Overview # A line plot (or line graph, whichever you prefer) is a way to show how something changes as something else changes. Visually, a line plot connects point to point with lines. In many instances, the points themselves are not shown prominently, and the plot itself relies more on conveying data through the sequence of lines. Line plots are frequently used to show how some value changes over time. ...
Overview # A bubble plot is simply an enhanced version of a scatter plot. A simple, flat scatter plot typically only utilizes two numerical fields and conveys data based on the position of points. A bubble plot on the other hand utilizes a third numerical field. In a bubble plot, the size of the points change based on the value of the third numerical field. Data # A bubble plot requires at least three numerical fields: ...
Overview # Heatmaps are used to display variations in numbers across different observations that also have other categorical attributes. These are also referred to as tile plots. Data # At a minimum, a heatmap must have at least one numerical field and one categorical field. A more common and information rich heatmap can leverage one numerical field and two categorical fields. R # A heatmap can be generated in R using the geom_tile() function in the ggplot2 package. ...
Overview # A waffle plot is an excellent way to display proportions. It can often be used in place of a pie chart. In fact, waffle charts are sometimes lovingly referred to as “square pie charts”. One of the major advantages of waffle charts over pie charts is the absence of ambiguity. Waffle charts are displayed in discrete blocks. Pie charts on the other hand are displayed in wedges, which can sometimes be difficult for the human eye to discern scale and proportions. ...
Overview # A scatter plot is a simple way to present at least two numerical types of data together. This sort of presentation is often useful to convey a sense of the relationship between two types of data. R # Let’s make a scatter plot in R with the ggplot2 package. We’ll also use tooling from tidyverse to manipulate the data. library(tidyverse) library(ggplot2) # a bit redundant, since this is included in tidyverse, but showing it explicitly here In this case, we’ll use the cars data set as an example. ...
Overview # A violin plot is used to display the distribution of numerical variables. The width along the cross section of any part of the plotted violin represents how many data points there are within that given section. A single violin plot can display the distribution of numerical variables for multiple categories. Data # A violin plot requires at least one categorical variable and one numerical variable. R # Let’s make a violin plot in R with the ggplot2 package. ...
Overview # A bar plot is a data visualization that shows the relationship between a categorical variable and a numerical variable. Think of a categorical value as a type of thing, and a numerical variable as anything that can be counted or measured. Data # A bar plot requires at least one categorical variable and one numerical variable. Additional variables can be reflected in the bar plot using such additional features as colors, textures, or outlines. ...