Introduction to R
[ Feedback ]
[ Course Materials ]
Note to WCM students and employees: in order to receive email announcements regarding workshops registration, if you are a student, please make sure to subscribe to the "Community" broadcast list by following the instructions found here.
WCM employees can subscribe here. Memorial Sloan Kettering and Rockefeller University students and employees should receive the email announcements automatically.
Registration has closed. All spots have been filled and the waiting list is full. If you registered for this course, you will receive an email notifying you of your enrollment status.
It is expected this course will be offered again.
Dates: [ one class in four parts ]
Part 1: Thursday, October 11th 2018, 12:30pm - 2:30pm
Part 2: Friday, October 12th 2018, 12:30pm - 2:30pm
Part 3: Thursday, October 18th 2018, 12:30pm - 2:30pm
Part 4: Friday, October 19th 2018, 12:30pm - 2:30pm
Part 2: Friday, October 12th 2018, 12:30pm - 2:30pm
Part 3: Thursday, October 18th 2018, 12:30pm - 2:30pm
Part 4: Friday, October 19th 2018, 12:30pm - 2:30pm
R is a freely available statistical computing package. This hands-on workshop will cover the basics of R, plotting and performing bioinformatics analysis with R.
Prerequisites:
- Students are expected to have some basic knowledge of statistics, including concepts like random variables and probability distributions, means and standard deviations, sampling, and be familiar with popular exploratory data analysis plots, like histograms, bar plots and scatter plots.
- Participants are asked to bring a laptop* with the following software installed:
- Download most updated version of R for the corresponding operating system: https://cran.r-project.org/mirrors.html
- Download most updated version of Rstudio: https://www.rstudio.com/
- *If you have a new Macbook, you must bring a USB-C ethernet connector.
There is a $100 fee for this workshop. The fee is waived for students.
Organizers: Han Yuan (MSK)
Prerequisites:
Students are expected to have some basic knowledge of statistics, including concepts like random variables and probability distributions, means and standard deviations, sampling, and be familiar with popular exploratory data analysis plots, like histograms, bar plots and scatter plots.