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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978-3-319-45598-3Springer International Publishing Switzerland 2016

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Maryam Chinipardaz,Mehdi DehghanIn this chapter you will learn the basics of working with numbers in R. This includes understanding how to manage the numeric type (integer vs. double), the different ways of generating non-random and random numbers, how to set seed values for reproducible random number generation, and the different ways to compare and round numeric values.

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辯論的終結(jié)

Dealing with NumbersIn this chapter you will learn the basics of working with numbers in R. This includes understanding how to manage the numeric type (integer vs. double), the different ways of generating non-random and random numbers, how to set seed values for reproducible random number generation, and the different ways to compare and round numeric values.

辯論的終結(jié)

Data Structure BasicsPrior to jumping into the data structures, it’s beneficial to understand two components of data structures - the structure and attributes.

碎片

Dealing with Missing ValuesA common task in data analysis is dealing with missing values. In R, missing values are often represented by . or some other value that represents missing values (i.e. .). We can easily work with missing values and in this chapter I illustrate how to test for, recode, and exclude missing values in your data.

虛度

https://doi.org/10.1007/978-3-319-59767-6 In spite of advances in technologies for working with data, analysts still spend an inordinate amount of time obtaining data, diagnosing data quality issues and pre-processing data into a usable form. Research has illustrated that this portion of the data analysis process is the most tedious and ti

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