Programme Overview
This programme is to introduce participants to the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R, and the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.
Participants who complete this program will be equipped to apply these skills in analysing data or becoming data analysts.
You will become familiar with the following:
- Describe the R programming language and its programming environment.
- Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors.
- Describe the options for generating visualizations in R.
- Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content
Programme Objective
The Programme will provide participants with the ability to:
- Examine the benefits of using the R programming language.
- Install and run the R software
- Install and use RStudio software to run R programs
- Describe the basic building blocks of data structures in R
- Describe what it means to say data is tidy
- tidy up messy data using R
- Learn about R Markdown for documenting R programming.
Programme Content
Participants in the programme will develop the following key competencies:
Module 1: Overview of RProgramming and data analytics
- R is a programming language that can help you in your data analysis process. In this part of the Programme, you’ll learn about R and RStudio, and the environment you’ll use to work in R. You’ll explore the benefits of using R and RStudio as well as the components of RStudio that will help you get started.
Module 2: Programming using Rstudio
- Using R can help you complete your analysis efficiently and effectively. In this part of the Programme, you’ll explore the fundamental concepts associated with R. You’ll learn about functions and variables for calculations and other programming. In addition, you’ll discover R packages, which are collections of R functions, code, and sample data that you’ll use in RStudio.
Module 3: Working with data in R (Cleaning and tidying data)
- The R programming language was designed to work with data at all stages of the data analysis process. In this part of the Programme, you’ll examine how R can help you structure, organize, and clean your data using functions and other processes. You’ll learn about data frames and how to work with them in R. You’ll also revisit the issue of data bias and how R can help.
Module 4: More about visualization
- R is a tool well-suited for creating detailed visualizations. In this part of the Programme, you’ll learn how to use R to generate and troubleshoot visualizations. You’ll also explore the features of R and RStudio that will help you with the aesthetics of your visualizations and annotating and saving them.
Module 5: Documentation and report
- When you’re ready to save and present your analysis, R has different options to consider. In this part of the Programme, you’ll explore R Markdown, a file format for making dynamic documents with R. You’ll find out how to format and export R Markdown, including how to incorporate R code chunks in your documents.
Who Should Attend?
The programme is suitable a specifically beneficial for:
- All participants who want to know more about Data Analysis using R
Registration
Click HERE to register
Facilitator
Dr Nana Baah Gyan
Dr. Nana Baah Gyan is a Senior Lecturer and the current Head of Department of
Computer Science in Central University. He holds a Ph.D. in Computer Science from
Vrije Universiteit in Amsterdam. His research area primarily focuses on the use Web and
Media and other tools of Information Communication Technology for development in
resource-challenged communities. His other areas of research include application of ICT
tools in Health research, Data Analytics and Computing Statistics, machine learning and
artificial intelligence. He is an ardent advocate for open-source tools and an avid user as
well in all his research, teaching and learning. He is well-published and is on panels for
reviews of international journals such as EJISDC.