Introduction to Data Visualization

Figure 1 below is an example where several 2D graphs are plotted on the same axes. The figure depicts not only how the growth of plants is related to the amount of water they receive but also how the growth is related to the amount of sunlight they receive. The graph represents plant growth as a function of the amount of water and sunlight. Plants are also affected by the amount of nutrients in the soil, which we will refer to as the rate of fertilizer application. If we speculate that the rate of fertilizer application was constant at a medium level in Figure 1, then the whole plant growth experiment could be run again with a different rate of fertilizer application. A new 2D graph like Figure 1 could be generated. Figure 1 - How the amount of water affects plant growth with different amounts of sun per day (assuming constant fertilizer application rate).

The plant experiment could be conducted with two, ten, or ten thousand different rates of fertilizer application. A 2D graph could be generated from the data at each of these specific fertilizer levels. If two or three different experiments are run, it is not difficult to compare their 2D graphs. Comparing 2D graphs becomes difficult when there are a large number of 2D graphs (ten or ten thousand).

How can a scientist compare multiple 2D graphs?

Sometimes it is possible for the scientist to make a 3D graph. Multiple graphs like Figure 1 could be plotted by putting the third independent variable (fertilizer) on the z-axis and plotting a 2D graph for each level of fertilizer. However, since there are five line graphs for each level, that would be very confusing!! So let's look at another way scientists can use to visualize data.

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