## 3 Results and Discussion
### Effectiveness Analysis
#### 3.2.5 Discussion  
The VR and R settings primarily differed in one aspect only--interaction. We expected that users would perform better in the VR setting than in R when generating visualizations, filtering data, and merging data because IVEE does not require its users to have any prior experience with either or any other statistical software. We further predicted that users would prefer interacting with IVEE because it supports intuitive, natural interactions with the software. The results obtained from instant IVEE ratings and through direct comparison with R support our expectations for most participants. The results indicate that, on average, the participants took less time to complete 21 out of 26 assigned questions. There was a major difference in the time it took to complete 17 of these 21 questions--in favor of IVEE. [PERSON] et al. [34] emphasized the importance of natural, smooth interaction when handling data and making data analytics an enjoyable experience for users by simplifying the interface that stands between users and their data. This is achieved here by introducing an NUI that is supported by immersion--i.e., IVEE--where users primarily rely on their intuition for handling data, thus reducing the users' cognitive load and skipping the hurdle of having to learn a new language/software in order to perform analytics. Furthermore, this new VR interface allows for the generation of full 3D objects, offering a spatial value that is not provided by a computer interface. The results also show that the participants with expert R knowledge took more time to complete the tasks in the VR environment. Nevertheless, this time difference was marginal, especially when taking into consideration the fact that these experts managed to complete the tasks faster in R because they were already extremely familiar with the software, its interface, and its interaction methods. The marginal time difference between both conditions indicates that IVEE, although used for the first time, allowed experts to complete their tasks in almost the same amount of time as they took to do so in an environment with which they were very familiar. This outcome is favorable because it shows that first-time users benefit from the new VR interface in terms of enhancing their time performance. On the other hand, novice users took less time to complete the tasks in VR. In fact, novice users took approximately half the time to complete the tasks in the VR environment than in the R environment. It should also be noted that novice participants relied on using a cheat sheet, which contained code lines and instructions on how to use R, to complete the tasks in R. Without this cheat sheet, most of the novice participants would have quit, failing to finish the R portion of the experiment--and there would be no data to which we would be able to compare IVEE results. This is also promising because it shows how important advanced interaction is for enhancing the performance time of novice users. A closer examination of the distinct techniques that the participants used to interact with their data suggests that those interfaces that join users and machines greatly influence their performance time. The VR design survey results indicate that most participants found IVEE's interface to be easy to use, easy to remember, and intuitive when visualizing, subsetting, and merging data. Handling multidimensional data on traditional 2D interfaces necessitates a variety of summary representations that need to be combined to inform about the data. These methods have disadvantages--complicated relationships between data points are not easily distinguishable, and complex datasets are hard to analyze using