| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000045957643 | |
| 005 | 20181019111052 | |
| 008 | 181018s2018 flua b 001 0 eng d | |
| 010 | ▼a 2017056456 | |
| 020 | ▼a 9781498730235 (pbk. : acid-free paper) | |
| 020 | ▼a 9781138480605 (hardback : acid-free paper) | |
| 035 | ▼a (KERIS)REF000018576965 | |
| 040 | ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a QA76.9.D343 ▼b P429 2018 |
| 082 | 0 0 | ▼a 006.3/12 ▼2 23 |
| 084 | ▼a 006.312 ▼2 DDCK | |
| 090 | ▼a 006.312 ▼b P362e | |
| 100 | 1 | ▼a Pearson, Ronald K., ▼d 1952-. |
| 245 | 1 0 | ▼a Exploratory data analysis using R / ▼c Ronald K. Pearson. |
| 260 | ▼a Boca Raton, FL : ▼b CRC Press/Taylor & Francis Group, ▼c c2018. | |
| 300 | ▼a xiv, 547 p. : ▼b ill. (some col.) ; ▼c 23 cm. | |
| 490 | 1 | ▼a Chapman & Hall/CRC data mining and knowledge discovery, ; ▼v 45 |
| 500 | ▼a "A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc." | |
| 504 | ▼a Includes bibliographical references and index. | |
| 520 | ▼a "This textbook will introduce exploratory data analysis (EDA) and will cover the range of interesting features we can expect to find in data. The book will also explore the practical mechanics of using R to do EDA. Based on the author's course at the University of Connecticut, the book assumes no prior exposure to data analysis or programming, and is designed to be as non-mathematical as possible. Exercises are included throughout, and a Solutions Manual will be available. The author will also provide a supplemental R package through the Comprehensive R Archive Network that will include implementations of some of the features in this book, along with data examples, tools, and datasets"-- ▼c Provided by publisher. | |
| 650 | 0 | ▼a Data mining ▼x Computer programs. |
| 650 | 0 | ▼a R (Computer program language). |
| 830 | 0 | ▼a Chapman & Hall/CRC data mining and knowledge discovery, ▼v 45. |
| 945 | ▼a KLPA |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 006.312 P362e | Accession No. 121246280 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Book Introduction
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" ? good, bad, and ugly ? features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data.
The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing.
The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available.
About the Author:
Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).
Information Provided By: :
Table of Contents
I Analyzing Data Interactively with R
1 Data, Exploratory Analysis, and R
2 Graphics in R
3 Exploratory Data Analysis: A First Look
4 Working with External Data
5 Linear Regression Models
6 Crafting Data Stories
II Developing R Programs
7 Programming in R
8 Working with Text Data
9 Exploratory Data Analysis: A Second Look
10 More General Predictive Models
11 Keeping It All Together
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