Correlation
Last updated
Last updated
The standard way to visualize the relationship between two measured data variables is with a scatterplot. Scatterplots are fine when there is a relatively small number of data values. The plot of stock returns in Figure 1-7 involves only about 750 points. For data sets with hundreds of thousands or millions of records, a scatterplot will be too dense, so we need a different way to visualize the relationship.
The correlation coefficient is a standardized metric so that it always ranges from –1 (perfect negative correlation) to +1 (perfect positive correlation).
OTHER CORRELATION ESTIMATES
Statisticians have long ago proposed other types of correlation coefficients, such as Spearman’s rho or Kendall’s tau. These are correlation coefficients based on the rank of the data. Since they work with ranks rather than values, these estimates are robust to outliers and can handle certain types of nonlinearities. However, data scientists can generally stick to Pearson’s correlation coefficient, and its robust alternatives, for exploratory analysis. The appeal of rankbased estimates is mostly for smaller data sets and specific hypothesis tests.
Figure 1-8 is a hexagon binning plot of the relationship between the finished square feet versus the tax-assessed value for homes in King County. Rather than plotting points, which would appear as a monolithic dark cloud, we grouped the records into hexagonal bins and plotted the hexagons with a color indicating the number of records in that bin. In this chart, the positive relationship between square feet and tax-assessed value is clear. An interesting feature is the hint of a second cloud above the main cloud, indicating homes that have the same square footage as those in the main cloud, but a higher tax-assessed value. Figure 1-8 was generated by the powerful R package ggplot2, developed by Hadley Wickham [ggplot2]. ggplot2 is one of several new software libraries for advanced exploratory visual analysis of data; see “Visualizing Multiple Variables”.
A useful way to summarize two categorical variables is a contingency table — a table of counts by category. Table 1-8 shows the contingency table between the grade of a personal loan and the outcome of that loan. This is taken from data provided by Lending Club, a leader in the peer-to-peer lending business. The grade goes from A (high) to G (low). The outcome is either paid off, current, late, or charged off (the balance of the loan is not expected to be collected). This table shows the count and row percentages. High-grade loans have a very low late/charge-off percentage as compared with lower-grade loans. Contingency tables can look at just counts, or also include column and total percentages. Pivot tables in Excel are perhaps the most common tool used to create contingency tables. In R, the CrossTable function in the descr package produces contingency tables, and the following code was used to create Table 1-8:
A violin plot, introduced by [Hintze-Nelson-1998], is an enhancement to the boxplot and plots the density estimate with the density on the y-axis. The density is mirrored and flipped over and the resulting shape is filled in, creating an image resembling a violin. The advantage of a violin plot is that it can show nuances in the distribution that aren’t perceptible in a boxplot. On the other hand, the boxplot more clearly shows the outliers in the data.
The corresponding plot is shown in Figure 1-11. The violin plot shows a concentration in the distribution near zero for Alaska, and to a lesser extent, Delta. This phenomenon is not as obvious in the boxplot. You can combine a violin plot with a boxplot by adding geom_boxplot to the plot (although this is best when colors are used).
Visualizing Multiple Variables
The types of charts used to compare two variables — scatterplots, hexagonal binning, and boxplots — are readily extended to more variables through the notion of conditioning. As an example, look back at Figure 1-8, which showed the relationship between homes’ finished square feet and tax-assessed values. We observed that there appears to be a cluster of homes that have higher tax-assessed value per square foot.
Diving deeper, Figure 1-12 accounts for the effect of location by plotting the data for a set of zip codes. Now the picture is much clearer: tax-assessed value is much higher in some zip codes (98112, 98105) than in others (98108, 98057). This disparity gives rise to the clusters observed in Figure 1-8.
The concept of conditioning variables in a graphics system was pioneered with Trellis graphics, developed by Rick Becker, Bill Cleveland, and others at Bell Labs [Trellis-Graphics]. This idea has propogated to various modern graphics systems, such as the lattice ([lattice]) and ggplot2 packages in R and the Seaborn ([seaborne]) and Bokeh ([bokeh]) modules in Python. Conditioning variables are also integral to business intelligence platforms such as Tableau and Spotfire. With the advent of vast computing power, modern visualization platforms have moved well beyond the humble beginnings of exploratory data analysis. However, key concepts and tools developed over the years still form a foundation for these systems.
