Soil scientists routinely sample, characterize, and summarize patterns in soil properties in space, with depth, and through time. Invariably, some samples will be lost or sufficient funds required for complete characterization can run out. In these cases the scientist is left with a data table that contains holes (so to speak) in the rows/columns that are missing data. If the data are used within a regression, missing values in any of the predictor or the response variable result in row-wise deletion– even if 9/10 variables are present. Furthermore, common multivariate methods (PCA, RDA, dissimilarity metrics, etc.) cannot effectively deal with missing data. The scientist is left with a couple options: 1) row-wise deletion of cases missing any variable, 2) re-sampling or re-characterizing the missing samples, or 3) estimating the missing values from other variables in the dataset. This last option is called missing data imputation. This is a broad topic with countless books and scientific papers written about it. Here is a fairly simple introduction to the topic of imputation. Fortunately for us non-experts, there is an excellent function (aregImpute()) in the Hmisc package for R.