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Titlebook: R Data Science Quick Reference; A Pocket Guide to AP Thomas Mailund Book 20191st edition Thomas Mailund 2019 R.data science.tidyr.analytics

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發(fā)表于 2025-3-21 19:04:32 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)R Data Science Quick Reference
副標(biāo)題A Pocket Guide to AP
編輯Thomas Mailund
視頻videohttp://file.papertrans.cn/821/820004/820004.mp4
概述The first quick reference of its kind dealing with data science using R.Covers the specific APIs and packages that let you build R-based data science applications.Also covers how to use these packages
圖書(shū)封面Titlebook: R Data Science Quick Reference; A Pocket Guide to AP Thomas Mailund Book 20191st edition Thomas Mailund 2019 R.data science.tidyr.analytics
描述In this handy, practical book you will cover each concept concisely, with many illustrative examples. You‘ll be introduced to several R data science packages, with examples of how to use each of them.?.In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more..After using this handy quick reference guide, you‘ll have the code, APIs, and insights to write data science-based applications in the R programming language.? You‘ll also be able to carry out data analysis.??.What You Will Learn.Import data with readr.Work with categories using forcats, time and dates with lubridate, and strings with stringr.Format data using tidyr and then transform that data using magrittr and dplyr.Write functions with R for data science, data mining, and analytics-based applications.Visualize data with ggplot2 and fit data to models using modelr.Who This Book Is For.Programmers new to R‘s data science, data mining, and analytics packages.? Some prior coding experience with R in general is recommended.??.
出版日期Book 20191st edition
關(guān)鍵詞R; data science; tidyr; analytics; purrr; ggplot; modelr; broom; markdown; knitr; shiny; tidyr; stingr; forcats; l
版次1
doihttps://doi.org/10.1007/978-1-4842-4894-2
isbn_ebook978-1-4842-4894-2
copyrightThomas Mailund 2019
The information of publication is updating

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Pipelines: ,l construction. In practical terms, this means that your R code will consist of a series of function calls where the output of one is the input of the next. The pattern is typical, but a straightforward implementation of it has several drawbacks. The Tidyverse provides a “pipe operator” to alleviate this.
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Working with Models: , of R packages that support this is well beyond the scope of this book. The main concern when choosing and fitting models is not the syntax, and this book is, after all, a syntax reference. We will look at two packages that aim at making a tidy interface to models.
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Introduction,R is a functional programming language with a focus on statistical analysis. It has built-in support for model specifications that can be manipulated as first-class objects, and an extensive collection of functions for dealing with probability distributions and model fitting, both built-in and through extension packages.
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Representing Tables: ,The data that the . package returns are represented as . objects. These are tabular data representations similar to the base R data frames but are a more modern version.
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