🏃 Rhythm

Ups and Downs, Rhymes and Reasons, Seasons and Rhythms

What graphs will we see today?

Variable #1	Variable #2	Chart Names	Chart Shape
Quant	Quant	Line Plot

What kind of Data Variables will we choose?

No	Pronoun	Answer	Variable/Scale	Example	What Operations?
1	How Many / Much / Heavy? Few? Seldom? Often? When?	Quantities, with Scale and a Zero Value.Differences and Ratios /Products are meaningful.	Quantitative/Ratio	Length,Height,Temperature in Kelvin,Activity,Dose Amount,Reaction Rate,Flow Rate,Concentration,Pulse,Survival Rate	Correlation

Inspiration

Ek Ledecky bheegi-bhaagi si, is it?

Figure 1: LinePlot Inspiration https://fivethirtyeight.com/features/katie-ledecky-is-the-present-and-the-future-of-swimming/

Yeh Ledecky hai, ya jal-pari?

In Figure 1, the black line is the average of the 50 best times at each distance since 2000. The top 200 times for each distance since 2000 are also plotted, with light orange lines each representing one swimmer.

Her races and her career essentially follow the same pattern — the more she swims, the more she separates from the field.

Her 1500 metres record timing is better than the best time for 800m!!😱

How do these Chart(s) Work?

Line Plots take two separate Quant variables as inputs. Each of the variables is mapped to a position, or coordinate: one for the X-axis, and the other for the Y-axis. Each pair of observations from the two Quant variables ( which would be in one row!) give us a point. All this much is identical with the Scatter Plot.

And here, the points are connected together and sometimes thrown away altogether, leaving just the line.

Looking at the lines, we get a very function-al sense of the variation: is it upward or downward? Is it linear or nonlinear? Is it periodic or seasonal…all these questions can be answered with Line Charts.

When one variable is Time?

Line charts often have one variable as a time variable. In such case the data is said to be a time series. We might deal with Time Series later.

Plotting a Scatter Plot

Using Orange

Although this widget exists, , it does not provide quite the kind of Line Chart is considered standard in the data viz industry.

Nonetheless, let us at least look at this data in Orange, before plotting it elsewhere. Yes, peasants.

Import this into Orange and see…(sigh)

Figure 2: RIAA Revenues Data Table

Figure 2 states that there are 432 data points, with 7 variables in the dataset; some missing data.

For now, the variables we need are :

Quantitative Data

• Year: (int) Year in which RIAA revenue was logged
• Value (For Charting): (int) Revenue in million USD We can ignore the rest for now, unless we plan to work more with this data, and need to know more. The other numerical data showing billions of USD are not easily decipherable, an example of data that is not documented well…

Qualitative Data

• Category: (chr) Form of the Music released ( CD etc..)

Using RAWgraphs

Upload this into https://app.rawgraphs.io/ and play! Here is something we can create there:

Using DataWrapper

https://academy.datawrapper.de/article/23-how-to-create-a-line-chart

Here be dragons: DataWrapper wants the data in wide format: each Format of music needs to have its figures in a separate column! 🤦. And this is not a data transformation that we can achieve within DataWrapper. Bah.

We are probably better off plotting a regular scatter plot. Here too there seem to be limitations because we are not able to colour the series based on type of music Format.

What is the Story here?

• Over the years different music formats have had their place in the sun
• All physical forms are on the wane; streaming music is the current mode of music consumption.

The Shape of You Data

Never mind that silly song now.

As mentioned above, data can be in wide or long form. How does one imagine this shape-shifting that seems needed now and then? Let’s see.

Long Form and Wide Form Data

Several tools such as DataWrapper (and others, yes, I admit, even with code, as we will see) need data transformed to a specific shape. We should now look at this idea of shape in data. Consider the data tables below:

Product	Power	Cost	Harmony	Style	Size	Manufacturability	Durability	Universality
G1	0.5858003	0.2773750	0.7244059	0.0731445	0.1000535	0.4551024	0.9622046	0.9966129
G2	0.0089458	0.8135742	0.9060922	0.7546750	0.9540688	0.9710557	0.7617024	0.5062709
G3	0.2937396	0.2604278	0.9490402	0.2860006	0.4156071	0.5839880	0.7145085	0.4899432

Product	Parameter	Rating
G1	Power	0.5858003
G1	Cost	0.2773750
G1	Harmony	0.7244059
G1	Style	0.0731445
G1	Size	0.1000535
G1	Manufacturability	0.4551024
G1	Durability	0.9622046
G1	Universality	0.9966129
G2	Power	0.0089458
G2	Cost	0.8135742
G2	Harmony	0.9060922
G2	Style	0.7546750
G2	Size	0.9540688
G2	Manufacturability	0.9710557
G2	Durability	0.7617024
G2	Universality	0.5062709
G3	Power	0.2937396
G3	Cost	0.2604278
G3	Harmony	0.9490402
G3	Style	0.2860006
G3	Size	0.4156071
G3	Manufacturability	0.5839880
G3	Durability	0.7145085
G3	Universality	0.4899432

What we have done is:
- convert all the variable names into a stacked column Parameter
- Put all the numbers into another column Rating
- Repeated the Product column values as many times as needed to cover all Parameters (8 times).

See the gif below to get an idea of how this transformation can be worked reversibly. (Yeah, never mind the code also.)

So how can we actually do this? Turns out there are some nice people at U. San Diego who have built an R-oriented app called Radiant for Business Analytics that can do this pretty much click-and-point style, though it is nowhere as much fun as Orange. Head off there:

https://vnijs.shinyapps.io/radiant

We upload our original data, pivot it, and download the pivotted data. Peasants. Now the pivotted wide-form data should work in DataWrapper.

Whatever.

Dataset: Cancer

Examine the Data

Data Dictionary

Quantitative Data

Qualitative Data

• Diagnosis: (text) (B)enign, or (M)alignant

Research Questions

Question

Q1.

Question

Q2.

What is the Story Here?

Your Turn

Wait, But Why?

References

1. Charles Chambliss (1989). The Mundanity of Excellence: An ethnographical report on Stratification and Olympic Swimmers.

2. Nijs V (2023). radiant: Business Analytics using R and Shiny. R package version 1.6.0, https://github.com/radiant-rstats/radiant.

Figure 1: LinePlot Inspiration https://fivethirtyeight.com/features/katie-ledecky-is-the-present-and-the-future-of-swimming/ Figure 2: RIAA Revenues Data Table