Foundations of Data and Digital Journalism

205 Pages·2023·12.727 MB·English

by  Alex Richards

Foundations of Data and
Digital Journalism
This accessible, step-by-step guide is written for students and working professionals
who want to better understand data journalism, web design, and the visualization of
information.
Foundations of Data and Digital Journalism recognizes a growing need for general
data knowledge in newsrooms across the globe, including an understanding of what’s
possible for both data reporting and presentation and how it can be achieved. It serves
as a roadmap for students and working journalists who seek to understand what data is
and how to find it; how to harness it most effectively for news; how to think critically
about analysis results, potential shortcomings in the data, and the inclusion of appro-
priate context; and how to present compelling, data-driven stories online. Interviews
with a diverse range of current practitioners help the reader gain a deeper understand-
ing of how these tools and techniques are used in digitally focused newsrooms today.
Taking a holistic approach to data journalism, this book enables readers to:
• Assess a data set with a critical eye, understanding what it shows, how it was
created, and for what purpose.
• Master prominent and easily accessible software tools, including Google Sheets
and R.
• Translate findings and conclusions into plain English for a news audience with-
out overstating what the data can show or being misleading.
• Create impactful, attractive visualizations for an audience to explore.
• Understand how the modern web works, including HTML5, CSS3, and respon-
sive webpage frameworks, like Bootstrap.
This is an ideal textbook for undergraduate and postgraduate journalism students and
for working professionals looking to expand their skillset.
The book is supported with online student resources, including example datasets to
support the material covered, available at Routledge.com.
Alex Richards is an assistant professor at Syracuse University’s S.I. Newhouse School
of Public Communications. Richards was a Pulitzer Prize finalist in 2011, and his
reporting has been honored with the Goldsmith Prize for Investigative Reporting
and the Taylor Family Award for Fairness in Journalism, among others. He previously
worked as a data reporter and editor for the Chicago Tribune, Nerdwallet, The Chron-
icle of Higher Education, and the Las Vegas Sun. Richards is also a former training
director for Investigative Reporters & Editors, where he taught in-depth reporting and
data journalism techniques in newsrooms across the country.

Foundations of Data and
Digital Journalism
Alex Richards
Designed cover image: © Floriana/ iStock via Getty Images
First published 2023
by Routledge
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and by Routledge
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2023 Alex Richards
The right of Alex Richards to be identified as author of this work has
been asserted in accordance with sections 77 and 78 of the Copyright,
Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced
or utilised in any form or by any electronic, mechanical, or other means,
now known or hereafter invented, including photocopying and recording,
or in any information storage or retrieval system, without permission in
writing from the publishers.
Trademark notice: Product or corporate names may be trademarks
or registered trademarks, and are used only for identification and
explanation without intent to infringe.
ISBN: 9781032017778 (hbk)
ISBN: 9781032017747 (pbk)
ISBN: 9781003182238 (ebk)
DOI: 10.4324/9781003182238
Typeset in Goudy
by codeMantra
Access the Support Material: www.routledge.com/9781032017747
For Meg, without whom none of this would have been possible.

Contents
List of figures xi
Acknowledgments xvi
1 Introduction: Why data journalism? 1
Finding data 3
Working with data 4
Representing data 6
Why data journalism? 7
2 Data, numeracy, and how to bulletproof information 10
Gaining a better understanding of your data: first steps 10
What is each row in my data? 12
Spotting common data problems 13
Problems with the data universe 16
Understanding your data: an early checklist 17
Three ways to talk about your data 18
3 Where data comes from—and how to get it 21
Routes to public data 22
Open records in action 23
Record retention schedules: a guide to what governments keep 24
Writing an official request 26
Non-public data 30
Sticking to what’s “gettable” 31
4 Starting with spreadsheets 35
Four ways spreadsheets excel 35
Where spreadsheets (traditionally) lag 36
Some background on Google Sheets 36
Getting started with Google Sheets 37
viii Contents
Basic spreadsheet concepts 38
Other important interface features 41
Getting data into Google Sheets 42
Navigating the data display and controlling its structure 44
Exploring a data set to learn something new 46
5 Sort, filter, pivot: The building blocks of data analysis 51
Reordering your data table with sorting 51
Focusing on the most relevant information with filter 54
Filtering by values 55
Filtering by condition 56
Working with filtered data 57
Other things to know about filtering 58
Summarizing data sets with pivot tables 58
Finding meaningful groups in your data 59
Creating a pivot table 59
Building a simple pivot table from a question 61
Pivot table summary types 63
Sorting a pivot table 65
Leaning into the flexibility of pivot tables 65
Filtering a pivot table 67
More than one way to group data 68
6 Clean and repair: Techniques for more advanced analysis 70
The filter menu and frequency tables 70
Adding columns for cleaned values 72
Functions to help you clean text 74
Using functions in a cleaning column 75
Getting your changes to stick with “Paste Special” 75
Using more than one function at the same time 76
Combining and separating cell contents 76
Using “Split text to columns” 77
Merging values 80
Splitting cells at specific positions: focus on dates 81
Adding new categories based on a condition 83
7 Simple tools for everyday data visualization 86
Common types of visualizations and when to use them 86
The process of creating a data visualization 92
Beyond the data visualization: other critical things to consider 97
Using color in data visualizations 98
What can go wrong? 100
Contents ix
8 Introduction to R and the tidyverse 104
The best way to work with R 105
Overview of the RStudio interface for R 105
Using R: understanding variables 107
Expanding base R with outside packages 108
Installing and loading packages within RStudio 109
Importing a simple data table from CSV 111
Where is R looking for files? 112
An example with read_csv() 112
Data tables, tibbles, and adjusting data types 113
Putting it all together in an R script 115
9 Using R for data analysis 117
Changing the data’s column headers 118
Sorting and filtering data in RStudio and the tidyverse 118
Using “pipes” in the tidyverse to keep your data analysis straight 122
Expanding your analysis into new columns 123
Summarizing a data set by different groups 125
A tidyverse superpower: joining data sets using a column with
shared values 129
The types of joins 130
Returning to the crash data: merging it with populations 134
Joining perils: ending up with duplicated data 136
Next steps in R 137
10 Making the modern web with HTML and CSS 139
An HTML primer 139
What you need to create HTML 141
The parts of an HTML element 142
Basic webpage structure and other common HTML elements 143
Where CSS fits in, and how it works with HTML 146
Writing a CSS selector 147
Some common CSS properties 148
Giving your CSS selectors more specific targets 150
Styling HTML based on classes and IDs 150
When CSS selectors clash 152
11 More advanced CSS: Layouts, Bootstrap, and more 154
Block vs. inline display 155
The “box model” in HTML 155
Simple responsiveness for block elements using width 158