Radar plots are exceptional for visualizing the properties of individual objects. Here, I demonstrate how to draw radar plots in R by plotting the properties of whiskeys from several distilleries.
There is a large number of different types of plots for visualizing data.
The following plots are frequently used:
- The bar plot shows the extent of values according to the height of bars. If the data are normally distributed, they can be display variation by including error bars.
- The box plot indicates variation by showing the most frequently observed measurements in terms of the first, second, and third quartile.
- The histogram consists of bars that indicate the frequency of measurements and is ideal for showing the distribution of a variable.
- The line plot connects individual measurements using lines. It is most suited for time-series data.
- The scatter plot shows the value of two variables as points and is ideal for identifying correlated variables.
The following plots are less frequently used than the basic plots. Nevertheless, these plots may be very useful for specific applications.
- The beeswarm plot is an alternative to the box plot that draws individual data points in a well-defined manner.
- The Q-Q plot can be used to compare whether two samples have similar distributions.
- The radar plot shows the values of several properties in a circular layout.
- The violin plot is an alternative to the box plot that shows a density estimate.
- The geospatial plot is concerned with drawing the locations of entities on a map.
Posts about plots
The following posts exemplify the use of plots in R.
Box plots are limited since they only show Q1, Q2, and Q3. Box plot alternatives such as the beeswarm and violin plot, however, provide more information about the overall distribution of the data.
Line plots are ideally suited for visualizing time series data. Using some stock market data, I demonstrate how line plots can be generated using native R, the MTS package, and ggplot.
Bar plots are frequently used due to their simplicity. However, they also do not convey a lot of information. Here, I discuss how error bars can be used to visualize variance and under which circumstances bar charts should not be used.
Box plots are ideal for showing the variation of measurements because they do not only visualize the first, second, and third quartile, but also outliers.
Scatter plots are a great tool for learning about individual data points. Here, I demonstrate the use of scatter plots for visualizing the correlation between two variables.
Histograms are an ideal tool for visualizing the distribution of a variable and frequently used for data exploration. Here, I show how a histogram acan aid in differentiating two distributions.