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Keywords: R software  or R 

Cleaning Biodiversity Data in R

This book is a practical guide for cleaning geo-referenced biodiversity data using R. It focuses specifically on the processes and challenges you’ll face with biodiversity data. As such, this book isn’t a general guide to data cleaning but a targeted resource for those working with or interested...

Keywords: R, Data cleaning, Biodiversity data, Rstats, Ecology, Reproducibility, Beginner R coding, data wrangling, Coding

Cleaning Biodiversity Data in R https://dresa.org.au/materials/cleaning-biodiversity-data-in-r This book is a practical guide for cleaning geo-referenced biodiversity data using R. It focuses specifically on the processes and challenges you’ll face with biodiversity data. As such, this book isn’t a general guide to data cleaning but a targeted resource for those working with or interested in ecology, evolution, and geo-referenced biodiversity data. Atlas of Living Australia support@ala.org.au R, Data cleaning, Biodiversity data, Rstats, Ecology, Reproducibility, Beginner R coding, data wrangling, Coding
ALA Labs

ALA Labs provides resources and articles from the Atlas of Living Australia's Science and Decision Support team. On the website, you can find:

  • Posts: Code, articles, analyses and visualisations that will hopefully help you in your own work
  • Research: Highlighted summaries of scientific...

Keywords: Ecology, R, Python, Rstats, Biodiversity data, Open science, Reproducibility, Coding, Data cleaning, Data visualisation, Species Distribution Modelling, Beginner R coding

ALA Labs https://dresa.org.au/materials/ala-labs ALA Labs provides resources and articles from the Atlas of Living Australia's Science and Decision Support team. On the website, you can find: - Posts: Code, articles, analyses and visualisations that will hopefully help you in your own work - Research: Highlighted summaries of scientific research that has used data from the Atlas of Living Australia - Software: R & Python packages that the Science & Decision Support team manage - Books: Long-form resources with best-practice data wrangling and visualisation - Gallery: Showcasing external work that uses tools from ALA Labs Atlas of Living Australia support@ala.org.au Ecology, R, Python, Rstats, Biodiversity data, Open science, Reproducibility, Coding, Data cleaning, Data visualisation, Species Distribution Modelling, Beginner R coding
Tutorials to learn how to use STAN

Stan tutorials offer links to exceptional tutorial papers, videos and statistics to learn Bayesian statistical methods and applied statistics.

Keywords: Statistics, applied statistics, Bayesian statistics, R software, Python, MATLAB

Tutorials to learn how to use STAN https://dresa.org.au/materials/tutorials-to-learn-how-to-use-stan Stan tutorials offer links to exceptional tutorial papers, videos and statistics to learn Bayesian statistical methods and applied statistics. https://mc-stan.org/about/team/ Statistics, applied statistics, Bayesian statistics, R software, Python, MATLAB
Species Distribution Modelling in R

This set of scripts and videos provide an introduction to running SDMs in R and include some steps to consider that go beyond what's available in the EcoCommons SDM point-and-click tools.

Five videos include: 1. An introduction to SDM in R, 2. occurrence data, 3. environmental data, 4. fitting...

Keywords: Species Distribution Modelling, Ecology, R software, EcoCommons

Species Distribution Modelling in R https://dresa.org.au/materials/species-distribution-modelling-in-r This set of scripts and videos provide an introduction to running SDMs in R and include some steps to consider that go beyond what's available in the EcoCommons SDM point-and-click tools. Five videos include: 1. An introduction to SDM in R, 2. occurrence data, 3. environmental data, 4. fitting your model, 5. model evaluation Scripts and files are available here: https://github.com/EcoCommons-Australia/educational_material/tree/main/SDMs_in_R/Scripts Scripts for all four modules are here: https://www.ecocommons.org.au/wp-content/uploads/EcoCommons_steps_1_to_4.html https://www.ecocommons.org.au/contact/ Species Distribution Modelling, Ecology, R software, EcoCommons ugrad mbr phd
WORKSHOP: Single cell RNAseq analysis in R

This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022.

Event description

Analysis and interpretation of single cell RNAseq (scRNAseq) data...

Keywords: Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq

WORKSHOP: Single cell RNAseq analysis in R https://dresa.org.au/materials/workshop-single-cell-rnaseq-analysis-in-r-4f60b82d-2f1e-4021-9569-6955878dd945 This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022. Event description Analysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data.  We will discuss the ‘why’ behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data. This workshop is presented by the Australian BioCommons and Queensland Cyber Infrastructure Foundation (QCIF) with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.   Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. scRNAseq_Slides (PDF): Slides used to introduce topics scRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshop scRNAseq_Resources (PDF): A list of resources recommended by trainers and participants scRNAseq_QandA(PDF): Archive of questions and their answers from the workshop Slack Channel.   Materials shared elsewhere: This workshop follows the tutorial ‘scRNAseq Analysis in R with Seurat’ https://swbioinf.github.io/scRNAseqInR_Doco/index.html This material is based on the introductory Guided Clustering Tutorial tutorial from Seurat. It is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here. Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq
WORKSHOP: R: fundamental skills for biologists

This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022.

 

Event description

Biologists need data analysis skills to be able to...

Keywords: Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation

WORKSHOP: R: fundamental skills for biologists https://dresa.org.au/materials/workshop-r-fundamental-skills-for-biologists-81aa00db-63ad-4962-a7ac-b885bf9f676b This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022.   Event description Biologists need data analysis skills to be able to interpret, visualise and communicate their research results. While Excel can cover some data analysis needs, there is a better choice, particularly for large and complex datasets.  R is a free, open-source software and programming language that enables data exploration, statistical analysis, visualisation and more. The large variety of R packages available for analysing biological data make it a robust and flexible option for data of all shapes and sizes.  Getting started can be a little daunting for those without a background in statistics and programming. In this workshop we will equip you with the foundations for getting the most out of R and RStudio, an interactive way of structuring and keeping track of your work in R. Using biological data from a model of influenza infection, you will learn how to efficiently and reproducibly organise, read, wrangle, analyse, visualise and generate reports from your data in R. Topics covered in this workshop include: Spreadsheets, organising data and first steps with R Manipulating and analysing data with dplyr Data visualisation Summarized experiments and getting started with Bioconductor   This workshop is presented by the Australian BioCommons and Saskia Freytag from WEHI  with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Schedule (PDF): A breakdown of the topics and timings for the workshop Recommended resources (PDF): A list of resources recommended by trainers and participants Q_and_A(PDF): Archive of questions and their answers from the workshop Slack Channel. Materials shared elsewhere:   This workshop follows the tutorial ‘Introduction to data analysis with R and Bioconductor’ which is publicly available. https://saskiafreytag.github.io/biocommons-r-intro/ This is derived from material produced as part of The Carpentries Incubator project https://carpentries-incubator.github.io/bioc-intro/ Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation
WORKSHOP: Working with genomics sequences and features in R with Bioconductor

This record includes training materials associated with the Australian BioCommons workshop ‘Working with genomics sequences and features in R with Bioconductor’. This workshop took place on 23 September 2021.

Workshop description

Explore the many useful functions that the Bioconductor...

Keywords: R software, Bioconductor, Bioinformatics, Analysis, Genomics, Sequence analysis

WORKSHOP: Working with genomics sequences and features in R with Bioconductor https://dresa.org.au/materials/workshop-working-with-genomics-sequences-and-features-in-r-with-bioconductor-8399bf0d-1e9e-48f3-a840-3f70f23254bb This record includes training materials associated with the Australian BioCommons workshop ‘Working with genomics sequences and features in R with Bioconductor’. This workshop took place on 23 September 2021. Workshop description Explore the many useful functions that the Bioconductor environment offers for working with genomic data and other biological sequences.  DNA and proteins are often represented as files containing strings of nucleic acids or amino acids. They are associated with text files that provide additional contextual information such as genome annotations. This workshop provides hands-on experience with tools, software and packages available in R via Bioconductor for manipulating, exploring and extracting information from biological sequences and annotation files. We will look at tools for working with some commonly used file formats including FASTA, GFF3, GTF, methods for identifying regions of interest, and easy methods for obtaining data packages such as genome assemblies.  This workshop is presented by the Australian BioCommons and Monash Bioinformatics Platform with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Schedule (PDF): schedule for the workshop providing a breakdown of topics and timings   Materials shared elsewhere: This workshop follows the tutorial ‘Working with DNA sequences and features in R with Bioconductor - version 2’ developed for Monash Bioinformatics Platform and Monash Data Fluency by Paul Harrison. https://monashdatafluency.github.io/r-bioc-2/ Melissa Burke (melissa@biocommons.org.au) R software, Bioconductor, Bioinformatics, Analysis, Genomics, Sequence analysis
VOSON Lab Code Blog

The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages.

Keywords: visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics

Resource type: tutorial, other

VOSON Lab Code Blog https://dresa.org.au/materials/voson-lab-code-blog The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages. robert.ackland@anu.edu.au visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics researcher support phd masters
Programming and tidy data analysis in R

A workshop to expand the skill-set of someone who has basic familiarity with R. Covers programming constructs such as functions and for-loops, and working with data frames using the dplyr and tidyr packages. Explains the importance of a "tidy" data representation, and goes through common steps...

Keywords: R, Tidyverse, Programming

Resource type: tutorial

Programming and tidy data analysis in R https://dresa.org.au/materials/programming-and-tidy-data-analysis-in-r A workshop to expand the skill-set of someone who has basic familiarity with R. Covers programming constructs such as functions and for-loops, and working with data frames using the dplyr and tidyr packages. Explains the importance of a "tidy" data representation, and goes through common steps needed to load data and convert it into a tidy form. To be taught as a hands on workshop, typically as two half-days. Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given. Paul Harrison paul.harrison@monash.edu R, Tidyverse, Programming phd ecr researcher
Introduction to R

An introduction to R, for people with zero coding experience.

To be taught as a hands on workshop, typically as two half-days.

Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and...

Keywords: R

Resource type: tutorial

Introduction to R https://dresa.org.au/materials/introduction-to-r An introduction to R, for people with zero coding experience. To be taught as a hands on workshop, typically as two half-days. Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given. Paul Harrison paul.harrison@monash.edu R phd ecr researcher
Introduction to Jupyter Notebooks

This workshop will introduce you to Jupyter Notebooks, a digital tool that has exploded in popularity in recent years for those working with data.

You will learn what they are, what they do and why you might like to use them. It is an introductory set of lessons for those who are brand new,...

Keywords: jupyter, Introductory, training material, CloudStor, markdown, Python, R

Resource type: tutorial

Introduction to Jupyter Notebooks https://dresa.org.au/materials/introduction-to-jupyter-notebooks This workshop will introduce you to Jupyter Notebooks, a digital tool that has exploded in popularity in recent years for those working with data. You will learn what they are, what they do and why you might like to use them. It is an introductory set of lessons for those who are brand new, have little or no knowledge of coding and computational methods in research. This workshop is targeted at those who are absolute beginners or ‘tech-curious’. It includes a hands-on component, using basic programming commands, but requires no previous knowledge of programming. sara.king@aarnet.edu.au Mason, Ingrid jupyter, Introductory, training material, CloudStor, markdown, Python, R
Introduction to Machine Learning using R: SVM & Unsupervised Learning

Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...

Keywords: R

Introduction to Machine Learning using R: SVM & Unsupervised Learning https://dresa.org.au/materials/introduction-to-machine-learning-using-r-svm-unsupervised-learning Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. Know the differences between various core Machine Learning models. Understand the Machine Learning modelling workflows. Use R and its relevant packages to process real datasets, train and apply Machine Learning models. \\Either \Learn to Program: R\ and \Data Manipulation in R\ or \Learn to Program: R\ and \Data Manipulation and Visualisation in R\needed to attend this course. If you already have experience with programming, please check the topics covered in the courses above and \Introduction to ML using R: Introduction & Linear Regression\ to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training.\\Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.\\ training@intersect.org.au R
Introduction to Machine Learning using R: Introduction & Linear Regression

Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...

Keywords: R

Introduction to Machine Learning using R: Introduction & Linear Regression https://dresa.org.au/materials/introduction-to-machine-learning-using-r-introduction-linear-regression Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. Understand the difference between supervised and unsupervised Machine Learning. Understand the fundamentals of Machine Learning. Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. Understand the Machine Learning modelling workflows. Use R and and its relevant packages to process real datasets, train and apply Machine Learning models \\Either \Learn to Program: R\ and \Data Manipulation in R\ or \Learn to Program: R\ and \Data Manipulation and Visualisation in R\needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts and familiarity with dplyr, tidyr and ggplot2 packages.\\Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.\\ training@intersect.org.au R
Exploring ANOVAs in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.This half-day course covers one and two-way Analyses of Variance (ANOVA) and their...

Keywords: R

Exploring ANOVAs in R https://dresa.org.au/materials/exploring-anovas-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.This half-day course covers one and two-way Analyses of Variance (ANOVA) and their non-parametric counterparts in R. ANOVA (Analysis of Variance) is a statistical method used to determine whether there are significant differences between the means of three or more groups. It helps analyse the effect of independent variables on a dependent variable by comparing the variance within groups to the variance between groups. ANOVA tests assume normality, homogeneity of variances, and independence of observations, and can be used to explore relationships in datasets, such as how factors like study time or parental education affect student performance. - Basic statistical theory behind ANOVAs - How to check that the data meets the assumptions - One-way ANOVA in R and post-hoc analysis - Two-way ANOVA plus interaction effects and post-hoc analysis - Non-parametric alternatives to one and two-way ANOVA This course assumes an intermediate level of programming proficiency, plus familiarity with the syntax and functions of the dplyr and ggplot2 packages. Experience navigating the RStudio integrated development environment (IDE) is also required. If you’re new to programming in R, we strongly recommend you register for the \Learn to Program: R\, \Data Manipulation and Visualisation in R\ workshops first.  training@intersect.org.au R
Data Manipulation and Visualisation in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

In this workshop, you will learn how to manipulate, explore and get insights...

Keywords: R

Data Manipulation and Visualisation in R https://dresa.org.au/materials/data-manipulation-and-visualisation-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). You will also explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. DataFrame Manipulation using the dplyr package DataFrame Transformation using the tidyr package Using the Grammar of Graphics to convert data into figures using the ggplot2 package Configuring plot elements within ggplot2 Exploring different types of plots using ggplot2 Either \Learn to Program: R\ or \Learn to Program: R\ and \R for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: R\ and \R for Research\ courses to ensure that you are familiar with the knowledge needed for this course. training@intersect.org.au R
Traversing t tests in R

R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

The primary goal of this workshop is to familiarise you with basic statistical concepts in R...

Keywords: R

Traversing t tests in R https://dresa.org.au/materials/traversing-t-tests-in-r R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. Read in and manipulate data Check assumptions of t tests Perform one-sample t tests Perform two-sample t tests (Independent-samples, Paired-samples) Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect’s following courses to get up to speed: \Learn to Program: R\, \Data Manipulation and Visualisation in R\ training@intersect.org.au R
Introduction to Machine Learning using R: Classification

Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...

Keywords: R

Introduction to Machine Learning using R: Classification https://dresa.org.au/materials/introduction-to-machine-learning-using-r-classification Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. Know the differences between various core Machine Learning models. Understand the Machine Learning modelling workflows. Use R and its relevant packages to process real datasets, train and apply Machine Learning models. \\Either \Learn to Program: R\ and \Data Manipulation in R\ or \Learn to Program: R\ and \Data Manipulation and Visualisation in R\needed to attend this course. If you already have experience with programming, please check the topics covered in courses above and \Introduction to ML using R: Introduction & Linear Regression\ to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training.\\Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.\\ training@intersect.org.au R
Data Visualisation in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

In this workshop, you will explore different types of graphs and learn how to...

Keywords: R

Data Visualisation in R https://dresa.org.au/materials/data-visualisation-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. Using the Grammar of Graphics to convert data into figures using the ggplot2 package Configuring plot elements within ggplot2 Exploring different types of plots using ggplot2 Either \Learn to Program: R\ or \Learn to Program: R\ and \R for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: R\ and \R for Research\ courses to ensure that you are familiar with the knowledge needed for this course. We also strongly recommend attending the \Data Manipulation in R\ course. training@intersect.org.au R
Exploring Chi-square and correlation in R

This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures...

Keywords: R

Exploring Chi-square and correlation in R https://dresa.org.au/materials/exploring-chi-square-and-correlation-in-r This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures for computing reliability and correlation (Pearson’s r, Spearman’s Rho and Kendall’s tau) in real world datasets. Obtain inferential statistics and assess data normality Manipulate data and create graphs Perform Chi-Square tests (Goodness of Fit test and Test of Independence) Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau) This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts, as well as familiarity with data manipulation (dplyr) and visualisation (ggplot2 package).  Please consider attending Intersect’s following courses to get up to speed: \Learn to Program: R\, \Data Manipulation and Visualisation in R\ training@intersect.org.au R
Data Manipulation in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

In this workshop, you will learn how to manipulate, explore and get insights...

Keywords: R

Data Manipulation in R https://dresa.org.au/materials/data-manipulation-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. DataFrame Manipulation using the dplyr package DataFrame Transformation using the tidyr package Either \Learn to Program: R\ or \Learn to Program: R\ and \R for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: R\ and \R for Research\ courses to ensure that you are familiar with the knowledge needed for this course. training@intersect.org.au R
Learn to Program: R

R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.

But getting started with R can be...

Keywords: R

Learn to Program: R https://dresa.org.au/materials/learn-to-program-r R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. Introduction to the RStudio interface for programming Basic syntax and data types in R How to load external data into R Creating functions (FUNCTIONS) Repeating actions and analysing multiple data sets (LOOPS) Making choices (IF STATEMENTS – CONDITIONALS) Ways to visualise data in R No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found \here\. training@intersect.org.au R
Start Coding without Hesitation: Programming Languages Showdown

Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are...

Keywords: Python, R, Matlab, Julia

Start Coding without Hesitation: Programming Languages Showdown https://dresa.org.au/materials/start-coding-without-hesitation-programming-languages-showdown Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are exploring four of the most popular programming languages that are widely used in academia, namely Python, R, MATLAB, and Julia. Why use Programming An overview of Python, R, MATLAB, and Julia Code comparison of the four programming languages Popularity and job opportunities Intersect’s comparison General guidelines on how to choose the best programming language for your research The webinar has no prerequisites. training@intersect.org.au Python, R, Matlab, Julia
R for Social Scientists

R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.

But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this...

Keywords: R

R for Social Scientists https://dresa.org.au/materials/r-for-social-scientists R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment. But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Data Carpentry. Basic syntax and data types in R RStudio interface How to import CSV files into R The structure of data frames A brief introduction to data wrangling and data transformation How to calculate summary statistics A brief introduction to visualise data No prior experience with programming needed to attend this course. training@intersect.org.au R
R for Research

R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.

This workshop is an introduction to data...

Keywords: R

R for Research https://dresa.org.au/materials/r-for-research R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. This workshop is an introduction to data structures (DataFrames) and visualisation (using the ggplot2 package) in R. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. Project Management with RStudio Introduction to Data Structures in R Introduction to DataFrames in R Selecting values in DataFrames Quick introduction to Plotting using the ggplot2 package \Learn to Program: R\ or any of the \Learn to Program: Python\, \Learn to Program: MATLAB\, \Learn to Program: Julia\, needed to attend this course. If you already have some experience with programming, please check the topics covered in the \Learn to Program: R\ course to ensure that you are familiar with the knowledge needed for this course. training@intersect.org.au R
A showcase of Data Analysis in Python and R: A case study using COVID-19 data

In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and...

Keywords: Python, R

A showcase of Data Analysis in Python and R: A case study using COVID-19 data https://dresa.org.au/materials/a-showcase-of-data-analysis-in-python-and-r-a-case-study-using-covid-19-data In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and Python. Cleaning up a dataset for analysis Using Jupyter lab for interactive analysis Making the most of the tidyverse (R) and pandas (python) Simple data visualisation using ggplot (R) and seaborn (python) Best practices for readable code The webinar has no prerequisites. training@intersect.org.au Python, R