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雷达卡
Efficiently perform data manipulation using the split-apply-combine strategy in R
Data Manipulation with R, 2nd Edition.pdf
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About This Book
- Perform data manipulation with add-on packages such as plyr, reshape, stringr, lubridate, and sqldf
- Learn about factor manipulation, string processing, and text manipulation techniques using the stringr and dplyr libraries
- Enhance your analytical skills in an intuitive way through step-by-step working examples
This book is for all those who wish to learn about data manipulation from scratch and excel at aggregating data effectively. It is expected that you have basic knowledge of R and have previously done some basic administration work with R.
What You Will Learn- Learn about R data types and their basic operations
- Work efficiently with string, factor, and date variables using stringr
- Understand group-wise data manipulation
- Work with different layouts of R datasets and interchange between layouts for varied purposes
- Manage bigger datasets using pylr and dpylr
- Perform data manipulation with add-on packages such as plyr, reshape, stringr, lubridate, and sqldf
- Manipulate datasets using SQL statements with the sqldf package
- Clean and structure raw data for data mining using text manipulation
This book starts with the installation of R and how to go about using R and its libraries. We then discuss the mode of R objects and its classes and then highlight different R data types with their basic operations.
The primary focus on group-wise data manipulation with the split-apply-combine strategy has been explained with specific examples. The book also contains coverage of some specific libraries such as lubridate, reshape2, plyr, dplyr, stringr, and sqldf. You will not only learn about group-wise data manipulation, but also learn how to efficiently handle date, string, and factor variables along with different layouts of datasets using the reshape2 package.
By the end of this book, you will have learned about text manipulation using stringr, how to extract data from twitter using twitteR library, how to clean raw data, and how to structure your raw data for data mining.
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