Krishna Choudhary, PhD
The scripting language R is considered one of the most powerful languages for quantitative analysis, statistics, and graphics. This workshop will help you get started using R to analyze your datasets and create graphs for visualization. You'll do hands-on exercises to demystify data analysis using R.
This workshop is designed for those who have no background whatsoever in programming/R. You’ll learn:
- RStudio interface
- Addition, subtraction, basic math operations
- Assigning values to variables
- Commenting in a script
- Logical operators
- Intro to functions and libraries
- Reading data
- Troubleshooting error messages
- Exploring data (basic summaries such as mean, median, etc.)
- Selecting subsets of data
- Plotting data
- Data structures available in R
Novice Level: This is an introductory workshop in the R Scripting series. No prior experience with programming or R/RStudio is required for this course. No prerequisites. Absolute beginners are especially welcome!
View the upcoming workshops in the Gladstone Bioinformatics Program.
Note: This workshop will be held over 2 days, September 28–29, 2020. Attendance in the first session is mandatory in order to attend the second session.
The Gladstone Data Science Training Program was started in 2018 to provide trainees with learning opportunities and hands-on workshops to improve their skills in bioinformatics and computational analysis. This program offers a series of workshops throughout the year to enable trainees to gain new skills and get support with their questions and data.
Diversity, Equity, and Inclusion
At Gladstone, we are committed to providing events and professional development activities that resonate with our community’s diverse members. Our goal is to develop creative programming that encompasses a wide variety of ideas and perspectives to inspire, educate, and engage with everyone within our walls.
We want to effect positive change through our events and activities by providing a platform for discussions on important topics related to increasing diversity and inclusiveness in the sciences.