laptak2003/AmazonDataScience huggingface dataset:
Iâ€™ve been working with data for ten years and I really wish that I had a copy when I started my career. I borrowed a coworkers copy and then immediately bought my own so I could lend it to my family members.The best part is the author identifies the truly horrendous examples out there then through a step by step process shows how to make them more effective. Itâ€™s a very handy reference that I recommend to anyone that makes a living communicating data to decision makers.
Simple - if you ever present data to anyone, you should read this book unless by some miracle you were born a data visualization designer or you already learned this stuff in school.This book is full of guidance backed by detailed examples and step by step instructions.This book also doesnâ€™t get hung up on any specific tool - itâ€™s pretty much tool agnostic, and thatâ€™s a good thing.I enjoyed reading it and have already started to implement it my work as a program manager at Microsoft.
This is probably the best book on creating data visualizations I've come across to date.Amazing attention to detail: not a tick mark is out of place. The author doesn't just copy and paste visualizations from other sources (unless used to illustrate common mistakes). Instead she carefully constructs each example putting thought into every bit of ink on the page. As a result, the charts are concise and get to the point without distracting you with details that don't add information or emphasis. This book helps you understand what's important in data visualization and helps you highlight the important thing in all the charts you create.Some examples of skills this book taught me:- how to use muted colors combined with a few highlights to drastically improve the storytelling aspect of a chart- add less: unnecessary grid lines, markers, et cetera, can detract from a chart's storynits: I like pie charts. I think they make intuitive sense and I even think the pie chart on page 5 would be fine if the author applied the color scheme in the bar chart below that to the pie chart. I agree that 3D pie charts are bad, but 2D pie charts are OK in my book.I really liked the before and after charts on pages 4 - 6. Would have loved another appendix with a dozen more of these for future fast reference.
This book has become my right-hand resource for creating reports and presentations that clearly communicate their intended story/message. As a market research consultant, I often have to walk that fine line between a data/statistics-heavy report (to please the research department), and a higher level, "give me the top 5 take-aways" presentation for senior leadership.  Cole has done an excellent job demonstrating both the how and the why of information displays in a way that is clear, concise, and compelling. I am both a better researcher and a better communicator for having bought and read this book. I hope Cole keeps on writing...!  I would highly recommend this to anyone who is tasked with summarizing and communicating data visually. I promise - you will not be disappointed.
If I could go back in time to one point in history I would go back to the start of my career to give myself this book. I stumbled across this just by happenstance after leaving my bossâ€™ office completely frustrated. I had spent a good amount of time on a report for him that showed number of sales leads, conversions, locations, etc. just to have him proceed to rip apart everything I had worked so hard on. I kept telling him that the data was all there and that he just wasnâ€™t looking at it right. I went to the internet to find something to help me build better dashboards. I found â€œstorytelling with dataâ€. I read the entire book as fast as I could. I then spent the next few days fixing my entire report. Turns out that it wasnâ€™t my boss that was looking at the report wrong, he just had a very busy story to try and read to get the data he was looking for (complete with pie charts). After reading Coleâ€™s book I completely changed the way I approach reports and dashboards. Now, I can send them out to our team and not get a hundred email responses to clarify everything. The story is getting told. I would recommend this book to anyone and everyone. I currently have 52 books (Iâ€™m somewhat of a book nerd) on my desk and shelf at workâ€¦ this one is my favorite of them all.
This is such an incredible book. One can read UX (user design books) and Dashboard books, but this book looks behind the scenes at how our brains work and how we process data, and gives us such an incredible depth and breadth of insight into the mind and into how it sees things, and how one can use this knowledge to direct someone to the important things in our presentations. Remember the white space! Music is made by the pauses between the notes, as much as the notes themselves. Thanks Cole for some incredible insights, just like reading a Malcolm Gladwell book.
The first portion of the book is incorrectly cut and bound together at an angle. You donâ€™t notice right away because the edges are cut to the overall book shape but the printing could definitely use some work
This is a well constructed discourse on thinking required behind the creation of data visualization output.  The author is effective as the book has a conversational tone with adequate examples which enhance the narrative.  Useful as a book club read for those in the technical arena where telling a story with data is vital.
Iâ€™ve had so many â€œAha!â€ and â€œHow could I have gone so long without realizing this?â€ moments while reading this book. The author presents ideas with extreme clarity. By the end, you will be thinking methodically about your use of color, clutter, and graphs. If you're someone who uses data to explain anything to anybody, I could not more strongly recommend this book. It's the best data visualization resource I've read.
so many bla bla blaall is basic concepts and must of them you can figured out meantime read them
It's hard to put down! It works very well to help researchers understand many phases of the research process including the writing, surprisingly. It truly builds the muscle for sustaining complex research and how to reduce time demands by thinking upfront. Very, very well done. Of course, it is strong on how to communicate findings which is the true reason we research anyway. Thank you, Knaffic, for this contribution.
This is more than a book on the topic, it's a set of instructions. Also, though the book is short in page count, it's very sense and rewarding in content. Be prepared to have to slow down and study as you read. Changed my approach and opened up my horizons. Thanks Cole!
Awesome book on how to present your data in the right way. It has definitely helped me to improve my data story telling skills as well as my data visualization abilities.
I purchased this book as required reading for a graduate study data analytics class. As with most text books, I expected it to be full of â€œcookie cutterâ€ graphs and charts that had no real world application...I couldnâ€™t have been more wrong! This book was not only an enjoyable read, it was also filled with insightful explanations on the thought behind WHY certain details are emphasized in graphs and how to get your point across to your audience through data visualization.A must read for anyone in the data science field or anyone who uses charts and graphs in a presentation format.
This book was very easy to read since it utilized lots of visuals and was organized well. I think I got through it in one or two weekends.I'm an experienced analyst, but this still had immediate use cases for me since this book focuses on putting yourself in the shoes of your audience rather than presenting data like an analyst wants to. I've revamped all of my recurring tables/graphs, and even presented my before & afters to my team - and their minds were blown.
This is a beautifully written, helpful book that no data vizzer (amateur or pro) should be without. Knaflic touches on key elements of producing not just an informative visualization, but one that truly engages the audience and helps them immediately see the story it tells. The book is an easy read and full of very helpful "before" and "after" visualizations that really drive her points home and are practical regardless of the platform you're using to share your data -- from Excel to Tableau and beyond. I read the whole book in an hour and immediately went to work incorporating her recommendations into a visualization being prepared for a client. The end result was cleaner, more aesthetically pleasing and did a better job telling the data's story. A must read.
..."I found the pearl, let's see if your can too."STORYTELLING WITH DATA is an essential introduction to the basic graphic and story concepts essential to conveying information with simple charts available to everyone in common programs like Excel. Knaflic teaches how to understand and frame context, choose between fundamental visual displays, eliminate clutter, focus attention to where you want it, and tell a story. All together, she shows how to make data a pivotal part of the story you want to tell.
I'm only like halfway through this book so far, and it's already massively helped me put together powerpoints or emails that require presenting data.
I had to buy this book for a class but I love it. Easy to read, fun approach. I know I'll be using this often.
The book is well written and the charts are extremely polish. The author has done a great job explaining concepts on all the different attributes that make a good story when using data.I recommend this book to anyone trying to improve the way they use and design charts, as well as those that need to present data to others.
As a career counselor, I bought this to help my clients incorporate graphs and charts into their resumes, as well as make better pitches for my data-driven products. I thought this would be a snooze-fest like a Stats textbook, yet I couldn't put this thing down! I was enthralled with every detail. Ms. Nussbaumer Knaffic has a quick wit and sense of humor that makes the topic extremely fun. I actually felt energized by reading this book, which was not what I was expecting AT ALL. I simply thought it'd be another boring, albeit important, reference. It's transformed the way I look and think about data. I'm so thankful I stumbled onto this book!
This is a great book. Easy to understand but very  deep in results and foundations.Cole makes clear all the concepts of charts design and explains in easy way the main concepts of storytelling.  Combining both, makes and excellent book.I appreciate  every chapter and enjoyed it.I am inspired to do a better  explanations and never see a chart in a same way.
As a professional working in one-to-one marketing, I feel like too often we succumb to flashy "junk charts" or otherwise ineffective presentations of data. This book seeks to break this habit by teaching typical BI professionals about how to more effectively communicate using data.The depth at which the author discusses each of the primary topics is appropriate for the majority of applications and the visuals presented are top notch. That praise aside, if you're looking to go (even) deeper about individual parts and pieces of charts and tables, I'll also recommend you pick up Stephen Few's "Show Me The Numbers," as he gives an incredibly comprehensive view on the micro-level of visualization design. In my opinion though, this book covers enough ground for the majority of readers and is a great way to "up your game" when it comes to presenting to a variety of audiences. I'm going to recommend it to my colleagues as a way to lift the quality of data visualization in my department as a whole.
I straddle the line between engineering and project management, and thus often find myself making presentations to customers to explain difficult events or analyses, to request more funding for projects, or to provide courses of action for projects.  All of these presentations require my colleagues and me to reduce large amounts of data down to a few key points.  While we're good with data, we're not so fantastic sometimes at presenting it in a simple, compelling way.  This book, I am certain, will fix all that.The book itself is a minimalist, beautiful work of art.  The author walks you through 6 key topics for creating interesting, concise, and gorgeous presentations that will get your point across and compel your audience to take action.  I read the book in a few days and immediately started adopting the approach in my graphs and presentations.  I truly think that everyone - entire teams, really - who must make persuasive presentations should own this book.
Grateful to come across this book for several reasons. 1) My path to better data visuals was to save PowerPoint decks from other internal teams who had great design and message communication, I didnâ€™t understand what those good design elements were until this book came along.  2) How many times have you read a comprehensive topic wondering â€œwhat did I just learn?â€. The author concludes each chapter with a Conclusion summary AND concludes the book in the same way with a summary of the key points. Have copied them into my own â€œcliff notesâ€ summary for quick reference. 3) Author provides numerous references to further advance our interest.This is one resource that will stay in my active read list on the kindle app.
Just skimming the pictures gave me some insight into why some of my presentations didn't go over as well as they could have. A bit wordy in spots, but still worth the time invested. I'll probably also refer to it in the future when I need presentation ideas.
This book is about creating charts and graphs not reading and analyze data. Not what I wanted to read or purchase. I don't need to create charts I need to learn to read and analyze data already presented to me. This book is a rip off.
This is, far and away, the most useful book (and now desktop reference) I have purchased on data visualization and data storytelling.The narrative is clear and easy to follow and the advice is very practical. I have been able to optimize my own charts and graphs considerably by applying the 'less is more' techniques laid out in the text. No single recommendation here is groundbreaking, but in concert, they help users transform the 'off the shelf' Microsoft office charts into something that are C-suite approved.I have since purchased copies for every member of my consumer insights team, and use it as an on-boarding tool for all new team members.
This book resonated with me for a few reasons:- it is well structured and has a great flow- it contains both "dos" and "don'ts" scenarios and explains the thought process of how to improve bad graphs- it has interesting bits on the fundamentals of perception and all recommendations are also staffed using other references (well researched)This book reads extremely easy, is visually pleasing and will rewire the way that you perceive information graphics.
Years ago I heard about storytelling to present the messages, that I wanted to get through to the audience, But as a financial analyst my main focus was to create the data and present it in nice ways. And as an instructor in Excel Dashboards I was more focused on techniques for graphing presented by authors like Edward Tufte and Stephen Few. But when I came across Cole's book "Storytelling with data" it added an extra dimension to my understanding of how I could get the message to the audience. Finally I understood storytelling, and how it can benefit me as an analyst.
Amazing book. The auther repeat the information over and over again throughout the book which make the information easy to remember. It improved my final work dramatically and my managers are happy about it. Well constructed. I am not patient reader myself but I finished this book in two days. I was able to take notes too and illuminate others about storytelling. Buy it! It worth every penny.TIP: buy kindle version and audio book together and take notes on your macbook = MAGIC
Most people spend all of their time mining the data, and very little time deciding how to use that same data to tell their findings.  This book is simple to read, yet impactful.  Lots' of good solid advise of which charts to use, when.  How to more accurately portray data.  How to simplify the message.  .I've recommended it to everyone I know.
this book is written by an ex-google engineer ... I would say this book is very preliminary and gives you outline how you should think about making data visualization. chapter 9 is an only application based and it's huge.I would say that make a mind map of each chapter as you read and then when you read chapter 9. check how each concept is applied in data visualisations.
I am a corporate trainer and I specialize in communication skills strategies. I loved this book. The book does a terrific job of making "storytelling with data" seem simple. It is written in a conversational manner and provides so many relevant ideas and examples of different ways to share complicated data in a visual way. This book is a must read for anyone who teaches presentation and communication skills. It is so helpful!
As a geek and lover of data, I could not get my point across to audiences and after reading Cole's book, I now know why!  Her book is so informative, with a can-do approach that I'm ready to start TODAY to make my data presentations more impactful.  My boss will appreciate our organization learning this required skill because he is the recipient of many terribly organized data.  Sometimes it takes us an hour to get to the SO WHAT?!  I highly recommend this book to everyone who puts together presentations with data.
I've consumed this book quickly and have already used its advice in my published reports. I recommend this book to anyone producing reports written or online.I do think there is a slight miss with more and more reports being online these days and their consumption being of an interactive nature. The core principals and rules still apply.
This is a really good quick-reference guide for people who need to represent discoveries and ideas from data. Not the most sophisticated study but is great for the growing population of people who need to include data as a part of their conversations.
EXCELLENT BOOK! Recommending it to anybody who is willing to listen. If they don't, their loss. I'm increasing my job skills. So well written.
Lots of practical tips and great visual examples! Like the author says, I will never look at data visualization the same way again.
Great resource to improve your own data visualization skills or to help coworkers make their work more persuasive.  The book covers both practical aspects of charts creation along with more complex theoretical design principles.I first bought the book several years ago and have revisited it several times while creating presentations or summaries of my work.
I thoroughly enjoyed the fundamentals which were explained very easy for even a beginner to understand.Cole reinforced the importance of understanding the context and business needs before we pick a visual and how certain design thinking leads to better business decisions.This book is strongly recommended for anyone in data visualization.
This book is very straight forward and well written. Lays out good visualizations well.
The title is misleading! Nothing even mentioned or discussed for Data Science Python, R, or Julia! One would be able to discern if Amazon offered 'Look inside this book' for this. Waste of my hard earned money!
I was once told the best performers take the time to learn their craft.  This book helps those that work with visualizations do just that.  Great, basic concepts that any data viz, data science or analytics person should be aware of.  This is basic stuff, but all too often basic is what is overlooked.  The author is well-versed and experienced in this subject matter.  This book was recommended to me when I was enrolled in a Tableau-sponsored classroom training workshop.
Really clean and practical tips on how to improve your design game when reporting or presenting data. Love it. Author knows her stuff!
A book that gives you several hints on how to present your data more clearly and in a more friendly manner. However, most recommendations require you NOT to be restrained by some "corporate design". In my previous job we had w PowerPoint template that virtually rendered most of the advice here impossible to implement. But the book is very good with numerous examples based on real life cases.
I will never look at charts and graphs the same way again (and that is a very good thing.) I am telling everyone I work with to read it.
As she mention, having an analytical mind can make it difficult to understand and apply effective visualization techniques. This guideline help you focus your attention to the specific attributes you should pay attention to.
Cole presents a very clear and effective approach to building visualizations that can tell a story with minimal amount of clutter. Each chapter addresses a key step in the process finally wrapping up all up with great examples. Her approach just makes sense. I have applied many of these techniques and it's a noticeable difference when your audience just gets it because they are focused on the right information.Highly recommended!
This book is an easy read, but you'll get even more by following Cole on her blog. Like her blog, this book provides before and after examples of charted data. She transforms a confusing mess into a clear and compelling story with her techniques. It's worth a read.
As the title suggests, this book walks you through the steps of telling a story with data- from asking the right questions to choosing the appropriate charts/graphs. One of my favorite things about this book is the engaging/concise writing style, which really helped with digesting the information. The before/after examples were great as well because you can actually see how to apply the visualization concepts that are being discussed in the chapters. Best of all I was able to apply what I learned from this book at work and received plenty of compliments!
I'm halfway through the book and it has already proven to be thoroughly valuable.  I only expect it to get better and better.  Knaflic continually backs up data viz theory with real-life samples, contrasting the good with the bad.  I've already implemented many of these data viz best practices in my line of work (I work a lot in Excel and Power BI as an Energy Supply Analyst and a Freelancer) and have been able to provide valuable instruction to fellow colleagues based on what I've learned.  This is a very enjoyable book that will surely improve the way you visualize your data, thus drawing out more insight rather than hiding it.
Emily and Jacqueline cover all the data science skills that no one talks about in an elegant and fun way to read. The four parts range from getting started in data science to growing in your current role and each chapter ends with an in-depth interview with a current data scientist in industry. My personal favorite chapter covers the interview experience, which can often be another skill in and of itself.Build A Career in Data Science will prove popular not only for those looking to begin a career in data science but also for those who simply want to take their career to the next level. The in-depth interviews with professional data scientists help the reader understand how data scientists in industries approach and solve actual problems that move the business as well as further their career. Consider this read your go-to mentor for breaking into the field.
This is one of the best books on data visualization I have read. I was immediately able to apply to learning at my job with tremendous success. If you want to stand out from the crowd, read this book and apply the principles!
If you present a lot and are good at it - you will recognize the value of this book.If you are new to the field - start here and stay here.
Very useful. The most useful parts are the examples of "before" and "after".  The concepts are also quite useful.  I hope she comes out with a workbook online that mimics her workshops.
Good book, well written.  If you turn data into reports - this book is for you.  I learned a lot.
This book is amazing and really highlights easy practical ways to use our understanding of how our brains process information, and how and where we focus to draw an audience to particular data. I can't wait to put many of these principles into practice and create standout and compelling presentations.
Helped me look at using data to tell a story with real tools to do that.
Thank you for this book!
I was writing a course curriculum and read a number of books trying to find a good textbook for my class, and once I found this one I knew it would be a winner. It's practical, concise, and replete with examples.
After reading this book you can that data visualization is easier than you expected :)It is book for all whose preparing reports and presentations.
There are apparently fakes of this copy out there. This is not one of them. Ordered this book to further develop my understanding of data visualization practices and I have adopted many (if not all) of the tips and tricks offered as personal best practices for visualizations. Be sure to read the purple boxes as they can offer suggestions for other books to read once you've finish this one.
I work with MS Analytics students at a large university - and I think this book is a great resource for anyone interested in a career in data science.  Very practical A-Z guide to entering the exciting field of data analytics & data science!  Highly recommend
I teach data science to undergraduates at UC San Diego, and am so thankful this book came out - it is my go-to recommendation when I am asked for advice about the job side of data science.This isn't like those "10 tricks to getting a data science job" guides, filled with endless lists of skills, but no real advice.  This book is filled with thoughtful, timely information about the whole data science career arc.The book is in four parts:1. Getting started with data science2. Finding your data science job3. Settling in to data science4. Growing into your data science roleThis book is full of the kind of knowledge that is usually either found the hard way or given from mentors. One example: there is section the pros and cons of working at different types of data science companies, from big tech to startups to direct to consumer retail.
This is a GREAT book for someone looking to get into the field of Data Science! I had a background in Statistics going into looking for my first role working with data. I didn't know it at the time, but I had no foundational idea of what kind of job I wanted working with data, what kind of company I wanted to work for, nor how to make myself the most marketable candidate. Ultimately, I didn't know what I didn't know.This book goes beyond what you can find through a simple Google search about getting a job in Data Science - This book does a great job going into detail on understanding the industry from an insider's perspective. For me, the most helpful part was the guidance on building a portfolio. There's also plenty of guidance on things to think about once you actually get into your new role - SO helpful.I highly recommend this book to anyone looking to start a career in Data Science. It was a complete game changer for me, and I hope it's one for you too!
I'm an experienced data scientist, and I often get asked by aspiring data scientists how to get started. I always recommend this book, because it brings together so much more experience than I have to offer on my own. The interviews give a wide range of advice and perspectives.The authors have done a fantastic job of summarizing the options available at different companies, the types of data science jobs, and the tradeoffs with each. I found the sections on the job application process and working with stakeholders to be particularly informative.I highly recommend this book to anyone who wants to start or grow their career in data science!
This is a wonderful book for those getting started in their Data Science careers. It covers a lot of career building tips and tricks you don't get in technical books. With advice you could only get from experts, I appreciate that they highlight real-world scenarios I can relate to in my career. The book is perfectly tailored to aspiring Data Scientists by outlining how to negotiate a job offer, what to do when a project fails, and even how to leave roles gracefully. I'm love love love the section on managing yoru first months on the job as it helps fill the gaps when going from academia or bootcamp to a Data Science role. I would reccomend this book to newbies as well as people deep into their careers for the insights on how to analyze data effectively, deploy models into production, and even working with stakeholders. It covers a surprising amount of what is left out of most college programs and online courses. 10/10 would reccomend.
I purchased this book to help guide me through the process of learning more about the ins-and-outs of data science positions and how to break into data science. I am currently working in as a software engineer and this book will give you practical knowledge about what to do to start your journey as well as give you various paths to becoming a data scientist. There is no one path and both Emily and Jacqueline lay out various paths that can be taken to get more into data science as a career.They will also walk you through the various types of companies that hire data scientists and what those types of companies expect from you as an employee as well as what you can expect to learn as a data scientist at each type of company.They walk you through how to stand out in a sea of people entering the data science field as well as how to continue to grow and learn more about the vast niches inside of the umbrella that is data science.One aspect that I love about the way the book is structured is that they have interviews with other data scientists in throughout the chapters that continue to re-enforce the things that they are teaching with real world testimonials/insights/scenarios.A must have for anyone that is interested in learning more about what data scientists do to getting a job as a data scientist and also navigating your career in data science. Buy this book now!
I'm currently a graduate student in social psychology looking to transition into data science. Over the past year, I've spent an inordinate amount of time researching data science and attempting to plan out my transition from academia to industry. Despite my best attempts, I felt completely overwhelmed and confused by everything.  There were no mentors in academia that were willing or able to help me in my data science endeavor, so I was ready to give up.On a whim, I emailed the author of an article I had read about transitioning from academia to industry (Emily Robinson) asking for help and guidance regarding my own transition. She helpfully pointed me to the new book she was working on with Jacqueline Nolis that she promised would help me make a smooth transition to a career in data science.The book, Build a Career in Data Science, proved to be exactly what I needed. In clear terms, the book exhaustively details every step of the process for not only getting your first job in data science, but how to advance and succeed in that job. The book went over different kinds of data science jobs, the most common kinds of companies that hire data scientists, which software programs are essential, how to go about learning data science skills, how to get involved in the data science community, and so much more. After a few days spent devouring the entire book, I was finally able to create a clear, comprehensive plan for transitioning from academia to data science. I now feel confident that I can reach my data science career goals, which is a huge relief.I cannot recommend this book highly enough. If you are at all interested in becoming a data scientist but aren't sure how to do so, this needs to be the very first book you read.
As someone who recently made a transition from academic research to data science, this book has served as a compass for navigating analytics projects, communicating with peers and managers, setting up analysis infrastructure, and providing value to stakeholders. I especially like their analysis plan template to provide structure and organization to everyday projects; I now use it for most of my work. Overall, it gives a good understanding of what to expect and how to prepare for every element of a data science job in industry.
I enjoyed this book, it had tons of tidbits that would have been very helpful when I started out in data science a few years ago. I'm definitely going to buy this for people I know just starting out in the field.
I mainly bought it because of the interview questions appendix and stayed because rest of the content is amazing.The book is very well writing and offers a truly detailed journey of how to start in data science and grow in the field.
I really enjoy learning from this book. It has an easy to follow conversational tone, and yet is packed with insights. I like that it contains reproducible code for the examples, so itâ€™s easy to practice from. I do wish the end of section problems had answers in the back.This book is available to read online for free from Hadleyâ€™s website (easily found in a google search).  There are a few errors in this printed copy (just a few), but the one on his website has the corrections.
When I started my job as a research assistant, I started learning statistics to fill a niche. Later that introduced me into the R programming language. I've taken many free online classes, looked at videos, and worked through Github examples. This book(and the Wiley Series in general) have been the greatest resource I have used in developing R mastery.  The structure of the book is unique and very well planned, the information is still every relevant, and you will be referring to themes and concepts from this book for the rest of your career in Data Science. If you are on the fence, I highly recommend this book.
Excellent introduction to the "tidy" approach to use R.  It has bundled several R packages and created a user interface to R and particularly RStudio that makes is much easier to use R for statistics, graphing, and data and text mining.  Strongly recommend new R users to adopt this approach.
I'll preface this review by saying that while I think this book has some significant problems, R seems to be much harder to find good tutorials for than any other programming language or computer topic I've ever learned (I've been a programmer for 15+ years).  I tried a Udacity course on R which ended up being so shallow that I feel like I learned more about R from 20 pages of this book than from hours of Udacity videos.  While I am becoming proficient in R from this book, I think that this book has some problems that have made it slower and more difficult for me to learn R.With the exception of the final few chapters, R for Data Science is informative and easy to understand during its explanation of R concepts and commands.  The biggest problem that I have with this book is with the exercises in each chapter.  Essentially, each chapter shows you to use a hammer, how to use a screwdriver, and how to use a saw, and then says build a birdhouse.  Building a birdhouse is more complex than using a hammer or a saw in isolation, and you may also need additional tools (such as a tape measure), which were never described in the chapter.  It would be much easier to figure out how to build a complete birdhouse if the book first showed how to build smaller scale projects, such as a small wooden box, rather than only showing how to use each tool mostly in isolation.  In that way, you'd get some examples of using tools together to construct something more complicated.Each topic has a few simple exercises, and also two or three exercises which are considerably more complicated than anything which was demonstrated earlier.  While I can figure out the solution eventually, I bought this book so that I can learn R as painlessly as possible.  It's frustrating to spend so much time trying to figure out a problem which I could have figured out much faster had I been give some better examples prior to the exercises.  Furthermore, no answers are given in the back of the book and there are sometimes exercises where a necessary command hasn't been shown in the book (I found a page online where another person had solved the exercises).In addition to the above problems, the last few chapters about formulas and modelling seem rushed and some concepts are poorly explained or barely explained at all (even though those concepts are used in the examples).This book will teach you enough about R to be manipulate and graph datasets and write some simple functions, although it may not give you enough information by itself to solve more complicated problems.  Once you start getting beyond dataset manipulation, modelling, and writing simple functions, you'll probably need another book to sufficiently understand those more advanced concepts.
I picked this up to try and help me learn Program R, as I am a neophyte. I have some limited R experience,  this was way over my head, and seemed hard to understand. I have since purchased R for Beginners, and R for Dummies, and hope to  tackle this book  again with a bit more introductory material under my belt.
This book is available in its latest version as a free online resource whether you buy this book or not. However if you are someone who likes to have an actual book in your hands then is the way to go. As you work through the examples you will find some errors where the R-code does not match the output presented. However, as this is a teaching book, you can look upon these small detective tasks as part of your learning process. If all else fails you can generally find the correct code in the updated online version. Make sure you write in the corrections as you find them to prevent frustration in the future.This book explains some of Hadley Wickham's initiatives to bring "tidiness" to the fairly chaotic universe which new R-coders must navigate.
I am truly enjoying learning r programming with this book. The tone and language are honest and empathetic to the challenges of learning. I've done my best to tackle the exercises throughout it and it has definitely given some comfort.isMy one request is for a binder style book, it's a pain to keep pages open between coding. Dear O'Reilly crew, knowing this I will be $5-10 more for that style :)
Excellent book. After watching R YouTube videos (Roger Peng, Google Developers) the book brings it all together and shows so much more. Amazing resource to learn R. I could be wrong but the book is more detailed than the online version, but very grateful to have a digital resource as well.
This book literally changed my life as it caused me to realize that data science is my calling.  The writing is lucid, the examples are instructive, and the tools the book teaches border on magical.  I can't recommend this book highly enough!
This book was recommended to me by an R instructor at my university. Even after reading through just a bit of it, it seems extremely straightforward and like it's going to be great at explaining data science and beginning programming which is great since I am just starting with programming of any sort. I am hoping to use some of the techniques in this book for my Ph.D. research. Great book!
I think this book is a perfect blend of technical training, visual reporting, sample datasets and clear communication.  The authors teach concepts in small chunks of code, and the code is pretty easy to interpretI will also be honest, it took me a few months of playing with â€œRâ€ to get to a point where the syntax made sense(im still learning).  Im at the point, where i can read each chapter and im NOT getting confused at any of the topics(through chapters 1-5 so far).Im really starting to feel confident enough to take the breakthroughs to develop solutions that will be able to use at work very soon(maybe in a month or so)I had invested several years into Python and its great but my company locks python down but leaves R completely open (probably concerning the kernal access).  So i had to switch to R from scratch.  The transition was intimidating at first, but i plugged away and got the basics on my own.  Now im somewhat confident about subsetting, db connections, dataframes, ggplot and dplyr.  Im looking forward to defining functions and proc+packages in the near future.I really have to commend Hadley and the Grolemund for putting together a VERY DIGESTIBLE book.  The book is long, but it is easy to follow.I bought the physical copy and im so happy, i bought the digital copy(hard to find)Thanks,Chris Buck
I think it's always easy to criticize what is missing from a book like this, but after reading a couple others, I found this to be good for my needs. I was taking an online course created by some Johns Hopkins analysts, and I spent so much time listening to their lectures and finding so much was missing it began to be a waste of time. This book was exactly what I needed to fill in many gaps, have some meaningful but not complicated practice, etc...
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 models5. Presenting information (Communicate)Hereâ€™s a brief overview of each section:ExploreI 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.WrangleThis 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 â€œtidying upâ€ your data and cleaning data points (string and dates).ProgramThis 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.ModelNow, 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 â€œwhyâ€ questions. Definitely planning to come back to this chapter later.CommunicateThis 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. Iâ€™m looking forward to further exploring R and learning how to create my own data visualizations.
Made me realize how powerful that my old friend SAS is, and how easy it is to use R to perform the same data manipulations. The book has brief chapters filled with examples of each functional group. Not sure if the book would be as good for student or new comer who does not realize that tools like SAS and/or R are such necessary and powerful tools if they are not willing to climb steep learning curves to achieve the required end.
Practical way to approach learning data wrangling and manipulation in R. Writing is easy to read and doesnâ€™t get bogged down in coderâ€™s jargon. Used this to teach myself how to handle my thesis data in R. I appreciated the bookâ€™s structure which starts light on several topics, leaves them, and comes back to dive into more detail. However, I ended up flipping from section to section to figure out my specific needs. I also appreciated the authors talked about the overall workflow in R, previous classes and online resources can be too specific and forget to teach efficient ways to manage files and projects.
I really like this book.  Like all R books, if you are a beginner, its hard to find a particular concept.  Googling the question is quicker.  But, it is a great logical tutorial.  I recommend it.  I have to confess I have only worked through the first six chapters.  I think this would be a good book for a class in Data Science.The one thing I don't like, (which is not the author's fault), is that you cannot cut and paste the code into R.  Or at least I don't know how.  The Kindle people could and should make this happen.
As a college student trying to learn R without any programming background, I often found the materials suggested by professors to be confusing and boring. I tried online courses, textbooks, and video tutorials, but still struggled to grasp important concepts. Finally, at the recommendation of a potential employer I gave Wickham's R for Data Science a try. He strongly recommended the book for both beginners, as well as intermediate R users looking to sharpen their skills.Unlike other books I had used previously, Wickham's explanations were concise, clear, and interesting. He teaches important fundamentals of the language, without overwhelming newcomers to R. Wickham's explanations are always accessible, even for those without programming experience.Hadley Wickham is practically a celebrity in the R community, and for good reason. He has developed many of the best packages in use. And unlike many experts, he is very conscious about teaching in a way that anyone can understand. Before you give up on learning R, I would definitely recommend giving this book a try.Note: The book is available for free on the author's website. However, I prefer to have a physical copy and have been pleased with the quality.
This is a good book for someone just getting started with R and Data Science.  The book is straightforward with nothing too complicated.  Covers the essentials, including data wrangling, visualizations, modeling and communication of results.  All the packages needed for the examples are the stand-by's, like ggplot2, dplyr, etc.  There is certainly more to R than provided by the book, but for that, one should check out Advanced R, also by Hadley Wickham.  I did like that the book counter-intuitively began with visualizations, which is usually after basic programming in other R books.  I appreciated the insight that many business user's may come to R for the visualizations, but maybe are scared away by the scripting.  This book attempts to help the reader get over those fears and get to using R quickly.
This is an excellent book for beginners, and teaches R in a consistent and fun way. Especially appreciate Hadley's "tidyverse" approach which makes things easier for beginners, since R can be a mess of inconsistency and mystery otherwise. The book is not so good as a reference however, as there is no summary of each chapter where you can quickly remind yourself of the commands. The book is available free online, so check it out there to see if this is what you need.
The authors are mercifully brief in their explanations but generous in the code samples supplied.  They focus on important details but don't get bogged down in minutia, giving excellent references to on-line material for additional information.  I am still in the process of reading the text, but so far the coverage has been excellent.  I am new to the R code base, so this book is a treasure trove.  The print quality of the book is also refreshingly high, with carefully chosen fonts and effective  use of color.