https://github.com/3mmarand/bio00017c-data-analysis-in-r-2020

University of York, Department of Biology, C-level module: Data Analysis in R section of Laboratory and Professional Skills for Bioscientists

https://github.com/3mmarand/bio00017c-data-analysis-in-r-2020

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University of York, Department of Biology, C-level module: Data Analysis in R section of Laboratory and Professional Skills for Bioscientists

https://github.com/3mmaRand/BIO00017C-Data-Analysis-in-R-2020/blob/main/

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# Overview

## Introduction

*Learning data Analysis is important...*

Reproducible data management, handling, wrangling, modelling and visualisation underpin both the scientific process and many of the most in-demand hard skills. They additionally develop general the computational skill needed for other in-demand hard skills. See, for example [LinkedIn's global list of the most in-demand hard skills](https://business.linkedin.com/talent-solutions/blog/trends-and-research/2020/most-in-demand-hard-and-soft-skills).

*... and takes time ...*

Part of the reason that these skills are in-demand is that many view them as difficult and they take time to learn. They are skills you learn, not facts to memorise, and take time and practice in the same way that playing an instrument, speaking another language or playing a sport well take time and practice. You have keep going until you've mastered some aspect and tolerate a lot of mistakes. We are all uncomfortable in that process, myself included! I've have spent many frustrating hours trying to work out how to express my data question as code both in terms of the logic required and the syntax needed.

*... but can be great fun ...*

However, the process is often fun! There is a lot of problem solving which is engaging work because there's always something to 'chase'. I can concentrate on coding for much longer than I can concentrate on writing or reading because it is such an active learning process. There are impressive looking figures, which you can reproduce on a different dataset in moments, exciting biological insights revealed in a test and whole world of 'techy' tricks you had no idea you'd be able to do!

All materials are indexed here: 

## **Learning objectives**

By the end of "Data Analysis in R" the successful student will be able to:

-   Explain the purpose of data analysis.
-   Name, identify and choose classical univariate statistical tests (and some non-parametric 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.

## Materials


Data Analysis in R for BIO00017C by Emma Rand is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Please cite as:

Emma Rand. (2022). Data Analysis in R (BIO00017C) 2020: 2022 (v1.1). Zenodo. https://doi.org/10.5281/zenodo.6359475

You can obtain all the workshop materials by using the green 'Clone or download' button above.

Emma

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