Outside the Fusion interfaces to R, a number of interfaces provide extremely useful functionality to a q instance from R and vice-versa.
Although it is not the recommended method, if R is running on Windows, the q ODBC3 driver can be used to connect to kdb+ from R.
The RODBC package should be installed in R. An example is given below.
# install RODBC
> install.packages("RODBC")
# load it
> library(RODBC)
# create a connection to a predefined DSN
> ch <- odbcConnect("localhost:5000") # run a query
# s.k should be installed on the q server to enable standard SQL
# However, all statements can be prefixed with q) to run standard q.
> res <- sqlQuery(ch, paste('q)select count i by date from trade'))R contains a maths library which can be compiled standalone. The functions can then be exposed to q by wrapping them in C code which handles the mapping between R datatypes and q datatypes (K objects). See rwinston/kdb-rmathlib for an example of integrating q with the R API (i.e. making use of some statistical functions from q).
q)\l rmath.q
q)x:rnorm 1000 / create 1000 normal variates
q)summary x / simple statistical summary of x
q)hist[x;10] / show histogram (bin count) with 10 bins
q)y:scale x / x = (x - mean(x))/sd(x)
q)quantile[x;.5] / calculate the 50% quantile
q)pnorm[0;1.5;1.5] / cdf value for 0 for a N(1.5,1.5) distribution
q)dnorm[0;1.5;1.5] / normal density at 0 for N(1.5;1.5) distribution👉 Andrey’s althenia.net/qml for an embedded math lib
Rserve allows applications to connect remotely to an R instance over TCP/IP. The methods are the same as those outlined above, the difference being that all data is passed over TCP/IP rather than existing in the same memory space.
Every connection to Rserve has a separate workspace and working directory, which means user-defined variables and functions with name clashes will not overwrite each other. This differs from the previous method where, if two users are using the same q process, they can overwrite each other’s variables in both the q and R workspaces.