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# R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

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desertcart.com: R for Data Science: Import, Tidy, Transform, Visualize, and Model Data: 9781491910399: Wickham, Hadley, Grolemund, Garrett: Books

Review: learning R - As a beginner to R, I bought this book at the recommendation from Data Science for Fundraising: Build Data-Driven Solutions Using R and am so glad that I did. R4DS provides useful content to get started with R. The book consists of 5 main sections: 1. Getting started (Explore) 2. Data manipulation (Wrangle) 3. Scripting (Program) 4. Build models 5. Presenting information (Communicate) Here’s a brief overview of each section: Explore I was a bit puzzled as the book jumps directly into plotting using the ggplot library. Although the authors are clear that the purpose of introducing ggplot is to keep our motivation high, seeing the ggplot syntax without a whole lot of background can be confusing at first. But then the authors do a great job at explaining the various aspects of ggplot. So my advice to other R beginners is to just keep going forward! After the intro to ggplot, you learn about the basics of R, such as variables and functions, as well as intermediate topics such as writing scripts, manipulating data using dplyr, and lastly EDA. This section ends with a good overview of how to use RStudio to saves script files. Wrangle This section starts with an intro to tibbles, which is a new concept for handling data in R. This section is important since most of the functions in later chapters use tibbles, such as reading data from various sources. There are many important concepts that seem useful such as “tidying up” your data and cleaning data points (string and dates). Program This is a function section that introduces data pipes. This section explores how to chain complicated data operations together. You also get to see some good practice in writing human readable code. Then the book jumps into using the power of R as a functional programming language. Once I feel more proficient, I will take a deeper look at purr. Model Now, depending on your situation, you may not be interested in this section, which is focuses on predictive modeling. There are quite a few recipes that explore “why” questions. Definitely planning to come back to this chapter later. Communicate This section is useful because it introduces how to create reusablereports. Overall, this is my go-to book along with the book I previously mentioned. This book is a solid reference book for learning R. Another good thing is that the companion website is regularly updated and you can copy and paste the code examples directly into RStudio. I’m looking forward to further exploring R and learning how to create my own data visualizations.
Review: An excellent introduction to using R for Exploratory Analysis. - Wickham and Grolemund have produced an excellent book that would help a beginning R user become very efficient in explanatory analysis. Unsurprisingly the approach that they expound utilises the "hadleyverse" a collection of packages (ggplot2 for visualisation, tidyr for reshaping, dplyr for selecting and filtering, purrr for functional programming, broom for linear models etc) that dramatically speed up most of the common steps involved in an analysis. One benefit of Wickham's involvement in these packages has been a coherent philosophy that sits behind them. It can be a little tricky when learning this philosophy, but the long term benefits are enormous. The book is broken up into a number of sections that effectively builds up the ability to ingest, transform, visualise and model datasets. A good portion of the book is available in an online version, to give you a taste of how it is written. Many have been following it as it was written. I have passed on copies of the book to a number of colleagues who were just starting out and the response has been uniformly positive. In my own case I was familiar with some of the these packages; ggplot2, dplyr, tidyr, but found the book taught me purrr and how to better use the packages together. Probably my two biggest caveats to readers are that there are situations where packages from outside the "hadleyverse" maybe required. The authors do a great job of pointing this out, but it does pay in my experience to know data.table and lattice for example. Both because they can occasionally fit a problem better but also because you inevitably come across other people's code where these packages are used. The other caveat is that the modelling is a little rudimentary. Most of the examples are just fitting independent regression models, whereas it seems to me that a hierarchical model would be a better fit. Still these are small things and it would be silly to expect a single book to cover all of these areas. In short this is the book I would give to someone who was keen to learn about how to use R for data science. It reads really well building up the different components whilst still being a valuable reference if you just need a reminder of a particular package (what is the difference between tibbles and data frames again?). Even though a good portion of the book is available online, it is well worth it to have the full thing on your bookshelf (digital or otherwise). On a broader note with Max Kuhn (author of the excellent "Applied Predictive Modelling" with Kjell Johnson) joining Wickham and Grolemund at RStudio, it is a great time to start your R journey.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #99,657 in Books ( See Top 100 in Books ) #16 in Mathematical & Statistical Software #29 in Data Processing #137 in Probability & Statistics (Books) |
| Customer Reviews | 4.7 4.7 out of 5 stars (1,628) |
| Dimensions  | 5.9 x 1.2 x 8.8 inches |
| Edition  | 1st |
| ISBN-10  | 1491910399 |
| ISBN-13  | 978-1491910399 |
| Item Weight  | 1.76 pounds |
| Language  | English |
| Print length  | 518 pages |
| Publication date  | January 31, 2017 |
| Publisher  | O'Reilly Media |

## Images

![R for Data Science: Import, Tidy, Transform, Visualize, and Model Data - Image 1](https://m.media-amazon.com/images/I/91on5Tt+MVL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ learning R
*by N***C on May 8, 2018*

As a beginner to R, I bought this book at the recommendation from Data Science for Fundraising: Build Data-Driven Solutions Using R and am so glad that I did. R4DS provides useful content to get started with R. The book consists of 5 main sections: 1. Getting started (Explore) 2. Data manipulation (Wrangle) 3. Scripting (Program) 4. Build models 5. Presenting information (Communicate) Here’s a brief overview of each section: Explore I was a bit puzzled as the book jumps directly into plotting using the ggplot library. Although the authors are clear that the purpose of introducing ggplot is to keep our motivation high, seeing the ggplot syntax without a whole lot of background can be confusing at first. But then the authors do a great job at explaining the various aspects of ggplot. So my advice to other R beginners is to just keep going forward! After the intro to ggplot, you learn about the basics of R, such as variables and functions, as well as intermediate topics such as writing scripts, manipulating data using dplyr, and lastly EDA. This section ends with a good overview of how to use RStudio to saves script files. Wrangle This section starts with an intro to tibbles, which is a new concept for handling data in R. This section is important since most of the functions in later chapters use tibbles, such as reading data from various sources. There are many important concepts that seem useful such as “tidying up” your data and cleaning data points (string and dates). Program This is a function section that introduces data pipes. This section explores how to chain complicated data operations together. You also get to see some good practice in writing human readable code. Then the book jumps into using the power of R as a functional programming language. Once I feel more proficient, I will take a deeper look at purr. Model Now, depending on your situation, you may not be interested in this section, which is focuses on predictive modeling. There are quite a few recipes that explore “why” questions. Definitely planning to come back to this chapter later. Communicate This section is useful because it introduces how to create reusablereports. Overall, this is my go-to book along with the book I previously mentioned. This book is a solid reference book for learning R. Another good thing is that the companion website is regularly updated and you can copy and paste the code examples directly into RStudio. I’m looking forward to further exploring R and learning how to create my own data visualizations.

### ⭐⭐⭐⭐⭐ An excellent introduction to using R for Exploratory Analysis.
*by S***S on December 26, 2016*

Wickham and Grolemund have produced an excellent book that would help a beginning R user become very efficient in explanatory analysis. Unsurprisingly the approach that they expound utilises the "hadleyverse" a collection of packages (ggplot2 for visualisation, tidyr for reshaping, dplyr for selecting and filtering, purrr for functional programming, broom for linear models etc) that dramatically speed up most of the common steps involved in an analysis. One benefit of Wickham's involvement in these packages has been a coherent philosophy that sits behind them. It can be a little tricky when learning this philosophy, but the long term benefits are enormous. The book is broken up into a number of sections that effectively builds up the ability to ingest, transform, visualise and model datasets. A good portion of the book is available in an online version, to give you a taste of how it is written. Many have been following it as it was written. I have passed on copies of the book to a number of colleagues who were just starting out and the response has been uniformly positive. In my own case I was familiar with some of the these packages; ggplot2, dplyr, tidyr, but found the book taught me purrr and how to better use the packages together. Probably my two biggest caveats to readers are that there are situations where packages from outside the "hadleyverse" maybe required. The authors do a great job of pointing this out, but it does pay in my experience to know data.table and lattice for example. Both because they can occasionally fit a problem better but also because you inevitably come across other people's code where these packages are used. The other caveat is that the modelling is a little rudimentary. Most of the examples are just fitting independent regression models, whereas it seems to me that a hierarchical model would be a better fit. Still these are small things and it would be silly to expect a single book to cover all of these areas. In short this is the book I would give to someone who was keen to learn about how to use R for data science. It reads really well building up the different components whilst still being a valuable reference if you just need a reminder of a particular package (what is the difference between tibbles and data frames again?). Even though a good portion of the book is available online, it is well worth it to have the full thing on your bookshelf (digital or otherwise). On a broader note with Max Kuhn (author of the excellent "Applied Predictive Modelling" with Kjell Johnson) joining Wickham and Grolemund at RStudio, it is a great time to start your R journey.

### ⭐⭐⭐⭐⭐ Great book - Very useful!
*by X***3 on June 12, 2018*

I am very happy with this book. Its easy to read and the exercises/code work and are applicable. The text is clearly written and the supporting graphics are well constructed. I have started working through some of the exercises in R and find them very helpful. I bought this book after completing a graduate course in data analysis using R. Much of the course was constructed around Hadley Wickham's work. We used ggplot2 and tidyverse extensively. We did not use this book in the course and I now wish we had used it as a desk reference. This book is answering many of the questions I had. It is also providing an overview of many basic concepts and tools in R and would have been helpful to have at my fingertips during my graduate course. Google is a great resource, but a well written and easily sourced desktop resource like this is indispensable. I am learning something new about R constantly...I would consider myself a late beginner to early intermediate user of R for data science/data analysis. I often find books that cover the basics extremely useful, because its easy to forget. So for me, this was a good buy - regardless of my skill level. Others will have to make their own decision. This book does not cover mapping in R. In fact, there is a sentence in Chapter 1 that states - this book does not cover mapping. I would like to have had more on that topic or a follow-up book committed to mapping and integration with other tools like Leaflet or ArcGIS.

## Frequently Bought Together

- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- The Book of R: A First Course in Programming and Statistics
- Hands-On Programming with R: Write Your Own Functions and Simulations

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