Assignment1 - About R and Me :)

Mercan Karacabey

About myself

I’m Mercan Karacabey, I am a computer engineer. I work as a software engineer in the software development department in Borusan Danismanlik. i have just graduated this year and I did my graduation projects using data analytics, machine learning, natural language processing, deep learning. My only work on the data at work is on relational database. But I will soon take on the role of data scientist at the work. Therefore, what I learned in master’s degree is very important to me. This subject will be both my area of interest and my future.

About the video I’ve watched

I watched this video Subject of this video is: What’s in a name? 20 years of R release management. Content of this video:

• History of R release
• The role of the R Team
• Release Management Issues
• War Stories

They called yourself this: R consortium

Generally in this video, Phased of R release procedure -> alpha , beta, RC phases. When first release happens : 1997 How they find the name of R? At the last time, was there other names in their mind?

About 3 R Posts

1. Analytics Administration for R

Analytic administrator is a role that data scientists assume when they onboard new tools, deploy solutions, support existing standards, or train other data scientists. Analytic admins are important for any organization that wants to:

• Modernize their analytic tools
• Take advantage of all their data
• Build analytic products and applications
• Develop a best-in-class data science team

This post is significant because data is everywhere. And we should analyze it for mining useful information.

2. REST APIs and Plumber

Plumber is an R package that allows existing R code to be exposed as a web service through special decorator comments. With minimal overhead, R programmers and analysts can use plumber to create REST APIs that expose their work to any number of internal and external systems. This solution provides real-time access to processes and services created entirely in R, and can effectively eliminate the need to perform batch operations or technical hand-offs in order to move R code into production.

Nowadays, service architecture is widely used. Therefore, it is important that R supports this.Therefore,this post is significant post.

3. Cost Effective Big Query with R The more cost-effective solution described on StackOverflow is to ARRAY_AGG the entire table into one record for each day. This requires one query over the table’s data to ARRAY_AGG each day you are interested in, and then multiple UNNEST queries using a single query on a single column.

This solution queries the full table’s worth of data twice, instead of the number of days. That’s a cost of $100, saving $54,650.

Cost is always important :) So we need to pay attention to this post