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Mastering Data Analysis with R
Gergely Daróczi 著
更新時間：2021-07-09 21:59:19
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最新章節(jié)：
Index
IfyouareadatascientistorRdeveloperwhowantstoexploreandoptimizeyouruseofR’sadvancedfeaturesandtools,thisisthebookforyou.AbasicknowledgeofRisrequired,alongwithanunderstandingofdatabaselogic.
最新章節(jié)
- Index
- Chapter 14 – Analysing the R Community
- Chapter 13 – Data Around Us
- Chapter 12 – Analyzing Time-series
- Chapter 11 – Social Network Analysis of the R Ecosystem
- Chapter 10 – Classification and Clustering
品牌：中圖公司
上架時間：2021-07-09 18:47:45
出版社：Packt Publishing
本書數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- Index 更新時間：2021-07-09 21:59:19
- Chapter 14 – Analysing the R Community
- Chapter 13 – Data Around Us
- Chapter 12 – Analyzing Time-series
- Chapter 11 – Social Network Analysis of the R Ecosystem
- Chapter 10 – Classification and Clustering
- Chapter 9 – From Big to Smaller Data
- Chapter 8 – Polishing Data
- Chapter 7 – Unstructured Data
- Chapter 6 – Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth)
- Chapter 5 – Building Models (authored by Renata Nemeth and Gergely Toth)
- Chapter 4 – Restructuring Data
- Chapter 3 – Filtering and Summarizing Data
- Chapter 2 – Getting Data from the Web
- Chapter 1 – Hello Data!
- General good readings on R
- Appendix A. References
- Summary
- R-related posts in social media
- The number of R users in social media
- Analyzing overlaps between our lists of R users
- The R-help mailing list
- R package maintainers
- R Foundation members
- Chapter 14. Analyzing the R Community
- Summary
- Spatial statistics
- Alternative map designs
- Interactive maps
- Satellite maps
- Rendering polygons around points
- Plotting thematic maps
- Finding polygon overlays of point data
- Visualizing point data in space
- Geocoding
- Chapter 13. Data Around Us
- Summary
- Advanced time-series analysis
- More complex time-series objects
- Outlier detection
- Autoregressive Integrated Moving Average models
- Holt-Winters filtering
- Seasonal decomposition
- Visualizing time-series
- Creating time-series objects
- Chapter 12. Analyzing Time-series
- Summary
- Further network analysis resources
- Visualizing network data
- Centrality measures of networks
- Loading network data
- Chapter 11. Social Network Analysis of the R Ecosystem
- Summary
- Machine learning algorithms
- Logistic regression
- Discriminant analysis
- Latent class models
- Cluster analysis
- Chapter 10. Classification and Clustering
- Summary
- Multidimensional Scaling
- Principal Component Analysis versus Factor Analysis
- Factor analysis
- Principal Component Analysis
- Adequacy tests
- Chapter 9. From Big to Small Data
- Summary
- Using robust methods
- Extreme values and outliers
- Data imputation
- Filtering missing data before or during the actual analysis
- Getting rid of missing data
- By-passing missing values
- Identifying missing data
- The types and origins of missing data
- Chapter 8. Polishing Data
- Summary
- The segmentation of documents
- Some other metrics
- Analyzing the associations among terms
- Further cleanup
- Visualizing the most frequent words in the corpus
- Cleaning the corpus
- Importing the corpus
- Chapter 7. Unstructured Data
- Summary
- Models for count data
- Logistic regression
- The modeling workflow
- Chapter 6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth)
- Summary
- Discrete predictors
- How well does the line fit in the data?
- Model assumptions
- Linear regression with continuous predictors
- The motivation behind multivariate models
- Chapter 5. Building Models (authored by Renata Nemeth and Gergely Toth)
- Summary
- The evolution of the reshape packages
- Reshaping data in a flexible way
- Merging datasets
- Computing new variables
- dplyr versus data.table
- Rearranging data
- Filtering data by string matching
- Transposing matrices
- Chapter 4. Restructuring Data
- Summary
- Summary functions
- Running benchmarks
- Aggregation
- Drop needless data
- Chapter 3. Filtering and Summarizing Data
- Summary
- R packages to interact with data source APIs
- Scraping data from other online sources
- Reading data from HTML tables
- Other popular online data formats
- Loading datasets from the Internet
- Chapter 2. Getting Data from the Web
- Summary
- Loading Excel spreadsheets
- Importing data from other statistical systems
- Loading data from databases
- Loading a subset of text files
- Benchmarking text file parsers
- Loading text files of a reasonable size
- Chapter 1. Hello Data!
- Customer support
- Reader feedback
- Conventions
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- Support files eBooks discount offers and more
- www.PacktPub.com
- About the Reviewers
- About the Author
- Credits
- Mastering Data Analysis with R
- coverpage
- coverpage
- Mastering Data Analysis with R
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Support files eBooks discount offers and more
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Chapter 1. Hello Data!
- Loading text files of a reasonable size
- Benchmarking text file parsers
- Loading a subset of text files
- Loading data from databases
- Importing data from other statistical systems
- Loading Excel spreadsheets
- Summary
- Chapter 2. Getting Data from the Web
- Loading datasets from the Internet
- Other popular online data formats
- Reading data from HTML tables
- Scraping data from other online sources
- R packages to interact with data source APIs
- Summary
- Chapter 3. Filtering and Summarizing Data
- Drop needless data
- Aggregation
- Running benchmarks
- Summary functions
- Summary
- Chapter 4. Restructuring Data
- Transposing matrices
- Filtering data by string matching
- Rearranging data
- dplyr versus data.table
- Computing new variables
- Merging datasets
- Reshaping data in a flexible way
- The evolution of the reshape packages
- Summary
- Chapter 5. Building Models (authored by Renata Nemeth and Gergely Toth)
- The motivation behind multivariate models
- Linear regression with continuous predictors
- Model assumptions
- How well does the line fit in the data?
- Discrete predictors
- Summary
- Chapter 6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth)
- The modeling workflow
- Logistic regression
- Models for count data
- Summary
- Chapter 7. Unstructured Data
- Importing the corpus
- Cleaning the corpus
- Visualizing the most frequent words in the corpus
- Further cleanup
- Analyzing the associations among terms
- Some other metrics
- The segmentation of documents
- Summary
- Chapter 8. Polishing Data
- The types and origins of missing data
- Identifying missing data
- By-passing missing values
- Getting rid of missing data
- Filtering missing data before or during the actual analysis
- Data imputation
- Extreme values and outliers
- Using robust methods
- Summary
- Chapter 9. From Big to Small Data
- Adequacy tests
- Principal Component Analysis
- Factor analysis
- Principal Component Analysis versus Factor Analysis
- Multidimensional Scaling
- Summary
- Chapter 10. Classification and Clustering
- Cluster analysis
- Latent class models
- Discriminant analysis
- Logistic regression
- Machine learning algorithms
- Summary
- Chapter 11. Social Network Analysis of the R Ecosystem
- Loading network data
- Centrality measures of networks
- Visualizing network data
- Further network analysis resources
- Summary
- Chapter 12. Analyzing Time-series
- Creating time-series objects
- Visualizing time-series
- Seasonal decomposition
- Holt-Winters filtering
- Autoregressive Integrated Moving Average models
- Outlier detection
- More complex time-series objects
- Advanced time-series analysis
- Summary
- Chapter 13. Data Around Us
- Geocoding
- Visualizing point data in space
- Finding polygon overlays of point data
- Plotting thematic maps
- Rendering polygons around points
- Satellite maps
- Interactive maps
- Alternative map designs
- Spatial statistics
- Summary
- Chapter 14. Analyzing the R Community
- R Foundation members
- R package maintainers
- The R-help mailing list
- Analyzing overlaps between our lists of R users
- The number of R users in social media
- R-related posts in social media
- Summary
- Appendix A. References
- General good readings on R
- Chapter 1 – Hello Data!
- Chapter 2 – Getting Data from the Web
- Chapter 3 – Filtering and Summarizing Data
- Chapter 4 – Restructuring Data
- Chapter 5 – Building Models (authored by Renata Nemeth and Gergely Toth)
- Chapter 6 – Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth)
- Chapter 7 – Unstructured Data
- Chapter 8 – Polishing Data
- Chapter 9 – From Big to Smaller Data
- Chapter 10 – Classification and Clustering
- Chapter 11 – Social Network Analysis of the R Ecosystem
- Chapter 12 – Analyzing Time-series
- Chapter 13 – Data Around Us
- Chapter 14 – Analysing the R Community
- Index 更新時間：2021-07-09 21:59:19
