Course Overview on Data Visualization with R

To interpret your data and draw insights, you need to be able to visualise it. R is a programming language enjoyed by data scientists for its ability to process and utilise data. With R, you'll be able to take your data and use it to tell insightful stories with compelling visualisations to convince stakeholders and build trust.

What You Will Learn

In this course, you will learn:

Data visualization principles
How to communicate data-driven findings
How to use ggplot2 to create custom plots
The weaknesses of several widely-used plots and why you should avoid them

Delivery Methods

Delivery Method	Duration
Classroom Instructor-led classroom-based training. Classes are scheduled at various conference centres in the Sandton area or your premises. Stationary and printed manuals or online resources are included. Refreshments, including 2 tea breaks and a cooked meal for lunch are provided. Training is between 9 am to 4 pm. Classroom	4 Days	Get a Quote
Live Virtual Training This course is delivered live via Microsoft Teams. You will be able to see and hear the instructor, view their whiteboard, and ask questions or communicate in the chat. Attend from the comfort of your own home or private office. Live Virtual Training	4 Days	Get a Quote

Discounts Available

Brochure:

Information may change without notice.

Audience

Data scientists
Programmers
Business analysts
Engineers
Scientists

Pre-Requisites

Leading Training's R Programming Course or equivalent knowledge

Course Outline / Curriculum

Introduction to data visualization

ggplot2

The components of a graph
ggplot objects
Geometries
Aesthetic mappings
Layers
- Tinkering with arguments
Global versus local aesthetic mappings
Scales
Labels and titles
Categories as colors
Annotation, shapes, and adjustments
Add-on packages
Putting it all together
Quick plots with qplot
Grids of plots
Exercises

Visualizing data distributions

Variable types
Case study: describing student heights
Distribution function
Cumulative distribution functions
Histograms
Smoothed density
- Interpreting the y-axis
- Densities permit stratification
Exercises
The normal distribution
Standard units
Quantile-quantile plots
Percentiles
Boxplots
Stratification
Case study: describing student heights (continued)
Exercises
ggplot2 geometries
- Barplots
- Histograms
- Density plots
- Boxplots
- QQ-plots
- Images
- Quick plots
Exercises

Data visualization in practice

Case study: new insights on poverty
- Hans Rosling’s quiz
Scatterplots
Faceting
- facet_wrap
- Fixed scales for better comparisons
Time series plots
- Labels instead of legends
Data transformations
- Log transformation
- Which base?
- Transform the values or the scale?
Visualizing multimodal distributions
Comparing multiple distributions with boxplots and ridge plots
- Boxplots
- Ridge plots
- Example: 1970 versus 2010 income distributions
- Accessing computed variables
- Weighted densities
The ecological fallacy and importance of showing the data
- Logistic

Data visualization principles

Encoding data using visual cues
Know when to include
Do not distort quantities
Order categories by a meaningful value
Show the data
Ease comparisons
- Use common axes
- Align plots vertically to see horizontal changes and horizontally to see
vertical changes
- Consider transformations
- Visual cues to be compared should be adjacent
- Use color
Think of the color blind
Plots for two variables
- Slope charts
- Bland-Altman plot
Encoding a third variable
Avoid pseudo-three-dimensional plots
Avoid too many significant digits
Know your audience
Exercises
Case study: vaccines and infectious diseases
Exercises

Robust summaries

Outliers
Median
The inter quartile range (IQR)
Tukey’s definition of an outlier
Median absolute deviation
Exercises
Case study: self-reported student heights

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