Numbers
Values of the number type are, unsurprisingly, numeric values. In a JavaScript program, they are written as follows:
13
Use that in a program, and it will cause the bit pattern for the number 13 to come into existence inside the computer’s memory.
JavaScript uses a fixed number of bits, 64 of them, to store a single number value. There are only so many patterns you can make with 64 bits, which means that the number of different numbers that can be represented is limited. With N decimal digits, you can represent 10 N numbers. Similarly, given 64 binary digits, you can represent 2 64 different numbers, which is about 18 quintillion (an 18 with 18 zeros after it). That’s a lot.
Computer memory used to be much smaller, and people tended to use groups of 8 or 16 bits to represent their numbers. It was easy to accidentally overflow such small numbers—to end up with a number that did not fit into the given number of bits. Today, even computers that fit in your pocket have plenty of memory, so you are free to use 64-bit chunks, and you need to worry about overflow only when dealing with truly astronomical numbers.
Not all whole numbers less than 18 quintillion fit in a JavaScript number, though. Those bits also store negative numbers, so one bit indicates the sign of the number. A bigger issue is that nonwhole numbers must also be represented. To do this, some of the bits are used to store the position of the decimal point. The actual maximum whole number that can be stored is more in the range of 9 quadrillion (15 zeros)—which is still pleasantly huge.
Fractional numbers are written by using a dot.
9.81
For very big or very small numbers, you may also use scientific notation by adding an e (for exponent), followed by the exponent of the number.
2.998e8
That is 2.998 × 10 8 = 299,800,000.
Calculations with whole numbers (also called integers) smaller than the aforementioned 9 quadrillion are guaranteed to always be precise. Unfortunately, calculations with fractional numbers are generally not. Just as π (pi) cannot be precisely expressed by a finite number of decimal digits, many numbers lose some precision when only 64 bits are available to store them. This is a shame, but it causes practical problems only in specific situations. The important thing is to be aware of it and treat fractional digital numbers as approximations, not as precise values.
