Although there are many types of figures, like tables, they share some typical features: captions, the image itself, and any necessary contextual information which will vary depending on the type of figure you use. Figures should be labeled with a number followed by a descriptive caption or title.
Captions should be concise but comprehensive. They should describe the data shown, draw attention to important features contained within the figure, and may sometimes also include interpretations of the data. Figures are typically read from the bottom up, so captions go below the figure and are left-justified. The most important consideration for figures is simplicity. Choose images the viewer can grasp and interpret clearly and quickly. Consider size, resolution, color, and prominence of important features.
Figures should be large enough and of sufficient resolution for the viewer to make out details without straining their eyes.
Also consider the format your paper will ultimately take. Journals typically publish figures in black and white, so any information coded by color will be lost to the reader. On the other hand, color might be a good choice for papers published to the web or for PowerPoint presentations. In any case, use figure elements like color, line, and pattern for effect, not for flash.
Figures should be labeled with a number preceding the table title; tables and figures are numbered independently of one another. Also be sure to include any additional contextual information your viewer needs to understand the figure.
For graphs, this may include labels, a legend explaining symbols, and vertical or horizontal tick marks. Every graph is a figure but not every figure is a graph. Graphs are a particular set of figures that display quantitative relationships between variables. Some of the most common graphs include bar charts, frequency histograms, pie charts, scatter plots, and line graphs, each of which displays trends or relationships within and among datasets in a different way.
More details about some common graph types are provided below. Some good advice regarding the construction of graphs is to keep it simple. Remember that the main objective of your graph is communication. If your viewer is unable to visually decode your graph, then you have failed to communicate the information contained within it. Pie charts are used to show relative proportions, specifically the relationship of a number of parts to the whole.
However, if you want your reader to discern fine distinctions within your data, the pie chart is not for you. Humans are not very good at making comparisons based on angles. We are much better at comparing length, so try a bar chart as an alternative way to show relative proportions. Additionally, pie charts with lots of little slices or slices of very different sizes are difficult to read, so limit yours to categories. Examples of bad pie charts: Figure 1. Elements in Martian soil. It is also hard to distinguish fifteen colors when comparing the pie chart to the color coded key.
Figure 2. Leisure activities of Venusian teenagers. The chart shows the relative proportion of five leisure activities of Venusian teenagers tanning, trips to Mars, reading, messing with satellites, and stealing Earth cable. Bar graphs are also used to display proportions. In particular, they are useful for showing the relationship between independent and dependent variables, where the independent variables are discrete often nominal categories.
Some examples are occupation, gender, and species. Bar graphs can be vertical or horizontal. In a vertical bar graph the independent variable is shown on the x axis left to right and the dependent variable on the y axis up and down. In a horizontal one, the dependent variable will be shown on the horizontal x axis, the independent on the vertical y axis.
The scale and origin of the graph should be meaningful. If the dependent numeric variable has a natural zero point, it is commonly used as a point of origin for the bar chart.
However, zero is not always the best choice. You should experiment with both origin and scale to best show the relevant trends in your data without misleading the viewer in terms of the strength or extent of those trends.
Example of a bar graph: Figure 3. Genders of spaceship crew members in popular television series. Because the television series are arranged chronologically on the x-axis, the graph can also be used to look for trends in these numbers over time. Although the number of crew members for each show is similar ranging from 9 to 11 , the proportion of female and male crew members varies.
Star Trek has half as many female crew members as male crew members 3 and 6, respectively , Battlestar has fewer than one-fourth as many female crew members as male crew members 2 and 9, respectively , Star Trek: TNG has four female crew members and six male crew members, Stargate SG-1 has less than one-half as many female crew members as male crew members 3 and 7, respectively , and Firefly has four female and five male crew members.
Frequency histograms are a special type of bar graph that show the relationship between independent and dependent variables, where the independent variable is continuous, rather than discrete. This means that each bar represents a range of values, rather than a single observation. It also uses bold-faced and varying font sizes to distinguish the column headings from the table spanners. Spacing beneath the column headings creates additional contrast.
Using a border around the table also makes it stand out more and contains the data nicely. Notice that all numbers are aligned by decimal place and that all text is centered. The next example shows this same table without the use of "best practice.
With all the gridlines, lack of contrast, and poor use of space and alignment, this table is difficult to read. This table makes use of appropriate groupings in order to break the list apart and make it easy to follow.
Good use of space and varying size and bold-faced fonts also create a nice contrast and make it easy on the eyes. Below is the same table following poor practice. Notice that this table lacks grouping of any kind, making it difficult to sort through the list.
Title and header formatting is not consistent throughout, and the numerical data is centered instead of left-aligned, making it difficult to compare values. There is no contrast or use of space, so this table is a lot less easy on the eyes than the one above.
Birchman, J. Enhancing the appearance of information graphics. Engineering Design Graphics Journal , 67 1 , Hall, R. Handbook of Tabular Presentation. Nicol, A. Wainer, H. Understanding graphs and tables. Educational Researcher , 21, Wright, P. Presenting technical information: A survey of research findings. Instructional Science , 6, Example: Column Headings : Each column has a heading in order to identify what data are listed below in a vertical arrangement.
When the column heading is above the leftmost column, it is often referred to as the "stubhead" and the column is the "stub column. The data that follow the stub column are known as the "stub.
Column Spanner : A heading that sits above two or more columns to indicate a certain classification or grouping of the data in those columns. A column spanner may also specify units, when appropriate. Return to Top Tabulating Raw Data When you are collecting data during a laboratory experiment, it is important that you record it in tabular format in a spreadsheet, lab manual, lab notebook, or word processing software.
Example 2 : Raw Data Spreadsheet Return to Top Tabular Presentation of Data Once you are ready to include your results in your report, you may decide that the best representation for your data is a tabular presentation. To accomplish this task, you'll need to be familiar with the basic rules for tabular presentation: Limit your table to data that are relevant to the hypotheses in the experiment.
Be certain that your table can stand alone without any explanation. Make sure that your table is supplementary to your text and does not replicate it. Refer to all tables by numbers in your text, e. Describe or discuss only the table's highlights in your text. Always give units of measurement in table headings. Align decimal places. Round numbers as much as possible.
Try to round to two decimal places unless more decimals are needed. Unless using a specific format style that requires that you place tables separately at the end of the report, place the tables near the text that refers to them. Decide on a reasonable amount of data to be represented, not too little so that the reader does not understand you results, but not too much so that the reader is overwhelmed and confused. Only include the necessary number of tables in your paper, otherwise, it may be redundant or confusing to the reader.
Do not use tables if you only have two or fewer columns and rows. In such cases, a textual description is enough. Organize your tables neatly so that the meaning of the table is obvious at first glance.
If the reader spends too much time deciphering your table, then it is too complicated and not efficient. With these data table templates , you can highlight the sections of the table you want your audience to notice first. Using this tool will help you visualize your data while avoiding boring or dull tables.
Creating a data table like this with other powerpoint alternatives is nowhere near as intuitive — or fun! Every service-based company or startup will need a pricing structure template like this.
But how many have you seen out there on different software or service websites that look confusing, hard to understand or just plain ugly? This smart template from Beautiful. In the pricing structure below, the highlighted column is the Pro option. The creator of the data table wants the user to see this tier first, hoping that they will choose it leveraging simple psychology principles of visual impact.
These smart table templates will save you lots of time by making the rows and columns look great and easy to read. But what goes where? If the values along the horizontal line are time-based, place the data in order from oldest to newest from left to right. If the horizontal line is for monetary money values, set it up from lowest to highest again, from left to right. For vertical values, it can vary according to what the table is about. A good rule of thumb is to order from most important to least, in descending order from top to bottom.
Another powerful way to create impact with your data is with the colors that you use for the data table. Thankfully, Beautiful. When you click on the color palette you want, content throughout the entire table will instantly change to that color — voila!
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