You might be wondering why we are using R for data visualisation. The R Bootcamp pages also provide useful links to resources to help with learning R. If you need to brush up on the basics, I encourage you to work through the R Bootcamp modules from Introduction to Statistics before continuing. As such, this course assumes you have a good understanding of using R and the RStudio integrated development environment. This course will make use of the very powerful ggplot2 package implemented in R. Critically appraise the advantages and disadvantages of common univariate and bivariate visualisations and exercise judicious choice when selecting an appropriate display of the data.enhance univariate and bivariate data visualisations by adding markers, annotations, statistical summaries and error bars to represent statistical uncertainty.manipulate and summarise data into a structured data frame needed prior to visualisation.one qualitative and one quantitative variable.Identify and apply common methods used to visualise univariate and bivariate data using ggplot2 including the following combinations of variables:.Assign colour scales appropriately to the colour and fill aesthetics in ggplot2 including ColourBrewer palettes.Assign colours in R using R’s inbuilt list of 657 colour names and colour hex codes.Use the ggplot() function of ggplot2 to create layered data visualisations.Use the qplot() function of ggplot2 to create quick data visualisations.Define and differentiate between terms and components related to the Layered Grammar of Graphics used by ggplot2 including the following:.The learning objectives for this chapter are as follows: 7.5.3 Case Study - The City of Melbourne’s Urban Forest.6.3 Multivariate Data Visualisation Strategies.5.18.5 One Quantitative and One Qualitative Variable.5.14.5 Coxcomb Diagram (Polar Area Diagram).5.14 Qualitative Univariate Visualisations.3.13.11 Try to avoid colour scales that use red and green.3.13.10 Non-data elements should not compete with the data.3.13.9 Use colour scales to encode important information.3.13.8 Saturated colours can be used for small data points. 3.13.7 Reserve bright colours to highlight important information.3.13.5 Define objects with equiluminous colour using thin borders.3.13.2 Use colour to differentiate important features.3.4.3 Change and Inattentional Blindness.3.3 Our Visual Information Processing System.2.5 Well Known Data Visualisation Storytelling Sites.2.3 Storytelling and Data Visualisation.1.9.4 Privacy and Sensitive Information.1.7 Publication Ready Data Visualisations.1.5.3 Focusing, justifying and choosing methods.1.5.2 Identifying a targeted audience and a data visualisation design objective.
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