Radar plot

A radar chart also known as a spider or star chart is a visualization used to display multivariate data across three or more dimensions, using a consistent scale. Not everyone is a huge fan of these charts, but I think they have their place in comparing entities across a range of dimensions in a visually appealing way. A radar chart is useful when trying to compare the relative weight or importance of different dimensions within one or more entities.

radar plot

The example we'll use here is with cars. Cars have different fuel efficiency, range, acceleration, torque, storage capacity and costs. We can use a radar chart to benchmark specific cars against each other and against the broader population. Let's start with getting our data. Each record is a car with its specs across a range of attributes. We need to transform those attributes into a consistent scale, so let's do a linear transformation of each to convert to a scale. We're all set with the data - we have a car in each row with five attributes, each with a value between zero and Let's create some radar charts.

Creating a radar chart in Matplotlib is definitely not a straightforward affair, so we'll break it down into a few steps. First, let's get the base figure and our data plotted on a polar aka circular axis. It's a start but still lacking in a few ways. Some things to highlight before we move on. We create the data plot itself by sequentially calling ax.

The two main arguments are angleswhich is a list of the angle radians between each axis emanating from the center, and valueswhich is a list of the data values. You can see numerous things are wrong with the chart though - the axes don't align with the shape, there are no labels, and the grid itself seems to have two lines right around The axis labels though aren't perfect though; several of them overlap with the grid itself and the alignment could be better.

Let's fix that. Still a few subtle problems though.Cluster analysis involves splitting multivariate datasets into subgroups 'clusters' sharing similar characteristics. Radar plots can help to visually profile the resulting subgroups. For example, this graph, from Vickersshows the profile of one of the clusters from an area classification published by the UK Office for National Statistics:.

The blue plot line compares the cluster average relative to the national average 0 across all of the 41 range-standardised dimensions used as inputs to the clustering process.

radar plot

The purpose of this post is to report on a function I have developed that produces plots similar to that above using gpplot2. The full R code for this function can be found here. In addition there there are a number of other properties of the plot that pose potential challenges, at least to my novice usage of gpplot Non ggplot2 solutions to this problem may already exist, but I want to minimise the number of flavours of R graphics that I have to get my head round.

Hence, for better or worse, I took the decision to create a function capable of producing the required plots via ggplot, armed, as a minimum, with only:. The function has been heavily paramterised, as detailed below, to allow the user to closely manage most aspects of the resulting plot.

The one aspect of plot appearance that I have been unable to control satisfactorily is the colour assigned to each plot path. All suggestions welcome. Input data plot. Grid lines grid. Plot centre centre.

Radar Chart

Plot extent Parameters to rescale the extent of the plot vertically and horizontally, in order to allow for ggplot default settings placing parts of axis text labels outside of plot area. Scaling factor is defined relative to the circle diameter grid. Grid lines includes separate controls for the appearance of some aspects the 'minimum', 'average' and 'maximum' grid lines. Grid labels grid. Axis and Axis label axis.

radar plot

Cluster plot lines group.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, but various heuristics, such as algorithms that plot data as the maximal total area, can be applied to sort the variables axes into relative positions that reveal distinct correlations, trade-offs, and a multitude of other comparative measures.

The radar chart is also known as web chartspider chartspider web chartstar chart[2] star plotcobweb chartirregular polygonpolar chartor Kiviat diagram [3] [4]. It is equivalent to a parallel coordinates plot, with the axes arranged radially. 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: [5]. Radar charts are a useful way to display multivariate observations with an arbitrary number of variables. Typically, radar charts are generated in a multi-plot format with many stars on each page and each star representing one observation. 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 if the variables are assigned to the rays of the star in some meaningful order.

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 radar charts. Radar charts are primarily suited for strikingly showing outliers and commonalityor when one chart is greater in every variable than another, and primarily used for ordinal measurements โ€” 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 โ€” 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. Radar charts can distort data to some extent especially when areas are filled in, because the area contained becomes proportional to the square of the linear measures. For example, the alternating data 9, 1, 9, 1, 9, 1 yields a spiking radar chart which goes in and outwhile reordering the data as 9, 9, 9, 1, 1, 1 instead yields two distinct wedges sectors.

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 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 or rather the closely related "polar area graph" is: [16]. 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.

Radar chart

After that, they tend to be overwhelming. The chart on the right [5] contains the star plots of 15 cars. The variable list for the sample star plot is:. 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 the last one on the image and see that it is one of the most expensive cars, gets below average but not among the worst gas mileage, has an average repair record, and has average-to-above-average roominess and size.

We can then compare the Cadillac models the last three plots with the AMC models the first three plots. 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. 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 ballswhich are used extensively by Consumer Reports. An excellent way for visualising structures within multivariate data is offered by principal component analysis PCA.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. Each variable is provided an axis that starts from the center. All axes are arranged radially, with equal distances between each other, while maintaining the same scale between all axes.

Grid lines that connect from axis-to-axis are often used as a guide. Each variable value is plotted along its individual axis and all the variables in a dataset and connected together to form a polygon. Radar Charts are useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance, such as Skill Analysis of Employee or sport players, product comparison, etc. This is an open source visual.

Apps Consulting Services. Search Microsoft AppSource. Sell Blog. Skip to main content Apps Radar Chart. Power BI visuals. Microsoft Corporation. Version 2. License Agreement Privacy Policy. Radar Chart. Overview Reviews.

Multiple measures plotted over a categorical axis. Useful to compare attributes 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. Add-in capabilities When this add-in is used, it. Other apps from Microsoft Corporation.Radar Charts are a way of comparing multiple quantitative variables.

This makes them useful for seeing which variables have similar values or if there are any outliers amongst each variable. Radar Charts are also useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance.

radar plot

Each variable is provided with an axis that starts from the centre. All axes are arranged radially, with equal distances between each other, while maintaining the same scale between all axes.

Grid lines that connect from axis-to-axis are often used as a guide. Each variable value is plotted along its individual axis and all the variables in a dataset and connected together to form a polygon. Having multiple polygons in one Radar Chart makes it hard to read, confusing and too cluttered. Especially if the polygons are filled in, as the top polygon covers all the other polygons underneath it. Having too many variables creates too many axes and can also make the chart hard to read and complicated.

So it's good practice to keep Radar Charts simple and limit the number of variables used. Even with the aid of the spiderweb-like grid guide. Comparing values all on a single straight axis is much easier.

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Radar Plotting _PART 1

The Data Visualisation Catalogue. However, there are some major flaws with Radar Charts: Having multiple polygons in one Radar Chart makes it hard to read, confusing and too cluttered.

Functions Comparisons. Similar Charts Parallel Coordinates. Need to access this page offline? Download the eBook from here. About Blog Shop Resources. Page top Previous Homepage Next. Click Here Need to access this page offline?The fmsb package allows to build radar chart in R. Next examples explain how to format your data to build a basic version, and what are the available option to customize the chart appearance.

How to customize the chart appearance, polygon, net, labels and more. It is totally doable to show several groups on the same spider chart, still using the fmsb package. The examples below will guide you through this process. But keep in mind that displaying more than 2 or 3 groups will result in a cluttered and unreadable figure. There is a lot of criticism going around spider chart. Before using it in a project, you probably want to learn more about it.

Most basic radar Start with a basic version, learn how to format your input dataset. Radar chart customizations How to customize the chart appearance, polygon, net, labels and more. Basic multi-group radar chart Start with a basic version, learn how to format your input dataset. Customization Customization option offered by the fmsb package. About axis limits Learn how to control chart axis limits.

Related chart types.I know that the navigators get fed up with this irritating question during third-party inspections.

Radar Charts in Python

In this post, I will definitely be discussing how to do radar plotting. But before we start on that, we need to agree on the point if all this is really required and is the radar plotting helpful to the navigators in any way. ARPA of one or both of your radars stopped working in the mid sea. Master will report the fact to the company and company will seek dispensation from the flag to sail and arrive at next port without working ARPA function of the radar.

The flag will give the dispensation on a condition that the navigators will use radar plotting for all the targets on the radar.

Even when your ARPA is working, you may notice that the radar is not holding on to the some of the targets. By this I mean, the acquired vector is moving away from the target. The only issue is that like the celestial sight, to be effective radar plotting too need practice.

With radar plotting, we aim to get all the information that ARPA can give. These pieces of information are. When we see the target on the radar, we take the bearing and range of the target and note down the time of observation. Let us plot this on the radar plotting sheet. Now after some interval, take the 2nd and 3rd set of observations of the target and plot it on the radar plotting sheet.

Plot these on the radar plotting sheet and extend the line joining all three points. This is the line of relative approach of the target towards our vessel which we have assumed at the center of the sheet. To do that just draw a line perpendicular to the line of approach and measure the distance of this line from the scale in the radar plotting sheet. Now with simple mathematics calculate the time the target would take to cover 5. In 12 minutes your ship with speed 12 knots will travel 2.

One what is the aspect and how to calculate the Aspect of the target. And second what is the significance of the aspect. Now let us come back to the radar plotting sheet and the situation we have discussed so far and let us get the aspect of the target vessel. Just to put this in perspective, here is what will be the aspect of the target vessel with different headings.

If you had noticed, I have denoted the own ship with just a dot and have not shown the heading of own ship. It is the easiest way to find the CPA of the targets.

But the issue with the relative approach is that we do not know with certainty the angle at which the target vessel is approaching us.


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