Current generation of Reconfigurable Match-Action Tables switches are highly programmable, able to support stateful operations and pipeline specifications using languages like P4. Nevertheless, these switches do not offer primitives to support real-valued operations on the data plane, thus requiring support from external servers or middle boxes to perform advanced operations. We introduce InREC, a system that extends the capabilities of programmable switches to support in-network real-valued operations using the IEEE half-precision floating point representation. InREC relies on decomposing real-valued functions into lookup tables taking into account the RMT model constraints to reach the right trade-off between accuracy and resource usage. It also supports state management for the computation of recursive function over time series. InREC prototype on Barefoot Tofino switches demonstrates the efficiency of InREC for in-network computation of different types of operations and its application for in-network logistic regression models used for classification problems. We also demonstrate the use of InREC to implement an ARIMA model on a Tofino switch for DDoS detection. Our evaluation of InREC shows that it is possible to implement complex in-network applications with high accuracy and low latency.