Data Management with R Language (Introduction to Advanced)
R Language Description
R is a programming language and software environment for statistical analysis, graphics representation, and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac. This programming language was named R, based on the first letter of the first name of the two R authors (Robert Gentleman and Ross Ihaka)
This course serves as a great boot camp starting from introduction to more complex topics. It is also suitable for anybody who uses a spreadsheet to work with data, while no prior programming knowledge is required.
If you already know a little R and want to kick your data wrangling ability into high gear or want to create beautiful and informative visualizations? This course is for you!
The course is aimed at students, academics, and professionals who need to generate data-driven reports or deepen their understanding of the language, and prepare themselves to write code for automation.
Course objectives
- Introduction in R
- Understand Data analyses in R
- Master Plotting with ggplot in R
- R Markdown automated reporting in R
- Functional programming
Duration
The standard duration of this course is 7 weeks, 2 sessions per week, and 3 hours per session.
Introduction in R Session 1:
- Background: What is R?
- Data transformation
- Data Visualization
- Modeling
- Automated reporting
- Interactivity
- Shiny
- Production
- How to learn
Introduction in R Session 2:
- RStudio Tour
- Loading Data
- R packages
- LAB
Understand Data analyses in R Session 1:
- Tidyverse: An Overview
- Exploring dplyr functions
- Working with rows: filter() and arrange()
- LAB
Understand Data analyses in R Session 2:
- Exploring dplyr functions
- Interactive challenge: Implementing dplyr to a new dataset
- LAB
Understand Data analyses in R Session 3:
- Exploring tidyr functions
- LAB
Understand Data analyses in R Session 4:
- string
- forcats
- lubridate
- Iteractive challenge: Implementing stringr, forcats and lubridate functions
- LAB
Master Plotting with ggplot in R Session 1:
- ggplot2: An Overview
- Terminology
- Introduction to aesthetics
- Calls of ggplot2 functions
- Plotting types (geoms)
- LAB
Master Plotting with ggplot in R Session 2:
- ggplot2: Intermediate
- Facetting
- Positions
- Saving plots
- Scales
- LAB
Master Plotting with ggplot in R Session 3:
- ggplot2: Advanced
- Exploring different colour palettes
- Creating basic 2D maps
- Plotting timeseries data
- LAB
R Markdown automated reporting in R Session 1:
- Automated Reporting
- Introduction to Mark(up|down)
- R Markdown
- LAB
R Markdown automated reporting in R Session 2:
- Advanced Reporting
- Extensions to R Markdown
- Automation
- Emailing Reports
- LAB
Functional programming Session 1:
- Programming: An overview
- What is programming?
- Anatomy of a programme
- Functions
- User-defined functions
- Creating a function
- Closures
- Anonymous functions
- LAB
Functional programming Session 2:
- Conditional Expressions
- Writing conditional statements
- Combining conditionals with dplyr functions
- LAB
Functional programming Session 3:
- Base R iteration and Interaction with the purrr package
- Exploring the purrr package
- LAB

Adam Smith Canada
Excellent