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R for data science cookbook : over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
[2016]
|
| Colección: | Quick answers to common problems.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Table of Contents
- Preface
- Chapter 1: Functions in R
- Introduction
- Creating R functions
- Matching arguments
- Understanding environments
- Working with lexical scoping
- Understanding closure
- Performing lazy evaluation
- Creating infix operators
- Using the replacement function
- Handling errors in a function
- The debugging function
- Chapter 2: Data Extracting, Transforming, and Loading
- Introduction
- Downloading open data
- Reading and writing CSV files
- Scanning text files
- Working with Excel files
- Reading data from databases
- Scraping web data
- Accessing Facebook data
- Working with twitteR
- Chapter 3: Data Preprocessing and Preparation
- Introduction
- Renaming the data variable
- Converting data types
- Working with the date format
- Adding new records
- Filtering data
- Dropping data
- Merging data
- Sorting data
- Reshaping data
- Detecting missing data
- Imputing missing data
- Chapter 4: Data Manipulation
- Introduction
- Enhancing a data.frame with a data.table
- Managing data with a data.table
- Performing fast aggregation with a data.table
- Merging large datasets with a data.table
- Subsetting and slicing data with dplyr
- Sampling data with dplyr
- Selecting columns with dplyr
- Chaining operations in dplyr
- Arranging rows with dplyr
- Eliminating duplicated rows with dplyr
- Adding new columns with dplyr
- Summarizing data with dplyr
- Merging data with dplyr
- Chapter 5: Visualizing Data with ggplot2
- Introduction
- Creating basic plots with ggplot2
- Changing aesthetics mapping
- Introducing geometric objects
- Performing transformations
- Adjusting scales
- Faceting
- Adjusting themes
- Combining plots
- Creating maps.
- Chapter 6: Making Interactive Reports
- Introduction
- Creating R Markdown reports
- Learning the markdown syntax
- Embedding R code chunks
- Creating interactive graphics with ggvis
- Understanding basic syntax and grammar
- Controlling axes and legends
- Using scales
- Adding interactivity to a ggvis plot
- Creating an R Shiny document
- Publishing an R Shiny report
- Chapter 7: Simulation from Probability Distributions
- Introduction
- Generating random samples
- Understanding uniform distributions
- Generating binomial random variates
- Generating Poisson random variates
- Sampling from a normal distribution
- Sampling from a chi-squared distribution
- Understanding Student's t-distribution
- Sampling from a dataset
- Simulating the stochastic process
- Chapter 8: Statistical Inference in R
- Introduction
- Getting confidence intervals
- Performing Z-tests
- Performing student's T-tests
- Conducting exact binomial tests
- Performing Kolmogorov-Smirnov tests
- Working with the Pearson's Chi-squared tests
- Understanding the Wilcoxon Rank Sum and Signed Rank tests
- Conducting one-way ANOVA
- Performing two-way ANOVA
- Chapter 9: Rule and Pattern Mining with R
- Introduction
- Transforming data into transactions
- Displaying transactions and associations
- Mining associations with the Apriori rule
- Pruning redundant rules
- Visualizing association rules
- Mining frequent itemsets with Eclat
- Creating transactions with temporal information
- Mining frequent sequential patterns with cSPADE
- Chapter 10: Time Series Mining with R
- Introduction
- Creating time series data
- Plotting a time series object
- Decomposing time series
- Smoothing time series
- Forecasting time series
- Selecting an ARIMA model
- Creating an ARIMA model
- Forecasting with an ARIMA model.
- Predicting stock prices with an ARIMA model
- Chapter 11: Supervised Machine Learning
- Introduction
- Fitting a linear regression model with lm
- Summarizing linear model fits
- Using linear regression to predict unknown values
- Measuring the performance of the regression model
- Performing a multiple regression analysis
- Selecting the best-fitted regression model with stepwise regression
- Applying the Gaussian model for generalized linear regression
- Performing a logistic regression analysis
- Building a classification model with recursive partitioning trees
- Visualizing a recursive partitioning tree
- Measuring model performance with a confusion matrix
- Measuring prediction performance using ROCR
- Chapter 12: Unsupervised Machine Learning
- Introduction
- Clustering data with hierarchical clustering
- Cutting tree into clusters
- Clustering data with the k-means method
- Clustering data with the density-based method
- Extracting silhouette information from clustering
- Comparing clustering methods
- Recognizing digits using the density-based clustering method
- Grouping similar text documents with k-means clustering methods
- Performing dimension reduction with Principal Component Analysis (PCA)
- Determining the number of principal components using a scree plot
- Determining the number of principal components using the Kaiser method
- Visualizing multivariate data using a biplot
- Index.