Parallel Computing in R

In my work, I usually deal with dataset of products from different customers across different market places. Basically, each product has its own time series dataset. The size of each dataset is not big but we have millions of them. Before finding any convincing reasons to combine dataset from different products, now we just treat them all as independent dataset. And, since they are all independent, it is definitely a good idea to use parallel computing to push the limit of our machine and to make code executed efficiently.

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Connect SQL Server Database to R

When I am working on my personal projects, all the data I have can be easily saved as .csv or .txt files. Things are not complicated. However, when things are in scale, it is hard to not to talk about database. In my company, all the data are stored in SQL Server database. So, it is important for me to know how to connect R with SQL Server. And, also good to know how to interact with database in R by passing variables in R environment to database and then return the desired dataset.

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ioslides vs. Slidify in R Markdown Presentation

The Github repository for this website : choux130/slide_thesis_ioslides. The link to my slides : https://choux130.github.io/slide_thesis_ioslides/#1 Having an opportunity to give a presentation for my master thesis, I decided to give it a try on R Markdown Presentation with interactive graphs and planned to publish it online after the presentation. There are three main choices in R Studio for the R Markdown Presentation: ioslides, Slidy, and Beamer. Beamer is for .

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