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Foundations of Data and Digital Journalism [Kõva köide]

  • Formaat: Hardback, 188 pages, kõrgus x laius: 234x156 mm, kaal: 453 g, 121 Tables, black and white; 57 Halftones, black and white; 57 Illustrations, black and white
  • Ilmumisaeg: 14-Feb-2023
  • Kirjastus: Routledge
  • ISBN-10: 1032017775
  • ISBN-13: 9781032017778
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Foundations of Data and Digital JournalismSuurem pilt
  • Formaat: Hardback, 188 pages, kõrgus x laius: 234x156 mm, kaal: 453 g, 121 Tables, black and white; 57 Halftones, black and white; 57 Illustrations, black and white
  • Ilmumisaeg: 14-Feb-2023
  • Kirjastus: Routledge
  • ISBN-10: 1032017775
  • ISBN-13: 9781032017778
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032017778.html
Märksõnad:

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 appropriate context; and how to present compelling, data-driven stories online. Interviews with a diverse range of current practitioners help the reader gain a deeper understanding 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 without 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 responsive 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.



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.

List of figures
xi
Acknowledgments xvi
1 Introduction: Why data journalism?
1(9)
Finding data
3(1)
Working with data
4(2)
Representing data
6(1)
Why data journalism?
7(3)
2 Data, numeracy, and how to bulletproof information
10(11)
Gaining a better understanding of your data: first steps
10(2)
What is each row in my data?
12(1)
Spotting common data problems
13(3)
Problems with the data universe
16(1)
Understanding your data: an early checklist
17(1)
Three ways to talk about your data
18(3)
3 Where data comes from---and how to get it
21(14)
Routes to public data
22(1)
Open records in action
23(1)
Record retention schedules: a guide to what governments keep
24(2)
Writing an official request
26(4)
Non-public data
30(1)
Sticking to what's "gettable"
31(4)
4 Starting with spreadsheets
35(16)
Four ways spreadsheets excel
35(1)
Where spreadsheets (traditionally) lag
36(1)
Some background on Google Sheets
36(1)
Getting started with Google Sheets
37(1)
Basic spreadsheet concepts
38(3)
Other important interface features
41(1)
Getting data into Google Sheets
42(2)
Navigating the data display and controlling its structure
44(2)
Exploring a data set to learn something new
46(5)
5 Sort, filter, pivot: The building blocks of data analysis
51(19)
Reordering your data table with sorting
51(3)
Focusing on the most relevant information with filter
54(1)
Filtering by values
55(1)
Filtering by condition
56(1)
Working with filtered data
57(1)
Other things to know about filtering
58(1)
Summarizing data sets with pivot tables
58(1)
Finding meaningful groups in your data
59(1)
Creating a pivot table
59(2)
Building a simple pivot table from a question
61(2)
Pivot table summary types
63(2)
Sorting a pivot table
65(1)
Leaning into the flexibility of pivot tables
65(2)
Filtering a pivot table
67(1)
More than one way to group data
68(2)
6 Clean and repair: Techniques for more advanced analysis
70(16)
The filter menu and frequency tables
70(2)
Adding columns for cleaned values
72(2)
Functions to help you clean text
74(1)
Using functions in a cleaning column
75(1)
Getting your changes to stick with "Paste Special"
75(1)
Using more than one function at the same time
76(1)
Combining and separating cell contents
76(1)
Using "Split text to columns"
77(3)
Merging values
80(1)
Splitting cells at specific positions: focus on dates
81(2)
Adding new categories based on a condition
83(3)
7 Simple tools for everyday data visualization
86(18)
Common types of visualizations and when to use them
86(6)
The process of creating a data visualization
92(5)
Beyond the data visualization: other critical things to consider
97(1)
Using color in data visualizations
98(2)
What can go wrong?
100(4)
8 Introduction to R and the tidyverse
104(13)
The best way to work with R
105(1)
Overview of the RStudio interface for R
105(2)
Using R: understanding variables
107(1)
Expanding base R with outside packages
108(1)
Installing and loading packages within RStudio
109(2)
Importing a simple data table from CSV
111(1)
Where is R looking for files?
112(1)
An example with read_csv ()
112(1)
Data tables, tibbles, and adjusting data types
113(2)
Putting it all together in an R script
115(2)
9 Using R for data analysis
117(22)
Changing the data's column headers
118(1)
Sorting and filtering data in RStudio and the tidyverse
118(4)
Using "pipes" in the tidyverse to keep your data analysis straight
122(1)
Expanding your analysis into new columns
123(2)
Summarizing a data set by different groups
125(4)
A tidyverse superpower: joining data sets using a column with shared values
129(1)
The types of joins
130(4)
Returning to the crash data: merging it with populations
134(2)
Joining perils: ending up with duplicated data
136(1)
Next steps in R
137(2)
10 Making the modern web with HTML and CSS
139(15)
An HTML primer
139(2)
What you need to create HTML
141(1)
The parts of an HTML element
142(1)
Basic webpage structure and other common HTML elements
143(3)
Where CSS fits in, and how it works with HTML
146(1)
Writing a CSS selector
147(1)
Some common CSS properties
148(2)
Giving your CSS selectors more specific targets
150(1)
Styling HTML based on classes and IDs
150(2)
When CSS selectors clash
152(2)
11 More advanced CSS: Layouts, Bootstrap, and more
154(27)
Block vs. inline display
155(1)
The "box model" in HTML
155(3)
Simple responsiveness for block elements using width
158(2)
Content divisions
160(1)
Repositioning a content division element using margins
161(2)
Nesting div elements
163(2)
Keeping images in check
165(1)
Shorthand properties for margins, padding, and borders
166(1)
Content divisions and the semantic web
167(1)
Creating more complex webpage layouts with the Bootstrap Grid
167(1)
What is Bootstrap?
168(1)
Adding Bootstrap to your webpage
169(1)
How the Bootstrap grid works: using containers, rows, and columns
169(2)
Column classes
171(5)
Giving instructions for more than one breakpoint
176(1)
Aligning and distributing content
177(2)
Next Bootstrap steps
179(2)
12 Where to learn more
181(4)
Index 185
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 Chronicle 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.