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Efficiently perform data manipulation using the split-apply-combine strategy in RAbout 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
Who This Book Is For
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
In Detail
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.
- Sales Rank: #1003906 in eBooks
- Published on: 2015-03-31
- Released on: 2015-03-31
- Format: Kindle eBook
About the Author
Jaynal Abedin
Jaynal Abedin currently holds the position of senior statistician at the Centre for Communicable Diseases (CCD) at the International Centre for Diarrhoeal Disease Research, Bangladesh (http://www.icddrb.org/). He attained his bachelor's and master's degrees in statistics from the University of Rajshahi, Bangladesh. He has extensive experience in R programming and Stata, and has good leadership qualities. He has contributed to two books on R and also developed an R package named edeR, short for e-mail data extraction using R, which is available at CRAN (http://cran.rproject.org/web/packages/edeR/index.html). He is currently leading a team of statisticians. He has hands-on experience in developing training material and facilitating training in R programming and Stata, along with statistical aspects in public health research. His primary areas of interest in research include causal inference and machine learning. He is currently involved in several ongoing public health research projects, and is a coauthor of nine peer-reviewed scientific papers. Moreover, he is involved in several work-in-progress manuscripts. He works as a freelance statistician in online marketplaces and has obtained a good reputation for his work.
Kishor Kumar Das
Kishor Kumar Das is a statistician at the International Centre for Diarrhoeal Disease Research, Bangladesh, an internationally recognized organization that focuses mainly on public health research. He completed his MSc and BSc in applied statistics from the Institute of Statistical Research and Training, University of Dhaka, Bangladesh. He has extensively used R for data processing, statistical analysis, and graphs for more than 10 years. His research interests are survival analysis, machine learning, and statistical computing.
Most helpful customer reviews
11 of 12 people found the following review helpful.
Too consise for beginners, too basic for intermediate R users
By ctlr
First a disclaimer: Packt Publishing asked me to review this book and post a review on Amazon. They gave me the book, I did not buy it. However, my review does not pull punches. This review is of the PDF version of the book.
Data Manipulation with R (2nd Ed) consists of 6 small chapters. The first two chapters introduce the novice user to R. The third chapter covers data manipulation with plyr and dplyr packages. The fourth chapter demonstrates how to reshape data. The fifth covers some strategies for dealing with data too big for memory. The final chapter touches on text manipulation. The book is small and comes in at just over 100 pages. What’s new in the 2nd edition? I don’t know. They don’t say.
I regret to say I didn’t like this book. My biggest complaint is the lack of real-world examples. Most of the data manipulation is done with either small fake data sets or built-in R data sets. I would have liked to have seen a case study where the authors start with realistic raw data and actually walk through the process of cleaning and manipulation. I appreciate that small, simple examples are needed to introduce a new function. But it would have been nice to see these functions creatively combined and deployed in manipulating a complex data set.
For example, take the center piece of the book, chapter 3, which covers the plyr and dplyr packages. The examples are minimal and work exclusively with the simple “iris” data set included with R. The iris data is clean and has only 150 records of 5 variables. In other words, it’s great for examples but doesn’t actually look like real-world data. In contrast, the tutorial (aka, vignette) that comes with dplyr uses flight data from the US Bureau of Transportation. It has over 300,000 records and 16 variables. It’s large, kind of messy and presents all sorts of challenges. I honestly can’t say that chapter 3 adds value to the tutorial that comes with dplyr. On the other hand, the chapter also covers the older plyr package, which doesn’t have a tutorial in R. The simple examples presented may be useful to a novice user interested in learning the plyr package.
Another complaint is the various statements made in the book with no follow-up or evidence. For example, on page 2, a bullet point in the "summary of why we should use R" states "It has a cloud-computing feature." What is this feature? Why is that useful? I don’t know, it's never mentioned again. On page 52 we’re told "the dplyr package offers a complete set of functions to perform every kind of data manipulation we need in the process of analysis." Every kind? Every single kind? I like dplyr too, but let’s not get carried away!
There’s also some things that are just plain wrong. On page 7, they say, "The R console allows only one line of command at a time." Not true, we can separate commands with a semicolon. On page 26, we’re told "an array is a multiply subscripted data entry that allows the storing of data frames, matrices, or vectors of different types." No, per the documentation for array: "An array in R ... is simply a vector which is stored with additional attributes giving the dimensions." In other words, it cannot store data frames. On page 92 the authors state, “We can perform group-wise processing and aggregation using sqldf, which is a faster alternative to the aggregate function.” Evidence? None provided. I tested this myself and came to the opposite conclusion. The aggregate function is actually about 4 times faster than sqldf, at least on my computer using their example:
library(sqldf)
system.time(iris_avg
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