A Thoughtful Catalog of the Sigma Visual Library
You’ve collected billions of rows of data.
You’ve established join-links between dozens of tables.
Your head swirls with the possibilities; You’ve entered a multi-dimensional new world, where an incomprehensibly large network of variables clamor for attention in brightly colored graphics — my goodness, X vs. Y vs. Z all laid at your fingerprints in a resplendent rainbow of comparison and causality!
But first, stop. Breathe.
The purpose of analytical displays is to assist thinking.
Thus, before you flood a Workbook with visuals, let’s walk through how Sigma Visual Elements can help you address the different kinds of thinking inherent in Sigma analytics.
A bar chart, also known as a bar graph, is a type of chart that uses rectangular bars to represent data. The bars can be either horizontal or vertical, and their lengths are proportional to the values they represent. Bar charts are commonly used to compare data across different categories or to show changes in data over time. They are a simple and effective way to visually communicate information, and often serve as the first line of data investigation.
When to use bar charts?
- Highlighting and comparing different categories of data can be well displayed through a bar chart.
- When you are showing piece of a larger dataset
When to avoid bar charts?
- If there are too many categories, it may be better to use different method of presenting
- When smaller changes exist, it may be better to use line graphs rather than bar graphs.
A line chart, also known as a line graph, is a type of chart that displays information as a series of data points connected by straight lines. Line charts are commonly used to show changes in data over time, with the horizontal axis representing time and the vertical axis representing the values being measured. They can also be used to compare multiple data series on the same chart, or to visually represent forecast periods.
When to use line charts?
- Time based continuous data can be best suited for line charts
- Multiple series can be displayed in line chart well
- Larger datasets tend to work well with line charts
When to avoid line charts?
- If you have smaller datasets, bar charts may be a better option to present your data.
- Not the best option for comparing multiple categories at one time
An area chart is a type of chart that displays data in a series of data points connected by straight lines, and the area below the lines is filled with color or shading. The resulting graph depicts the trend in data over time or across different categories. Area charts are similar to line charts in that they are used to show trends, but they also show the relative magnitude of each data series as the area between the line and the horizontal axis is filled with color or shading.
When to use area charts?
- Area chart can be a good option if you want to show the volume of the data
- When you want to show part to whole relationship
When to avoid area charts?
- Comparing multiple categories and exact data value may be challenging for area charts
A scatter chart, also known as a scatter plot or scatter graph, is a type of chart that displays the relationship between two variables by plotting them as a series of individual data points. Each data point represents a combination of values for the two variables, and the position of the point on the chart is determined by its values.
Scatter charts are useful for identifying patterns and trends in data and for determining the strength and direction of the relationship between two variables. They can also be used to detect outliers, which are data points that are far from the expected values based on the pattern of the other data points. Scatter charts are commonly used in fields such as statistics, engineering, and social sciences to analyze and visualize complex data. They are also often used in business to identify correlations between different variables, such as the relationship between marketing expenditures and sales revenue.
When to use scatter charts?
- If there are correlations in the data, scatter charts will do a good job presenting them.
- Statistical analysis
- When there are patterns or trends in data
When to avoid scatter charts?
- When the data is small, scatter chart may not be the best option
- Also if there is too much data, it’s easy to make a blob that doesn’t make much sense.
- If there are no correlation in your data, it may not be the best option
You start by taking a standard bar chart and giving each bar its own little area chart, like a colorful hat. But then you realize that the hat isn't enough. You need more, something that will really make your chart stand out. So you add a line chart to the mix, weaving it in and out of the bars and the areas like a tangled mess of spaghetti.
"Why?" you ask yourself. "Why am I doing this?" But it's too late to turn back now. You've gone too far, delved too deep into the madness of chart-making. And so you continue, adding more and more elements to the chart, until it becomes a swirling, chaotic vortex of data, a maelstrom of color and lines and bars and areas, each one vying for your attention, each one begging to be noticed.
And then, finally, you step back and behold your creation, this monstrous chart that defies all logic and reason, that mocks the very concept of data visualization. You have created the ultimate chart, a chart that no one will ever understand or be able to use, but that will live on forever in the annals of chart-making history as a testament to your madness and your genius.
Combo charts are a type of visualization that uses a mixture of chart types. Sigma combo charts support bars, lines, areas, and scatter plots.
When to use combo charts?
- Compare data with different values
- If you want to have one main value and have comparison values
When to avoid combo charts?
- If you want to show more than 3-4 types of charts, it’s better to separate them out rather than cluttering them into one chart.
Box Plot Charts
A box plot, also known as a box-and-whisker plot, is a type of chart that displays the distribution of a set of numerical data through its quartiles.
A box plot is composed of a rectangle (the "box") and two "whiskers" extending from it. The box represents the middle 50% of the data, with the bottom of the box indicating the first quartile, (25th percentile,) and the top of the box indicating the third quartile, (75th percentile). The line inside the box represents the median value of the data. The whiskers extend from the box to the minimum and maximum values of the data, or to a specified distance from the box called the "fences". Any data points outside the whiskers or fences are considered outliers and are plotted as individual points.
Box plots are useful for quickly summarizing the distribution of a dataset, particularly its central tendency and variability. They can also be used to compare the distribution of different datasets, particularly when the datasets have similar measures of central tendency and variability.
When to use box plot charts?
- Box plot is good at displaying and comparing the distribution of a set of numerical data
When to avoid box plot charts?
- When there is only one group of distribution, it may not be the best option.
- Box plot only gives high level summary, so if you are trying to show more detail, box plot may not be the best option.
A pie chart is a type of chart that displays data as a circle divided into slices, with each slice representing a proportion or percentage of the whole. The size of each slice is proportional to the value it represents in relation to the total value of the data being displayed.
Pie charts are often used to display data that can be divided into categories or subgroups, and to show how each category or subgroup contributes to the whole. For example, a pie chart could be used to show the proportion of sales for different product categories in a given period of time, or the percentage of a company's revenue generated by different geographic regions.
Pie charts are generally easy to read and can be visually appealing, but they have some limitations. For example, it can be difficult to compare the size of different slices, especially when there are many slices or the values are close together. Additionally, pie charts can be less effective for showing changes over time or comparing multiple sets of data.
When to use pie charts?
- Representing a proportion or percentage of the whole
- When there are less than 7 categories
When to avoid pie charts?
- When there are more than 7 categories
- If you are looking for more detailed comparison rather than proportion
- When you are looking to graph exact values
In the chart before us, we see a world turned upside down, a reality distorted and reshaped by the whims of data. The bars are not bars at all, but strange and otherworldly forms, twisting and bending in ways that defy reason and logic. This chart is a dream, a nightmare, a portal to a realm beyond our understanding.
A Sankey chart is a type of flow diagram that shows the movement of data, resources, or quantities through a system or process. Sankey charts are typically used to visualize complex processes or systems, such as energy flows, material balances, or website user flows. The chart consists of nodes and links, where the nodes represent the sources, destinations, or intermediate steps in the process, and the links represent the flows or quantities being transferred between the nodes.
The width of the links in a Sankey chart is proportional to the quantity of data or resources being transferred, making it easy to compare the relative magnitudes of the flows. Sankey charts can be useful for identifying inefficiencies, bottlenecks, or areas for improvement in a process or system. They can also be used to communicate complex information or data in a clear and easy-to-understand format. However, Sankey charts can become cluttered and difficult to read if there are too many nodes or links, so it is important to use them judiciously and with a clear purpose.
When to use Sankey charts?
- When you are trying to present data in a process
When to avoid Sankey charts?
- If there are too many nodes or links, it can be difficult to comprehend
- If you are trying to see exact value, may not be the most intuitive way to present data
Single Value Charts
A Single Value Chart chart typically displays one or more KPIs over a period of time, such as a week, month, or year, and compares the actual values to a target or goal. Single Value charts can display quantitative and categorical displays, and are often color-coded to indicate whether the KPI is on track (green), at risk (yellow), or behind schedule (red).
When to use single value charts?
- Summarizing time series data into a single value
A funnel chart is a type of chart that displays the stages of a process or system, and the conversion rates between each stage. It is called a funnel chart because the shape of the chart resembles a funnel, with a wide top and a narrow bottom.
Each stage of the process is represented by a segment of the funnel, with the width of the segment proportional to the number or percentage of items or people that enter that stage. The segments are arranged vertically, with the top segment representing the initial stage, and the bottom segment representing the final stage.
These are most helpful in displaying how volumes move through stages, or in our examples, how different stages make up a hierarchy of volume.
When to use funnel charts?
- When there are 3 or more stages
- Good for conversion rate type of data
- When first stage is expected to be greater in volume than last stage
When to avoid funnel charts?
- If there are less than 3 stages, funnel charts may not be the best option
- If stages are approximately similar in sizes, funnel charts may not be the best option.
A gauge chart is a type of chart that displays a single value within a range of possible values, often represented as a semi-circular or circular gauge. Gauge charts are commonly used to display performance metrics or key performance indicators (KPIs) in a visual and easy-to-understand format.
When to use gauge charts?
- Showing performance metric compared to target
When to avoid gauge charts?
- Only helpful to for simpler information
- Cannot show multiple variable
Sigma workbooks support three distinct map types: Region, Point, and GeoJSON.
You should choose your map type based on your available data and the results you wish to see.
- If you want to map a text column [State] with cell values like "Colorado" and "California," or a text column [ZIP Code], you should choose a Region map type.
- If you want to map point coordinates, and you have Latitude and Longitude values, you should choose the Point map type
- If you want to plot custom GeoJSON shapes, and you have a column that contains that information, you can use the GeoJSON map type to fill those areas.
When to use map charts?
- Compare values within geographical regions
- High level summary based on geography
When to avoid map charts?
- If data does not include geographical components
- If alternative options are available to display data
Where can I learn more about Sigma features and use cases?
Our online documentation is a great way to get high-level information on product features along with as much fine detail as you want.
Sigma QuickStarts provide “step-by-step” guides to using Sigma, exploring specific features and use-cases.