Line chart

Line charts allow you to visualize change over a continuous range, such as time or distance. Visualizing change with a line chart allows the overall trend to be displayed at once, and multiple trends to be compared simultaneously.


Create a line chart to visualize trends in crime incidents in 2014 and 2015.

  • X-axis date or number—Date
  • Aggregation—Count
  • Numeric fields—None
  • Split by—Crime type
  • Interval—Six days
Crime incidents in 2014 and 2015


The Data configurations include the variables that are used to create the line chart.


Line charts require a continuous X-axis date or number variable and an associated numeric value on the y-axis.

The x-axis of a line chart displays a continuous variable such as time or distance, and a line is drawn visualizing the change in value between each consecutive time or distance interval. Each interval is marked with a point corresponding to a numeric value measured by the y-axis.

When a number field is chosen for the x-axis and each number appears in the table only once, no aggregation is necessary, but a Numeric fields variable must be specified.

When a date field is chosen for the x-axis, the dates are binned, or aggregated, into time intervals. When a line chart is drawn using a date field or an aggregated number field, an aggregation method must be chosen to specify how the data will be summarized.

If no numeric fields are specified, the chart will use the Count aggregation method, which totals the number of records that appear at each time or distance interval. For example, in a dataset of crime incidents, a date field is binned into two-week intervals with a Count aggregation method. The resulting chart will display a marker for every two-week interval representing how many crimes took place during those two weeks.

If Numeric fields values are specified, the aggregation method can be one of the following:

  • Sum
  • Mean
  • Median
  • No aggregation

Multiple series

Multiple lines, or series, can be visualized in the same chart to compare trends.

Multiple series line charts can be created by adding multiple numeric fields, or by setting a Split by category field.

When multiple Numeric fields values are added, one line is drawn to visualize the change for each numeric field. For example, in a dataset of crime incidents, a date field is binned into two-week intervals with a Sum aggregation method and two Numeric fields values, PropertyLoss and TotalDamage. The resulting chart will display two lines, one representing the sum of PropertyLoss and the other the sum of TotalDamage for every two-week interval.

A category field can also be used to split a line chart into multiple series. For example, in a dataset of crime incidents, a date field is binned into two-week intervals with a Count aggregation method and a CrimeType Split by field. The resulting chart will display lines representing the total number of crimes of each type (for example, Theft, Vandalism, and Arson) that took place during those two weeks.


A Split by category cannot be applied when more than one Numeric field value has been added.

Category fields with many unique values are not appropriate for splitting a field into multiple series.

Time binning

Time aggregation, or binning, occurs automatically when a date field is chosen for the x-axis. Several options control the interval size and related settings applied to the binning.


Temporal data is binned into time intervals along the x-axis. A default interval is chosen based on the temporal extent of the dataset and can be manually changed using the Interval parameter.

Interval alignment

Time intervals may align to the first data point or to the last data point.

Example data

Snap to the first data point initiates binning with the earliest date and works forward.

Binning with alignment at the start of the dataset

Snap to the last data point initiates binning with the most recent date and works backward.

Binning with alignment at the end of the dataset

Interval alignment is important to consider because the last bin created may be partially empty, which can give the misleading impression that there is a dip in the value or count during that time, when really the data collection began or ended during the span of that bin. To avoid bin bias, check the Trim incomplete interval option. This removes the partially filled bin from the visualization:

Trim incomplete interval

Empty bins

Depending on the sparsity of the dataset and the time interval size chosen for binning, there may be bins that contain no data. Empty bins may be treated as zero when a lack of data truly represents a value of zero (for example, no illnesses were reported in May or no rain was collected during a week span). It is not appropriate to assign a zero value to a bin in which no data exists because none was collected (for example, no reading from a temperature gauge does not mean there was a temperature of zero).

There are three options available for dealing with empty bins:

  • Treat them as zero (Treat as zero)—This is most appropriate when counting incidents, as no incidents counted likely means zero incidents took place.
  • Interpolate neighboring values (Connect line)—Null values can be visually interpolated by connecting the line between the bins on either side of the empty bin.
  • Break the line (Break line)—Leave a blank space where an empty bin falls.


You can turn on labels that display the value of each line or series by enabling Show data labels.


You can use the Series configurations to change the color, width, style, and label of a line on the line chart.


The Axes configurations are used to change the specifications for the x-axis and y-axis.

You can format the way the x- and y-axes display numeric values by specifying the number of decimal places and whether to include a thousands separator. The x-axis can only be formatted if it displays a number field rather than a date field.

The default minimum and maximum y-axis bounds are set based on the range of data values represented on the y-axis. You can customize these values by typing new Minimum bounds and Maximum bounds values. Click Reset to revert the axis bound to the default value.

Logarithmic scale

By default, line chart axes are displayed on a linear scale. Numeric (nondate) axes can be displayed on a logarithmic scale using the Logarithmic scale slider.

Logarithmic scales are useful when visualizing data with large positive skew when the majority of data points have a small value, with a few data points with very large values. Changing the scale of the axis does not change the value of the data—only the way it is displayed.

Linear scale axis

On a logarithmic scale, increments increase by magnitudes. In the axis diagram below, each increment on the axis increases by multiplying by 10.

Logarithmic scale axis

Logarithmic scales cannot display negative values or zero. If you use a log scale for a variable containing negative values or zeros, those values will not appear on the chart.


You can configure the look of the chart by formatting text and symbol elements. Chart formatting options include the following:

  • Text elements—Size, color, and style of the font used for the chart title, x-axis title, y-axis title, legend title, description text, legend text, axis labels, and data labels. You can change the format for multiple elements at once by pressing Ctrl and clicking to select the elements.
  • Symbol elements—Color, width, and style (Solid, Dot, or Dash) for grid and axis lines and the background color of the chart.


The default titles for charts and axes are based on the variable names and chart type. You can edit or turn off the titles on the General tab. You can also provide a title in the Legend title parameter and a chart description in the Description parameter. A description is a block of text that appears at the bottom of the chart window.