transforming environmental data in R

Date:

The Tue 22 & Wed 23 Sep 2020 just completed.

next Date; Spring 2021

Location:

ONLINE! 

Cost:

Students £399

Professionals £499

(above Early Bird discount, then £50 more)

Please express your interest in this course here, so we can fix a new date ASAP. (you will also benefit from the Early-bird discount rate)

Short Course Description:

This 2-day interactive online course will help you understand the benefits of data transformation tools (such as R). You will learn about aspects such as auditable workflow, repeatability, time-saving, improving efficiency and reduced risk of data loss. You will participate in practical data transformation tool exercises using real environmental datasets to combine and manipulate datasets in different formats from different sources, leading to analysis-ready data. The course also covers cleaning and validation of datasets and best-practice for documentation of scripts and workflows.

We will do many practical exercises. For these, you will be in virtual breakout rooms in pairs or small groups. The facilitators will move from room to room to help you with your exercises

Learning outcome:

By the end of the course you will have gained sufficient data transformation skills and knowledge to apply this to your own datasets and projects.

Course objectives:

The course will help you to understand and improve your knowledge and skills on:

  • The benefits of data transformation tools
  • How data transformation tools (DTT) help users meet quality standards
  • How to read in multiple datasets from source in different formats
  • How DTTs can be used to clean and validate data.
  • How to tidy data and get it 'analysis-ready'.
  • Common data transformation operations
  • How to combine and integrate datasets from different sources
  • How to write data to different formats
  • How to combine the use of different DTTs (e.g. R and Python)
  • Data management issues and best practice when working with data

Hardware/ Software requirements:

You will need a laptop or desktop computer. A second external screen will be an advantage (but is not essential).

We will use Zoom to deliver the training course. There are 5 ways to join Zoom (and at least one of them will work for you!). We will provide more information about Zoom with the joining instructions and at the start of the course. You can find more information about Zoom on our FAQ page.

We will do lots of practical exercises, so you can continue workinig on your skills immediately after the training course.  You will need to install this software before the course starts

We will explain in the joining instructions how to download and install the software.

Places:

18 places

Level:

Beginner – Intermediate (some basic knowledge of R will be an advantage, but not essential)

We are expecting you to have basic data management skills in MS Excel.

Target Audience:

Anyone who is looking to work with data in a reproducible manner and currently works mainly in spreadsheets, or is looking to prepare data for analysis in R.

e.g. MSc /PhD/ early career researchers/ Ecologists / Environmental Scientists / Environmental Consultants

Please express your interest in this course here, so we can fix a new date ASAP. (you will also benefit from the Early-bird discount rate)

Previous Course participants said:

A great course, well pitched and addressing key and practical issues of data transformation.

really happy, a valuable and useful piece of training.

Course leader:

David Leaver, Environmental Data Scientist  and Informatics Liaison officer, UKCEH

David has a background in Chemistry and Atmospheric Sciences and works with scientists, application developers and data managers to improve data management, dissemination and science capabilities in UKCEH. He has developed tools to organise, transform and analyse data from UK-wide pollutant monitoring, ensuring the quality and traceability of results submitted to stakeholders. David has developed and delivered successful courses in relational databases and data transformation in UKCEH over a number of years.