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

Site map

 

About this site

Getting started with R

  • Installing R and RStudio
  • Using RStudio notebooks
  • Basic project management
  • Data entry
  • Importing data and data cleaning
  • Data types & structure

Data manipulation

  • Reshaping data (wide and long formats)
  • Subsetting data
  • Summarising data
  • Combining data sets
  • Making new variables

Coding skills

  • 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 variable
  • Plotting with ggplot
    • ggplot1 basics
    • ggplot 2 formatting: overall appearance
    • ggplot 2 formatting: titles and axis names
    • ggplot 2 formatting: colours and symbols
    • ggplot: bar plots with error bars
  • Visualising multivariate data sets
    • Multi-dimensional scaling
    • Principal components analysis
    • Classification (cluster diagrams)
    • Heat maps
  • Visualising spatial data
    • Making maps of your study sites
    • Making simple maps

Statistics

  • t tests
    • One sample t test
    • Independent samples t test
    • Paired t test
  • Linear models
    • Linear regression
    • Analysis of variance (one-way)
    • Analysis of variance (factorial)
    • Understanding interactions
    • Interpreting coefficients in linear models
  • Generalised linear models
    • GLM 1: introduction/binomial data
    • GLM 2: count data
    • Interpreting coefficients in GLMs
  • Mixed models
    • Mixed models 1: Linear mixed models with one random effect
    • Mixed models 2: Linear mixed models with several random effects
    • Mixed models 3: Generalised linear mixed models
  • Generalised additive models
  • Categorical data analyses
    • Goodness of fit tests
    • Contingency tables
  • Power analyses
  • Introduction to mvabund
  • Forecasting with time series
  • Meta-analyses
    • Meta-analyses 1: introduction and calculating effect sizes
    • Meta-analyses 2: Fixed effect and random effect models
    • Meta-analyses 3: More complex models

Upcoming Events

There are no upcoming events.

View Calendar
Add
  • Add to Timely Calendar
  • Add to Google
  • Add to Outlook
  • Add to Apple Calendar
  • Add to other calendar
  • Export to XML

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

Other UNSW quantitative resources

  • Eco-stats research blog
  • Stats Central @ UNSW