Multiple series scatter plot ggplot2 how to#

A total of 150 observations, 50 for each species of Iris, are all the make up the dataset. There are four numerical variables, or features, that are represented in this dataset. All that is required to access it is to refer to it by its name (“iris”). This dataset is available by default within R. In adherence with the style of the previous articles (box plot, line plot, etc), this article will use the Iris dataset. This article will cover all of them and leave the decision up to the reader. However, multi-variable plotting may often come in handy, especially during data point exploration.

They should not have to frantically search for what is meant with the plot and what the plot is attempting to communicate. The audience’s eyes should fall naturally on the important messages being reported about the data. There should be only one or two main focus points in the graphic- even a 3d scatter plot can be too confusing, with too many axes to be able to understand axis labels, the linear regression line, or see the correlation coefficient. Good practice in data visualization is to keep the amount of information being communicated per plot to a minimum. Too much information is often worse than no information. However, as enticing, as this may seem to do, it is not often advised to plot too many variables on one plot. This allows you to map a trend line on the ggplot scatter plot, plotting a linear regression line and seeing the correlation coefficient of your data frame directly on the graph using a plot function. Like different color y values, shapes, sizes, and transparency can all be mapped to other variables, extending the range of variables visible at one glance in the plot. The different color of the points would thus represent a third categorical variable. For example, color may be added to the scatter plot. Now, by mapping the aesthetics of the points to other variables, the plot may be extended from a two-variable simple scatter plot to a multi-variable 3d scatterplot. The position value of the point on the x-axis represents one variable, while the value of the positions of the points on the y-axis represents the second variable. The data, or the values in the dataset, are displayed as points. Scatter plots are most commonly used to plot, or display, two variables of a given dataset. This will be explained more clearly in the following section on creating a scatterplot in R.

The reason for this is that scatterplots are not limited to only two variables.

Notice how the term “multi-variable” is used, as opposed to “two-variable”. Therefore, it is only natural for the transition from single-variable plots to multi-variable plots be made through scatterplots.

The ggplot scatter plot is one of the most common of these plots. In fact, most linear model plots are actually used for plotting multiple variables. This article, however, will instead be concerned with plots that plot more than one variable. It is, therefore, a relatively simple plot and quite fitting to be used as an introduction to plots. One of the distinguishing characteristics of a histogram, as mentioned in the article, is that it plots only one variable. The tutorial went into detail explaining what a histogram is and how it is plotted using ggplot2. The article immediately preceding this one concerned the histogram.

Multiple series scatter plot ggplot2 how to#

The tutorials have been delving into how to create different kinds of plots. The last couple articles have been concerned more with the data related side of ggplot2. This included themes, creating facets, and customizing legends, among other things. The first half of the series was primarily concerned with the aesthetics of the ggplot2 package. This article is a continuation of the ggplot2 series.