Category: TERR

RStudio Error – “ERROR r error 4”

A few weeks ago, I wrote a post on TERR compatible versions of R and RStudio. At that time, I realized I needed to update my RStudio and R versions. In doing that, I ran into an RStudio error when launching my newly updated RStudio.  Since I ran into this problem, I am sure other users will too.  
RStudio Error
This error occurs when RStudio can’t find the installation of R. Fixing it is super simple. Simply hold down the Ctrl key when launching R and a popup will appear that allows you to specify the R installation location.
Fixing Error
Now, I’m unsure what order of operations I followed and/or if the order of operations caused my problem, but I would recommend the following…
  1. Uninstall R
  2. Uninstall RStudio
  3. Install new R
  4. Install new RStudio

Hopefully, your installation goes easier than mine did.

TERR Scripts to Read/Write to MS Access recently published a free template on the Exchange that reads and writes data from/to MS Access.  Under the covers, you’ll find two property controls and two data functions working with the RODBC package.  Now, we know that templates are good, but being able to replicate the work is better.  Users want to be able to recreate that functionality in their own files, which is why I am writing this post to explain the code and how everything fits together so you can recreate this functionality in your own DXP files.  Before reading any farther, use this link to download a copy of the template and familiarize yourself with how it works.

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Missing Value Imputation with Data Augmentation in R

Incomplete data is a problem that Data Scientists face every day. Most common practices vary from complete deletion of the observations with missing values, substitution by a fixed value, or performing imputation using statistics like the mean or median. Since these approaches have limitations on capturing the structure of the data, scientists have developed more sophisticated methods.

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Using the “spTimer” Package to Model Spatio-Temporal Data in R

The “spTimer” package uses three Bayesian models to fit Spatio-Temporal Data. The data may be given at sparse spatial stations, where observations at each station are considered time series. The package can model the residual spatio-temporal variation to measure uncertainty. It also gives flexibility to customize covariance function selection, the hyper-parameters of the prior distributions and the tuning parameters for the implemented MCMC algorithms.

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Using Support Vector Machines in Spotfire

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Support Vector Machines (SVMs) is one of the most popular and most widely used machine learning algorithms today. It is robust and allows us to tackle both classification and regression problems. In general, SVMs can be relatively easy to use, have good generalization performance, and often do not require much tuning. Follow this link for further information regarding support vector machines. To help illustrate the power of SVMs, we thought it would be useful to go through an example using a custom template we have created for SVMs.

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