**R Programming in Data Science: High Velocity Data**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 21m | 218 MB

High-velocity data—such as the information that springs from Twitter and IoT devices—comes barreling in at a speed beyond normal comprehension, demanding high-performance from both hardware and software. While it might not initially appear up to the challenge, the R programming language can be revved up to operate with high-velocity data. Written close to the metal by sitting directly on top of the C programming language, R provides a rich set of data structures and concepts. This course drills down into efficient R programming, providing practical strategies that can help you work your mojo on high-velocity data.

Instructor Mark Niemann-Ross begins by sharing a framework for understanding the different types of high-velocity data. He then covers how to use R to acquire high-velocity data, as well as how to leverage profiling tools and optimize R code for use with high-velocity data. He wraps up by exploring how to use R to present data, including how to use Shiny—an R package that allows you to build web apps straight from R—for interactive dashboards.

Topics include:

- Problems and opportunities with high-velocity data
- Characteristics of high-velocity data
- Real-time processing of high-velocity data with R
- Using R to acquire high-velocity data
- Polling for data with an R program
- Using Profvis, Rprof, and microbenchmark
- Optimizing R code for use with high-velocity data
- Using R to present high-velocity data
- Using R Markdown for static dashboards

**Introduction**

1 How can you use R with high-velocity data

**Problems and Opportunities with High-Velocity Data**

2 Perspectives on high-velocity data

3 Simulating high-velocity data

4 Concepts of batch data

5 Handling batch data with R

6 Working with near real-time data

7 Handling near real-time data with R

8 Concepts of real-time data

9 Handling real-time data with R

10 Setting a default CRAN mirror

**Using R to Acquire High-Velocity Data**

11 Polling for data in R

12 Interrupt-driven data acquisition with R

**Profiling Tools for R**

13 Tools

14 Profvis

15 Rprof

16 microbenchmark

**Optimizing R to Process High-Velocity Data**

17 Improving the speed of loops

18 Optimizing if… then… else with ifelse

19 Avoid copying data

20 Combining optimizations

21 Use RCPP to speed up functions

22 Using microbenchmark to check results

**Using R to Present High-Velocity Data**

23 Static and dynamic reports

24 Use R Markdown for static dashboards

25 Flexdashboard and other enhancements for static reports

26 Use Shiny for interactive dashboards

27 Use plumber to create APIs

28 Cran task view for WebTechnologies

**Conclusion**

29 Summary