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

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 25m | 224 MB

Data fills all available space, and now that storage is cheap, the amount of data has exploded. However, all that information is useless without analysis and context. The R programming language is designed to make it easier to analyze and visualize massive amounts of data. For example, R provides the ability to multiply one block of variables by another—an assumption that provides inherent advantages over other languages. This course shows why R is ideal for high volumes of data, introduces more efficient ways to use the language, and explains how to avoid the problems and capitalize on the opportunities of big data. Learn how to determine if you have enough memory and processing power, produce visualizations of big data, optimize your R code, and use advanced techniques such as parallel processing to speed up your computations. Plus, discover how to integrate R with big-data solutions such as SQL databases and Apache Spark.

Topics include:

- Accessing memory and processing power
- Visualizing high-volume data
- Profiling and optimizing R code
- Compiling R functions
- Parallel processing with R
- Using R with other big data solutions

**Table of Contents**

1 Wrangling high-volume data with R

2 Sample data set

3 Perspectives on high-volume data

4 Big data and available memory

5 Code Finding available memory

6 Big data and CPU cycles

7 Code How fast is your computer

8 High-volume data and visualizations

9 Code Graphs for high-volume data

10 Code rug() and jitter()

11 Code Applying statistics to plots

12 Code Subsampled graphs for high-volume data

13 Code Trellising data across multiple charts

14 R programming tools for high-volume data

15 Downsampling

16 Profile R code to find inefficiencies

17 Code Profile R code to find inefficiencies

18 Avoid the copy-on-modify problem with R

19 Code Avoid copy-on-modify with data.table

20 Optimization versus readability

21 Compile R functions

22 Parallel processing with R

23 Code Parallel R functions

24 bigmemory, LaF, and ff packages

25 Store high-volume data in a database

26 Code R with databases

27 Cloud computing with R

28 Sparklyr with R

29 Code R with Sparklyr

30 Summary of high-volume data with R

Resolve the captcha to access the links!