Visualization of data allows one to present it in a graphic form. Such a form enables to understand data, find patterns and dependencies which are important in making decisions. Anyone, who tried to convince the investor or help the manager in making a decision by showing a huge data sheet or a complicated report knows that it only leads to frustration and misunderstanding. Presenting data in graphic form makes understanding of complicated data and conclusions easier.

Currently, R is one of the most popular tools used in data analysis. Due to the number of useful functions and packages as well as its rapid development people became interested in using R. What makes it even more attracting is the fact that you can use it for free. It is a perfect, easy to use tool for data visualization.

The training is aimed at people who are willing to learn creating readable graphs in R. We always try to match the level of our training with the level of students knowledge but to make it even more efficient participants should know the basics of using R.

We recommend the training to:

Analysts, consultants, managers and statisticians.

The participants of our training work with R in a number of different branches(finance, production, medicine, etc.). We always try to put great emphasis on such elements of R, which are the most needed in everyday work.

1. Introduction to data visualization in R
   • Plot types
   • Data preparation, filtering, recoding, etc.
   • Choosing the right data to plot
   • Choosing the right plot to data
   • Overview of R visualisation packages– plot, ggplot2, lattice, leaflet, plotly)
2. Basic plots
   • Bar plot
   • Histogram
   • Pie chart
   • Linear plot
   • Scatter plot
   • Box plot
   • Exercises
3. Formatting
   • Color palletes
   • Symbols and Shapes
   • Legend, axesOpisy osi i legenda
   • Scale
   • Trend lines
   • Exercises
4. Advanced plots
   • Heatmap
   • Surface plot
   • Tree map
   • Bubble chart
   • Mosaic plot
   • Correlogram
   • Exercises
5. Mapping data
   • Maps available in R
   • leaflet package
   • Mapping data
   • Interactive maps
   • Exercises
6. 3D plots
7. Plotly – interactive plots
8. Common mistakes and how to avoid them