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 timeseries graphs – consider using a bar graph instead

Time series
Shows how data values for a measure(s) change over time (eg populationadjusted breast cancer diagnoses recorded in Australia every year, for a 20year 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 timeseries 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 previousyear 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 sidebyside horizontal bar graphs would better communicate the association

Sidebyside 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
