Below are useful and advanced functionalities of the dplyr package in comparison with basic R-base commands to group, filter and efficiently slice data.

To model complex patterns better than nearly any other algorithm, Artificial neural networks are beginning to gain attention at the era of big data. Applied to various fields from image recognition to industry, their utility can help solve classification and numeric prediction problems as well. In this paper i will show an example where it is able to measure the performance of a building component in the domain of engeneering and provide accurate estimates of solidity on unseen data, without making assumptions on the relationships between other variables. This steps from data exploration to model improvement, trying also to tweek the model using more complex multi-layer networks. This case study is from Brett Lantz's "Machine Learning with R". it can be refered to in order to have a more complete insight.