# Charting and VIZ Best Practices for Usability

By pjain      Published Dec. 30, 2019, 7:43 a.m. in blog AI-Analytics-Data

# Charting BPR: How to Use properly to show stats data

## Which charts for what?

SRC:http://pandeynishant.blogspot.com/

The choice of graphic display, therefore, depends on what information is important for your purposes: percentages (parts of the whole), running total, comparisons of categories, and so forth.

• Pie Charts

• Good for segment contribution "to the overall pie"
• Bar Charts

• Good for comparing values - eg sales/region, satisfaction/product
• Things to watch out

• Different Scales, or Chart doesn't start with Zero => lose proportional judgement ability

## Pie Chart

• What it is: A pie chart is used to show visually the proportions of parts of something being studied. The area of each slice of the pie shows the slice’s proportion to the entire category being studied and to the other slices. A pie chart shows data at one point in time, like a snapshot; it does not show change in data over time like a line chart does.

• How to use it - Determine proportions: Find the total value for the entire category being studied and calculate the percentage for each segment or part.

• Calculate degrees: Convert the percentage values for each segment into degrees relative to the 360 degrees in the circle. (For example, 12% X 360 degrees = 43 degrees)

• Construct the chart. Draw a circle and divide it into appropriately sized segments.

• Add labels and a title. Label each segment or add a legend to identify the segments. Then clearly title the chart.

## A bar graph, or bar chart

• It is used to represent values in relation to other values. They’re often used to compare data taken over long periods of time, but they’re most often used on very small sets of data.

These graphs can be horizontal or vertical. If it’s horizontal, the “categories” for what the actual data being represented is across the bottom and at the side, horizontally, are numbers that represent the actual data.

There are many characteristics of bar graphs that make them useful. Some of these are that: 1. They make comparisons between different variables very easy to see. 2. They clearly show trends in data, meaning that they show how one variable is affected as the other rises or falls. 3. Given one variable, the value of the other can be easily determined.

## Line graphs

Line graphs compare two variables. Each variable is plotted along an axis. A line graph has a vertical axis and a horizontal axis. So, for example, if you wanted to graph the height of a ball after you have thrown it, you could put time along the horizontal, or x-axis, and height along the vertical, or y-axis.

Each type of graph has characteristics that make it useful in certain situations. Some of the strengths of line graphs are that:

1. They are good at showing specific values of data, meaning that given one variable the other can easily be determined.

2. They show trends in data clearly, meaning that they visibly show how one variable is affected by the other as it increases or decreases.

3. They enable the viewer to make predictions about the results of data not yet recorded.

Unfortunately, it is possible to alter the way a line graph appears to make data look a certain way. This is done by either not using consistent scales on the axes, meaning that the value in between each point along the axis may not be the same, or when comparing two graphs using different scales for each. It is important that we all be aware of how graphs can be made to look a certain way, when that might not be the way the data really is.

## Histogram

A frequency distribution is simply a grouping of the data together, generally in the form of a frequency distribution table, giving a clearer picture than the individual values.
The most usual presentation is in the form of a histogram and/or a frequency polygon.

A Histogram is a pictorial method of representing data. It appears similar to a Bar Chart but has two fundamental differences:

The data must be measurable on a standard scale; e.g. lengths rather than colours.

The Area of a block, rather than its height, is drawn proportional to the Frequency, so if one column is twice the width of another it needs to be only half the height to represent the same frequency.

## Cumulative Data Plots

• Ogive (Cumulative Line Graphs) Data may be expressed using a single line. An ogive (a cumulative line graph) is best used when you want to display the total at any given time. The relative slopes from point to point will indicate greater or lesser increases; for example, a steeper slope means a greater increase than a more gradual slope. An ogive, however, is not the ideal graphic for showing comparisons between categories because it simply combines the values in each category, thus indicating an accumulation (a growing or lessening total). If you simply want to keep track of a total and your individual values are periodically combined, an ogive is an appropriate display.

For example, if you saved \$300 in both January and April and \$100 in each of February, March, May, and June, an ogive would look like Figure 1.

An ogive displays a running total. Although each individual month's savings could be expressed in a bar chart (as shown in Figure 2), you could not easily see the amount of total growth or loss, as you can in an ogive.

• Vertical bar chart of accumulated savings for one year.

```- http://github.com/raspu/RPRadarChart
```

## Gauges

• eg Restore d3 charts

• Bar Charts

• Gauge