Titlebook: Beginning Data Science in R; Data Analysis, Visua Thomas Mailund Book 20171st edition Thomas Mailund 2017 R.programming.statistics.data sci

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Reproducible Analysis,ses, written in various scripts, perhaps saving some intermediate results along the way or maybe always working on the raw data. You create some plots or tables of relevant summaries of the data, and then you go and write a report about the results in a text editor or word processor. It is the typic

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Data Manipulation,e statistical models or machine learning algorithms we want to analyze them with. The first stages of data analysis are almost always figuring out how to load the data into R and then figuring out how to transform it into a shape you can readily analyze. The code in this chapter, and all the followi

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Unsupervised Learning,king prediction models. Sometimes we are just trying to find out what structure is actually in the data we analyze. There can be several reasons for this. Sometimes unknown structures can tell us more about the data. Sometimes we want to explicitly avoid an unknown structure (if we have datasets tha

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Advanced R Programming,p of the quick introduction you got in the last chapter. Except, perhaps, for the functional programming toward the end, 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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Testing and Package Checking,th a couple of chosen parameters, but to build robust software you need to approach testing more rigorously. And to prevent bugs from creeping into your code over time, you should test often. Ideally, you should check all your code anytime you make any changes to it.

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Book 20171st editionrn.Perform data science and analytics using statistics and the R programming language.Visualize and explore data, including working with large data sets found in big data.Build an R package.Test and check your code.Practice version control.Profile and optimize your code.Who This Book Is For.Those wi