Information visualization has been here for quite a long time now. It is one of the best and easy ways to make a common man understand the data without going through huge tables of data and guessing around. So what is it and what are the types of visualization?
Visualization is a catalyst for discussion and collective insight about data. It gives birth to that magical moment: an unwieldy, unyielding data set is transformed into an image on the screen, and suddenly the user can perceive an unexpected pattern out of the raw data.
Types of Visualization are listed below:
Line graph
A line graph is a classic method for visualizing continuous change. A line graph can chart one or more numeric variables.
A line graph takes a table where each column represents one data series. One special column corresponds to the x-axis labels. For example, in this table the “Year” column would be used for the x-axis labels, and the two other columns would define two data series to be graphed.
A sample data table in the line graph format is:
| Year |
Price of Doughnut ($) |
Price of Coffee ($) |
| 2005 |
.75 |
1.42 |
| 2006 |
.78 |
2.52 |
| 2007 |
.81 |
2.15 |
A complication is that in some data sets the rows and columns are flipped, like this:
| Year |
|
2005 |
2006 |
2007 |
| Price of Doughnut ($) |
.75 |
.78 |
.81 |
| Price of Coffee ($) |
1.42 |
2.52 |
2.15 |
Line graphs are classic diagrams that usually give a good picture of the data. There are just a few pitfalls. The most important thing is that line graphs should only be used when the positions on the x-axis have a natural ordering. If your labels are “2000, 2001, 2002″ that’s fine; if your labels are “US, England, Germany” you should consider a bar graph instead.
Stack Graph
A stack graph is a classic method for visualizing change in a set of items, where the sum of the values is as important as the individual items. A stack graph is excellent for looking at revenue over time across several products, for example. Because a stack graph uses areas to convey numbers, they don’t work for negative values. And in some situations it might not make sense to add up different data series (say, prices of different stocks over time). For these cases you may want to use a line graph.

A stack graph takes a table where each column corresponds one data series. One special column corresponds to the x-axis labels. For example, in this table the “Year” column would be used for the x-axis labels, and the two other columns would define two data series to be graphed. Note that this example is natural for a stack graph since the values are positive, and when you add them up you get something meaningful: the price of a (not so healthful) breakfast.
A sample data table in the stack graph format is:
| Year |
Price of Doughnut ($) |
Price of Coffee ($) |
| 2005 |
.75 |
1.42 |
| 2006 |
.78 |
2.52 |
| 2007 |
.81 |
2.15 |
A complication is that in some data sets the rows and columns are flipped, like this:
| Year |
|
2005 |
2006 |
2007 |
| Price of Doughnut ($) |
.75 |
.78 |
.81 |
| Price of Coffee ($) |
1.42 |
2.52 |
2.15 |
Stack graphs are a standard, useful chart. They can convey multiple levels of meaning at once, but have some drawbacks. It can be hard to judge the exact widths of a stripe in a stack graph, or compare the widths of two stripes. If exactitude or comparisons are of primary importance, consider a line graph instead. Another consideration is that some time series don’t make sense to add. Adding the stock price of GE to the stock price of Intel is pointless.
Stack Graphs for Categories
A stacked graph is meant for visualizing the total change over time of a group of quantities. Because items are “stacked,” this type of graph is especially useful when it makes sense to add up the underlying data points. For example, a stacked graph is good for sales data, since you may be interested not only in sales of individual items but also want to know how total sales varies over time. For simple data sets you may be able to use a simple stack graph. But if your data items are arranged into categories and subcategories, it makes sense to use a stack graph for categories.

Each ribbon of color in the graph represents a data item changing over time. In this graph the ribbons represent United States government spending on different budget items. The height, or thickness, of each ribbon represents the dollar figure for spending. The overall height of the graph shows the total spending.
A stacked graph requires a somewhat complex data table. To create the categories and subcategories, Many Eyes will look at all the text columns, in order from left to right. The leftmost text column will be uswed for the highest-level category, the next for the level below that, and so on. Numeric columns will be used for the values of the graph, again in order from left to right.
An example data set suitable for a stacked graph is:
| Type |
Food |
2000 |
2001 |
2002 |
2003 |
| Fruit |
Banana |
1456 |
1456 |
1456 |
1456 |
| Fruit |
Lemon |
23 |
23 |
23 |
23 |
| Fruit |
Orange |
981 |
981 |
981 |
981 |
| Meat |
Pork |
111 |
111 |
111 |
111 |
| Meat |
Beef |
442 |
442 |
442 |
442 |
| Meat |
Chicken |
1456 |
1456 |
1456 |
1456 |
The numbers in the value column should not be negative, since the stacking technique in the graph would no longer make sense. Negative values will be treated as zeros.
Bar Chart
A bar chart is a classic method for numerical comparisons. A bar chart can show one or more sets of variables.

A bar chart takes a table where each column corresponds one data series. One special column corresponds to the x-axis labels. For example, in this table the “Year” column would be used for the x-axis labels, and the two other columns would define two data series to be graphed. In certain cases you may want the x-axis labels to be taken from the column headers (many data spreadsheets have one column per year, for example). When you configure the visualization, you’ll have an option to flip rows and columns.
| Animal |
Lifespan |
Wingspan |
| Gryphon |
100 |
12 |
| Phoenix |
1000 |
6 |
| Pegasus |
50 |
20 |
Bar charts are classic diagrams that usually give a good picture of the data. Their main problem is that when there are many bars, labeling becomes problematic. They also imply that the data is discrete; if your data is something that is plausibly continuously changing over time, for instance, you might consider a line graph instead.
To Be Continued….