Missing months are ignored in visualisation but flagged internally.

Only explicit gaps in numeric variables are filled with zeros.

Missing months are added with NA values for measured variables.

Missing months are interpolated linearly.

Use the same colour for both lines and rely on the legend for distinction.

Use multiple overlapping line geometries to show uncertainty.

Use subtle colours and different line types (e.g. solid vs dashed) for accessibility.

Use thick solid lines in bright contrasting colours (e.g. red and blue).

dmy(01/2/2025)

mdy("01/2/2025")

dmY("01/2/2025")

dmy("01/2/2025")

Simplify the chart to highlight key patterns and events.

Use complex colour gradients to highlight every monthly change.

Include all raw data points to ensure transparency.

Emphasise model residuals to show predictive uncertainty.

It causes missing values to appear as zeros.

It makes trends appear smoother.

It ignores population differences, exaggerating larger regions.

It reduces seasonal variation.

To create aesthetic balance through colour contrast.

To replace the need for axis titles.

To indicate uncertainty or highlight a specific period of interest.

To group data by category rather than time.

Vertical position in a lollipop chart.

Angle of circles in a Pie chart.

Length of bars.

Position along a common scale.

The data being plotted is misleading/inaccurate.

A line graph could never be appropriate for this type of data.

x-axis has a very wide range, making it hard to see changes.

y-axis has a very wide range, making it hard to see changes.

To add visual appeal to reports.

To make data look interesting.

To simplify complex information for better understanding.

To show all the different variables in the visualisation, so as not to be misleading.

Avoid distorting what the data have to say.

Show the data! 

Minimise the data density.

Minimise chart junk to avoid unnecessary distractions.