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FREE RECORDED 1 DAY INTRO TO R AND R STUDIO (FIRR01)
20th October 2021 - 22nd October 2021
Wednesday, April 13th, 2022
Free seminar
This is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Introduction To Python And Programming In Python”.
Time
17:00 GMT
Speaker
Course Instructor Dr. Mark Andrews
About this course
Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in data science, machine learning, and in other scientific computing applications. In order to use Python confidently and competently for these applications, it is necessary to have a solid foundation in the fundamentals of general purpose Python. This two day course provides a general introduction to the Python environment, the Python language, and general purpose programming in Python. We cover how to install and set up a Python computing environment, describing how to set virtual environments, how to use Python package installers, and overview some Python integrated development environments (IDE) and Python Jupyter notebooks. We then provide a comprehensive introduction to programming in Python, covering all the following major topics: data types and data container types, conditionals, iterations, functional programming, object oriented programming, modules, packages, and imports. Note that in this course, we will not be covering numerical and scientific programming in Python directly. That is provided in a subsequent two-day course, for which the topics covered in this course are a necessary prerequisite.
About This Course
In this free one day course, we provide a comprehensive introduction to R and how it can be used for data science and statistics. We begin by providing a thorough introduction to RStudio, which is the most popular and powerful interfaces for using R. We then introduce all the fundamentals of the R language and R environment: variables and assignment, data structures, operators, functions, scripts, packages, projects, etc.
IF YOU ARE IN A POSITION TO CONTRIBUTE TOWARDS YOUR PLACE IT WOULD BE APPRECIATED SO WE CAN USE ANY REVENUE TO FUND OTHER FREE COURSES – THERE IS THE OPTION TO PURCHASE A TICKET FOR £20.00
WE ALSO RECOMMEND ORGANISING A SAMALL GROUP IF YOU HAVE COLLEAGUES WHO ALSO WANT TO ATTEND, THIS WAY WE CAN MAXIMISE HOW MANY PEOPEL CAN ACCESS THE COURSE
THIS IS A FREE INTRODUCTORY COURSE – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE PAID COURSES IN THIS SERIES
Intended Audiences
This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.
Course Details
Venue – Delivered remotely
Time zone – NA
Availability – NA
Duration – 1 days
Contact hours – Approx. 6 hours
ECT’s – NA
Language – English
Teaching Format
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.
Assumed quantitative knowledge
We will assume only a minimal amount of familiarity with some general statistical and mathematical concepts. These concepts will arise when we discuss statistics and data analysis. Anyone who has taken any undergraduate (Bachelor’s) level course on (applied) statistics can be assumed to have sufficient familiarity with these concepts.
Assumed computer background
No prior experience with R or any other programming language is required. Of course, any familiarity with any other programming will be helpful, but is not required.
Equipment and software requirements
Attendees of the course will need to use a computer on which RStudio can be installed. This includes Mac, Windows, and Linux, but not tablets or other mobile devices. Instructions on how to install and configure all the required software, which is all free and open source, will be provided before the start of the course. We will also provide time during the workshops to ensure that all software is installed and configured properly.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
Course Programme
Topic 1
The What and Why of R. We’ll start by briefly explaining what R is, what is used for, and why is has become so popular.
Topic 2
Guided tour of RStudio. RStudio is the most widely used interface to R. We will provide a tour of all its parts and features and how to use it effectively.
Topic 3
First steps in R. Now, we cover all the fundamentals of R and the R environment. These include variables and assignment, data structures such as vectors, data frames, lists, etc, operations on data structures, functions, scripts, installing and loading packages, using RStudio projects, reading in data, etc. This topic will be detailed so that everyone obtains a solid grasp on these fundamentals, which makes all subsequent learning much easier.
Mark Andrews
Works At
Senior Lecturer, Psychology Department, Nottingham Trent University, England
- Teaches
- Free 1 day intro to r and r studio (FIRR)
- Introduction To Statistics Using R And Rstudio (IRRS03)
- Introduction to generalised linear models using r and rstudio (IGLM)
- Introduction to mixed models using r and rstudio (IMMR)
- Nonlinear regression using generalized additive models (GAMR)
- Introduction to hidden markov and state space models (HMSS)
- Introduction to machine learning and deep learning using r (IMDL)
- Model selection and model simplification (MSMS)
- Data visualization using gg plot 2 (r and rstudio) (DVGG)
- Data wrangling using r and rstudio (DWRS)
- Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
- Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
- Bayesian data analysis (BADA)
- Bayesian approaches to regression and mixed effects models using r and brms (BARM)
- Introduction to stan for bayesian data analysis (ISBD)
- Introduction to unix (UNIX01)
- Introduction to python (PYIN03)
- Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
- Machine learning and deep learning using python (PYML03)
- Python for data science, machine learning, and scientific computing (PDMS02)
Personal website
Google Scholar
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.