How to visualize data with extreme value differences?

1. How do you show vastly different values in one chart?
2. Which graph is best for showing differences of data?
3. Which type of visualization is best when you want to compare proportions in large volume?
4. Which type of visualization is best when you want to compare proportions in a large volume of data with multiple categories and subcategories?
5. What is the best technique to visualize changes in prices over time?
6. Which visualization type is best for comparing data points?

How do you show vastly different values in one chart?

A common approach to chart a wide range of values is to break the axis, plotting small numbers below the break and large numbers above the break. An advantage here is that it generally uses a linear scale.

Which graph is best for showing differences of data?

a Bar Graph. Bar graphs are used to compare things between different groups or to track changes over time.

Which type of visualization is best when you want to compare proportions in large volume?

Scatter plot is not only fun to say – it's what you need when looking for the correlation in a large data set.

Which type of visualization is best when you want to compare proportions in a large volume of data with multiple categories and subcategories?

Treemaps are a great choice: To display large amounts of hierarchical data. When a bar chart can't effectively handle the large number of values. To show the proportions between each part and the whole.

What is the best technique to visualize changes in prices over time?

Line Chart – Your Best Chart to Show Trends Over Time. Line charts are the best visual presentation for emphasizing change over time. Consider two variables, one on the vertical axis and the second on the horizontal axis.

Which visualization type is best for comparing data points?

Bar charts are good for comparisons, while line charts work better for trends. Scatter plot charts are good for relationships and distributions, but pie charts should be used only for simple compositions — never for comparisons or distributions.