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230404 ||| eng |
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|a 1803237651
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| 050 |
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4 |
|a QA76.9.D343
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| 100 |
1 |
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|a Santos, Gustavo R.
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| 245 |
0 |
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|a Data Wrangling with R
|h [electronic resource]
|b Load, Explore, Transform and Visualize Data for Modeling with Tidyverse Libraries
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| 260 |
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|a Birmingham
|b Packt Publishing, Limited
|c 2023
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| 300 |
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|a 385 p.
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| 505 |
0 |
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|a Managing lengths -- Mutating strings -- Joining and splitting -- Ordering strings -- Working with regular expressions -- Learning the basics -- Creating frequency data summaries in R -- Regexps in practice -- Creating a contingency table using gmodels -- Text mining -- Tokenization -- Stemming and lemmatization -- TF-IDF -- N-grams -- Factors -- Summary -- Exercises -- Further reading -- Chapter 5: Working with Numbers -- Technical requirements -- Numbers in vectors, matrices, and data frames -- Vectors -- Matrices -- Data frames -- Math operations with variables -- apply functions
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| 505 |
0 |
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|a Descriptive statistics -- Correlation -- Summary -- Exercises -- Further reading -- Chapter 6: Working with Date and Time Objects -- Technical requirements -- Introduction to date and time -- Date and time with lubridate -- Arithmetic operations with datetime -- Time zones -- Date and time using regular expressions (regexps) -- Practicing -- Summary -- Exercises -- Further reading -- Chapter 7: Transformations with Base R -- Technical requirements -- The dataset -- Slicing and filtering -- Slicing -- Filtering -- Grouping and summarizing -- Replacing and filling -- Arranging
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| 505 |
0 |
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|a Data distributions -- Visualizations -- Basic Web Scraping -- Getting data from an API -- Summary -- Exercises -- Further reading -- Chapter 3: Basic Data Visualization -- Technical requirements -- Data visualization -- Creating single-variable plots -- Dataset -- Boxplots -- Density plot -- Creating two-variable plots -- Scatterplot -- Bar plot -- Line plot -- Working with multiple variables -- Plots side by side -- Summary -- Exercises -- Further reading -- Part 2: Data Wrangling -- Chapter 4: Working with Strings -- Introduction to stringr -- Detecting patterns -- Subset strings
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| 505 |
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|a Creating new variables -- Binding -- Using data.table -- Summary -- Exercises -- Further reading -- Chapter 8: Transformations with Tidyverse Libraries -- Technical requirements -- What is tidy data -- The pipe operator -- Slicing and filtering -- Slicing -- Filtering -- Grouping and summarizing data -- Replacing and filling data -- Arranging data -- Creating new variables -- The mutate function -- Joining datasets -- Left Join -- Right join -- Inner join -- Full join -- Anti-join -- Reshaping a table -- Do more with tidyverse -- Summary -- Exercises -- Further reading -- Chapter 9: Exploratory Data Analysis
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| 505 |
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|a Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Part 1: Load and Explore Data -- Chapter 1: Fundamentals of Data Wrangling -- What is data wrangling? -- Why data wrangling? -- Benefits -- The key steps of data wrangling -- Frameworks in Data Science -- Summary -- Exercises -- Further reading -- Chapter 2: Loading and Exploring Datasets -- Technical requirements -- How to load files to RStudio -- Loading a CSV file to R -- Tibbles versus Data Frames -- Saving files -- A workflow for data exploration -- Loading and viewing -- Descriptive statistics -- Missing values
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| 653 |
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|a Data mining / fast
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| 653 |
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|a R (Langage de programmation)
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| 653 |
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|a R (Computer program language) / fast
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| 653 |
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|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
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| 653 |
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|a R (Computer program language) / http://id.loc.gov/authorities/subjects/sh2002004407
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| 653 |
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|a Exploration de données (Informatique)
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Description based upon print version of record
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| 776 |
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|z 9781803237657
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781803235400/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3/12
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| 520 |
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|a Take your data wrangling skills to the next level by gaining a deep understanding of tidyverse libraries and effectively prepare your data for impressive analysis Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore state-of-the-art libraries for data wrangling in R and learn to prepare your data for analysis Find out how to work with different data types such as strings, numbers, date, and time Build your first model and visualize data with ease through advanced plot types and with ggplot2 Book Description In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you'll need plenty of tools that enable you to extract the most useful knowledge from data.
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| 520 |
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|a What you will learn Discover how to load datasets and explore data in R Work with different types of variables in datasets Create basic and advanced visualizations Find out how to build your first data model Create graphics using ggplot2 in a step-by-step way in Microsoft Power BI Get familiarized with building an application in R with Shiny Who this book is for If you are a professional data analyst, data scientist, or beginner who wants to learn more about data wrangling, this book is for you. Familiarity with the basic concepts of R programming or any other object-oriented programming language will help you to grasp the concepts taught in this book. Data analysts looking to improve their data manipulation and visualization skills will also benefit immensely from this book
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| 520 |
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|a Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization. The book begins by teaching you how to load and explore datasets. Then, you'll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you'll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards. By the end of this book, you'll have learned how to create your first data model and build an application with Shiny in R.
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