Have you ever been tasked with creating a chart, diagram, or graph? There needs to be more understanding surrounding these similar phrases, particularly because they are frequently used interchangeably. Nevertheless, there are some slight discrepancies in their application.
Visual representations aid us in swiftly comprehending the material. Whether comparing sales figures or emphasizing a pattern, your report or presentation improves precision and credibility when you use an excellent diagram, graph, or chart. Read on as we cover the main differences between charts, graphs, and diagrams and how they all aid in data visualization.
Visual representations are powerful tools for presenting and analyzing data. They offer several advantages in conveying information effectively:
When utilizing visual representations, it’s crucial to ensure they are accurate, clear, and aligned with your intended message. Choosing the appropriate type of visualization based on the data and purpose, using proper labeling and scaling, and presenting data ethically are essential considerations for creating impactful visualizations.
Corporations and businesses widely use graphs and charts for various purposes, such as representing numbers, reports, and performance. They are almost similar in terminology. A graph is a diagram or a mathematical function that can display statistical data.
A figure depicts the association among two varying quantities, often involving two variables. The evaluation will involve taking measurements along one of two perpendicular axes. Conversely, charts are graphical representations of information, while line charts are simply one variant. A graph represents numerical information in a table format and depicts functions.
Graphs and charts are visual representations of data, but they have different purposes and distinct characteristics. Here’s a comparison between charts vs. graphs:
In sum, graphs are more focused on illustrating quantitative relationships and patterns, while charts are better suited for presenting categorical or qualitative data concisely and visually appealing. Both graphs and charts have their uses depending on the type of data and the purpose of visualizing it.
Charts and diagrams are visual tools used to represent information, but they have distinct characteristics and serve different purposes. Here’s a comparison between charts and diagrams:
Overall, charts are primarily focused on presenting numerical or quantitative data clearly and concisely, while diagrams are more versatile and used to illustrate complex concepts, systems, or relationships. Charts are commonly used in data analysis and business contexts, while diagrams find applications in various fields for visualization, planning, and problem-solving.
Each graph displays data on diagram plots (such as a bar, pie, or line chart), demonstrating graph patterns and correlations between variables. While there are many different forms of graphs, this article contains all the typical kinds of statistical graphs and charts that are frequently used in all fields of study.
A column chart is a visualisation of data approach where vertical columns are used to indicate categories. Each category’s column height corresponds to the values plotted. As a result, a vertical bar chart is another name for a column chart.
It is simpler to compare the chart’s columns against a wide range of items because each column begins at the zero baseline. The format of the data in the column chart makes it simpler to read and interpret. Users may quickly spot patterns and gather important information from the data displayed in the column chart.
Different kinds of column charts exist. Some of them are the following:
Because they are simple to grasp, column charts are practical and excellent for use in a variety of scenarios. Important information can be shown and communicated rationally and effectively with column charts. Column charts should generally be utilised when:
A column chart may be necessary for the following numerous reasons:
A bar chart is a graphical representation that uses rectangular bars to show and contrast discrete data categories. Each bar’s height or length corresponds to the frequency or value of the associated category.
A bar chart’s implications are endless, ranging from tracking patterns over time to mapping regional distributions and understanding frequency distributions. Despite their many variations—stacked, horizontal, grouped, and more—they all share the ability to highlight patterns and facilitate rapid, efficient comparisons.
Knowing a bar chart’s structure is the first step towards analysing it. The visual clarity of the bar chart is what makes it simple, but every element of its construction is essential to understanding the information it displays.
Simply consider the length of each bar to be the magnitude or frequency, and each bar as a category. A bar chart allows the user to quickly compare and contrast data since the length of each bar provides a brief overview of the quantity for each category. Among these essential elements that provide the data displayed clarity are its labels, titles, and scales. Parts of the Bar Chart are:
In this article, we explore some of the main advantages of bar charts, explaining why they are such a valuable tool for data visualisation.
A donut chart is a form of pie chart with a hole in the center, much like a donut. Donut charts provide categories in the form of arcs rather than slices. Although, at first glance, the hole in the middle appears to serve merely cosmetic purposes, it serves a more important purpose by assisting viewers in avoiding uncertainty about the area parameter.
For years, people have used donut charts to convey complex data. Although donut charts resemble pie charts, they are significantly easier to comprehend because they allow viewers to concentrate on the variations in values rather than the relative sizes of the sectors. In addition, donut charts are simple to design and have a more up-to-date appearance than other graphical options.
Donut charts, compared to other charts, are composed of two categories and options: Simple and Exploded.
To comprehend how to decipher a donut chart, one must first thoroughly understand each component of its construction. Each donut chart component has a crucial purpose, allowing readers to effectively analyze the data given. Here are some elements of the donut chart listed below:
Line charts are visual illustrations that clearly show trends and patterns in statistical information. Line charts are used in quantitative analysis to represent continuous data over a given period. This allows for quick and comprehensive interpretation for enterprises.
Line charts are much more than just visual tools. They are a dynamic gateway for understanding the fundamentals of data evolution. Line charts depict the shift of a variable over time or another continuous interval using a series of displayed dots joined by lines.
These infographics effectively depict data dynamics, revealing swings, patterns, and irregularities that may be hidden in raw data. Concerning line charts, you must first identify and comprehend their basic components.
A line chart, also known as a line graph or curve chart, is a visual representation that depicts data points connected by straight lines. This sort of chart is very effective for illustrating trends, changes, and relationships in data over a continuous period. Each data point on the chart reflects a value connected to a certain category or time. The connecting lines between these spots make it easy for viewers to identify patterns and variances in the data.
Line charts originated in the 18th century when mathematicians and scientists utilized them to demonstrate various mathematical functions. Nevertheless, the contemporary concept of line charts gained popularity in the nineteenth century, particularly through the works of notable statisticians such as William Play Fair. His breakthroughs in data visualization paved the way for the charts we know today. With the development of computers and powerful software, line charts have become an essential tool for analysts, researchers, and decision-makers in a variety of industries.
Line charts excel at representing variations over time, which makes them perfect for analyzing trends and spotting patterns in data sequences. Line charts tell a clear story about how variables evolve by organizing data points along an x-axis (horizontal) depicting time and a y-axis (vertical) showing data values. This temporal viewpoint helps identify growth, decrease, fluctuations, and probable anomalies in datasets. Line charts reveal tales hidden inside the temporal dimension of data, such as financial trends, variations in the weather, and population dynamics.
A pie chart is a sort of graph that depicts data in a circular format, with each slice indicating some percentage or proportion of the total. The sum of all the pie slices equals 100 percent and 360 degrees.
The pie chart depicts data in a pictorial format, making it easier to see and comprehend the proportionate sections or composition of a data set. It is also known as a circle chart.
The portions of the pie are sometimes depicted as percentages, indicating how much they contributed to the whole. This is not required; a pie chart can be created by changing data values directly to degrees. However, translating facts to percentages makes it more understandable to the ordinary reader.
In comparison to other types of charts and graphs, pie charts have very few use-case applications. A pie chart can only be used to show individual components of a whole. In other words, the information and its categories must be subsets of an entire data group for a single category to be represented by a pie chart.
An area chart is a visual representation of data that conveys information through both lines and filled regions. This sort of chart is especially useful for demonstrating data trends and changes over a given period or across several categories.
It is an important tool used by organisations to convert insights into attractive visuals. It is part of a more substantial chart network used in business intelligence to help make smarter decisions.
As corporations, researchers, and individuals deal with massive volumes of data, the requirement to show it in a comprehensible and visually appealing manner has become critical. An area chart is an important tool for data visualisation.
It carries out this by giving data between categories or over time in a clear and understandable format. Using lines and shaded regions, area charts reveal the meaning behind the numbers that make up their body. Understanding intricate data linkages, trends, and swings is facilitated by this.
An area chart’s basic idea is to link data points with lines, and then fill the space beneath the lines with the x-axis. Every data series plays a role in creating a different coloured area. This highlights its role in the broader trend.
The shaded areas enlarge or decrease in response to variations in the data points. Consequently, the area offers a quick visual representation of the data set’s changes. In an area chart, the areas that are shaded have important meanings.
The values of the data points at a specific time or category are represented by the vertical space between the line and the x-axis. By evaluating the breadth and height of these darkened regions, viewers may comprehend the individual values of the data as well as their overall impact. It improves understanding of the links, contrasts, and patterns in the dataset as a result.
Graphs called scatter plots show how two variables in a data collection relate to one another. It displays data points as a Cartesian system or on a two-dimensional plane. Plotting the dependent variable on the Y-axis corresponds to the independent variable, or attribute, on the X-axis. These plots are frequently referred to as scatter diagrams or scatter graphs.
There are other names for a scatter plot, including an XY graph, a scatter chart, and a scattergram. Numerical data pairs are graphed in a scatter diagram with one variable on each axis to illustrate their relationship. For all of us, the question now is:
Either of the following scenarios makes use of scatter plots.
Scatter plots provide an instantaneous report of a lot of data. It is advantageous in the subsequent circumstances:
Multiple data points can be shown on a bubble chart, making it simple to assess the relationships between them visually. Effective visualisations like bubble charts make it simple for viewers to spot patterns in the data by enabling them to swiftly assess data from several sources.
Bubble charts can help simplify complicated tales and reveal insights about potential correlations or trends between several datasets, even if they can be time-consuming to set up properly.
Bubble charts are frequently used for rapid and simple data comparison. By examining the bubble’s size, position, and colour, we may comprehend the relationship between several variables. The best-selling goods or services can be determined using a bubble chart that shows the performance of various goods or services in terms of income, quantity sold, and client happiness. In this manner, companies can examine the data and use it to inform their decisions.
A histogram is a pictorial illustration of a frequency distribution divided into continuous classes. It is an area diagram, which can be described as a set of rectangles with bases and intervals between class boundaries, as well as areas proportionate to frequencies in the respective classes.
All rectangles in such representations are nearby because the base encompasses the intervals between class boundaries. The heights of rectangles are proportional to the associated frequencies of comparable classes, whereas the heights of distinct classes are proportional to the corresponding frequency density.
In other words, a histogram is a diagram made up of rectangles with areas proportional to the frequency of a variable and widths equal to the class interval.
The histogram graph is used in particular situations. They are:
The histogram can be divided into several forms based on the distribution of frequencies of the data. There are numerous sorts of distributions, including normal distribution, skewed distribution, bimodal distribution, multimodal distribution, comb distribution, edge peak distribution, dog food distribution, heart cut distribution, and so on.
The histogram can be used to represent the various sorts of distributions. There are various types of histograms:
A box and whisker plot is a technique for abstracting a set of data that has been approximated using an interval scale. It is also known as a box plot. These are primarily used for data interpretation. It is one of the graphical approaches for displaying the variation of data in a dataset. We may also use the histogram to visualise the data. However, the histogram gives a suitable display. Box and whisker plots are superior to histograms because they provide more information by displaying various groups of data in the same graph.
We utilise these box plots or graphical representations to learn:
The moment we create a box plot graph, we draw a box from the first to the third quartile. The median is a vertical line that passes through the centre of the box. The whiskers (little lines) move from each quartile to the minimum or maximum value.
Box and whisker diagrams make it possible to read data quickly and easily. It summarizes data from numerous sources and presents it in a single graph. It allows us to make more informed decisions by comparing data from several categories.
The box and whisker graphic is utilised when we have several datasets from various sources that are related to one another. For example, compare test scores across classrooms.
A waterfall chart is an illustration tool that depicts the cumulative effect of successive positive and negative variables on an original starting point. In basic terms, it provides a well-structured and clear representation of the progressive transitions, stressing the complicated procedure by which several components contribute to an outcome, along with their net significance.
Typically, a waterfall diagram consists of a series of vertical columns or bars, each representing a different data aspect or classification. This figure begins with an initial benchmark or base value, followed by a variety of bars that either ascend (showing positive contributions) or descend (meaning negative contributions) from that benchmark. The length of each bar provides a visual indicator of its size for affecting the overall shift, terminating in the ultimate value shown at the end of the diagram, which represents the sum of all contributing factors.
Waterfall charts are useful for distinguishing financial statements, analysing budget variations, and sifting project expenses. They offer a clear visual representation of how separate components affect the output. In this respect, waterfall diagrams are useful for financial analysts, decision-makers, and project managers who want to understand and clarify the driving forces behind specific performance measures or outcomes.
The waterfall chart can be traced back to its use in presentations by the renowned consulting firm McKinsey & Company. This novel charting methodology is an effective data visualisation method that reveals the transforming journey of an initial value as it interacts with subsequent positive and negative inputs.
Its popularity now reflects its adaptability and efficacy in representing sequential and categorical data. The chart uses a succession of bars to represent gains and losses, providing a crystal-clear depiction of how an initial figure transforms in reaction to numerous events, ending in the final closing figure.
A funnel chart is a graphical representation that has the shape of a funnel, with each segment becoming increasingly narrower. The portions are positioned vertically to indicate the hierarchy. Each section on the funnel chart represents a phase or stage in a sequential process. They show the movement of data points as they go through the stages.
A funnel pours liquid into a container by being broad at the top and thin at the bottom. In the same manner, a funnel graphic can help you understand how data flows and transforms inside a specific environment.
A funnel chart’s power stems from its simplicity, which hides the quantity of information it can represent. Breaking down the essential components of a funnel chart allows us to acquire a better grasp of how it efficiently visualises data transitions and advancement through different stages.
A funnel chart is a distinct style of data visualisation that depicts data as it moves through various phases of a process. This one-of-a-kind chart is named from its shape, which resembles a funnel with gradually narrowing portions.
Each segment of the funnel represents a distinct step in the process, reflecting the changing data values as they go from the first to the last stage. A funnel chart’s primary aim is to visually demonstrate the progression or turnover that occurs at each stage of a process.
A radar chart shows multiple variables piled along an axis with a single central point. The chart includes at least three numerical variables for comparison, referred to as radii. The map resembles a spider web, hence the name “spider chart.”
The data sets are represented using transparent colors, tints, and trends, making it easy to identify differences and similarities. Simply said, a radar/spider chart is ideal for displaying the fluctuation between several data points.
Here are the key components of a radar chart:
Center Point: The center point of the radar chart represents its core. This is the point from which several axes are drawn.
Grids: Whenever the radar chart’s axes are connected, the graph breaks down into several grids. Grids like this help to represent information.
Values: Values represent data points. These are shown on the axis with unique colors.
Axis: Each axis of the radar chart indicates a data element. It contains a label via a name and many values.
There are three types of Radar Chart. Names are listed below:
Here are some of the limits you should know:
A Gantt chart is a project management tool that enables administrators to establish project schedules. It displays the sequence in which project-related duties will be accomplished, as well as when they are due and how long they will take to complete. Every Gantt chart has two primary sections: a grid or list of duties on the left side and a project timeframe on the right.
Henry Gantt devised the Gantt chart in the first decade of the twentieth century to help with the preparation, organization and tracking by comparing accomplished work to anticipated work. Today, project managers and teammates use Gantt charts to plan projects, set schedules, distribute resources, and track progress all in one project management application.
At its most basic, a Gantt chart enables project, program, and portfolio managers to rapidly lay out the project’s schedule by grouping project tasks on a graphical timeline. However, Gantt charts are also used for preparing projects, arranging them, tracking, and handling resources.
The Project Manager’s Gantt chart allows you to distribute tasks to team members, define due dates, calculate costs, and track outcomes in real-time. The best feature is that it can be used in conjunction with other project management tools like Kanban organization boards, real-time project reports, timesheets, and teamwork applications.
Heatmaps use color changes to visually represent data. Heatmaps, when used in the form of tables, are effective for interrogating varied information by arranging components in rows and columns and shading the cells inside the table. Heatmaps are useful for visualizing variation across numerous variables, exposing patterns, determining whether variables are comparable, and discovering correlations.
Usually, all rows belong to one category (labels are presented on the left or right side), while all columns belong to another. Single rows and columns are separated into subdivisions, all of which match up in a matrix. Cells are the intersections of rows and columns and can hold either qualitative or numerical data.
To be read properly, a heat map must be accompanied by a legend. Categorical data is color-coded, however numerical data necessitates a color scale that transitions from one color to a different one to depict the distinction between high and low numbers. A selection of solid colors can be used to represent several value bands (0-10, 11-20, 21-30, etc.), or one may employ a color gradient scale to symbolize a single range (for example, 0 – 100) by merging two or more colors.
Heatmaps are a graphic more suited to providing a more generic view of numerical data because they rely on color to express values, making it more difficult to correctly distinguish between colors and extract individual data points. However, this issue can be resolved by presenting the data values within the cells.
Heatmaps may also be employed to demonstrate how data evolves as time passes if any of the rows or columns is set to a period. For example, you could use a heat map to examine variations in temperature during the year in several cities to determine where the hottest or coldest regions are. So each row might list the cities for comparison, the columns each month, and the cells containing the temperature figures.
A treemap chart is made using a data visualization technique that represents data in a hierarchy as layered rectangles. The tree-like structure is made up of decreasing-sized rectangles, hence the name “nesting.” The data in a treemap chart is grouped into rectangles. The plot colors and rectangle dimensions are determined based on the quantity variables.
Every rectangle represents two integer values. That’s the reason why you can filter the data to an infinite number of levels. So, a treemap chart is an excellent way to easily discern between data values.
Treemap charts are particularly useful for discovering the relationships and patterns between two components displayed in a hierarchical data structure. It allows us to visualize data input in a linear and easy-to-read manner.
A treemap chart consists of the three components listed below.
Rectangles: The data in a treemap chart is symbolized by rectangles. Rectangles are one of the fundamental building components, also called “nodes” or “branches.” Every rectangle in a treemap chart can hold two numerical values.
Hierarchy: A treemap chart can depict data in multiple layers. These graphs integrate data into an organizational framework represented by nested rectangles.
The measurements and plot colors are recognized and determined based on the quantitative variables of the individual rectangles. Color mapping is an excellent way to add details to treemap charts.
The treemap chart consists of the subsequent three sections:
Treemap charts have revolutionized the way data is presented and interpreted in a compact space. It remains a versatile and attractive tool for plotting and displaying hierarchical data from a variety of areas.
In many situations at business or school, the pie chart may be one of the most regularly utilised charts. It can be used to display the contents of a specific category. However, when dealing with multilevel data, pie charts are insufficient.
At the time, the sunburst chart was introduced, which solved the problem. Sunburst Chart, also known as Ring Chart, Multi-level Pie Chart, and Radial Map, is commonly used to depict hierarchical data structures.
A Sunburst Chart is made up of an inner circle encircled by rings representing deeper hierarchical levels. Every segment’s angle is either proportionate to a value or evenly divided under a single node.
According to the above, three advantages of sunburst charts can be seen:
First, high-quality work includes enhanced material. A sunburst chart can represent the contents of many pie charts. It can reduce work time, eliminate wasted effort, and increase work quality. At the same time, enhanced content can encourage greater thinking. The sunburst chart combines the material of all aspects and levels into a whole, allowing us to overlook details.
Secondly, the sunburst chart highlights the focus. Whether the data is reflected with regard to each part or different colours are used, the important point is always visible. Based on this, we can determine the emphasis of the analysis. Reasonable conclusions can only be made by getting to the foundation of the issue.
Lastly, consider how inclusive the sunburst graphic is. Many charts require you to type at least two numbers to create them. However, when it comes to actual work and study, numerous expressions do not require numbers. As a result, when confronted with textual statements, people rarely contemplate creating charts. Using words to express the link between objects is not always the best option. The inclusivity of the sunburst chart is now displayed. Even when there is no data, the role of the sunburst map remains unchanged because it primarily displays the link between multiple levels of material.
What is the Pareto Chart? The Pareto Chart is a sophisticated bar chart that includes a line graph. It displays individual values in descending sequence using bars. Meanwhile, a line indicates the cumulative total of each of the numbers in percentage terms. The chart was created by Italian economist Vilfredo Pareto’s 80/20 rule, which claims that 80% of outcomes result from 20% of causes.
In the present age of advanced data analysis, the Pareto Principle is extremely important. It has far-reaching consequences for many corporate applications, including research, manufacturing, marketing, and sales. The Pareto chart allows users to extract meaningful insights from data distribution and learn about the elements that contribute to a given outcome.
A Pareto chart consists of the following elements that work collectively to give an actionable depiction of data, indicating key elements attributing to an outcome or issue:
A Pareto chart provides several valuable advantages that apply across various applications and sectors, including (but not limited to) making it a perfect data analytics and BI reporting tool:
A Sankey Diagram is a visualisation tool used to depict flows. Rectangles or text represent several different entities (nodes). Their connections are depicted by arrows or arcs with widths proportionate to the importance of the flow. Here’s an example of how many people migrate from one country (left) to another (right).
The data used is from this scholarly paper. Sankey diagrams are used to represent balanced networks, or flows. This can happen with a variety of data structures:
Evolution: The nodes are replicated into two or more groups representing stages. Connections depict the progression of two states, such as in the migration example above. This is more commonly shown as a chord diagram.
From beginning to end: Imagine a complete amount; the graphic depicts where it originates from and where it ends up, with various intermediary phases. Every node is unique. (This example comes from the network D3 package).
Variation: Sankey diagrams are prone to several visual alterations, even if the basic concept remains the same.
Common Mistakes: The position of nodes is critical; algorithms exist to reduce the number of crossings between links.
Mind over cluttering renders the figure illegible. It is advisable to disregard weak connections.
A slope graph resembles a line graph, but there is one key difference: each line has just two data points. This may appear to be a minor distinction. Still, in reality, it signifies something significant: we can contrast two categorical variables, something we couldn’t accomplish with a typical line graph.
When we utilize slope graphs, we are attempting to demonstrate one specific point: is the value in the first column greater, less, or equal to the value in the second column? When we connect those numbers with lines, we can notice the change because those lines will slope up or down in the same direction as the change in the numbers.
The higher the slope, the greater the change; and, if one thing is rising faster than the others, a slope graph will make this more obvious than a typical line graph.
Slope graphs may be used on either continuous or categorical data.
A chord diagram depicts flows or relationships between multiple elements (nodes). Each entity can be seen by a fragment located on the outer edge of the circular pattern. Then lines are drawn connecting each entity. The magnitude of the arc is related to the significance of the flow.
Here’s an example that shows the number of people moving from one country to another.
Chord diagrams are eye-catching and often used in data visualization. They allow you to see balanced relationships between several things. They are customized for numerous unique conditions, slightly affecting the output and the interpretation technique.
Flow: This is the example given in the chord diagram above. There are two methods for illustrating it:
The bipartite: nodes are classified into a few categories. Connections exist across categories, but not within them. In my perspective, Sankey diagrams are more suited to this case.
Violin Plot is a technique for visualizing the variation of numerical data over multiple variables. It is identical to the Box Plot but includes a rotated plot on each side, which provides additional details about the density estimation on the y-axis. The density is replicated and turned over, and the resultant shape gets filled in to form an image of a violin. A violin plot has the advantage of revealing distributional details that a boxplot cannot. On the contrary, the boxplot clearly illustrates the data’s outliers. Violin plots contain more information than box plots but are less popular.
The violin plot utilizes a kernel density estimate technique to determine its boundaries. Kernel density estimation (KDE) is a statistical approach for estimating the probability density function (PDF) of a variable that is random using a set of observed data points. It gives a smooth and continuous measurement of the fundamental distribution that the data is thought to originate.
A Stream Graph’s values are displaced around a shifting center baseline. Stream Graphs depict changes in statistics over time for many categories using flowing, organic shapes that imitate a river-like flow. This renders Stream Graphs more visually appealing and engaging.
In a Stream Graph, the dimension of each stream shape corresponds to the values in the different categories. The timescale is represented by the parallel axis of a Stream Graph. Color can be used to differentiate each category or to visually represent a different quantitative variable by modifying the color tone.
Stream Graphs are useful for visualizing large datasets to see trends and patterns that change over time across a variety of categories. For instance, Seasonal peaks and troughs in the stream form may indicate a periodic pattern. A Stream Graph could potentially be used to visually represent the volatility of a big group of assets over time.
The disadvantage of Stream Graphs is that they are frequently congested with enormous datasets, which makes them difficult to read. Categories with lower values are frequently drowned out in favor of categories with considerably higher values, rendering it impractical to observe all of the data. There is also no value axis to utilize as a reference, making it impossible to understand the exact values presented in a Stream Graph.
As a result, Stream Graphs should be saved for audiences who do not plan to spend much time analyzing the graph and studying its data. Stream Graphs are superior for providing a broader picture of the data. They also perform much better as interactive pieces than fixed or printed visuals.
Arc Diagrams are an alternative representation of two-dimensional network diagrams. Arc Diagrams put nodes over a single line (a one-dimensional axis) and utilize arcs to indicate connections between them.
The dimension of each arc line represents the frequency between the source and destination nodes. Arc Diagrams can be useful in identifying co-occurrences in data.
The disadvantage of arc diagrams is that they do not illustrate structure and relationships between nodes as well as 2D charts, and too many linkages can make the diagram difficult to read owing to clutter.
Arc diagrams are not as effective as 2D network charts in conveying the overall node structure. It has two major advantages, though:
A polar area diagram is a sort of chart that is comparable to a pie chart but with a few significant distinctions.
During the Crimean War, Florence Nightingale famously employed polar area diagrams known as “coxcomb charts” to emphasize the reasons for death among British soldiers. Her pictures demonstrated that infectious diseases induced by poor living circumstances killed more people than war wounds.
Some important advantages of polar area diagrams are:
To summarise, polar area diagrams are circular statistical graphics that use equal angles and changing radii to efficiently show and compare quantitative data, particularly over time.
A bullet graph is a bar with additional encodings that depict development toward a goal or performance against a reference line. Each bar concentrates the user on a single measure, incorporating other graphic components to convey additional information. The bullet graph, created by Stephen Few, substitutes the meters and gauges that characterized early dashboards and reports. It packs more details into less space, making it suitable for a compact dashboard.
The bullet graph represents a single major measure. It incorporates metrics from different domains to improve the graphical representation for analysis. One may show the current year’s revenue, evaluate it against a goal, and contrast it with success from the preceding year. Tick marks and labels are used on the data axis to aid in quick analysis. Bullet graphs, a type of bar chart, begin at zero to aid visual comprehension of the data.
A bullet graph is made up of bars that reflect the featured metric. This bar sticks out from the rest of the graph because of its vibrant color and bold line. It places itself in the center of the graph. A reference line, which represents a goal or other critical threshold, is drawn perpendicular to the bar on the quantitative scale axis. When the main bar reaches the reference line, the aim is achieved, or the state changes.
The employment of shade, color, or lines within and around the highlighted measure provides context. These can display historical performance, goals, and forecasts, or highlight additional dimensions. This enables simultaneous monitoring and investigation via a single display. For example, assume someone uses a bar graph to track their expenses year after year. The graph allows them to quickly determine whether year-to-date spending is lower than the previous year’s. Lines of reference or shading allow for the development of thresholds based on past data. They would follow up to see if the featured metric remained below the limit.
Marimekko charts employ stacked bar charts with various widths to symbolize and represent categorical data graphically. In this graph, both axes show variables with percentages. The proportional scale of each section determines its height and width. As a result, this chart allows you to identify interactions among categories and subcategories. The chart is also known as the Mekko chart or mosaic plot.
The Marimekko chart can be useful in the following situations:
A contour plot is a graphical approach for displaying a three-dimensional surface by drawing constant z slices, known as contours, in a two-dimensional format. That is, given a value for z, lines are created to connect the (x,y) locations where the z value occurs. The contour plot is an alternative to the three-dimensional surface plot.
This contour map demonstrates that the surface is symmetric with peaks in the middle.
The contour plot is composed of:
Independent variables are often constrained to a regular grid. The actual methods for determining the correct iso-response values are somewhat sophisticated and virtually invariably generated by computers.
An additional variable may be needed to define the Z values for drawing the iso-lines. Certain software packages need specified values. Other software packages will automatically calculate them. If the data (or function) does not form a regular grid, you will need to conduct a 2-D interpolation.
Network graphs are used to visualise and study the relationships between items, which might include people, events, transactions, and other objects. In a network graph, nodes represent things, while links or edges reflect their relationships. The weight or thickness of the edge might show how strong the link is.
Network graphs can be helpful for a wide range of applications, including intelligence analysis, fraud detection, and comprehending complicated systems. They let you identify related things, comprehend the nature of their relationships, and discover hidden patterns and insights.
A network graph’s essential elements are nodes, edges, and edge weights. Nodes can represent actual or intangible items, edges reflect the interconnections between nodes, and edge weights indicate the strength of those connections.
Machine learning and the processing of natural languages are frequently used to generate network graphs, which automatically recognize entities and their connections within huge, complicated datasets. Visualising these graphs can lead to tremendous analytical capabilities.
A ridgeline plot, also called a joy plot, is a data visualization that shows the distribution of quantitative variables over numerous categories on a single continuous axis. It illustrates the pattern of distribution by piling them vertically so that the overlapping lines resemble a mountain range. Ridgeline charts are very excellent for demonstrating how distribution patterns alter over different categories, periods, or scenarios, hence providing insights into variations in data and trends.
Multiple Distribution: Plot the distribution among multiple groups. It also permits multiple value comparisons.
Ridge Style: For a visually appealing presentation, smooth line styling, stroke, and marker options are offered.
Scaling: Easily identify patterns by adjusting the Y-axis scaling and density/area overlaps.
Colors: Apply color based on axis and category. There are over 30 palettes, 7 color schemes, and FX rules available.
Legend: A legend is offered when plotting data with numerous values.
Ranking: Remove the top and bottom N based on values or peaks in the data. Show the remaining values as “Others”.
Reference line: Add a line on the Y and X axes to emphasize a range or key data points.
Mode Line: Highlight peak points with labels.
Custom Tooltip: Include the highest, lowest, mean, and median points in the tooltip without using additional DAX/measures.
Themes: Use the JSON export/import functionality to easily save your designs, which can then be shared and used across different Power BI reports.
Annotation: Provide additional information and insights about certain data points.
Grid View: Turns the image into an interactive table. To quickly scan data, use pivot mode, filter, search, and sort options.
Show condition: Determine whether the visual should be shown or hidden.
Native features: Supported are cross-filtering, interactivity, selection, tooltip, bookmark, and context menu.
A ternary plot is a visual representation of three variables that add up to a constant, often 100%. It is represented as an equilateral triangle, with each vertex representing a single variable. The plot is widely used in chemistry, geology, and metallurgy to depict proportions such as mineral content in rocks. The three variables’ values are interconnected, therefore their ratios can be seen in two dimensions. Ternary plots are useful for analysing compositional data and creating phase diagrams.
A ternary plot has three primary components:
Ternary plots offer the following advantages:
A lollipop chart is an interpretation of a bar chart in which the bar is substituted with a line with a dot at the end. It is used for comparing different items or categories, ranking data, and displaying trends over time.
Lollipop charts are favoured over regular bar charts for displaying a large number of comparable high values because they eliminate a cluttered appearance and the Moiré pattern effect caused by multiple tall bars. A lollipop chart’s basic style makes it more visually appealing and clear.
A lollipop chart, like a bar chart, depicts the relationship between a numerical and a category variable. However, the value is marked by the centre of the dot, which may be less exact than a bar’s flat edge.
Lollipop charts are constructed utilising a scatter plot, with the line rooted from the x-axis and a dot at the end to indicate the value. They are often used in data visualisation applications such as Tableau.
The primary advantages of a lollipop chart are:
Error bar charts are visual illustrations that show data variability and indicate errors or uncertainties in reported readings. They often show one standard deviation, standard error, or confidence interval, which provides information about the precision of a measurement. Error bars can be used with a variety of graph styles, including bar charts and scatter plots, to examine data dependability and the statistical significance of discrepancies between datasets. They graphically represent how spread out the data is around a central value, which helps in the understanding of results.
Error bar charts have several critical elements:
These components help to visualise the fluctuation and unpredictability of the data displayed in the chart.
The following are examples of error bars commonly used in data visualisation:
Each type expresses distinct characteristics of data unpredictability and should be chosen based on the analysis environment and audience requirements.
A beeswarm plot, also known as a swarmplot, is a style of data visualisation in which individual data points are displayed so that they do not overlap, creating a “swarming” impression resembling a swarm of bees. It is intended to demonstrate the underlying distribution of the data while avoiding overlapping dots.
The beeswarm plot contains the following major features:
Beeswarm plots are excellent for understanding machine learning models because they provide a concise summary of how the top features influence the model’s output. They are a better alternative to boxplots since they disclose the underlying data distribution without obscuring the dataset.
A beeswarm plot has several critical components:
This representation is useful for visualising distributions and spotting patterns in datasets, particularly in statistical analysis and machine learning applications.
A Step Line chart, often known as a stair chart or a step chart, is a style of chart used to visualise changes in data over time. It is a type of line chart, but rather than linking the data points with a continuous line, it uses horizontal and vertical lines to produce a series of steps. The vertical lines show the change in value from one point to the next, whilst the horizontal lines show a time or category.
For these reasons, step-line charts are commonly used to depict data that varies over time, particularly when the changes occur at regular intervals. Some popular instances might be:
Stock prices: Step line charts can be used to illustrate the movement of stock prices over time, including highs and lows, along with any emerging trends or patterns.
Financial: Step line charts are useful for tracking financial data like sales, profits, and expenses. They can show how these variables vary over time and assist the user find trends or patterns.
Process Data: A Step Line chart is useful for showing how a process has advanced over time, such as when a project is completed or a product is progressing through a manufacturing process.
Time-series data: For data gathered at regular intervals, such as daily, weekly, or monthly, a step line chart can be an effective way to observe and evaluate how it changes over time.
A 3D Surface Chart (or 3D Surface Plot) represents three-dimensional data as a mesh surface, with two independent variables (X and Y) on the horizontal axes and a dependent variable (Z) on the vertical axis. This chart type is excellent for determining ideal data combinations as well as studying patterns, trends, and relationships across multiple datasets. The surface’s hue and shading show value ranges, much like a topographic map, making it useful for complex data display.
The primary components of a 3D Surface Chart are:
Using a 3D Surface Chart for data visualisation has multiple significant advantages:
A Ribbon Chart is a data visualisation tool that displays item rankings over time by connecting data points with ribbons. It is an extension of the stacked area chart, which successfully depicts changes in rank, trends, and performance. Ribbon charts are especially effective for comparing variables such as sales or market share across categories, illustrating which goods rise or fall in position over time. They are frequently used in tools such as Microsoft Power BI, where users may generate interactive and visually appealing representations of time series data.
Ribbon Charts, commonly referred to as Marimekko or Mekko charts, are powerful statistical visualisations used mainly in Microsoft Power BI for showing modifications to evaluations over time. In contrast to traditional stacked area charts, Ribbon Charts utilize flowing ribbons that demonstrate how values fluctuate, which makes them effective for contrasting categories such as sales or revenue over various time frames.
A windrose chart is a graphical tool that depicts the distribution of the wind’s speed and direction at a given area over time. It is often made up of a circle divided into pieces, each reflecting the frequency of wind flowing from a specific direction. The length of the segments or petals in each part represents the frequency or duration of the wind blowing in that direction.
Wind roses are widely used in meteorology, navigation, and environmental science to investigate and illustrate wind patterns and variations. They are also used in the design of buildings, structures, and landscapes to determine how wind affects their performance and comfort.
The % values (concentric circles) represent the percentage of time the wind blows from the given direction. For all directions, the magnitude of the coloured wedge shows the proportion of times the wind from that direction was in the stated speed range.
To generate a windrose chart, the raw wind speed and direction data must be converted into an organized format appropriate for study. This entails dividing the data into bins depending on wind speed and direction, calculating the number of observations in each bin, and maybe normalizing these frequencies.
Windrose charts can be made after the data has been prepared using Python tools such as Matplotlib and Plotly. Matplotlib may generate windrose-like plots by applying scripts on stacked bar charts curved into a circle, whereas Plotly has integrated assistance for windrose charts.
The major components of a wind rose chart are:
A Geo Chart is a data visualisation tool that shows how measurements change spatially.
Here are some practical examples of how to use geo charts:
Stock charts are visual illustrations of stock metrics like price and revenue movement over time. They are useful for traders as well as investors because they provide a visual tool to monitor stock performance and spot patterns. Stock charts can be used to monitor stock price fluctuations over time, uncover possibly inexpensive stocks, and make sound investing decisions.
There are numerous varieties of stock charts in Excel, but the most frequent are line charts, bar charts, pie charts, and Candlestick charts.
Each chart has advantages and shortcomings but line charts are the most fundamental and simple to interpret.
A 3D scatter plot is a data visualisation tool that depicts three quantitative factors in three dimensions, allowing for the investigation of complicated relationships and patterns between data points. Each point on the plot represents a unique combination of the three variables, and its position is determined by their values.
3D scatter plots are utilised in many domains, including:
3D scatter plots are widely used in the following industries:
Venn diagrams are graphical representations that show the relationships and commonalities between different sets or items. They consist of overlapping circles or ellipses, where each circle represents a set, and the overlapping regions represent the elements shared between the sets.
Venn diagrams are commonly used in various fields, such as mathematics, logic, statistics, and data analysis. They are especially useful for understanding committed relationships, identifying commonalities, and visualizing the intersection of different groups or categories. Venn diagrams can be helpful in problem-solving, decision-making, and exploring the relationships between complex data sets.
A matrix chart is a graphical tool that analyzes and displays relationships between multiple variables or factors. It organizes data in a grid-like structure, where the rows and columns represent different categories or criteria, and the intersections show the relationships or interactions between them. Matrix charts can be applied in various domains, including project management, quality management, risk assessment, product evaluation, and strategic planning. They allow for a comprehensive view of relationships between multiple factors and enable data-driven decision-making. By organizing information in a structured format, matrix charts provide a valuable tool for analyzing complex data and visualizing relationships clearly and concisely.