How can you use visual hierarchy to guide attention in a bar chart?

Yes, the order can be by X-axis or Y-aixs. Order by value helps when we're trying to detect Ranking on the other hand when each category sequence represents something they should be arranged by X-axis or category field

This may seem obvious, however one must be thoughtful about how the reader would interpret the graph. Some data may have inherent ordering - age ranges, chronology, or the likert scale - and it would be useful to visualize using that natural order. However sometimes the more important message is in the top few or bottom few bits of data, where ordering the data from smallest to largest or largest to smallest would make more sense depending on what you are trying to convey.

There are many color wheel tools out there to use for free. If you have the option, always make something custom. Sometimes the default colors are just... not it. Consider the topic of your visualization. There are resources that explain color psychology as well as regional or cultural ideas and feelings associated with various colors.

Sometimes what works best is using 1 solid color and use patterns, conditional formatting or labels to highlight important points.

A broader understanding of color, especially of hues, saturation and value, can help with selecting color effectively. A few color hacks to jumpstart this process: 1. If you have 1 data point that is the key message, use a color to help that stand out by making all other data points shades of gray. 2. Use warm colors (yellow, orange, red) and contrast with shades of blue when you need multiple colors 3. Avoid bright, saturated colors. Typically these appear "neon" and do not portray feelings of trust, or calm as per research. Color choice is highly dependent on the data, and what key message you want to deliver.

The key here is to maintain a healthy balance: too much white space makes the data less prominent, but if they overlap or touch each other they become a single shape as opposed to distinct data points. As a rule of thumb, the gap between the bars should be thinner than the bars themselves.

Highly effective annotations are those that serve a particular purpose. There are mainly three types of scenarios that annotations are best suited for: 1. A data point or series that you want to highlight and explain, such as an outlier. 2. Help the reader understand why the data looks the way it does, for example an external force at a point in time caused a dip in the future values. 3. A design element that needs further explaining - rare but necessary

Annotation is a great collaboration tool. It can explain & share information about data effectively. Because they are connected to a single point it make the impact more in-depth. Visuals should always show with analysis and conclusions.

It is important to minimize redundancy and visual clutter. If you can label your bars, an axis becomes redundant. If the title of your chart leaves no ambiguity about your axes, you don't need to label them. If your categories are clear, an axis title is unnecessary. Grid lines rarely serve an unavoidable purpose. The same could be said for zero lines. Reference lines can be helpful. Reference bands can add context. Reformatting axis and label values can be useful if it does not inhibit the meaning. It’s all about communicating your message clearly and avoiding distractions.

A bar chart is not a single type of chart, instead it covers an entire family of charts, each having its own purpose and domain of applicability. - The Bar Plot visualizes raw numerical values. - The Histogram summarizes them to visualize distribution. - The Frequency Chart displays counts of categorical values. Some variables cannot be visualized with bar charts directly, and derivative variables must be used instead. For example, the lengths of texts can be visualized with a Histogram. Other categorical variables might have a very large number of distinct values, making the Frequency Chart impractical. Then, use a Frequency of Frequency Chart instead. It is not easy to interpret, but it is a very powerful tool for Data Quality.

In my experience, leveraging visual hierarchy is paramount when crafting an impactful bar chart. To effectively guide attention, I recommend a few strategies. First, ensure that the most critical information is prominently displayed. This might involve using a distinct color for the most important bars or placing them at the forefront. Second, consider employing varying bar lengths or widths to emphasize disparities. Additionally, judiciously use labels, annotations, and gridlines to provide context and aid comprehension. Remember, a clutter-free design with careful attention to typography can make a world of difference.