Ordinal or nominal items, groups or categories

Compares data values across independent items, groups or categories (eg unemployment rates for each Australian state and territory)

Horizontal bar graph

• Order bars by size of data values to emphasise differences
• Use clustered bars for subcategories of groups, but limit clusters to 3 or 4 subcategories to enable comparisons across groups

Vertical bar graph

Dot plot

• Dots represent single data values for each item or group; a column of dots can represent summary values for each group
• Can be mistaken for scatter plots or time-series graphs – consider using a bar graph instead

Time series

Shows how data values for a measure(s) change over time (eg population-adjusted breast cancer diagnoses recorded in Australia every year, for a 20-year period)

Line graph (for large time series)

• Use to highlight trends or patterns in a measure over time
• Use for datasets that include data for more than about 8 time points
• Lines are connected, consecutive data values
• Lines always follow a horizontal direction, with time intervals on the x axis increasing from left to right, and the measurement variable plotted on the y axis
• Only connect consecutive values – intervals with missing data must be shown as a break in the line(s)

Vertical bar graph (for small time series)

• Use for time-series data with a small number of time points – about 8 or less
• Use to emphasise specific data values, rather than an overall pattern or trend

Dot plot

• Dots represent data values at each time point. If connected, these dots form a line graph
• Can be mistaken for scatter plots – consider using a bar graph or line graph instead

Part to whole (ie proportions of a total)

Shows how data values relate to, compare with or make up a total measure at 1 or more points in time (eg proportion of Australia’s total primary energy supply attributable to each major fuel type)

Horizontal bar graph

• Use to show the value (ie percentage or proportion of an absolute total) of each part for a single population
• This type of data is often shown as a pie graph, which is not generally recommended

Horizontal stacked bar graph

• Use to show proportions of a total measure for multiple populations
• Total(s) must add to 100% if values are percentages, or the total absolute value for other scales

Vertical stacked bar graph

• Use to show proportions of a total measure over time, for about 8 or fewer time points
• Use to emphasise changes in the relative size of parts over time

Stacked area graph

• Use to show proportions of a total measure over time, for about 8 or more time points
• Use to emphasise changes in the relative size of parts over time

Deviation

Shows the difference between data values and a baseline (eg differences between actual rainfall and predicted or previous-year rainfall for each month of a year)

Vertical bar graph

• Use when your goal is to highlight deviations between measurements and some meaningful baseline or reference
• Bars (ie data values) above the reference or x axis indicate positive differences from the baseline; bars below indicate negative differences
• The y axis can measure absolute differences or percentage change between data values and the reference

Line graph

• Use to show differences from a baseline or reference over time, when the dataset includes data for more than about 8 time points
• See above points for line graphs

Single frequency or distribution data

Shows how frequency or count values are distributed over the range of a measure (eg range of blood pressure measurements for men)

Histogram (for measures with a small range)

• Use a vertical bar graph to show frequency or count values across the range of a measure with few intervals
• Used as an alternative to a frequency polygon when individual data values must be emphasised

Frequency polygon (for measures with a large range)

• Use to show frequency or count values across the range of a measure with many intervals
• Use to emphasise the shape of a distribution

Strip plot

• Use to show the distribution of a measure for a small population
• If multiple measurements are recorded for the same value on the distribution, these points should be stacked or shown in a denser tone than other (nonrepeated) points

Box plot (horizontal or vertical)

• Use to summarise a measure’s distribution, rather than all individual data values
• May be unfamiliar to readers – consider plotting a simple histogram instead

Distribution of the same measure across multiple time points or categories

Shows how frequency or count values are distributed over the range of a measure, for more than 1 population (eg range of blood pressure measurements for men with 5 different medical conditions)

Vertical box plot

• Use to summarise multiple distributions of the same measure
• May be unfamiliar to readers – consider plotting summary values (eg medians of the distribution) as a bar chart for multiple groups or populations, or a line graph with or without upper and lower bounds for multiple distributions over time

Strip plot

• Multiple distributions are plotted side by side against the same y axis
• White space should separate each distribution
• See above points for strip plots

Line graph with upper and lower bounds

• Use to show distributions with a large number of time points – not multiple, discrete populations
• Median values for the distributions at each time point are connected to form a line
• The largest and smallest values for the distribution at each time point are connected to form (typically invisible) lines above and below the median line – the areas between the median line and these upper and lower bounds are shaded
• Upper and lower bounds may be an unfamiliar feature for readers – consider whether their inclusion adds meaning and whether this outweighs potential misperceptions among readers

Correlated measures

Shows an association between 2 measures or variables (eg children’s age and height)

Scatter plot

• Each dot or data point represents a subject’s measurement on x axis and y axis variables
• Use to show that data points form a meaningful shape that indicates the type (or lack) of association between 2 variables
• Consider including a trend line to highlight the type and strength of association
• Depending on the audience, readers may be unable to interpret scatter plots – consider whether side-by-side horizontal bar graphs would better communicate the association

Side-by-side horizontal bar graph

• Use to show an association between 2 measures when scatter plots are unfamiliar to readers
• Most effective for showing linear associations
• Two aligned bar graphs display each subject’s measurement on the first and second measures
• Order the bars by size on one of the graphs to emphasise the association between the 2 measures