Good news from Belgium: Course on Applied spatial modelling with R (April 13-14)

applied spatial

Within 2 weeks, our 2-day crash course on Applied spatial modelling with R (April 13-14, 2016) will be given at the University of Leuven, Belgium:
You'll learn during this course the following elements:

  • The sp package to handle spatial data (spatial points, lines, polygons, spatial data frames)
  • Importing spatial data and setting the spatial projection
  • Plotting spatial data on static and interactive maps
  • Adding graphical components to spatial maps
  • Manipulation of geospatial data, geocoding, distances, …
  • Density estimation, kriging and spatial point pattern analysis
  • Spatial regression

More information: Registration can be done at

applied spatial model

New RStudio add-in to schedule R scripts

With the release of RStudio add-in possibilities, a new area of productivity increase and expected new features for R users has arrived. Thanks to the help of Oliver who has written an RStudio add-in on top of taskscheduleR, scheduling and automating an R script from RStudio is now exactly one click away if you are working on Windows.

How? Just install these R packages and you have the add-in ready at the add-in tab in your RStudio session. Select your R script and schedule it to run any time you want. Hope this saves you some day-to-day time and feel free to help make additional improvements. More information:

install.packages("taskscheduleR", repos = "", type = "source")

taskscheduleR rstudioaddin


taskscheduleR: R package to schedule R scripts with the Windows task manager

If you are working on a Windows computer and want to schedule your R scripts while you are off running, sleeping or having a coffee break, the taskscheduleR package might be what you are looking for. 

taskscheduleR logo

The taskscheduleR R package is available at and it allows R users to do the following:

i) Get the list of scheduled tasks

ii) Remove a task

iii) Add a task

    - A task is basically a script with R code which is run through Rscript

    - You can schedule tasks 'ONCE', 'MONTHLY', 'WEEKLY', 'DAILY', 'HOURLY', 'MINUTE', 'ONLOGON', 'ONIDLE'

    - After the script has run, you can check the log which can be found at the same folder as the R script. It contains the stdout & stderr of the Rscript.

Below, you can find an example how you can schedule your R script once or daily in the morning. 
myscript <- system.file("extdata", "helloworld.R", package = "taskscheduleR")

## run script once within 62 seconds
taskscheduler_create(taskname = "myfancyscript", rscript = myscript,
schedule = "ONCE", starttime = format(Sys.time() + 62, "%H:%M"))
## run script every day at 09:10
taskscheduler_create(taskname = "myfancyscriptdaily", rscript = myscript,
schedule = "DAILY", starttime = "09:10")

## delete the tasks
taskscheduler_delete(taskname = "myfancyscript")
taskscheduler_delete(taskname = "myfancyscriptdaily")
  • When the task has run, you can look at the log which contains everything from stdout and stderr. The log file is located at the directory where the R script is located. 
## log file is at the place where the helloworld.R script was located
system.file("extdata", "helloworld.log", package = "taskscheduleR")

Who wants to set up an RStudio add-in for this?

Web scraping with R & novel classification algorithms on unbalanced data

Tomorrow, the next RBelgium meeting will be held at the bnosac offices. This is the schedule.

Interested? Feel free to join the event. More info:

• 18h00-18h30: enter & meet other R users

• 18h30-19h00: Web scraping with R: live scraping products & prices of

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• 19h15-20h00: State-of-the-art classification algorithms with unbalanced data. Package unbalanced: Racing for Unbalanced Methods Selection.

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Advanced R programming topics course in Leuven

wawitsrLast call for registration of the course on Advanced R programming topics.

Next week on February 17/18, the yearly R course on Advanced R programming topics in Leuven (Belgium) is scheduled.
Registration can be done at

The course is rewritten based on 3 years of extensive customer feedback and because of the tremendous evolution R has encountered in the last years.
You'll learn the following in this 2-day course:

  • functions and vectorisation
  • control flow
  • data handling using data.table (aggregation, rbinding, reshaping)
  • apply family of functions, split/apply/combine
  • parallelisation
  • error handling & debugging
  • building reports with latex & Sweave
  • building reports with latex/markdown and knitr
  • S3 & S4 classes, methods & generics
  • environments, search path, namespaces
  • creating your own R package
  • documenting your R package with Roxygen
  • building a vignette
  • R CMD check/build/install
  • unit testing of your functions
  • building your own corporate R package repository