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storytelling with data: a data visualization guide for business professionals storytelling with data storytelling with data a data visualization guide for business professionals cole nussbaumer knaflic Cover image: Cole Nussbaumer Knaflic Cover design: Wiley Copyright © 2015 by Cole Nussbaumer Knaflic. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748- 6008, or online at www.wiley.com/go/permissions. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762- 2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com. Library of Congress Cataloging-in-Publication Data: ISBN 9781119002253 (Paperback) ISBN 9781119002260 (ePDF) ISBN 9781119002062 (ePub) Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 http://www.copyright.com http://www.wiley.com/go/permissions http://booksupport.wiley.com http://www.wiley.com To Randolph vii contents foreword ix acknowledgments xi about the author xiii introduction 1 chapter 1 the importance of context 19 chapter 2 choosing an effective visual 35 chapter 3 clutter is your enemy! 71 chapter 4 focus your audience’s attention 99 chapter 5 think like a designer 127 chapter 6 dissecting model visuals 151 chapter 7 lessons in storytelling 165 chapter 8 pulling it all together 187 chapter 9 case studies 207 chapter 10 final thoughts 241 bibliography 257 index 261 ix foreword “Power Corrupts. PowerPoint Corrupts Absolutely.” —Edward Tufte, Yale Professor Emeritus1 We’ve all been victims of bad slideware. Hit‐and‐run presentations that leave us staggering from a maelstrom of fonts, colors, bullets, and highlights. Infographics that fail to be informative and are only graphic in the same sense that violence can be graphic. Charts and tables in the press that mislead and confuse. It’s too easy today to generate tables, charts, graphs. I can imagine some old‐timer (maybe it’s me?) harrumphing over my shoulder that in his day they’d do illustrations by hand, which meant you had to think before committing pen to paper. Having all the information in the world at our fingertips doesn’t make it easier to communicate: it makes it harder. The more information you’re dealing with, the more difficult it is to filter down to the most important bits. Enter Cole Nussbaumer Knaflic. I met Cole in late 2007. I’d been recruited by Google the year before to create the “People Operations” team, responsible for finding, keep- ing, and delighting the folks at Google. Shortly after joining I decided 1 Tufte, Edward R. ‘PowerPoint Is Evil.’ Wired Magazine, www.wired.com/wired/ archive/11.09/ppt2.html, September 2003. http://www.wired.com/wired/archive/11.09/ppt2.html http://www.wired.com/wired/archive/11.09/ppt2.html x foreword we needed a People Analytics team, with a mandate to make sure we innovated as much on the people side as we did on the product side. Cole became an early and critical member of that team, acting as a conduit between the Analytics team and other parts of Google. Cole always had a knack for clarity. She was given some of our messiest messages—such as what exactly makes one manager great and another crummy—and distilled them into crisp, pleasing imagery that told an irrefutable story. Her messages of “don’t be a data fashion victim” (i.e., lose the fancy clipart, graphics and fonts—focus on the message) and “simple beats sexy” (i.e., the point is to clearly tell a story, not to make a pretty chart) were powerful guides. We put Cole on the road, teaching her own data visualization course over 50 times in the ensuing six years, before she decided to strike out on her own on a self‐proclaimed mission to “rid the world of bad PowerPoint slides.” And if you think that’s not a big issue, a Google search of “powerpoint kills” returns almost half a million hits! In Storytelling with Data, Cole has created an of‐the‐moment complement to the work of data visualization pioneers like Edward Tufte.  She’s worked at and with some of the most data‐driven organizations on the planet as well as some of the most mission‐driven, data‐free institutions. In both cases, she’s helped sharpen their messages, and their thinking. She’s written a fun, accessible, and eminently practical guide to extracting the signal from the noise, and for making all of us better at getting our voices heard. And that’s kind of the whole point, isn’t it? Laszlo Bock SVP of People Operations, Google, Inc. and author of Work Rules! May 2015 xi acknowledgments  My timeline of thanks Thank you to… 2015 1980 2010−CURRENT My family, for your love and support. To my love, my husband, Randy, for being my #1 cheerleader through it all; I love you, darling. To my beautiful sons, Avery and Dorian, for reprioritizing my life and bringing much joy to my world. 2010−CURRENT My clients, for taking part in my effort to rid the world of ineffective graphs and inviting me to share my work with their teams and organizations through workshops and other projects. Thank you also to everyone who helped make this book possible. I value every bit of input and help along the way. In addition to the people listed above, thanks to Bill Falloon, Meg Freeborn, Vincent Nordhaus, Robin Factor, Mark Bergeron, Mike Henton, Chris Wallace, Nick Wehrkamp, Mike Freeland, Melissa Connors, Heather Dunphy, Sharon Polese, Andrea Price, Laura Gachko, David Pugh, Marika Rohn, Robert Kosara, Andy Kriebel, John Kania, Eleanor Bell, Alberto Cairo, Nancy Duarte, Michael Eskin, Kathrin Stengel, and Zaira Basanez. 2007−2012 The Google Years. Laszlo Bock, Prasad Setty, Brian Ong, Neal Patel, Tina Malm, Jennifer Kurkoski, David Hoffman, Danny Cohen, and Natalie Johnson, for giving me the opportunity and autonomy to research, build, and teach content on effective data visualization, for subjecting your work to my often critical eye, and for general support and inspiration. 2002−2007 The Banking Years. Mark Hillis and Alan Newstead, for recognizing and encouraging excellence in visual design as I first started to discover and hone my data viz skills (in sometimes painful ways, like the fraud management spider graph!). 1987−CURRENT My brother, for reminding me of the importance of balance in life. 1980−CURRENT My dad, for your design eye and attention to detail. 1980−2011 My mother, the single biggest influence on my life; I miss you, Mom. xiii about the author  Cole Nussbaumer Knaflic tells stories with data. She specializes in the effective display of quantitative information and writes the pop- ular blog storytellingwithdata.com. Her well‐regarded workshops and presentations are highly sought after by data‐minded individu- als, companies, and philanthropic organizations all over the world. Her unique talent was honed over the past decade through analyti- cal roles in banking, private equity, and most recently as a manager on the Google People Analytics team. At Google, she used a data‐ driven approach to inform innovative people programs and man- agement practices, ensuring that Google attracted, developed, and retained great talent and that the organization was best aligned to meet business needs. Cole traveled to Google offices throughout the United States and Europe to teach the course she developed on data visualization. She has also acted as an adjunct faculty member at the Maryland Institute College of Art (MICA), where she taught Introduction to Information Visualization. Cole has a BS in Applied Math and an MBA, both from the University of Washington. When she isn’t ridding the world of ineffective graphs one pie at a time, she is baking them, traveling, and embarking on adventures with her husband and two young sons in San Francisco. 1 introduction Bad graphs are everywhere I encounter a lot of less‐than‐stellar visuals in my work (and in my life—once you get a discerning eye for this stuff, it’s hard to turn it off). Nobody sets out to make a bad graph. But it happens. Again and again. At every company throughout all industries and by all types of people. It happens in the media. It happens in places where you would expect people to know better. Why is that? Figure 0.1 A sampling of ineffective graphs 16% 9% 7% 10% 10% 15% 10% 18% 10% 17% 32% 20% 15% 11% US Population Our Customers Our Customers Segment 7 Segment 6 Segment 5 Segment 4 Segment 3 Segment 2 Segment 1 (1.50) (1.00) (0.50) 0.00 0.50 1.00
Answered Same DayJun 16, 2021

Answer To: storytelling with data: a data visualization guide for business professionals storytelling with data...

Neha answered on Jun 21 2021
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Visualization in R
R Can be defined as a programming language which provides environment to perform the statistically computing and visualise the data using graphs for stop it is similar to the S environment and language and it was developed at the Bell laboratories. R Can be considered as the different implementation of the S language. There are few differences between both the languages but most of
the code written in S language can we run on the R language without making any changes. the R language is used to provide different types of statistically and graphical techniques. The statistically techniques can be classical test, classifications, linear and non linear modelling, clustering, time series analysis etc. the R language is highly extensible full stock the S language is mainly the choice of the researcher to perform a research in the statistically method ology but are provides turn environment which is open source to participate in the activities.
one of the main strength of R is that it is possible to produce the publication quality plots easily by including the formulas and mathematical symbols as per the requirement. There are few default designs are given for the graphs but the user has complete control over them and the design can be changed as per the requirement. The R can be defined as an integrated suite which includes the facilities to manipulate the data, calculate and display it graphically. The following are the facilities which are present in R language.
· It provides an effective method to handle the data and store it.
· it includes a suite of operators which can be used to calculate arrays.
· it has large and integrated collection of some intermediate tools which are useful for analysing the data.
· It comes with some graphical facilities to analyse the data and display it using the hard copy or on screen.
· it is a simple and effective programming language which is well developed and includes conditions, loops, input and output and the user defined recursive functions.
The R is a command line driven programming language. we can either right or just copy paste the commands after the command prompt symbol. After writing the command in the R console we can just press enter button and the command will show the output. If the command is incomplete then continuation prompt is issued which is signified by +. the another method is to write the script in the script window and then select a command. Run button can be used to execute the command. The R Language sales case sensitive and we should make sure that the spelling and capitalization is correct. The commands written in R language are also known as the function. The basic format to write a function is as follows:
object <- function.name(argument_1 = data, argument_2 = TRUE)
For this project I have used R studio to execute the programs and draw the graph. The R studio have different advantages.
1) The design of our studio supports easiest way to write the core. As we create a new script in the rstudio the windows adjust the session automatically so that we can see the script and results on the console when the commands are executed.
2) Rstudio helps the you Sir to view and interact with the objects which are present in the environment.
3) It is easy to set the working directory and access all the files on the computer using rstudio. The rstudio allows us to navigate to the folders using the files option.
4) The graphs in the rstudio are much more accessible for a casual user. it allows the user 2 move between the graphs and change the size of the graphs with out running the code again and again. It allows us to export or copy the graphs to some other document. It adds flexibility to the graphs.
In this report I have used a data set on the basis of this says turn for video games. I have taken the data from the Kaggle. the visualization in or can be done by using some basic libraries which helps hours to import the different types of graphs. the R provides us N number of libraries...
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