS table and applied in the browser as you edit and submit.

• NOTNULL - fails. A special missing is NULL as far as a NOT NULL (or primary key) constraint is concerned, so the rule rejects it - and so does a physical SAS NOT NULL constraint on the target table
• HARDREGEX - checked against the pattern like any other value; unlike blanks and the plain ., special missings are not exempt, so a numeric column that carries them needs a pattern which allows for a single letter
• SOFTREGEX - the same check, but a failure is only a warning rather than a block, and it is ignored entirely if the column also has a HARDREGEX rule
• SOFTSELECT - never blocks, so a special missing passes; HARDSELECT is a strict membership test and rejects one
• ROUND - harmless. It only rounds values that are numbers, so a special missing is left exactly as it was typed

Special missings are not supported in a column that carries a range rule or a formula:

• MINVAL / MAXVAL - a special missing sorts below every number, so MINVAL rejects it and MAXVAL accepts it while rejecting the column's real numbers. The rule's own value has to be a number too: put a special missing in MINVAL and every cell in the column fails, put one in MAXVAL and every real number fails
• HARDFORMULA / SOFTFORMULA - a formula that reads a special-missing cell does not compute; the grid's spreadsheet engine returns #VALUE! for that row, where the plain . contributes 0

CASE (UPCASE / LOWCASE) is a character rule and does not come into it: SAS hands the browser a special missing as an uppercase letter, so there is no case to enforce, and a CASE rule on a numeric column would reject the column's numbers rather than the missing.

One thing worth knowing about the regex rules: although the pattern is written in SAS PRX syntax, the check itself runs in the browser - SAS only parses the pattern (PRXPARSE) when the rule is saved - so the two engines can disagree on exotic patterns, and SAS pads a numeric-to-character conversion, so a pattern re-used in SAS needs strip() for an anchored match.

In short, a special missing counts as a value for the pattern and dropdown rules, and as a missing value for everything that asks for a number.

video: [Managing Special Missing Values with Data Controller for SAS](https://www.youtube-nocookie.com/embed/ggrcNr23Jzw)

The recording shows the full cycle: typing special missings into numeric cells, the rejections, submitting the changes, approving them, and reviewing the DIFF - including a change from one special missing to another, and the formatted / unformatted switch on date and datetime columns.