Speeding up package installation

Can’t be bothered reading, tell me now

A simple one-line tweak can significantly speed up package installation and updates.

See my post at the Jumping Rivers blog (no point duplicating content in two places)

 

UK R Courses

Over the next few months we’re running a number of R, Stan and Scala courses around the UK.

June (London)

• Mon Jun 26 – Introduction to R
• Tue Jun 27 – Statistical Modelling with R
• Wed Jun 28 – Programming with R
• Thu Jun 29 (2-day course) – Advanced R Programming

August (Belfast)

• Thu Aug 10 – Programming with R
• Fri Aug 11 – Building an R Package

September 2017 (Newcastle)

• Mon Sep 11 – Introduction to R
• Tue Sep 12- Statistical Modelling with R
• Wed Sep 13- Programming with R
• Thu Sep 14- Advanced Graphics with R
• Fri Sep 15- Automated Reporting (first steps towards Shiny)
• Mon Sep 18 (5-day course) – Bioconductor

October (Glasgow)

• Wed Oct 25 – Advanced Graphics with R
• Thu Oct 26 (2-day course) – Advanced R Programming

November (London)

• Mon Nov 06 (1-day course) – Efficient R Programming
• Tue Nov 07 (1-day course) – R for Big Data

December (London/Newcastle)

• Thu Dec 07 (2-day course) – Introduction to Bayesian Inference using RStan (Newcastle)
• Mon Dec 11 (3-day course) – Scala for Statistical Computing and Data Science (London)

See the website for course descriptions. Any questions, feel free to contact me: colin@jumpingrivers.com

On site courses available on request.

Security: the dangers of copying and pasting R code

Most of the time when we stumble across a code snippet online, we often blindly copy and paste it into the R console. I suspect almost everyone does this. After all, what’s the harm? Consider this simple piece of R code that performs simple linear regression

# Generate data
x = rnorm(10)
y = rnorm(10)

message(“All your base are belong to us.”)

# Simple linear regression 
m = lm(y ~ x)

Now highlight the above piece of R code and copy and paste it into your console; look carefully at what you’ve pasted. A new line has magically appeared.

# Generate data
x = rnorm(10)
y = rnorm(10)
message("All your base are belong to us.")
# Simple linear regression
m = lm(y ~ x)

Due to some sneaky CSS magic, I was able to hide the message() statement. If I was evil, I could have changed this to a system, source, or any other command.

The CSS code simply sets the message() function to the background color, changes the font size and makes it un-selectable (see this post for details).

So remember, be careful with your copy and pasting!

Training courses: R, Stan and Scala

Over the next few months we’re running a number of R, Stan and Scala courses around the UK.

Feburary

• Mon 13 – Introduction to R (London)
• Tue 14 – Programming with R (London)
• Wed 15 – Advanced Graphics with R (London)
• Thur 16 (2-day course) – Predictive Analysis (London)

March

• Tue Mar 21 – R for Big Data (Edinburgh)
• Mon Mar 27 – Survival/Churn Analysis with R (London)
• Tue Mar 28 – Building an R Package (London)
• Wed Mar 29 – Automated Reporting (first steps towards Shiny) (London)
• Thu Mar 30 – Interactive Graphics with Shiny (London)
• Fri Mar 31 – Spatial Data Analysis with R (London)

April

• Mon Apr 03 – Introduction to R (Newcastle)
• Tue Apr 04 – Statistical Modelling with R (Newcastle)
• Wed Apr 05 – Programming with R (Newcastle)
• Thu Apr 06 – Efficient R Programming (Newcastle)
• Fri Apr 07 – Advanced Graphics with R (Newcastle)
• Mon Apr 10 (2-day course) – Introduction to Bayesian Inference using RStan (London)

May

• Tue 16th (3-day course) – Scala for Statistical Computing and Data Science (London)

June

• Thu Jun 08 (2-day course) – Advanced R Programming (Newcastle)
• Mon Jun 26 – Introduction to R (London)
• Tue Jun 27 – Statistical Modelling with R (London)
• Wed Jun 28 – Programming with R (London)
• Thu Jun 29 (2-day course) – Advanced R Programming (London)

See the website for course descriptions. Any questions, feel free to contact me: colin@jumpingrivers.com

On site courses available on request.

Input/output benchmarks

To quantify the impact of the CPU on an analysis, I created the package benchmarkme. The idea is simple. If everyone runs the same R script, we can easily compare machines.

One of the benchmarks in the package is for comparing read/write speeds; we write a large CSV file (using write.csv) and read it back in using read.csv

The package is on CRAN can be installed in the usual way

install.packages("benchmarkme")

Running

library(benchmarkme)
## If your computer is relatively slow, remove 200 from below
res = benchmark_io(runs = 3, size = c(5, 50, 200))
## Upload you data set
upload_results(res)

creates three matrices of size 5MB, 20MB and 200MB, writes the associated CSV file to the directory

Sys.getenv("TMPDIR")

and then reads the data set back into R. The object res contains the timings which can compared to other users via

plot(res)

The above graph plots the current benchmarking results for writing a 5MB file (my machine is relatively fast).

Shiny

You can also compare your results using the Shiny interface. Simply create a results bundle

 create_bundle(res, filename = "results.rds")

and upload to the webpage.

Network drives

Often the dataset we wish to access is on a network drive. Unfortunately, network drives can be slow. The benchmark_io function has an argument that allows us to change the directory and estimate the network drive impact

res_net = benchmark_io(runs = 3, size = c(5, 20, 200), 
                           tmpdir = "path_to_dir")

 

benchmarkme Update

When discussing how to speed up slow R code, my first question is what is your computer spec? It always surprises me when complex biological experiments, costing a significant amount of money, are analysed using a six year old laptop. A new desktop machine costs around £1000 and that money would be saved within a month in user time. Typically the more the RAM you have, the larger the dataset you can handle. However it’s not so obvious of the benefit of upgrading the processor.

To quantify the impact of the CPU on an analysis, I created the package benchmarkme. The aim of this package is to provide a set of benchmarks routines and data from past runs. You can then compare your machine, with other CPUs.

The package is now on CRAN and can be installed in the usual way

install.packages("benchmarkme")

The benchmark_std() function assesses numerical operations such as loops and matrix operations. This benchmark comprises of three separate benchmarks: prog, matrix_fun, and matrix_cal. If you have less than 3GB of RAM (run `get_ram()` to find out how much is available on your system), then you should kill any memory hungry applications, e.g. firefox, and set `runs = 1` as an argument.

To benchmark your system, use

library("benchmarkme")
res = benchmark_std(runs = 3)

You can compare your results to other users via

plot(res)

My laptop is ranked around 50 out of 300. However, relative to the fastest processor, there’s not much difference.

Finally upload your results for the benefit of other users

## You can control exactly what is uploaded. See details below.
upload_results(res)

Shiny

You can also compare your results using the Shiny interface. Simply create a results bundle

 create_bundle(res, filename = "results.rds")

and upload to the webpage.

What’s uploaded

Two objects are uploaded:

1. Your benchmarks from benchmark_std or benchmark_io;
2. A summary of your system information (get_sys_details()).

The get_sys_details() returns:

– Sys.info();
– get_platform_info();
– get_r_version();
– get_ram();
– get_cpu();
– get_byte_compiler();
– get_linear_algebra();
– installed.packages();
– Sys.getlocale();
– The `benchmarkme` version number;
– Unique ID – used to extract results;
– The current date.

The function Sys.info() does include the user and nodenames. In the public release of the data, this information will be removed. If you don’t wish to upload certain information, just set the corresponding argument, i.e.

upload_results(res, args = list(sys_info=FALSE))

List of R conferences and useR groups

Recently Steph Locke asked on twitter if there was a list of R conferences. After some googling, all that I came up was a list of useR groups maintained by Microsoft. While the list was lengthy, it was missing a few groups (and twitter handles). So the other night I created a github repository listing conferences/groups.

If you spot any missing groups or conferences, feel free to make a pull request at the GitHub Repository.

R Courses at London, Leeds and Newcastle

Over the next few months we’re running a number of R courses at London, Leeds and Newcastle.

• September 2016 (Newcastle)
  • Sept 12th: Introduction to R
  • Sept 13th: Statistical modelling
  • Sept 14th: Programming with R
  • Sept 15th: Efficient R: speeding up your code
  • Sept 16th: Advanced graphics
• October 2016 (London)
  • Oct 3rd: Introduction to R
  • Oct 4th: Programming with R
  • Oct 5, 6th: Predictive analytics
  • Oct 7th: Building an R package
• November 2016 (Leeds)
  • Nov 21st, 22nd: Predictive analytics
  • Nov 23rd: Building an R package
• December 2016 (London)
  • December 5th, 6th: Advanced programming. Held at the Royal Statistical Society (booking form).
• January 2017 (Newcastle)
  • Jan 16th: Introduction to R
  • Jan 17th: Statistical modelling
  • Jan 18th: Programming with R
  • Jan 19th: Efficient R: speeding up your code
  • Jan 20th: Advanced graphics

See the website for course description. Any questions, feel free to contact me: csgillespie@gmail.com

On site courses available on request.

R Courses at Newcastle

Over the next two months I’m running a number of R courses at Newcastle University.

• May 2016
  • May 10th, 11th: Predictive Analytics
  • May 16th – 20th: Bioconductor
  • May 23rd, 24th: Advanced programming
• June 2016
  • June 8th: R for Big Data
  • June 9th: Interactive graphics with Shiny

Since these courses are on  advanced topics, numbers are limited (there’s only a couple of places left on Predictive Analytics). If you are interested in attending, sign up as soon as possible.

Getting to Newcastle is easy. The airport is 10 minutes from the city centre and has direct flights to the main airport hubs: Schiphol, Heathrow, and Paris.  The courses at Newcastle attract participants from around the world; at the April course, we had representatives from North America, Sweden, Germany,  Romania and Geneva.

Cost: The courses cost around £130 per day (more than half the price of certain London courses!)

 

Onsite courses available on request.

RStudio addins manager

RStudio addins let you execute a bit of R code or a Shiny app through the RStudio IDE, either via the Addins dropdown menu or with a keyboard shortcut. This package is an RStudio addin for managing other addins. To run these addins, you need the latest version of RStudio.

Installation

The package can be installed via devtools

## Need the latest version of DT as well
devtools::install_github('rstudio/DT')
devtools::install_github("csgillespie/addinmanager")


Running addins

After installing the package, the Addins menu toolbar will be populated with a new addin called Addin Manager. When you launch this addin, a DT table will be launched:

In the screenshot above, the highlighted addins, shinyjs and ggThemeAssit, indicate that this addins have already installed.

When you click Done

• Highlighted addins will be installed.
• Un-highlighted addins will be removed.

Simple!

Including your addin

Just fork and alter the addin file which is located in the inst/extdata directory of the package. This file is a csv file with three columns:

• addin Name/title
• Brief Description
• Package. If the package is only on github, use name/repo.

The initial list of addins was obtain from daattali’s repo.