Example
Numeric represents integers and doubles and is the default mode assigned to vectors of numbers. The function is.numeric() will evaluate whether a vector is numeric. It is important to note that although integers and doubles will pass is.numeric(), the function as.numeric() will always attempt to convert to type double.
x <- 12.3
y <- 12L
#confirm types
typeof(x)
[1] "double"
typeof(y)
[1] "integer"
# confirm both numeric
is.numeric(x)
[1] TRUE
is.numeric(y)
[1] TRUE
# logical to numeric
as.numeric(TRUE)
[1] 1
# While TRUE == 1, it is a double and not an integer
is.integer(as.numeric(TRUE))
[1] FALSE
Doubles are R's default numeric value. They are double precision vectors, meaning that they take up 8 bytes of memory for each value in the vector. R has no single precision data type and so all real numbers are stored in the double precision format.
is.double(1)
TRUE
is.double(1.0)
TRUE
is.double(1L)
FALSE
Integers are whole numbers that can be written without a fractional component. Integers are represented by a number with an L after it. Any number without an L after it will be considered a double.
typeof(1)
[1] "double"
class(1)
[1] "numeric"
typeof(1L)
[1] "integer"
class(1L)
[1] "integer"
Though in most cases using an integer or double will not matter, sometimes replacing doubles with integers will consume less memory and operational time. A double vector uses 8 bytes per element while an integer vector uses only 4 bytes per element. As the size of vectors increases, using proper types can dramatically speed up processes.
# test speed on lots of arithmetic
microbenchmark(
for( i in 1:100000){
2L * i
10L + i
},
for( i in 1:100000){
2.0 * i
10.0 + i
}
)
Unit: milliseconds
expr min lq mean median uq max neval
for (i in 1:1e+05) { 2L * i 10L + i } 40.74775 42.34747 50.70543 42.99120 65.46864 94.11804 100
for (i in 1:1e+05) { 2 * i 10 + i } 41.07807 42.38358 53.52588 44.26364 65.84971 83.00456 100
