Plot lets you visualize variable relationships using simple charts. There are different types of charts to plot different data types including time-series, heatmaps and bar charts.
Start by choosing a pair of variables to visualize. Next, pick a chart to match your analysis. Then, play around with the data inside your chart to focus in on specific patterns.
"There is magic in graphs. The profile of a curve reveals in a flash a whole situation - the life history of an epidemic, a panic, or an era of prosperity."
- Henry D. Hubbard
Bar charts are a simple representation of two variables. The variable represented on the Y axis must be quantitative. The variable on the X axis can either be quantitative or categorical.
Best used for ... Understanding how values from one or two variables are distributed.
Box plots are great for showing the value distribution belonging to a quantitative variable over a number of different categorical data segments. They represent quartile ranges and median values associated with these categories.
Best used for ... Understanding frequency distribution patterns.
Heat Maps are great at spotting correlation patterns between pairs of variables. They use a color spectrum to represent density of your dataset at points where values from two variables meet.
Best used for ... Measuring correlation between pairs of variables.
Overview charts are histograms representing the evolution of a variable over time and require a date variable. You can either represent a count of all your data points or chose a specific quantitative variable to plot.
Best used for ... Investigating general trends.
Segmented overview charts show how values from specific variables evolved over time and require a date variable. These are represented as a series of histograms, each representing a different value belonging to a specific variable.
Best used for ... Inspecting how date trends differ between values of a variable.
Compared segment charts represent data in time-series line charts and require a date variable. Similar to segmented overviews, they plot different values belonging to a specific variable over time. However, values are plotted as lines and are presented on the same axis.
Best used for ... Comparing date trends of one data segment with others.
Share charts represent your data in relative terms and require a date variable.. In share charts, values belonging to specific variables are divided into percentage shares, showing how each value contributes to the features of a variable over a date range.
Best used for ... Exploring how the features of a variable changed over time
Plot charts reveal patterns between one or two variable distributions. They can also be used to plot data evolution over time.
Different types of Plot charts require different data types. In the chart selector panel, you won’t be able to choose charts that are incompatible with the current variables for either the X and Y axis.
Use the X and Y axis dropdown menus inside of the chart selector panel to start visualizing your data with Plot charts.
Switching between charts in your project's Plot panel helps to reveal different perspectives on your dataset. Since, Plot charts require different types of data, you will only be able to choose between charts compatible with your current X and Y axis variables.
Changing the type of chart won't reset the active filters you have applied. Use the back button next to the variable dropdown menus to access the chart selector panel.
There are two ways to change the variables represented in Plot charts. If you have an active chart, you can choose variables compatible with the current chart from variable dropdown menus at the top of your Plot panel. Changing them from the chart selector panel lets you choose any variable in the dataset and will change the available chart types.
You can use your project’s sidebar filters to filter the data shown inside Plot charts. Each time you set a filter, Graphext will recalculate the data shown inside the chart so that it only represents the data matching the filter query.
In Plot charts that are designed to represent date variables, you can use our interactive date filter at the bottom of the chart to select specific date ranges to represent.
Click and drag your mouse over the date range histogram to filter your data by date. Drag the filter to the left or right to reposition it. Drag it's bars to resize it.
You can add different kinds of aggregation to Plot charts that represent date values. Choose the dropdown menu at the top left of your chart to change the way your data is aggregated over time.
Adding annotations to your Plot charts is a useful way to contextualize your analysis with significant events.
Annotations are placed at the bottom of x-axis and are marked by a vertical dashed line on your chart. Start adding annotations by clicking on the 'Add annotation' icon from the icon list at the top right of your project's Plot panel.
Annotations should be clear and short descriptions of significant events that affect trends in your data. Remove them by clicking on the description and selecting the trash icon below the text box.
The Plot panel is a quick way to find interesting patterns in your data. In fact, they are so interesting that you might want to immediately share them on social media. You can either save trends charts as insights or export them to your computer.
Click on the export icon in the top left of the Plot panel to export the current chart as an image.
Plot charts are often composed of separate elements which get downloaded independently. To export them as a group, save the chart as and download that.
When you save Plot charts as insights, you can add text, descriptions and statistics to supplement the chart. You can present Plot charts directly inside of your project's Insights panel.
Capturing Plot charts as insights then downloading that insight can be a useful way to collectively download all elements of a chart.
We know that data isn't always clean and simple.
Have a look through these topics if you can't see what you are looking for.