Introduction to R
A software environment for statistical computing
R is a free software environment for statistical computing and graphics. It is widely used in scientific data analysis. R is a great tool for biologists, since it provides cutting edge statisical techniques, excellent graph and figure plotting capabilities and bioinformatics tools all in one package.
Instructors
- Govind Chandra
Audience
Any students, postdocs or RAs who have an interest in bioinformatics and who intend to carry out statistical analysis of their experimental data using R. This two day course is planned to be a very gentle introduction to the very basic concepts of R. It should be seen as a prerequisite for the Introduction to Statistics course which will be based on R
Overview
R is a programming language and associated environment developed for statistical computing and data analysis. It provides many powerful tools for statistics, data visualisation and bioinformatics.
Schedule
Day 1
9:30 Start
11:00 Coffee break
12:30 Lunch
15:00 Coffee break
17:00 End
Day 2
9:30 Start
11:00 Coffee break
12:30 Lunch
15:00 Coffee break
17:00 End
Learning outcomes
By the end of the course you should be able to:
- What R is suitable for.
- How to use R for simple statistical analysis and plotting.
- About the different data types and data structures in R.
- How to get help regarding commands and functions in R.
- How to read your own data into R and how to write out data in R to text files for importing to other programs.
- How to make several different kinds of plots in R and how to control their appearance.
- How to take advantage of pre-existing packages in R to facilitate complicated data analysis.
Pre-requisites
It will help if you have already attended the Introduction to the HPC and the Introduction to Linux courses. These are however, not essential requirements but you are encouraged to attend these in the future if you have not done so because they provide complementary skills resulting in independent data analysis capability.
Full course syllabus
- Basic idea of how R works.
- Starting and quitting from R.
- Simple and complex data types in R. Objects.
- Getting help in R. Reading the documentation.
- Getting your data into and out of R.
- Basic plotting in R.
- Using packages in R.
Course materials
Course book
A software environment for statistical computing
Instructors
- Govind Chandra
Audience
Any students, postdocs or RAs who have an interest in bioinformatics and who intend to carry out statistical analysis of their experimental data using R. This two day course is planned to be a very gentle introduction to the very basic concepts of R. It should be seen as a prerequisite for the Introduction to Statistics course which will be based on ROverview
R is a programming language and associated environment developed for statistical computing and data analysis. It provides many powerful tools for statistics, data visualisation and bioinformatics.Schedule
Day 1
| 9:30 | Start |
| 11:00 | Coffee break |
| 12:30 | Lunch |
| 15:00 | Coffee break |
| 17:00 | End |
Day 2
| 9:30 | Start |
| 11:00 | Coffee break |
| 12:30 | Lunch |
| 15:00 | Coffee break |
| 17:00 | End |
Learning outcomes
By the end of the course you should be able to:- What R is suitable for.
- How to use R for simple statistical analysis and plotting.
- About the different data types and data structures in R.
- How to get help regarding commands and functions in R.
- How to read your own data into R and how to write out data in R to text files for importing to other programs.
- How to make several different kinds of plots in R and how to control their appearance.
- How to take advantage of pre-existing packages in R to facilitate complicated data analysis.
Pre-requisites
It will help if you have already attended the Introduction to the HPC and the Introduction to Linux courses. These are however, not essential requirements but you are encouraged to attend these in the future if you have not done so because they provide complementary skills resulting in independent data analysis capability.Full course syllabus
- Basic idea of how R works.
- Starting and quitting from R.
- Simple and complex data types in R. Objects.
- Getting help in R. Reading the documentation.
- Getting your data into and out of R.
- Basic plotting in R.
- Using packages in R.