Titlebook: Beginning Data Science in R 4; Data Analysis, Visua Thomas Mailund Book 2022Latest edition Thomas Mailund 2022 R.programming.statistics.dat

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Visualizing Data,Nothing tells a story about your data as powerfully as good plots. Graphics captures your data much better than summary statistics and often shows you features that you would not be able to glean from summaries alone.

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Working with Large Data Sets,The concept of Big Data refers to enormous data sets, sets of sizes where you need data warehouses to store it, where you typically need sophisticated algorithms to handle the data and distributed computations to get anywhere with it. At the very least, we talk many gigabytes of data but also often terabytes or exabytes.

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Project 1: Hitting the Bottle,To see a data analysis in action, I will use an analysis that my student, Dan S?ndergaard, did the first year I held my data science class. I liked his analysis so much that I wanted to include it in the book. I am redoing his analysis in the following with his permission.

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Working with Vectors and Lists,In this chapter, we explore working with vectors and lists a little further. We will not cover anything that is conceptually more complex that we did in the previous chapter. It is just a few more technical details we will dig into.

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Object-Oriented Programming,In this chapter, we look at R’s flavor of object-oriented programming. Actually, R has three different systems for object-oriented programming: S3, S4, and RC. We will only look at S3, which is the simplest and (is my impression) the most widely used.

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Building an R Package,Now we know how to write functions and create classes in R, but neither functions nor classes are the unit we use for collecting and distributing R code. That unit is the package. It is packages you load and import into your namespace when you write..