Data visualization

Purpose

1. Get you excited about storytelling with data

2. Show some tips and tricks to make your maps and charts pop

Hans Rosling’s the best stats you’ve ever seen

This is not about the software. Rather, it is about the theory behind communicating well with data

Structure

1. Improving your maps

2. Overcoming Excel

3. Telling a story with data

4. Reproducing figures for publication

A cartoon in the style of Aardman Animations depicting an animatronic computer fighting with a spreadsheet, vibrant green lighting

Everything is a story

Dan Harmon’s Story Circle

Our Story Circle

Zone of comfort: Data in excel

Want something: to be better at communicating with your data

Enter an unfamiliar situation: Looking closely at maps and charts

Adapt to it: Practice discussing the differences

Get what they wanted: Banging chart skills

Pay a heavy price: Hard to concentrate - relax with an inspiring video

Return to familiar situation: your projects

Having changed: I hope you learn something.

Improving your maps

Legend breaks

Recap from Lab 1 exercies

Make a map of the share of employment in industry in the year 2010 across the whole dataset

Recap from Lab 1 exercies

What is wrong with this map? 🗺️

Recap from Lab 1 exercies

01:00

Discuss with your neighbour:

• What do we like?

• What is confusing?

spmap employment_share_industry using "nutscoord.dta" 
  if year == 2010, 
  id(_ID) fcolor(Spectral) legstyle(2) 
    title("Employment Share Industry - 2010", size(large)) 
    osize(0.02 ..) ocolor(white ..) 
    clmethod(custom) clbreaks(0 (0.2) 1)
    legend(pos(9) size(medium) rowgap(1.5) 
    label(6 "80-100 %") label(5 "60-80 %") 
    label(4 "40-60 %") label(3 "20-40 %") label(2 "0-20 %") 
    label(1 "No Data")) 
    ndfcolor(gray) ndocolor(white ..) ndsize(0.02 ..)

Let’s plot the disribution of the data

histogram employment_share_industry if year == 2010, 
  color(midblue)

Let’s plot the disribution of the data

kdensity employment_share_industry if year == 2010

Now let’s make breaks based on this information

spmap employment_share_industry using "nutscoord.dta" 
  if year == 2010, id(_ID) fcolor(Spectral) legstyle(2) 
    title("Employment Share Industry - 2010", size(large)) 
    osize(0.02 ..) ocolor(white ..) 
    clmethod(custom) clbreaks(0 (0.075) 0.5) 
    legend(pos(9) size(medium) rowgap(1.5) 
    label(7 "37-45 %") label(6 "30-37 %") 
    label(5 "23-30 %") label(4 "15-23 %") 
    label(3 "8-15 %") label(2 "0-8 %") 
    label(1 "No Data")) 
    ndfcolor(gray) ndocolor(white ..) ndsize(0.02 ..)

Colour scales

Uses of color in data visualization

Distinguish categories (qualitative)

Qualitative scale example

Palette name: Okabe-Ito

Qualitative scale example

Palette name: Brewer Set1

Qualitative scale example

Palette name: Brewer Dark2

Uses of color in data visualization

Distinguish categories (qualitative)
Represent numeric values (sequential)

Sequential scale example

Palette name: inferno

Sequential scale example

Palette name: viridis

Uses of color in data visualization

Distinguish categories (qualitative)
Represent numeric values (sequential)
Represent numeric values (diverging)

Diverging scale example

Uses of color in data visualization

Distinguish categories (qualitative)
Represent numeric values (sequential)
Represent numeric values (diverging)
Highlight

Highlight example

Using density plots to set your legend breaks: quick example

Dataset: Solar panels in Sweden

Installed solar capacity in Sweden
Year: 2021
Swedish county	Installed solar capacity (megawatts)
Västra Götalands län	266.21
Skåne län	256.25
Stockholms län	182.25
Östergötlands län	106.81
Hallands län	94.31
Jönköpings län	88.53
Södermanlands län	79.71
Uppsala län	79.11
Kalmar län	59.01
Västmanlands län	49.45
Source: Energimyndigheten

How to decide on values for the bins?

Use a histogram or a density plot to see where the weight of the distribution is.

Map with appropriate breaks

Ask your neighbour:

1. what kind of palette is this?

2. Is it appropriate to use with this data?

01:00

Improving your maps

Great Choropleths

Examples of great maps

Financial Times analysis of Italian election results

Maps work best when they show an emerging spatial pattern, as was the case with this map from the recent Italian elections.

Showing the winner at municipality level clearly shows the political divisions in the country. In the north, the Northern League party triumphed largely on the back of an anti-immigration and anti-EU agenda. In the south, the anti-establishment Five Star Movement was even more successful, gaining a majority of votes in many areas

Examples of great maps

Financial Times analysis of Italian election results

Another interesting take on the results was to show the per cent share of vote. This helped to emphasise just how strong the support was for the two parties.

Examples of great maps

Human Terrain from The Pudding

Examples of great maps

The Coming Crisis: Exploring the U.S. Physician Shortage by Daniel Snow

Overcoming Excel

Motivation

Overcoming Excel

Excel itself is not a bad tool.

It is very popular! See the info on jobs that require it.

Overcoming Excel

Formby et al (2017) Microsoft Excel: Is It An Important Job Skill for College Graduates?

Overcoming Excel

Takeaways:

1. You will likely use Excel in the future 📊

2. Excel’s default plots and tables can be improved upon 📈

3. Simple rules can help you make your message clear 💎

Overcoming Excel

Charts

Overcoming Excel: Column plot

• We often encounter datasets containing simple amounts 🤏

• Here is some data on a sample of Swedish musical artists 🎵

• I put this data into Excel, and asked for a recommended chart 📊

Swedish musical artists
Rank	Artist	Monthly listeners (m)
1	Avicii	29.47
2	ABBA	23.48
3	José González	4.07
4	Robyn	3.11
5	Timbuktu	0.38
Datasource: Spotify charts Nov 2022

Your turn

02:30

Discuss with your neighbour:

• What do we like?

• What is confusing?

Tip 1: Avoid rotated axis labels

Ugly 🤢

Tip 1: Avoid rotated axis labels

Flip axes so that the text is easier to read 👓

Tip 2: Pay attention to the order of the bars

Bad 👎

Tip 2: Pay attention to the order of the bars

It is clear that José González recieves more streams than Robyn

Tip 3: Consider your titles, labels and axes

Uninformative️ ❗

Tip 3: Consider your titles, labels and axes

Note the title, x-axis title, x-axis labels 📙

Tip 3: Consider your titles, labels and axes

Titles and captions have different application areas

Figure 1: Monthly streams for Swedish musical artists. Data sources: Spotify charts in November 2022

We can use dots instead of bars

We can use dots instead of bars

Dots are preferable if we want to truncate the axes

Dataset: Solar panels in Sweden

Dots are preferable if we want to truncate the axes

Bar lengths do
not accurately
represent the
data values

Dots are preferable if we want to truncate the axes

Key features
of the data
are obscured

Dots are preferable if we want to truncate the axes

Overcoming Excel

Tables

Overcoming Excel: Tables

• We often encounter datasets containing simple amounts 🤏

• Here is some data on a sample of Swedish musical artists 🎵

• I put this data into Excel, and asked it to insert a table 🗃️

Swedish musical artists
Rank	Artist	Monthly listeners (m)
1	Avicii	29.47
2	ABBA	23.48
3	José González	4.07
4	Robyn	3.11
5	Timbuktu	0.38
Datasource: Spotify charts Nov 2022

Your turn again

02:30

Discuss with your neighbour:

• What do we like?

• What is confusing?

Key rules for table layout
Number	Rule
1	Do not use vertical lines.
2	Do not use heavy horizontal lines between data rows. (Horizontal lines as separator between the title row and the first data row or as frame for the entire table are fine.)
3	Text columns should be left aligned.
4	Number columns should be right aligned and should use the same number of decimal digits throughout.
5	Columns containing single characters are centred.
6	The header fields are aligned with their data, i.e., the heading for a text column will be left aligned and the heading for a number column will be right aligned.
Source: Claus Wilke’s Fundamentals of Data Visualization

Let’s apply these rules

01:30

Key rules for table layout
Number	Rule
1	Do not use vertical lines.
2	Do not use heavy horizontal lines between data rows. (Horizontal lines as separator between the title row and the first data row or as frame for the entire table are fine.)
3	Text columns should be left aligned.
4	Number columns should be right aligned and should use the same number of decimal digits throughout.
5	Columns containing single characters are centred.
6	The header fields are aligned with their data, i.e., the heading for a text column will be left aligned and the heading for a number column will be right aligned.
Source: Claus Wilke’s Fundamentals of Data Visualization

Table A

Table B

Let’s apply these rules

01:30

Key rules for table layout
Number	Rule
1	Do not use vertical lines.
2	Do not use heavy horizontal lines between data rows. (Horizontal lines as separator between the title row and the first data row or as frame for the entire table are fine.)
3	Text columns should be left aligned.
4	Number columns should be right aligned and should use the same number of decimal digits throughout.
5	Columns containing single characters are centred.
6	The header fields are aligned with their data, i.e., the heading for a text column will be left aligned and the heading for a number column will be right aligned.
Source: Claus Wilke’s Fundamentals of Data Visualization

Table C

Table D

Storytelling with data

Related time series

Plotting related time series

Dataset: Fertility and births outside of marriage in Denmark and Greece.

Default choice for plotting is two line plots

Plotting related time series

Pros 👍

• Familiar

Cons 👎

• Hard to keep track of each series

• Difficult to compare movements across short periods

An alternative: time on a third axis

What have we learned?

• Both countries saw a large drop in fertility from the 1960s until the 1980s

• In Denmark, after 1970 we see an increase in the share of children born outside of marriage

• In contrast, Greek families have relatively few children outside of marriage.

• After 1990, Danish fertility increased from 1.3 to 1.8, while Greek fertility remained at ‘lowest-low’ levels, below replacement.

What have we changed?

• Indicators on the x- and y-axis and then show time with text labels

• Legend is replaced with colour coded title

• Colours have meaning (main colour of country flag)

• Percentage labels on the y-axis

Storytelling with data

Giving context

Giving context

Sometimes we may want to show a particular series of data in its correct context.

For instance, in our line graph above which showed the evolution of the share of births outside of marriage in Denmark and Greece, we might want to know if these two represent the extremes within Europe.

Giving context

Do Denmark and Greece represent the extremes of the share of children born outside of marriage in Europe?

Giving context with an average

One way to do this would be to show an average for Europe

Giving context with an interval ribbon

Giving context with all of the data

This is silly

Giving context with all of the data

Here we highlight the series we are interested in and draw in the remaining series in grey

What have we changed?

• Shows each of the series

• We can see that Denmark is a leader in the beginning, but is caught up by other nations

• Does not hide outliers

• Makes clear the trends in your countries of interest

Storytelling with data

Tips for polished figures

Tips for polishing your figures

Where to get great colours from for your plots:

help spmap # Look for the palettes under fcolor

Recreating published figures

A FT chart published without the underlying data

Recreating published figures

Recreating published figures

You pay a heavy price

Additional data demo