Alright, perfect.
In this video we discussed when to use
bar, pie, doughnut, line and area charts.
Now we are ready to continue where we left
off.
Treemap charts
One type of chart that is not used as often
as it should be is the Treemap chart.
Here is what a Treemap looks like.
It allows us to split the sum of the whole
into hierarchies and then show an internal
breakdown of each of these hierarchies.
When to use Treemap charts
The company we have been looking at so far
has three divisions.
And each of them has its own products.
This is the perfect way to provide information
about the weight divisions have with respect
to the firm’s total revenue.
At the same time it shows how much each product
contributes to the revenue of its division.
Very informative, right?
When to avoid Treemap charts
As you can imagine it is quite difficult to
apply treemap charts to a context that is
not the one we just described.
Treemap charts are not suitable when the data
we are working with is not divisible into
categories and sub-categories.
Moreover, we can’t use treemap charts if
we want to track development over time.
Bridge chart
Bridge, also known as waterfall charts, take
their origins from consulting.
Several decades ago top tier “24/7 at your
service” consultants at McKinsey popularized
this type of visualization among their clients.
And ever since, the popularity of bridge charts
has continued to rise.
Bridge charts are made of bars showing the
cumulative effect of a series of positive
and negative values impacting a starting and
an ending value.
Here’s an example.
When to use bridge charts
There are two major use cases of bridge charts.
Both are very interesting and intuitive.
First, we can use this type of visualization
whenever we would like to bridge the difference
between two periods.
So, in our example from earlier, the company
registered different revenues in 2018 compared
to 2017, right?
The starting period for this chart is the
end of 2017 or 2018.
The ending period is the end of 2018.
With a simple bar chart, we would just see
an increase of 6 million.
The bridge chart gives us additional information
– how different divisions contributed to
this increase.
In fact, the revenues of two of the divisions
increased, while the other one didn’t.
In a similar fashion, a bridge chart can show
us how one variable was influenced by a series
of factors to obtain a specific output.
Let’s provide an easy to understand example,
which is heavily used in finance.
The company’s revenues were equal to 109
million $ in 2018, right?
What if we would like to create a visualization
showing how revenues are related to operating
profits?
We have the necessary information knowing
the intermediary steps in between.
Here’s the equation we will use.
Operating Profit = Revenue – Cost of goods
sold – Operating expenses – D&A.
There are three intermediary steps between
revenues and operating profit.
A bridge chart allows us to show the impact
of each of these steps.
Very nice, right?
When to avoid bridge charts
When we deal with data that does not involve
intermediary steps or segments, we will have
to use a different type of chart.
Simple as that.
Scatter plots
A scatter plot is a type of chart that is
often used in the field of statistics and
data science.
It consists of multiple data points plotted
across two axes.
Each variable depicted in a scatter plot would
have multiple observations.
If a scatter plot includes more than two variables,
then we would use different colours to signify
that.
When use scatter plots
A scatter plot chart is a great indicator
that allows us to see whether there is a pattern
to be found between two variables.
See the example we have here?
The x-axis contains information about house
price, while the y-axis indicates house size.
There is an obvious pattern to be found - a
positive relationship between the two.
The bigger a house is, the higher its price.
On the other hand, house size and the age
of the person who bought a house are two uncorrelated
variables, and a scatter plot helps us see
that easily.
So, this can be a very useful type of chart
whenever we would like to see if there is
any relationship between two sets of data.
When to avoid scatter plots
We can’t use scatter plots when we don’t
have bi-dimensional data.
In our example, we need information about
both house prices and house size to create
a scatter plot.
A scatter plot requires at least two dimensions
for our data.
In addition, scatter plots are not suitable
if we are interested in observing time patterns.
Finally, a scatter plot is used with numerical
data, or numbers.
If we have categories such as 3 divisions,
5 products, and so on, a scatter plot would
not reveal much.
Histogram charts
The last type of chart we will consider here
is the histogram chart.
A series of bins showing us the frequency
of observations of a given variable.
The definition of histogram charts is short
and easy.
Here’s an example.
An interviewer asked 267 people how much their
house cost.
Then a histogram was used to portray the interviewer’s
findings.
Some prices were in the range between $117-217k,
many more in the range $217-$317k, and the
rest of the houses were classified in more
expensive bins.
Here’s what the histogram looks like.
When to use histograms
Histograms are great when we would like to
show the distribution of the data we are working
with.
This allows us to group continuous data into
bins and hence, provide a useful representation
of where observations are concentrated.
When to avoid histograms
Be careful when the data you are working with
contains multiple categories or variables.
Multi-column histograms are to be avoided
when they look like this.
Conclusion
In this video, we were able to provide a great
summary of the different types of charts you
will need when working with data.
In addition, you learned something which is
even more important:
When to use these charts and When to avoid
using them
Clear and intuitive visualizations should
be the main focus.
There is no point in using sophisticated types
of charts that must be packaged with a translator
or a 5-page legend.
We are confident you understand that and will
be able to create stunning and crystal-clear
graphs right away.
Tableau is one of the most popular tools for
data visualization in the corporate world.
Follow this link to learn what makes Tableau
superior than traditional tools like Excel.
