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Here are some useful techniques and syntax which is available in C3 Expressions, but which may not be easy to find in current documentation

# Supported Syntax

**Comparisons**- Expressions like a > b, c >= d which return boolean values

**Ternary Operators**which return a value based on the result of a boolean expression- <conditional_expression> ? <value_if_true> : <value_if_false>

**Java basic math operators**- Addition ( + )
- Subtraction ( - )
- Multiplication ( * )
- Division ( / )
- Modulus ( % )
- etc...

**Java Standard Libraries and Functions**- Ex: Math - Math.abs, Math.cos, etc...
- Type casting - ex. dateTime('2020-01-01')

**ExpressionEngineFunctions (C3 defined functions)**- rolling(aggFunc, input_series, ...) - Computes a rolling aggregation of the input timeseries
- identity(value) - Produces a new timeseries consisting of repeating entries of 'value'.
- eval(aggFunc, interval, input_series, ...) - Forces evaluation of the input timeseries at the set interval, and aggregation with aggFunc.
- Most ExpressionEngineFunctions are designed to work with timeseries data

# Special keywords

`this`

Sometimes, it may be useful for a timeseries to refer to itself, or to get a reference to the 'current' timeseries. This is done with the this name. For example, we can specify the `transform`

field of a `TsDecl`

metric to do some transformation of the data before it heads to normalization. We can use the `fillMissing`

function to fill gaps in the data with a specified value. We'd specify this with `fillMissing(this, <value_to_fill>)`

.

# Esspecially Useful Expressions

## Ternary Operators

Ternary Operators are widely useful throughout the C3 AI Suite. They allow small conditional expressions which can affect the return value of your expression.

## Timeseries expressions

- rolling
- computes a rolling aggregation over a timeseries. It takes an aggregation function, a timeseries (which will be aggregated), and possibly another timeseries to signal when to restart the aggregation. rolling is like an expanding window function which may be dynamically reset.

- identity
- takes a value, and simply repeats this value when building the timeseries. it's useful occasionally if you need a timeseries consisting entirely of one value.

- eval
- generates a timeseries using a specific start/end date. This is useful for generating timeseries that rely on window functions which may rely on values before the start date of a requested timeseries. Essentially, eval builds an entire timeseries which is passed to the next function or on to the next step in the normalization process.

- rollingdiff
- returns a time series in which every value is computed by taking the difference between current and previous point.

- fillMissing
- Will impute missing values with some default value you specify. This is useful if you want to indicate missing values in some special way.

# Official Documentation

- General topic page: https://developer.c3.ai/docs/7.19.0/topic/metrics-expression-engine-functions-home
- A list of ExpressionEngineFunctions is available
- Online: https://developer.c3.ai/docs/7.19.0/type/ExpressionEngineFunction
- Through the Static Console: c3ShowType(ExpressionEngineFunction)