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.