Statistical graphics are powerful tools to communicate complex data and patterns, but they can also be misleading or confusing if not designed well. In this article, you will learn some of the best ways to make your statistical graphics more clear, based on the principles of data visualization and graphic design.
The first step to make your statistical graphics more clear is to choose the right type of graph for your data and purpose. Different types of graphs have different strengths and weaknesses, and you should match them to the message you want to convey. For example, if you want to show the distribution of a variable, you can use a histogram, a box plot, or a density plot. If you want to show the relationship between two variables, you can use a scatter plot, a line plot, or a bar plot. If you want to show the composition of a whole, you can use a pie chart, a stacked bar chart, or a treemap.
For instance if your data is highly skewed it is not a good idea to use boxplots with lots of outliers around. My favorite graph nowadays is the new stacked violin plot presented by the famous shap library.
Choosing the best type of graph for presenting data analysis depends on the nature of your data and the insights you want to communicate: 1. Nature of Data: - For Continuous Data: - Use histograms for distribution analysis. - Line charts are effective for showing trends over time. - Scatter plots can reveal relationships between two continuous variables. - For Categorical Data: - Bar charts are suitable for comparing categories. - Pie charts can be used to show the proportion of each category in a whole. - For Both Continuous and Categorical Data: - Box plots provide a summary of the distribution and help identify outliers.
Sometimes the best graph to use is not the most appropriate for the data but rather the one the end user can make the most of.
Graphs are a beautiful way of conveying information and oftentimes the purpose gets lost because we are too caught up in just inserting one. Scaling the graph right and labelling it in a way that leaves no room for doubt, makes all the difference.
The graph that could be translated in a very good storytelling, and someone that doesn′t know about the topic is able to understand with minimum effort.
The second step to make your statistical graphics more clear is to use appropriate scales and axes for your data. Scales and axes determine how your data are mapped to the visual space, and they can affect how your audience perceives and interprets your graphs. You should use scales and axes that are consistent, accurate, and meaningful. For example, you should avoid using distorted scales, such as logarithmic or exponential, unless they are necessary and well-explained. You should also avoid using unnecessary or misleading axes, such as truncated or reversed axes, unless they serve a specific purpose and are clearly labeled.
As i said in numerical sense, when disparities are large, adding depth via two dimensional display such as tree maps help. You may consider an effective coloring and maybe faceting strategy.
Avoid vertical axis if possible. Label your data ordinates directly, maintaining the abscissas on the horizontal axis. Prefer line or bar graphs. Use contrasting colors and be aware of the meaning of specific colors in the business context. Group less expressive classes to make your graph clean.
One of the common mistakes is to compare two quantitative data of different scales together on the same chart (eg a bar chart).
To identify patterns & trends it's very crucial to incorporate correct scales and axes. It's easy to read and interpret the meaning of the graph when axes are properly labeled.
The third step to make your statistical graphics more clear is to add labels and legends to your graphs. Labels and legends provide information and context to your data and help your audience understand what your graphs are showing. You should use labels and legends that are concise, informative, and readable. For example, you should use descriptive titles, subtitles, and captions to summarize your graphs and highlight the main findings. You should also use clear axis labels, tick marks, and units to indicate the values and dimensions of your data. You should also use consistent and distinguishable colors, symbols, and styles to represent different groups or categories of data, and explain them in a legend.
Even though this seems like obvious advice and quite basic, we can sometime make the error of not describing what is what in our graphs at all or perhaps not in an accessible way easy to understand for everyone. A good exercise to make sure that the labels and legends you created work well is to show your graph outside any context to a few friends or colleagues. If they can get it without added explanations on your side, you did a good job.
When enhancing your statistical graphics, incorporating labels and legends is key. Think of them as storytellers for your data, guiding your audience through the insights. Craft clear titles, subtitles, and captions to summarize findings. Ensure axis labels are informative, tick marks are well-defined, and units are crystal clear. Consistency is vital; use distinguishable colors, symbols, and styles to represent data categories, clarifying them in a neat legend. In Python and R, leverage natural language processing to seamlessly communicate the narrative of your visuals. Remember, these labels and legends are your data’s voice, making your graphics not just informative but engaging too!
The fourth step to make your statistical graphics more clear is to use colors and contrast wisely in your graphs. Colors and contrast can enhance or diminish the visual impact and clarity of your graphs, depending on how you use them. You should use colors and contrast that are appropriate, effective, and accessible. For example, you should use colors that are relevant, meaningful, and harmonious to your data and message. You should also use colors that are distinguishable, but not too many or too bright, to avoid visual clutter and confusion. You should also use contrast that is sufficient, but not too high or too low, to create emphasis and balance in your graphs. You should also consider the color blindness and the background of your audience, and choose colors and contrast that are accessible and visible to them.
-Use colors purposefully to differentiate between categories or highlight specific data points. Avoid overly bright or clashing colors that might distract or confuse the viewer. -Use a consistent color scheme throughout the graph and ensure color choices are accessible for all viewers, considering colorblindness or other visual impairments.
I always say this : the full spectrum heatmap hue destroyed the interpretability just because it looks cool. Best is using monochromatic hue, probably green if you need fine grained distinction. Do you know there s a reason green is preferred for night goggles. Mammal brain is evolved to detect prey and other things in grasslands.
Visual cues are essential in data graphics! Apart from using colours as a differentiator / highlighter, lengths and areas are good ways to emphasise/de-emphasise certain elements of your visuals.
The fifth step to make your statistical graphics more clear is to simplify and declutter your graphs. Simplicity and clarity are key to effective data visualization, and you should remove any unnecessary or distracting elements from your graphs that do not add value or meaning to your data and message. You should simplify and declutter your graphs by following the principle of data-ink ratio, which states that you should maximize the amount of ink that represents data, and minimize the amount of ink that does not. For example, you should remove or reduce grid lines, borders, backgrounds, shadows, and other decorative features that do not help your audience focus on your data. You should also remove or combine redundant or overlapping data points, labels, legends, and other elements that do not provide additional information or insight.
In the realm of statistical graphics, the art of simplification is a game-changer. In Python and R, harnessing the power of data-ink ratio elevates your visual storytelling. Shedding non-essential elements like grid lines and shadows sharpens focus. Use Python's Seaborn or R's ggplot2 to effortlessly trim redundancies, merging overlapping points for clarity. Think of your graph as a canvas; every stroke should contribute meaning. By embracing simplicity, your data's narrative takes center stage, ensuring that your audience connects effortlessly with the story you're telling.
Consider building up "layers" if you have a complicated graph. Explain each layer of the graph before combining them to ensure that the reader can grasp the full picture.
Data-ink ratio is a very important concept. Or, in an internet age, it could be rephrased as “Data-Attention” ratio. If you can draw your audience attention to the intended data as much as possible, I’d say it’s a very effective data visualisation.
Learning to keep your graph clean and clear will make the most impact when it comes to communicating a message with your visual representation of your data. Don't try to show too much in one graph - be sure what you want to draw attention to and remove or put into the second plan all else.
The sixth and final step to make your statistical graphics more clear is to test and refine your graphs. Testing and refining your graphs means checking their quality and accuracy, and making adjustments and improvements based on feedback and evaluation. You should test and refine your graphs by following the principle of graphical excellence, which states that you should design your graphs to reveal the truth, communicate the message, and stimulate the eye and the mind. For example, you should test your graphs for errors, inconsistencies, and biases in your data and methods. You should also test your graphs for clarity, readability, and aesthetics in your presentation and format. You should also test your graphs for relevance, interest, and impact in your audience and context. You should also solicit and incorporate feedback from others, and refine your graphs accordingly.
Testing and refining your statistical graphics is crucial for ensuring clarity and impact. In this final step, leverage the power of natural language processing in R and Python to enhance your graphs. Implement sentiment analysis to gauge audience reactions, ensuring your visuals resonate emotionally. Seek feedback from diverse perspectives to identify potential biases and refine accordingly. Prioritize readability and aesthetics, employing algorithms to assess graph quality objectively. By embracing these techniques, you'll not only reveal the truth in your data but also create visuals that captivate the mind and stimulate a meaningful connection with your audience.
An important element of testing and refining graphical analysis is to elicit user feedback. As a statistics/data science expert, what makes sense to you may not make sense to the audience for which your graphics are intended. In fact, in my experience, the end product is almost universally simpler than what I would choose to produce for myself. Assuming you aren't working with proprietary data, show your graphics to trusted friends or colleagues and ask them to give you some takeaways. Do their responses match what you hope your audience will take away from the graphic? If so, you're probably good. If not, refine further. When in doubt, simplify.
Show data that represents aggregate value/impact for the pain/topic that is being presented. The simpler and more assertive communication the better.
First of all we need to remove unnecessary elements from dataset. Then we need to select the right type of chart or graph to represent our data, we need to ensure axes, annotations and data points are labelled clearly. We needs to maintain consistent scales to accurately represent data relationships.