More at https://boards.greenhouse.io/twistbioscience/jobs/3135495?gh_src=9ecc0b941us

Candidates must have: a PhD degree in statistics, biostatistics or bioinformatics, extensive experience in analyzing high-dimensional data (microarray, SNP, CNVs) and of validation approaches. In addition, experience in penalized regression methods, data base manipulation; and strong programming skills in order to conduct Monte Carlo studies and applications (R). Candidate must have excellent communication skills (verbal, written and presentation), a strong proficiency in Linux system.

This position is available immediately and will be filled as soon as possible. Appointment could be extended beyond the first year based on additional funding.

For more information about the Department of Biostatistics and Bioinformatics, please visit our website: http://www.biostat.duke.edu.

For more info: http://biostat.duke.edu/sites/biostat.duke.edu/files/Halabi%20-%20Postdoc%20Job%20Posting%202013%20updated.pdf

Duke University is an Equal Opportunity/Affirmative Action Employer.

Address of the bookmark: http://www.rdatamining.com/

Address of the bookmark: https://practical-stats-med-r.netlify.app/

Address of the bookmark: http://www.programmingr.com/category/stype/r-job-listings/

Installation

To download the development version of pacman:

Download the zip ball or tar ball, decompress and run R CMD INSTALL on it, or use the devtools package to install the development version:

## Make sure your current packages are up to date
update.packages()
## devtools is required
devtools::install_github("trinker/pacman")

Note: Windows users need Rtools and devtools to install this way.

More at https://github.com/trinker/pacman

It is possible to create a vector by typing data directly into R using the combine function ‘c’

x

same as

x

creates the vector x with the numbers between 1 and 5.

You can see what is in an object at any time by typing its name;

x

will produce the output ‘[1] 1 2 3 4 5′

Note that names need to be quoted

daysofweek ← c(‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’);

Usually however you want to input from a file. We have touched on the ‘read.table’ function already.

mydata

Now mydata is a data frame with multiple vectors

each vector can be identified by the default syntax

#if any of these are typed it will print to screen

mydata$V1 mydata$V2 mydata$V3

By default the function assumes certain things from the file

• The file is a plain text file (there are function to read excel files: not covered here)
• columns are separated by any number of tabs or spaces
• there is the same number of data points in each column
• there is no header row (labels for the columns)
• there is no column with names for the rows** [I’ll explain].

If any of these are false, we need to tell that to the function

If it has a header column

mydata header=T also works

Note that there is a comma between different parts of the functions arguments

If there is one less column in the header row, then R assumes that the 1^st column of data after the header are the row names

Now the vectors (columns) are identified by their name

#if any of these are typed it will print to screen

mydata$A mydata$B mydata$C

# Summary about the whole data frame

summary(mydata)

# Summary information of column A

summary(mydata$A)

We can shortcut having to type the data frame each time by attaching it

attach(mydata)

# summary of column B as ‘mydata’ is attached

summary(B)

Two other important options for read.table

If is is separated only by tabs and has a header

mydata

Really useful if you have spaces in the contents of some columns, so R does not mess up reading the columns . However if the columns or of an uneven length it will tell you.

If you know that the file has uneven columns

mydata

This causes R to fill empty spaces in a columns with ‘NA’ .

The last two examples will still work with our file and give the same result as with only headers=T

Graphs

to get an idea of what R is capable of type

demo(graphics)

steps through the examples, and the code is printed to the screen

We will work with simpler examples that have immediate use to biologists.

Remember to get more information about the options to a function type ‘?function’

Histogram of A

hist(mydata$A)

If there was more data we could increase the number of vertical columns with the option, breaks=50 (or another relevant number).

boxplot(mydata)

We can get rid of the need to type the data frame each time by using the attach function

# if not already done so

attach(mydata)

boxplot(mydata$A, mydata$B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

same as

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Scatter plot

# if not already done so

attach(mydata)

plot(A,B) # or plot(mydata$A, mydata$B)

SAVING an image

Windows users (Rgui) RIGHT click on image and select which you want.

These instructions work for everyone.

You need to create a new device of the type of file you need, then send the data to that device

to save as a png file (easy to load into the likes of powerpoint, also great for web applications.

png(‘filename’)

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

or to save as a pdf

pdf(‘filename’)

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Note

• Nothing will appear on screen, the output is going to the file
• Also it may not be saved immediately but will once the device (or R) is turned quit.

To quit R type

q() # If you save your session, next time you start R, you will have your data preloaded.

Or if you want to remain in R

dev.off() #turns of the png (or pdf etc) device, thus forces the data to save

The OpenCPU JavaScript client library provides the most seamless integration of R and JavaScript available today.

OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source example apps are available from Github.

Installing your own OpenCPU server is super easy and only takes a few minutes.

More at https://www.opencpu.org/

For students interested in frontier research at the interface of biology, computation, physics and applied mathematics

IMSc is a leader in India in fundamental research in theoretical physics, mathematics and theoretical computer science, with several members actively pursuing research in interdisciplinary areas including computational biology. In 2013 IMSc started a unique Ph.D. programme in this subject, training students to apply cutting-edge computational and mathematical techniques to problems in modern biology, in collaboration with leading biology departments and institutions in India and abroad.
IMSc is an autonomous national research institute under the Department of Atomic Energy, Government of India, and a constituent institution of the Homi Bhabha National Institute (HBNI), Mumbai (a deemed university). Ph.D. degrees will be awarded by HBNI.
STRUCTURE OF PROGRAMME
Before embarking on their research, students have three semesters of coursework, which consists of seven core courses, to be carried out at IMSc; elective courses, which may be taken at IMSc or at other institutions by mutual consent; and lab rotations, at collaborating labs in other institutions. The core coursework covers essentials of modern biology, essential techniques from physics, mathematics, statistics and computer science, physics of proteins and biomolecules, biological sequence analysis and algorithms, and systems biology. Elective coursework covers various topics in greater depth. Following the coursework and a comprehensive examination, students will embark on research leading to a Ph.D. degree.
Selected candidates will be research fellows at IMSc and will receive fellowships, housing or house rent allowance, and contingency grants.

More at http://www.imsc.res.in/graduate_programme_0

Title : “Short-Term Research Fellowship”

Qualification : Candidates having M.Sc./M.Tech degree (with minimum of 60% marks overall) or equivalent in Bioinformatics/Biotechnology or any other related field, are eligible to apply. Candidate having prior experience in the area of next-generation sequencing data analysis, bioinformatics, molecular analysis of plant genes, and structural data analysis will be preferred.

No.of Post : 01
How to apply

Application should sent to Dr. Gitanjali Yadav, Staff Scientist-IV, National Institute of Plant Genome Research (NIPGR), Aruna Asaf Ali Marg, P.O. Box NO. 10531, New Delhi - 110067 on or before 26th April 2016

More at http://www.nipgr.res.in/careers/vacancies_latest.php#