Environmental Computing
About this site
Getting started with R
Installing R and R Studio
Using RStudio notebooks
Basic project management
Data entry
Importing data
Data types and structure
Data manipulation
Making new variables
Subsetting data
Summarising data
Combining data sets
Reshaping data
Coding skills
Good practice for writing scripts
Writing simple functions
Using loops
Version control
Asking code questions
Graphics
Basic plotting in R
Single continuous variable
Two continuous variables
Single continuous vs categorical variables
Plotting with ggplot
Plotting with ggplot: the basics
Plotting with ggplot: altering the overall appearance
Plotting with ggplot: : adding titles and axis names
Plotting with ggplot: colours and symbols
Plotting with ggplot: bar plots with error bars
Visualising multivariate data
Multidimensional scaling
Principal components analysis
Cluster analysis
Heat maps
Visualising spatial data
Making maps of your study sites
Making simple maps
Making interactive maps in R with leaflet
Statistics
t-tests
One sample t-tests
Independent samples t-tests
Paired t-tests
Linear models
Linear regression
Analysis of variance: single factor
Analysis of variance: factorial
Understanding interactions
Interpreting coefficients in linear models
Generalised linear models
Generalised linear models 1
Generalised linear models 2
Interpreting coefficients in glms
Mixed models
Mixed models 1
Mixed models 2
Mixed models 3
Generalised additive models (GAMs): an introduction
Categorical data analyses
Goodness of fit tests
Contingency tables
Power analysis
Introduction to mvabund
Forecasting with time series
Meta-analysis
Meta-analysis 1
Meta-analysis 2
Meta-analysis 3
Menu
About this site
Getting started with R
Installing R and R Studio
Using RStudio notebooks
Basic project management
Data entry
Importing data
Data types and structure
Data manipulation
Making new variables
Subsetting data
Summarising data
Combining data sets
Reshaping data
Coding skills
Good practice for writing scripts
Writing simple functions
Using loops
Version control
Asking code questions
Graphics
Basic plotting in R
Single continuous variable
Two continuous variables
Single continuous vs categorical variables
Plotting with ggplot
Plotting with ggplot: the basics
Plotting with ggplot: altering the overall appearance
Plotting with ggplot: : adding titles and axis names
Plotting with ggplot: colours and symbols
Plotting with ggplot: bar plots with error bars
Visualising multivariate data
Multidimensional scaling
Principal components analysis
Cluster analysis
Heat maps
Visualising spatial data
Making maps of your study sites
Making simple maps
Making interactive maps in R with leaflet
Statistics
t-tests
One sample t-tests
Independent samples t-tests
Paired t-tests
Linear models
Linear regression
Analysis of variance: single factor
Analysis of variance: factorial
Understanding interactions
Interpreting coefficients in linear models
Generalised linear models
Generalised linear models 1
Generalised linear models 2
Interpreting coefficients in glms
Mixed models
Mixed models 1
Mixed models 2
Mixed models 3
Generalised additive models (GAMs): an introduction
Categorical data analyses
Goodness of fit tests
Contingency tables
Power analysis
Introduction to mvabund
Forecasting with time series
Meta-analysis
Meta-analysis 1
Meta-analysis 2
Meta-analysis 3
Quantitative skills challenge I – Daniel Falster
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When:
October 19, 2018 @ 2:00 pm – 3:00 pm
2018-10-19T14:00:00+11:00
2018-10-19T15:00:00+11:00
Where:
K-E26-G006 - TchLab 5 Biosciences South Teaching Lab 5
Kensington NSW 2052
Australia
General linear mixed effect models, important things to know and assumptions to check — Eve Slavich
2019 BEES Course – Introduction to data analysis and linear regression in R
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Recently added tutorials
Making maps of your study sites
Generalised additive models (GAMs): an introduction
Meta-analysis
Asking code questions
Using RStudio notebooks
Introduction to mvabund
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Eco-stats research blog
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