"Clear explanations about functions in R and data visualistaion tools to perform statistical tests. Extremely useful to be able to cater the data to our company for examples."
JZ, Executive, R for Life Science Researchers, January 2021
• formatting problems and recommendation for a better formatting (helping following bioinformatics analyses)
• Limitations
• Introduction
• Define the following terms as they relate to R: object, assign, call, function, arguments, options.
• Call functions and use arguments to change their default options.
• Load external data from a .csv file into a data frame.
• Summarize and manipulate the contents of a data frame
• Introduction
• Describe the purpose of the dplyr and tidyr packages.
• Manipulate and reshape data
• Produce scatter plots, boxplots, and time series plots using ggplot package Stats with R:
• How to perform statistical tests in R (for qualitative and quantitative parametric data)
• Introduction
• Brief continuation of stats with R if necessary
• Bring your own data!!!!!!!! Discussion and help.
The workshop is aimed at researchers in the life sciences at all career stages and is designed for learners with little to no prior knowledge of programming, shell scripting, or command line tools
This course is aimed at beginners, no prior knowledge is required
"Clear explanations about functions in R and data visualistaion tools to perform statistical tests. Extremely useful to be able to cater the data to our company for examples."
JZ, Executive, R for Life Science Researchers, January 2021
“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and the Instructor handled this particularly well - very impressive”
Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022
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Our R for data analysts training course is designed for teams looking to replace or augment Excel, SPSS or SAS workflows with R.
This workshop is designed to teach basic concepts, skills and tools for working more effectively with data that usually a scientist would analyse in spreadsheets programs (i.e. Excel).
This is time consuming because it usually requires a lot of manual work. If you want to change a parameter or run an analysis with a new dataset, you usually have to redo everything by hand. It is also difficult to track or reproduce statistical or plotting analyses done in spreadsheet programs (i.e.macros) when you want to go back to your work or someone asks for details of your analysis.
The workshop is aimed at researchers in the life sciences at all career stages and is designed for learners with little to no prior knowledge of programming, shell scripting, or command line tools
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