Data Vusualizati8b Flashcards
Best chart type to show changes over time
Line
Best chart type to show changes over time
Line
Nightingale coxcomb
Like an exploded pie chart. 1850d. Shows sizes of pie slices better. Shows multiple axis per section
Data driven
Classic grapgs abd charts
Conceptual
Explain a process, abstract concept or idea. Like an org chart.
Column charts
use size to contrast and compare two or more values, using height or lengths to represent the specific values.
Heat maps
Similar to bar charts, heatmaps also use color to compare categories in a data set. They are mainly used to show relationships between two variables and use a system of color-coding to represent different values. The following heatmap plots temperature changes for each city during the hottest and coldest months of the year.
Pie charts
circular graph that is divided into segments representing proportions corresponding to the quantity it represents, especially when dealing with parts of a whole.
Pie charts
circular graph that is divided into segments representing proportions corresponding to the quantity it represents, especially when dealing with parts of a whole.
Scatterplots
show relationships between different variables. Scatterplots are typically used for two variables for a set of data, although additional variables can be displayed.
Does your data have only one numeric variable?
If you have data that has one, continuous, numerical variable, then a histogram or density plot are the best methods of plotting your categorical data. Depending on your type of data, a bar chart can even be appropriate in this case. For example, if you have data pertaining to the height of a group of students, you will want to use a histogram to visualize how many students there are in each height range:
Are there multiple datasets?
For cases dealing with more than one set of data, consider a line or pie chart for accurate representation of your data. A line chart will connect multiple data sets over a single, continuous line, showing how numbers have changed over time. A pie chart is good for dividing a whole into multiple categories or parts. An example of this is when you are measuring quarterly sales figures of your company. Below are examples of this data plotted on both a line and pie chart.
Are there multiple datasets?
For cases dealing with more than one set of data, consider a line or pie chart for accurate representation of your data. A line chart will connect multiple data sets over a single, continuous line, showing how numbers have changed over time. A pie chart is good for dividing a whole into multiple categories or parts. An example of this is when you are measuring quarterly sales figures of your company. Below are examples of this data plotted on both a line and pie chart.
Are you measuring changes over time?
A line chart is usually adequate for plotting trends over time. However, when the changes are larger, a bar chart is the better option. If, for example, you are measuring the number of visitors to NYC over the past 6 months, the data would look like this:
Do relationships between the data need to be shown?
When you have two variables for one set of data, it is important to point out how one affects the other. Variables that pair well together are best plotted on a scatterplot. However, if there are too many data points, the relationship between variables can be obscured so a heat map can be a better representation in that case. If you are measuring the population of people across all 50 states in the United States, your data points would consist of millions so you would use a heat map. If you are simply trying to show the relationship between the number of hours spent studying and its effects on grades, your data would look like this:
What are the 4 elements of successful visualization?
Information: reflects the conclusion you’ve drawn from the data, which you will communicate with visualization
Story: adds meaning to the data and makes it interesting
Goals: makes the data usable and useful
Visual form: creates both beauty and structure
5 seconds best practice
Your audience should know what they are observing within five seconds of being shown a data visualization. Visuals should be clear and easy to follow.
In the five seconds after that, your audience should understand the conclusion your visualization is making—even if they aren’t familiar with your research.
Simplicity best practice
As long as it’s not misleading, you should visually represent only the data that your audience needs to understand your findings. Including irrelevant data may confuse, distract, or overwhelm your audience.
What are the 9 principles of design?
Balance
Emphasis
Movement
Pattern
Repetition
Proportion
Rhythm
Variety
Unity
Balance
The design of a data visualization is balanced when the key visual elements, like color and shape, are distributed evenly. This doesn’t mean that you need complete symmetry, but your visualization shouldn’t have one side distracting from the other. If your data visualization is balanced, this could mean that the lines used to create the graphics are similar in length on both sides, or that the space between objects is equal. For example, this column chart (also shown below) is balanced; even though the columns are different heights and the chart isn’t symmetrical, the colors, width, and spacing of the columns keep this data visualization balanced. The colors provide sufficient contrast to each other so that you can pay attention to both the motivation level and the energy level displayed.
Emphasis
Your data visualization should have a focal point, so that your audience knows where to concentrate. In other words, your visualizations should emphasize the most important data so that users recognize it first. Using color and value is one effective way to make this happen. By using contrasting colors, you can make certain that graphic elements—and the data shown in those elements—stand out.
For example, you will notice a heat map data visualization below from The Pudding’s “Where Slang Comes From” article. This heat map uses colors and value intensity to emphasize the states where search interest is highest. You can visually identify the increase in the search over time from low interest to high interest. This way, you are able to quickly grasp the key idea being presented without knowing the specific data values.
Movement
Movement can refer to the path the viewer’s eye travels as they look at a data visualization, or literal movement created by animations. Movement in data visualization should mimic the way people usually read. You can use lines and colors to pull the viewer’s attention across the page.
For example, notice how the average line in this combo chart (also shown below) draws your attention from left to right. Even though this example isn’t moving, it still uses the movement principle to guide viewers’ understanding of the data.
