Titlebook: Data Wrangling with R; Bradley C. Boehmke, Ph.D. Book 2016 Springer International Publishing Switzerland 2016 R.data wrangling.data struct

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Dealing with Factorsthe most important uses of factors is in statistical modeling; since categorical variables enter into statistical models such as . and . differently than continuous variables, storing data as factors insures that the modeling functions will treat such data correctly.

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Dealing with Datesy in formats and accounting for time-zone differences and leap years. R has a range of functions that allow you to work with dates and times. Furthermore, packages such as . make it easier to work with dates and times.

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Managing Vectorsles illustrated in the previous section are considered vectors. In this chapter I will illustrate how to create vectors, add additional elements to pre-existing vectors, add attributes to vectors, and subset vectors.

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Managing Matricesistent mode (i.e. all elements must be numeric, or character, etc.). Therefore, a matrix can be thought of as an atomic vector with a dimension attribute. Furthermore, all rows of a matrix must be of same length. In this chapter I will illustrate how to create matrices, add additional elements to pr

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Importing Data This chapter covers how to import data into R by reading data from common text files and Excel spreadsheets. In addition, I cover how to load data from saved R object files for holding or transferring data that has been processed in R. In addition to the commonly used base R functions to perform da

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Managing Data Framesst way to think of a data frame is as an Excel worksheet that contains columns of different types of data but are all of equal length rows. In this chapter I will illustrate how to create data frames, add additional elements to pre-existing data frames, add attributes to data frames, and subset data frames.

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2197-5736 analyzing, and presenting data via a step-by-step tutorial .This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of inform

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Xu Huang,Muhammad Ahmed,Dharmendra Sharmano better way to learn than to immerse yourself in the environment! Although it’ll be painful early on and your nose will surely bleed, eventually you’ll learn the dialect and the quirks that come along with it.