最新章節

• Index
• Visualizing multivariate data using a biplot
• Determining the number of principal components using the Kaiser method
• Determining the number of principal components using a scree plot
• Performing dimension reduction with Principal Component Analysis (PCA)
• Grouping similar text documents with k-means clustering methods

• Index 更新時間：2021-07-14 10:52:04
• Visualizing multivariate data using a biplot
• Determining the number of principal components using the Kaiser method
• Determining the number of principal components using a scree plot
• Performing dimension reduction with Principal Component Analysis (PCA)
• Grouping similar text documents with k-means clustering methods
• Recognizing digits using the density-based clustering method
• Comparing clustering methods
• Extracting silhouette information from clustering
• Clustering data with the density-based method
• Clustering data with the k-means method
• Cutting tree into clusters
• Clustering data with hierarchical clustering
• Introduction
• Chapter 12. Unsupervised Machine Learning
• Measuring prediction performance using ROCR
• Measuring model performance with a confusion matrix
• Visualizing a recursive partitioning tree
• Building a classification model with recursive partitioning trees
• Performing a logistic regression analysis
• Applying the Gaussian model for generalized linear regression
• Selecting the best-fitted regression model with stepwise regression
• Performing a multiple regression analysis
• Measuring the performance of the regression model
• Using linear regression to predict unknown values
• Summarizing linear model fits
• Fitting a linear regression model with lm
• Introduction
• Chapter 11. Supervised Machine Learning
• Predicting stock prices with an ARIMA model
• Forecasting with an ARIMA model
• Creating an ARIMA model
• Selecting an ARIMA model
• Forecasting time series
• Smoothing time series
• Decomposing time series
• Plotting a time series object
• Creating time series data
• Introduction
• Chapter 10. Time Series Mining with R
• Mining frequent sequential patterns with cSPADE
• Creating transactions with temporal information
• Mining frequent itemsets with Eclat
• Visualizing association rules
• Pruning redundant rules
• Mining associations with the Apriori rule
• Displaying transactions and associations
• Transforming data into transactions
• Introduction
• Chapter 9. Rule and Pattern Mining with R
• Performing two-way ANOVA
• Conducting one-way ANOVA
• Understanding the Wilcoxon Rank Sum and Signed Rank tests
• Working with the Pearson's chi-squared tests
• Performing Kolmogorov-Smirnov tests
• Conducting exact binomial tests
• Performing student's T-tests
• Performing Z-tests
• Getting confidence intervals
• Introduction
• Chapter 8. Statistical Inference in R
• Simulating the stochastic process
• Sampling from a dataset
• Understanding Student's t-distribution
• Sampling from a chi-squared distribution
• Sampling from a normal distribution
• Generating Poisson random variates
• Generating binomial random variates
• Understanding uniform distributions
• Generating random samples
• Introduction
• Chapter 7. Simulation from Probability Distributions
• Publishing an R Shiny report
• Creating an R Shiny document
• Adding interactivity to a ggvis plot
• Using scales
• Controlling axes and legends
• Understanding basic syntax and grammar
• Creating interactive graphics with ggvis
• Embedding R code chunks
• Learning the markdown syntax
• Creating R Markdown reports
• Introduction
• Chapter 6. Making Interactive Reports
• Creating maps
• Combining plots
• Adjusting themes
• Faceting
• Adjusting scales
• Performing transformations
• Introducing geometric objects
• Changing aesthetics mapping
• Creating basic plots with ggplot2
• Introduction
• Chapter 5. Visualizing Data with ggplot2
• Merging data with dplyr
• Summarizing data with dplyr
• Adding new columns with dplyr
• Eliminating duplicated rows with dplyr
• Arranging rows with dplyr
• Chaining operations in dplyr
• Selecting columns with dplyr
• Sampling data with dplyr
• Subsetting and slicing data with dplyr
• Merging large datasets with a data.table
• Performing fast aggregation with a data.table
• Managing data with a data.table
• Enhancing a data.frame with a data.table
• Introduction
• Chapter 4. Data Manipulation
• Imputing missing data
• Detecting missing data
• Reshaping data
• Sorting data
• Merging data
• Dropping data
• Filtering data
• Adding new records
• Working with the date format
• Converting data types
• Renaming the data variable
• Introduction
• Chapter 3. Data Preprocessing and Preparation
• Working with twitteR
• Accessing Facebook data
• Scraping web data
• Reading data from databases
• Working with Excel files
• Scanning text files
• Reading and writing CSV files
• Downloading open data
• Introduction
• Chapter 2. Data Extracting Transforming and Loading
• The debugging function
• Handling errors in a function
• Using the replacement function
• Creating infix operators
• Performing lazy evaluation
• Understanding closure
• Working with lexical scoping
• Understanding environments
• Matching arguments
• Creating R functions
• Introduction
• Chapter 1. Functions in R
• Customer support
• Reader feedback
• Conventions
• Sections
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• eBooks discount offers and more
• www.PacktPub.com
• About the Reviewer
• About the Author
• Credits
• 版權頁
• 封面

• 封面
• 版權頁
• Credits
• About the Author
• About the Reviewer
• www.PacktPub.com
• eBooks discount offers and more
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Sections
• Conventions
• Reader feedback
• Customer support
• 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 更新時間：2021-07-14 10:52:04