R Studio Australia: Guide to Data Visualisation and Statistical Research

Box plots can help compare distributions and identify potential outliers.

The choice of chart should depend on the type of data and the question being investigated.

Using ggplot2 for Visualisation

ggplot2 is one of the most widely used packages for creating data visualisations in R. It follows a structured approach where data, variables, geometric objects, scales, and other components can be combined to create informative graphics.

For example, a student investigating the relationship between two numerical variables could use a scatter plot. Additional elements such as labels, titles, and trend lines can make the final graphic easier to interpret.

R Studio's documentation also demonstrates data visualisation workflows using ggplot2.

Statistical Analysis With R

After exploring the dataset, researchers need to select statistical techniques that match their research question and data.

Depending on the project, analysis might involve:

• Descriptive statistics
• Correlation analysis
• Regression
• Hypothesis testing
• Analysis of variance
• Classification
• Clustering
• Time-series analysis

The method should never be selected simply because it is available in R. Researchers should consider the research design, variable types, assumptions, sample characteristics, and purpose of the study.

Reproducible Research With R Studio