A radar chart is a graphical method of displaying multivariate data in the form of a two-dimensional
chart of three or more quantitative variables represented on axes starting from the same
point. The relative position and angle of the axes is typically uninformative.
The radar chart is also known as web chart, spider chart, star chart, star plot, cobweb
chart, irregular polygon, polar chart, or kiviat diagram.
Overview The radar chart is a chart and/or plot that
consists of a sequence of equi-angular spokes, called radii, with each spoke representing
one of the variables. The data length of a spoke is proportional to the magnitude of
the variable for the data point relative to the maximum magnitude of the variable across
all data points. A line is drawn connecting the data values for each spoke. This gives
the plot a star-like appearance and the origin of one of the popular names for this plot.
The star plot can be used to answer the following questions:
Which observations are most similar, i.e., are there clusters of observations? and to
locate similar points or dissimilar points.) Are there outliers?
Radar charts are a useful way to display multivariate observations with an arbitrary number of variables.
Each star represents a single observation. Typically, radar charts are generated in a
multi-plot format with many stars on each page and each star representing one observation.
The star plot was first used by Georg von Mayr in 1877. Radar charts differ from glyph
plots in that all variables are used to construct the plotted star figure. There is no separation
into foreground and background variables. Instead, the star-shaped figures are usually
arranged in a rectangular array on the page. It is somewhat easier to see patterns in the
data if the observations are arranged in some non-arbitrary order.
Application One application of radar charts is the control
of quality improvement to display the performance metrics of any ongoing program.
They are also used in sports to chart players' strengths and weaknesses, where they are usually
called spider charts. Limitations
Radar charts are primarily suited for strikingly showing outliers and commonality, or when
one chart is greater in every variable than another, and primarily used for ordinal measurements
– where each variable corresponds to "better" in some respect, and all variables on the
same scale. Conversely, radar charts have been criticized
as poorly suited for making trade-off decisions – when one chart is greater than another
on some variables, but less on others. Further, it is hard to visually compare lengths
of different spokes, because radial distances are hard to judge, though concentric circles
help as grid lines. Instead, one may use a simple line graph, particularly for time series.
Example
The chart on the right contains the star plots of 15 cars. The variable list for the sample
star plot is: Price
Mileage 1978 Repair Record
1977 Repair Record Headroom
Rear Seat Room Trunk Space
Weight Length
We can look at these plots individually or we can use them to identify clusters of cars
with similar features. For example, we can look at the star plot of the Cadillac Seville
and see that it is one of the most expensive cars, gets below average gas mileage, has
an average repair record, and has average-to-above-average roominess and size. We can then compare the
Cadillac models with the AMC models. This comparison shows distinct patterns. The AMC
models tend to be inexpensive, have below average gas mileage, and are small in both
height and weight and in roominess. The Cadillac models are expensive, have poor gas mileage,
and are large in both size and roominess. Artificial structure
Radar charts impose several structures on data, which are often artificial:
Relatedness of neighbors – radar charts are often used when neighboring variables
are unrelated, creating spurious connections. Cyclic structure – the first and last variables
are placed next to each other. Length – variables are often most naturally
ordinal: better or worse, though the degree of difference may be artificial.
Area – area scales as the square of values, exaggerating the effect of large numbers.
For example, 2, 2 takes up 4 times the area of 1, 1. This is a general issue with area
graphs, and area is hard to judge – see "Cleveland's hierarchy".
For example, the alternating data 9, 1, 9, 1, 9, 1 yields a spiking radar chart, while
reordering the data as 9, 9, 9, 1, 1, 1 instead yields two distinct wedges.
In some cases there is a natural structure, and radar charts can be well-suited. For example,
for diagrams of data that vary over a 24-hour cycle, the hourly data is naturally related
to its neighbor, and has a cyclic structure, so it can naturally be displayed as a radar
chart. One set of guidelines on the use of radar
charts is: you don't mind reading stacked areas instead
of position along a common scale, the data set is truly cyclic, not linear,
and there are two series to compare, one much
smaller than the other Data set size
Radar charts are helpful for small-to-moderate-sized multivariate data sets. Their primary weakness
is that their effectiveness is limited to data sets with less than a few hundred points.
After that, they tend to be overwhelming. Alternatives
Most simply, one may use a simple line graph, particularly for time series.
For graphical qualitative comparison of 2-dimensional tabular data in several variables, a common
alternative are Harvey Balls, which are used extensively by Consumer Reports. Comparison
in Harvey Balls may be significantly aided by ordering the variables algorithmically
to add order. An excellent way for visualising structures
within multivariate data is offered by principal component analysis.
Another alternative is to use small, inline bar charts, which may be compared to sparklines.
See also Plot
Polar area diagram Parallel coordinates
References This article incorporates public domain
material from websites or documents of the National Institute of Standards and Technology.
External links Star Plot