R – Sander Timmer

AzureSMR: handle your Azure subscription with R

By Sander Timmer | January 4, 2017 | Comments 0 Comment

Great new package for the people that use Microsoft Azure as their platform of choice and love R. With AzureSMR you are capable to handle the following services:

Azure Blob: List, Read and Write to Blob Services
Azure Resources: List, Create and Delete Azure Resource. Deploy ARM templates.
Azure VM: List, Start and Stop Azure VMs
Azure HDI: List and Scale Azure HDInsight Clusters
Azure Hive: Run Hive queries against a HDInsight Cluster
Azure Spark: List and create Spark jobs/Sessions against a HDInsight Cluster(Livy)

Install it from your interactive shell:

#Install devtools if(!require("devtools")) install.packages("devtools") devtools::install_github("Microsoft/AzureSMR") library(AzureSMR)

GitHub: https://github.com/Microsoft/AzureSMR

Source: http://blog.revolutionanalytics.com/2016/12/azuresmr.html

Evaluating Machine Learning models when dealing with imbalanced classes

By Sander Timmer | February 29, 2016 | Comments 1 comment

In this blog post I talk through an example of how to pick the best model when you deal with these kind of problems. I also touch the subject of cost-sensitive predictions, introducing some code to generate plots that will help you understand your model in cost fashion. Even more important, it will be essential for grasping the full business impact when moving to a data driven world!

#DataScience #R #MachineLearning #AzureML

Evaluating Machine Learning models when dealing with imbalanced classes – Developing Analytics Solutions with the Data Insights Global Practice – Site Home – MSDN Blogs

Sander Timmer, PhD. In real-world Machine Learning scenarios, especially those driven by IoT that are constantly generating data, a common problem is having an imbalanced dataset. This means, we have far more data representing one outcome class than the other. For example, when doing predictive …

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This sounds extremely promising for those using data frames in R!

By Sander Timmer | January 18, 2014 | Comments 0 Comment

#R #dplyr

Originally shared by +Sharon Machlis

If you use plyr, you'll probably want to know about what +Hadley Wickham tweeted is "the next iteration of plyr with more speed, a nicer API, and seamless database support" specifically for data frames.

http://blog.rstudio.org/2014/01/17/introducing-dplyr/

Introducing dplyr
dplyr is a new package which provides a set of tools for efficiently manipulating datasets in R. dplyr is the next iteration of plyr, focussing on only data frames. dplyr is faster, has a more cons…

ggplot2 awesomeness at play!

By Sander Timmer | September 9, 2013 | Comments 8 comments

Love this way how when combining #ggplot2 in #R with #gridExtra you can easily (very easy!!) add these density plots (thanks +Aylwyn Scally for pointing that out) on the top and side of your plot. Just pure awesome 🙂

For R code look at +Michael Kuhn blog:
http://blog.mckuhn.de/2013/04/2d-plot-with-histograms-for-each.html

Using ggplot2 in R for creating barplots with faced_grid is such a great way to quickly…

By Sander Timmer | June 11, 2012 | Comments 1 comment

Using ggplot2 in R for creating barplots with faced_grid is such a great way to quickly plot massive data frames in a pretty way. #winning #R #ggplot2

ggplot2: Barplots
One R Tip A Day uses R basic graphics to visualise migration to the United States during 1820-2006. Again, as usual, let’s reproduce this in ggplot2. First, load ggplot2 and dataset > libr…

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Using ggplot2 in R for creating barplots with faced_grid is such a great way to quickly plot massive data frames in a pretty way

By Sander Timmer | June 11, 2012 | Comments 0 Comment

Using ggplot2 in R for creating barplots with faced_grid is such a great way to quickly plot massive data frames in a pretty way. #winning #R #ggplot2