The Nebraska Agricultural Outlook for 1938

Each year the federal government gathers data relating to agriculture through the various departments of the United States Department of Agriculture. These data are classified and analyzed by the Bureau of Agricultural Economics at Washington and all information which may be helpful to farmers is published. For several years it has been the policy of the Department of Rural Economics and the Agricultural Extension Service of the College of Agriculture, Lincoln, to select from the federal information facts which may be especially helpful to Nebraska farmers. These facts and other economic conditions in Nebraska are published this year as the Agricultural Outlook for Nebraska, 1938. The Outlook should be helpful in the marketing of the crops and livestock on hand. It should also be helpful in making farm plans for 1938.

Review of Applied Spatial Data Analysis with R by R. S. Bivand, E. J. Pebesma, and V. Gomez-Rubio

Most authors struggle to pick a title that adequately conveys all of the material covered in a book. When I first saw Applied Spatial Data Analysis with R, I expected a review of spatial statistical models and their applications in packages (libraries) from the CRAN site of R. The authors’ title is not misleading, but I was very pleasantly surprised by how deep the word “applied” is here. The first half of the book essentially covers how R handles spatial data. To some statisticians this may be boring. Do you want, or need, to know the difference between S3 and S4 classes, how spatial objects in R are organized, and how various methods work on the spatial objects? A few years ago I would have said “no,” especially to the “want” part. Just let me slap my EXCEL spreadsheet into R and run some spatial functions on it. Unfortunately, the world is not so simple, and ultimately we want to minimize effort to get all of our spatial analyses accomplished. The first half of this book certainly convinced me that some extra effort in organizing my data into certain spatial class structures makes the analysis easier and less subject to mistakes. I also admit that I found it very interesting and I learned a lot.