Update course information for the November session

README.md

-R-Big data
+Effective Analysis of Big Data in R
 ==========
 
-Welcome to R Big Data Course repository. This contains information for a number
+Welcome to Effective Analysis of Big Data in R Course repository. This contains information for a number
 of editions of the course, under several names and with slightly
 different content:
 
 * [Slides](https://git.wageningenur.nl/warri004/r-big-data/tree/master/slides)
-* [Course documentation PE&RC edition, 2021](https://git.wageningenur.nl/warri004/r-big-data/raw/master/courseInfo_PERC_2021.pdf)
+* [Course documentation, edition: 2021](https://git.wageningenur.nl/warri004/r-big-data/raw/master/courseInfo.pdf)
 * [Data](https://git.wageningenur.nl/warri004/r-big-data/tree/master/data)
 * [Examples](https://git.wageningenur.nl/warri004/r-big-data/tree/master/examples)
 

courseInfo_PERC_2021.Rmd → courseInfo.Rmd

 ---
 title: "Course information"
 author: "Sven Warris"
-date: "October 14/15, 2021"
+date: "November 4/5, 2021"
 output: pdf_document
 ---
 
-Welcome to the R Big Data course. This document contains some basic information on the course. 
+Welcome to the *Effective Analysis of Big Data in R*. This document contains some basic information on the course. 
 
 ## Course introduction
 
@@ -25,12 +25,11 @@ plenty of time for hands-on exercises.
 
 ## RStudio server
 
-There is an RStudio server available at [warris.wurnet.nl](http://warris.wurnet.nl:8787), only during the course. 
+There is an RStudio server available at [rstudio.containers.wur.nl](https://rstudio.containers.wur.nl), only during the course. 
 
 ## Course code repository
 
-[Main Wageningen GIT repository R Big
-Data](https://git.wageningenur.nl/warri004/r-big-data)
+[Main Wageningen GIT repository Effective Analysis of Big Data in R](https://git.wageningenur.nl/warri004/r-big-data)
 
 ## The lecturers
 
@@ -39,33 +38,38 @@ Data](https://git.wageningenur.nl/warri004/r-big-data)
 
 ## Programme
 
-Wageningen University Campus, Orion Building, Room B3040. Please note that you have to bring your own laptop. Lunch is
-provided and there will be coffee breaks. The schedule is as follows:
-
-### October 14
-
-* 9.00 Welcome and introduction 
-* 9.30 The Big Data issue 
-* 10.30 Coffee break
-* 11.00 Profiling and efficient R programming 
-* 12.30 Lunch
-* 13.30 Profiling and efficient R programming (contd.) 
-* 15.00 Coffee break
-* 15.30 Reproducible research 
-* 17.00 Q&A 
-* 17.30 Closing
-
-### October 15
-
-* 9.00 R memory management and big data 
-* 10.30 Coffee break
-* 11.00 HPC in R 
-* 12.30 Lunch
-* 13.30 Big data analyses: a case of machine learning 
-* 15.00 Coffee break
-* 15.30 Big data analyses: a case of machine learning (contd.) 
-* 16.30 Q&A 
-* 17.00 Evaluation and closing
+Location: *Wageningsche Berg*. Please note that you have to bring your own laptop. Lunch and dinner are provided and there will be coffee breaks. The schedule is as follows:
+
+### November 4
+
+* 08:30 Registration and welcome with coffee and tea 
+* 09:00 Get to know each other & introduction 
+* 09:30 The Big Data issue by Ron Wehrens 
+* 10:30 Break 
+* 11:00 Profiling and efficient R programming by Ron Wehrens 
+* 12:30 Lunch 
+* 13:30 Profiling & efficient R programming (contd.) by Ron Wehrens 
+* 15:00 Break 
+* 16:00 Reproducible research by Sven Warris 
+* 17:00 Q&A 
+* 17.30 Closing day 1 
+* 18.00 Joined dinner at course venue 
+
+
+### November 5
+
+* 08:45 Welcome with coffee and tea 
+* 09:00 R memory management and big data by Ron Wehrens 
+* 10:30 Break 
+* 11:00 HPC in R by Sven Warris 
+* 12:30 Lunch 
+* 13:30 Big data analyses: a case of machine learning by Sven Warris 
+* 15:00 Break 
+* 15:30 Big data analyses: a case of machine learning (contd.) by Sven Warris 
+* 16:30 Q&A 
+* 17:00 Evaluation 
+* 17:15 Closing course
+
 
 ## Course information
 

slides/Sven/HPC.Rmd

 ---
 title: "High performance computing with R"
 author: "Sven Warris"
-date: "October 2021"
+date: "November 2021"
 output: beamer_presentation
 ---
 
@@ -138,6 +138,18 @@ Cons:
 
 # Exercise
 
+Let's go shopping!
+
+a) one person, one list
+b) group of 5 people, one list
+c) group of 5 people, random items from the list
+d) group of 5 people, items from the list per section of the shop
+
+How will this work out in practise with 1, 3, 5 or 7 lanes open?
+![Floor plan](./images/floorplan.jpg){width=50%}
+
+# Exercise
+
 On my computer (_20 cores, 0.5TB RAM_), I have 5 HiSeq read files (10Gb each,
 2mil reads per file) on a network share. The first step in processing these
 reads consists of trimming low quality base calls at the end of each read. Then
@@ -154,6 +166,8 @@ files at once without problems
 cluster. The files are stored in files of 100,000 reads each. Trimming is done
 through jobs streaming the data to local SSDs.
 
+
+
 # Algorithmic approaches
 
 Basic idea: 
@@ -430,17 +444,6 @@ c(12,41,53,8,9,2,11,7)
 * Design and implement an algorithm which find the _max_ value in _log2(N)_ steps.
 * Time _%do%_ and _%dopar%_ for several lengths of the list
 
-# Exercise
-
-Let's go shopping!
-
-a) one person, one list
-b) group of 5 people, one list
-c) group of 5 people, random items from the list
-d) group of 5 people, items from the list per section of the shop
-
-How will this work out in practise with 1, 3, 5 or 7 lanes open?
-![Floor plan](./images/floorplan.jpg){width=50%}
 
 # Serial vs. Parallel
 
@@ -485,6 +488,8 @@ scatter3D(x, y, z, theta=45,  xlab=xLabel, ylab=yLabel,
 Chloroplast vs Chloroplast
 
 
+
+
 # End of HPC
 
 This is the end of the part on High Performance Computing 
\ No newline at end of file

slides/Sven/machineLearning.Rmd

 ---
 title: "Big data and machine learning"
 author: "Sven Warris & Christina Papastolopoulou"
-date: "October 2021"
+date: "November 2021"
 output: beamer_presentation
 ---
 

slides/Sven/reproResearch.Rmd

 ---
 title: "Reproducible Research"
 author: "Sven Warris & Maikel Verouden"
-date: "October 2021"
+date: "November 2021"
 output: beamer_presentation
 ---
 
@@ -72,7 +72,7 @@ Examples:
 * Co-develop
 * Develop-test-release cycle support
 * Many, many tools available
-* Integrated into R
+* Integrated into R=
 * [WUR Git lab](https://git.wur.nl)
 * [github](http://www.github.com)
     * Allows for **DOI**!