In this coures, you will :
- R can assist you in finding your way.
- R is a statistical programming language used to analyse and visualise large amounts of data.
- This training series provides an in-depth introduction to R, including detailed instructions for installing and navigating R and RStudio, as well as hands-on examples ranging from exploratory graphics to neural networks.
- demonstrates how to install R and popular R packages and begin importing, cleaning, and converting data for analysis.
- Also demonstrates how to create visualisations such as bar charts, histograms, and scatterplots, as well as transform categorical, qualitative, and outlier data to best meet your research questions and algorithm requirements.
1. The Rules of Effective Data Visualization
- Why visualize data?
- What kind of visualization should you make?
2. Comparing Data Sets
- Visualize comparisons in data
- Bar charts across categories
- Line charts over time
- Sparklines for important events
- Gantt charts and time difference
- Treemaps for long tail data
- Highlight tables and heat maps
- Slope charts for changes between dates
- Optimize dashboard layout with small multiples
3. Data Relationships
- Visualize relationships in data
- Compare multiple variables within scatter plots
4. Distributions with Your Dataset
- Visualize data distributions
- Histograms for a single measure
- Box plots for multiple dimensions
5. Compositions of Data
- Visualize data composition
- Improve the use of pie charts
- Stacked bar charts
- 100% stacked bars
- Stacked area chart
- 100% stacked area chart
6. Geographic Data
- Visualize geographic data
- When to map geographic data
- Compare filled maps and symbol maps
- Alternative techniques with tilemaps