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Nanosecond Time Resolution for R


R has excellent tools for dates and times. The Date and POSIXct classes (as well as the ‘wide’ representation in POSIXlt) are versatile, and a lot of useful tooling has been built around them.

However, POSIXct is implemented as a double with fractional seconds since the epoch. Given the 53 bits accuracy, it leaves just a bit less than microsecond resolution. Which is good enough for most things.

But more and more performance measurements, latency statistics, … are now measured more finely, and we need nanosecond resolution. For which commonly an integer64 is used to represent nanoseconds since the epoch.

And while R does not have a native type for this, the bit64 package by Jens Oehlschlägel offers a performant one implemented as a lightweight S3 class. So this package uses this integer64 class, along with two helper functions for parsing and formatting, respectively, at nano-second resolution from the RcppCCTZ package which wraps the CCTZ library from Google. CCTZ is a modern C++11 library extending the (C++11-native) chrono type.


See the included demo script nanosecondDelayExample.R for a (completely simulated and hence made-up) study of network latency measured in nanoseconds resulting in the figure below


Simple Parsing and Arithmetic

R> x <- nanotime("1970-01-01T00:00:00.000000001+00:00")
R> print(x)
[1] 1
R> format(x)
[1] "1970-01-01T00:00:00.000000001+00:00"
R> x <- x + 1
R> print(x)
[1] 2
R> format(x)
[1] "1970-01-01T00:00:00.000000002+00:00"


R> options("width"=60)
R> v <- nanotime(Sys.time()) + 1:5
R> v
[1] 1481505724483583001 1481505724483583002
[3] 1481505724483583003 1481505724483583004
[5] 1481505724483583005
R> format(v)
[1] "2016-12-12T01:22:04.483583001+00:00"
[2] "2016-12-12T01:22:04.483583002+00:00"
[3] "2016-12-12T01:22:04.483583003+00:00"
[4] "2016-12-12T01:22:04.483583004+00:00"
[5] "2016-12-12T01:22:04.483583005+00:00"

Use with zoo

R> z <- zoo(cbind(A=1:5, B=5:1), v)
R> options("nanotimeFormat"="%H:%M:%E*S")  ## override default
R> z
                          A B
01:47:55.812513001 1 5
01:47:55.812513002 2 4
01:47:55.812513003 3 3
01:47:55.812513004 4 2
01:47:55.812513005 5 1

Use with data.table

R> library(data.table)
R> library(bit64)   # register some print methods for integer64
R> dt <- data.table(cbind(A=1:5, B=5:1), v)
R> fwrite(dt, file="datatableTest.csv")  # write out
R> dtcheck <- fread("datatableTest.csv") # read back, need to re-set class
R> dtcheck[, v:=nanotime(v)]             # need to re-class as nanotime

Use with data.frame

This requires version 0.0.2 or later.

R> df <- data.frame(cbind(A=1:5, B=5:1), v=v)

Technical Details

The bit64 package (by Jens Oehlschlägel) supplies the integer64 type used to store the nanosecond resolution time as (positive or negative) offsets to the epoch of January 1, 1970. The RcppCCTZ package supplies the formatting and parsing routines based on the (modern C++) library CCTZ from Google.


The package is in the very early stages. Expect changes, maybe even breaking ones. But the package has some tests, and code coverage.

See the issue tickets for an up to date list of potentially desirable, possibly planned, or at least discussed items.


The package is on CRAN and can be installed via a standard


whereas in order to install development versions a

remotes::install_github("eddelbuettel/nanotime")  # dev version

should suffice.


Dirk Eddelbuettel and Leonardo Silvestri


GPL (>= 2)