Data Wrangling with R Load, Explore, Transform and Visualize Data for Modeling with Tidyverse Libraries.

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 an...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Santos, Gustavo R.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited, 2023.
Temas:
Data mining.
R (Computer program language)
Exploration de données (Informatique)
R (Langage de programmation)
Data mining
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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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