Intro to Data Journalism

City, University of London
https://ddj.nicu.md/city/

👋 Welcome to Data Journalism

Who am I?

My name is Nicu Calcea.

I’m a data investigative journalist and City University alumnus.

I work at Global Witness, and I was previously doing data journalism at BBC News and the New Statesman.

Some stories

• The UK government met with oil lobbyists every day last year
• Eight out of ten firms pay men more than women
• How much territory does Ukraine control? Use this interactive map to find out
• Tory MPs would be over £1m worse off in six months with Boris Johnson’s second job ban

I graduated 4 years ago and did this module 5 years ago.

Get in touch

• Ion.Calcea.2@city.ac.uk
• James.Morris@city.ac.uk

You can email me with any questions about these slides, examples I’ve given, assignments, etc.

I do have a full time job, so for any administrative or urgent queries, please contact James Morris.

Who are you?

• What’s your name?
• What course are you in?
• Do you have any experience in data journalism?
• Why did you choose this module?

Sorry if I mispronounce any or all of your names and also sorry if I don’t remember everyone’s name. I will do my best.

I understand we are at different levels of experience. You have access to datacamp which will let you move ahead if you want to get a bit more from this module.

The plan

• Week 1: Introduction
• Weeks 2-3: Analysis
• Week 4: Cleaning
• Week 5: Stories
• Week 6: Visualisation
• Week 7: Maps
• Week 8: Projects
• Week 9: Scraping
• Week 10: Conclusions

Week 1

Introduction to Data Journalism

What is Data Journalism?

In its most simple definition, data journalism is the practice of using numbers and trends to tell a story. — Betsy Ladyzhets

Some people call it data-driven journalism, computer-assisted reporting or CAR (in the US), precision journalism. It’s history is even older than that though: the first edition of the Manchester Guardian had a data journalism article. So don’t focus too much on what you call it.

Data journalism [is] finding – in data – stories that are of interest to the public and presenting them in the most appropriate manner for public use and reuse. — Bahareh Heravi

Data journalism does not mean you have to limit yourself to data: we do everything else other reporters do, including developing contacts, interviewing sources, sending FOIs, doing field investigations, checking facts, writing, editing, multimedia (when relevant), etc. At the NS, data journalists are usually in charge of their stories from start to end, we’re not just a graphics desk.

Some people even have their own beats. I have data journalist colleagues who focus on particular beats, like politics, the environment, business, even fine wines (World of Fine Wines).

Data can be hard and complex, you’ll always want to reach out to experts who can explain things for you. Energy consumption map as an example.

History

Why do we need data journalism?

Tell richer stories

An increasing amount of human activity is recorded with data. This means there is a data angle for almost any subject.

Be more efficient

We tell some stories every year, month or day. We can greatly simplify or automate stories, giving us more time to focus on in-depth reporting.

Be more accurate

Though not without data quality issues and ethical considerations, accuracy is central to data journalism.

Unique angles

There are now stories where a data angle is the only or main angle. By using data, journalists can create news instead of covering them.

Personalise news

Make readers invested in a story by personalising it to their postcode, age or socio-economic status.

New audiences

Data journalism is exciting (I hope). The pandemic has shown that readers will reward publishers with their clicks.

Job trends

Since the pandemic, nearly every newsrooms has prioritised data journalism and has been massively hiring for data journalism positions.

New(-ish) platforms like Datawrapper and Flourish allow journalists to create and visualise data stories easier and without much technical expertise.

However, the increased supply of data journalists from courses like this means there are higher entry requirements (R, Python, SQL).

Data teams like the one at the FT and the BBC are now so big they need to be split into several smaller teams.

There are R, Python and SQL courses on Datacamp, which you have access to for free.

The process

Question

As with all journalism, data journalism starts with a question that the reporter wants to answer.

Source data

Data can come from government sources, third parties, or be collected by the reporter themselves.

Clean data

In most cases, you will need to filter, sort and clean up any errors or missing information in your dataset.

The process

Analyse data

How do you find the answer to your question in the data?

Review

While data doesn’t lie, data publishers do. Do your findings make sense? Can you verify them?

Present results

Communicate data in the most suitable way. Usually, but not always, you will visualise your findings.

Analyse data: be platform agnostic. Some tools die because APIs change, others are abandoned by their developers, some are replaced by better alternatives.

The process

Baby names

1. Make a copy of this spreadsheet and pick one tab to work in. Data from the ONS.
2. The yellow cells indicate where you need to fill in formulas.
3. What are some other potential stories that you can think of? Are there more babies named after the royals? What about Game of Thrones characters? What are the most popular gender-neutral names? Long-term trends?

Show the original dataset first

Some datasets (like the ONS one) come with a data dictionary.

For Nicu: choose boys or girls based on if there are more women or men in the group

[=A1+A2]

Returns one number added (+) or subtracting (*) another.

[=A1/B$1]

Returns one number divided (/) or multiplied (*) by another.

[=SUM()]

Returns the sum of a series of numbers and/or cells.

=AVERAGE()

Returns the numerical average value in a dataset, ignoring text.

=MEDIAN()

Returns the median value in a numeric dataset.

[=(NEW-OLD)/OLD]

Shows percentage change.

Averages

=MODE()

Finds the most common value in a range.

   

=MEDIAN()

Finds the value that’s right in the middle of a dataset.

   

=AVERAGE()

Sum all the values and divide by the number of records.

How did the Mail do it?

2018

2019

Assignments

Critique a data journalism project

• A 20-25 minute long narrated group PowerPoint presentation critiquing a data project that won or was shortlisted for the Sigma Awards.
• 500-word group reflection, with appropriate references.
• A 200-word reflection on your own learning.

Deadline: Friday, 13 December, 16:00 Marking: 40% of your final mark

Data journalism portfolio

• One news story (400 words).
• One EITHER feature story OR news investigation (800 words) substantially based on data techniques; and published digitally with appropriate visualisations.
• A 200 word reflective blog-post style log on you own learning journey.

Deadline: Friday, 24 January, 16:00 Marking: 60% of your final mark

No plagiarism, careful with AI.

Contact

• Ion.Calcea.2@city.ac.uk
• James.Morris@city.ac.uk

Week 2

Introduction to Data Journalism
https://ddj.nicu.md/city/

Sourcing data

Open Data

• UK
  • ONS
  • GOV.UK
  • Nomis (labour stats)
  • NHS Digital
  • Covid dashboard

• Other nations
  • US gov
  • Eurostat
• International
  • World Bank
  • United Nations
  • OECD
  • Our World in Data

Plan ahead

Closed data

FOIs

WhatDoTheyKnow

Be as specific as possible or you might get the “too much work” excuse, specify the format or you’ll get a PDF

Likelihood they’ll refuse (they refused my locations of CCTV cameras locations on national security grounds)

If you want to use FOIs, do it as soon as possible.

Scraping

Scraping (week 9)

pre-scraped data like Inside Airbnb

Parsehub, browser extensions, R/Python/Node

We’ll maybe talk about AI? as well?

Census exercise

1. Make a copy of this spreadsheet (here’s the original data).
2. Before you do anything else, what are some questions you would like to answer?
3. Free first row, filter the table to the region you’re from (or London)
4. Fill in the columns at the end
5. What are some other potential stories that you can think of?

1. Concatenate title =CONCATENATE(A2,“,”,B2, ” (“, C2,”)“)
2. Conditional formatting on success column

[=A1+A2]

Returns one number added (+) or subtracting (*) another.

[=A1/B$1]

Returns one number divided (/) or multiplied (*) by another.

[=SUM()]

Returns the sum of a series of numbers and/or cells.

=AVERAGE()

Returns the numerical average value in a dataset, ignoring text.

=MEDIAN()

Returns the median value in a numeric dataset.

[=(NEW-OLD)/OLD]

Shows percentage change.

[=IF()]

Returns one value if the result is true, another if it’s false.

[=COUNTIF()]

Count all the cells that match a condition.

[=SUMIF()]

Sum all the cells that match a condition.

[=CONCATENATE()]

Combine multiple bits of text together. Use =SPLIT() for the opposite.

[=VLOOKUP()]

Match the values in a cell with the corresponding row in another dataset.

[=XLOOKUP()]

Same as =XLOOKUP() but more flexible and easier to grasp.

Contact

• Ion.Calcea.2@city.ac.uk
• James.Morris@city.ac.uk

Week 3

Introduction to Data Journalis
https://ddj.nicu.md/city/

Toolbox

XLOOKUP

=XLOOKUP(search_term, col_to_search, col_to_return)

XLOOKUP exercise

1. Make a copy of this spreadsheet.
2. Fill in the empty columns with formulas we learned last time.

Pivot Tables

Pivot tables are extra tables in your spreadsheet, in which you can summarise data from your original table.

You can calculate averages, counts, max/min values or sums for numbers in a group.

Pivot table exercise

1. Make a copy of this spreadsheet.

Bonus points: Grab a CSV from police.uk and do it yourself.

Averages

=MODE()

Finds the most common value in a range.

   

=MEDIAN()

Finds the value that’s right in the middle of a dataset.

   

=AVERAGE()

Sum all the values and divide by the number of records.

Contact

• Ion.Calcea.2@city.ac.uk
• James.Morris@city.ac.uk