BAR CHART
The classic bar chart uses either horizontal or vertical bars to show discrete, numerical comparisons among categories. One axis of the chart shows the specific categories being compared, and the other axis represents a discrete value. Some bar graphs present bars clustered in groups of more than one (grouped bar graphs), and others show the bars divided into subparts to show cumulative effect (stacked bar graphs).
DEVIATION BAR CHART
When graphs display deviation relationships, they communicate how one or more set of metric values differ from a primary set of values. Deviation relationships can be effectively displayed using the following objects:
- Horizontal bars (except when combined with a time-series relationship)
- Vertical bars
- Vertical bars
DUAL AXIS BAR CHART
A Dual Axis bar chart uses either horizontal or vertical bars to show discrete, numerical comparisons among categories. It can be a combination of a bar and a line with 3 axes. One axis of the chart shows the categories and the other two axes show respective values.
STACKED BAR CHART
STACKED BAR CHART
Stacked Bar Graphs segment the bars of multiple datasets on top of each other. They are used to show how a larger category is divided into smaller categories and what the relationship of each part has on the total amount. There are two types of Stacked Bar Graphs:
- Simple Stacked Bar Graphs
- 100% Stacked Bar Graphs
Line charts are used to display quantitative values over a continuous interval or time span. They are most frequently used to show trends and relationships (when grouped with other lines). This gives the "big picture" over an interval, to see how it has developed over that period. Line graphs are drawn by first plotting data points on a Cartesian coordinate grid, then connecting a line between the points. Typically, the y-axis has a quantitative value, while the x-axis has either a category or sequenced scale.
PIE CHART
Pie charts help show proportions and percentages between categories, by dividing a circle into proportional segments. Each arc length represents a proportion of each category; the full circle represents the total sum of all the data, equal to 100%. Pie charts are used for making part-to-whole comparisons with discrete or continuous data. They are most impactful with a small data set.
Bubble Charts are typically used to compare and show the relationships between labeled/categorized circles, by the use of positioning and proportions. The overall picture of Bubble Charts can be used to analyze patterns/correlations. Bubble Charts use a Cartesian coordinate system to plot points along a grid where the X and Y axis are separate variables. Each point is assigned a label or category (either displayed alongside or on a legend). Each plotted point then represents a third variable by the area of its circle. Colors can also be used to distinguish between categories or to represent an additional data variable.
SCATTER PLOT
A Scatter plot can help you identify the relationships that exist between different values. By displaying a variable in each axis, you can detect if a relationship or correlation between the two variables exists. The Various types of correlations that can be interpreted are positive (values increase together), negative (one value decreases as the other increases), null (no correlation), linear, exponential and U-shaped. The strength of the correlation can be determined by how closely packed the points are to each other on the graph.
AREA CHART
Area Charts are Line Charts with the area below the line filled in with a certain color or texture. Area Graphs are drawn by first plotting data points on a cartesian coordinate grid, then joining a line between the points and finally filling in the space below the completed line. Like Line Charts, Area Charts are used to display the development of quantitative values over an interval or time period. They are most commonly used to show trends and relationships.
BOX PLOT
A box plot is a convenient way to visually display groups of numerical data through their quartiles. It shows distribution of data based on minimum, maximum, median, and percentiles. Typically used in descriptive statistics, box plots are a great way to quickly examine one or more data sets graphically. Although they may seem primitive in comparison to a histogram or density plot, they have the advantage of taking up less space, which is useful when comparing distributions between many groups or data sets.
GANTT CHART
GANTT CHART
Gantt charts (also referred to as project timelines) are bar charts that help plan and monitor project development or resource allocation on a horizontal time scale. They are essentially horizontal bar charts which provide graphical illustration of a schedule that can help users plan, coordinate, and track specific tasks in a project. The data analyzed in a Gantt chart has a defined starting and ending value; for example, Project A begins 4/15/06 and ends 5/10/06.
HILOW STOCK / CANDLESTICK
HILOW STOCK / CANDLESTICK
This chart control displays financial data as a series of candlesticks representing the high, low, opening, and closing values of a data series (four metrics). The top and bottom of the vertical line in each candlestick represent the high and low values for the data point, while the top and bottom of the filled box represent the opening and closing values.
HISTOGRAM
HISTOGRAM
A histogram visualizes the distribution of data over a continuous interval or a certain time period. Each bar in a histogram represents the tabulated frequency at each interval/bin. The total area of the histogram is equal to the total number of datasets. Histograms help give an estimate of where values are concentrated, what the extremes are and whether there are any gaps or unusual values.
PARETO CHART
POLAR CHART / RADAR CHART
A Pareto chart is designed to help identify the cause of a quality problem or loss. It includes a Histogram that shows how often a specific problem is occurring or the different types of problems that are occurring. In general, Pareto charts allow you to display the specific areas in which improvement or investigation is necessary. It contains both a bar and a line chart. The values are represented by descending bars and the running % to total is represented by the line. It depicts the percent journey to total & also displays actual values.
POLAR CHART / RADAR CHART
Radar Charts are a way of comparing multiple quantitative variables. This makes them useful for seeing which variables have similar values or if there are any outliers amongst each variable. They are also useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance.
WATER FALL
WATER FALL
A Waterfall visualization highlights the increments and decrements of the values of metrics over time. Analysts can use the widget to identify aspects of their business that are contributing to the fluctuations in the values. The visualization can also be used to perform “what-if” analyses. For e.g., % Revenue Y/Y Variance by Month. It shows how different aspects of the business positively or negatively affect the bottom line.
GAUGES
GAUGES
A Gauge visualization is a simple status indicator that displays a needle that moves within a range of numbers displayed on its outside edges. A real-world example of a gauge is a car's speedometer. Like the Cylinder and Thermometer widgets, this type of visualization is designed to display the value of a single metric. The needle within the gauge is a visual representation of that single metric value.
TIME SERIES
TIME SERIES
A Time Series Slider is an area graph that allows a document analyst to choose which section of the graph to view at a time. The visualization consists of two related graphs, one positioned above the other. The top graph is the controller, and contains a slider. The bottom graph is the primary graph. You use the slider on the controller to select some portion of the controller, which determines the range of data visible in the primary graph. It allows users to see a high level trend of one or more metrics and a detailed view by varying the window of the visible data. For e.g., Revenue trend by Date.
MAPS
FUNNEL
DATA CLOUD
A Map allows users to visualize the data so they can identify and analyze relationships, patterns, and trends in their data. Some of the functionalities available are:
- Displaying areas, points, and data that are color-coded based on metric values
- Using image markers, bubble markers, density maps, or color-coded areas to visualize data on the map
- Zooming/panning on the map and data
- Displaying an Information window with additional data for a marker or area
- Providing the ability to customize the Information window, such as providing additional details or metric information, including demographic content from the mapping service
- Drilling up to summary levels of data and down to detailed levels of data
HEAT MAP
HEAT MAP
A Heat Map presents a combination of colored rectangles, each representing an attribute element, that allow you to quickly grasp the state and impact of a large number of variables. Heat Maps are often used in the financial services industry to review the status of a portfolio. The rectangles contain varieties and shadings of colors, that emphasize on the status of various components. In a Heat Map, the size of each rectangle represents its relative weight and the color represents the relative change in the value of that rectangle. You can hover over each rectangle to see which attribute element the rectangle represents; and its metric values.
FUNNEL
A Funnel helps to quickly analyze various trends across several metric values. It is a variation of a stacked percent bar chart that displays data that adds up to 100%. Therefore, it can allow analysts to visualize the percent contribution of sales data. It can also show the stages in a sales process and reveal the amount of potential revenue for each stage. When the visualization is used to analyze a sales process, analysts can use the widget to drill down to key metrics such as deal size, profit potential, and probability of closing. The size of the area is determined by the series value as a percentage of the total of all values.
MICRO CHARTS
MICRO CHARTS
Micro chart visualizations gives the trend of a metric at a glance without having to know many additional details. The bar, sparkline, and bullet microcharts used in the microcharts convey information that an analyst can understand just by looking at the chart once. It consists of compact representations of data that allow analysts to quickly visualize trends in data. It conveys information so that a user can, at a glance, determine the trend of a metric over time or how a metric is performing compared to forecasted figures.
A Data Cloud displays attribute elements in various sizes to depict the differences in metric values between the elements. This type of visualization is similar to a Heat Map in that they both allow an analyst to quickly identify the most significant, positive, or negative contributions. A Data Cloud widget is basically a list of attribute elements. The first metric on the template determines the font size for the attribute elements. A bigger font for an element indicates a larger metric value.