Introduction to Data Analysis in RStudio

January 23, 2019
By

(This article was first published on Emma R, and kindly contributed to R-bloggers)

I’ve just started doing one of my favourite parts of my job – teaching a term of Data Analysis in R to about three hundred Bioscientists in their first year of higher education. My blog last week included a figure of their expected level of enjoyment:

data-1

However,  I find they become very competent in both statistics and coding considering they start as complete beginners and this is only part of their degree programme. I also have a lot of fun with them.

I thought would share their first workshop schedule. The aims of the term are:

  • to explain what matters in choosing methods of data analysis and give them practice in making those decisions.
  • To train them in analysing data in R specifically and help you develop an understanding of some core and highly transferable concepts in data analysis.

The “Learning Outcomes” called MLO of the whole term are that the successful student will be able to:

  • Explain the purpose of data analysis
  • Choose classical univariate statistical tests (and some nonparametric equivalents) appropriate to a given scenario and recognise when these are not suitable
  • Use R to perform these analyses on data in a variety of formats
  • Interpret, report and graphically present the results of covered tests

That first workshop is here! In this introduction they start working with RStudio and plotting data after independently studying two chapter of DataCamp‘s Introduction to R. They also start their journey on understanding the manual!

Introduction to module and RStudio

If you have any comments I’d love to hear them!

To leave a comment for the author, please follow the link and comment on their blog: Emma R.

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