Recently, I began a series on exploratory data analysis; so far, I have written about computing descriptive statistics and creating box plots in R for a univariate data set with missing values. Today, I will continue this series by analyzing the same data set with kernel density estimation, a useful non-parametric technique for visualizing the underlying distribution of a continuous variable.

Harlan also noted in the comment below that any truncated kernel density estimator (KDE) from density() in R does not integrate to 1 over its support set. Thanks to Julian Richer Daily for suggesting on AnalyticBridge to scale any truncated kernel density estimator (KDE) from density() by its integral to get a KDE that integrates to 1 over its support set. I have used my own function for trapezoidal integration to do so, and this has been added below. I thank everyone for your patience while I took the time to write a post about numerical integration before posting this correction. I was in the process of moving between jobs and cities when Harlan first brought this issue to my attention, and I had also been planning a major expansion of this blog since then.