R Programming Basics Course - Bangkok
Understand the basic concepts of R programming for data analysis
R is a programming language specifically developed for the statistical analysis of data and for producing graphical output. The functionality of the base R package is greatly extended by user-contributed packages which contain a range of functions. There is often more than one way to achieve an outcome in R.
This introductory R course, is the first in our series of R courses and provides an overview of the basics and introduces some of the functions and packages that can be used to work with data. The aim of this course is to provide a foundational:
- understanding of R
- R syntax
- data types and objects.
Learn the key concepts required to get you started using R scripts. View the full course outline below. We recommend students build on this on this knowledge with our R Programming Beginner course. The beginner course focuses on learning to apply a variety of techniques, rounding out the students know-how.
R Programming Basics Course - Bangkok
Frequently Asked Questions
Meet your Trainer
Tamara Shatar holds a PhD in Agricultural Data Science. She has extensive experience, including many years working as a research scientist focused on data analysis, modelling using machine learning, simulation and other techniques. While working in both academia and at the Australian CSIRO, part of her role included teaching a variety of data analysis skills. Tamara has designed our first Data Analytics Training Course, Using R, to provide beginners with the fundamental tools necessary to start using R for data analysis. She is consistently well reviewed by her students.
What is R?
R is an open source and free programming language that was developed for statistical analysis and production of high-quality graphics. It has long been popular with statisticians and academics who make up part of the large active user community behind R. This community has contributed over 15,000 packages that extend the base functionality of R, making it easy to implement a vast range of techniques for data manipulation, analysis and visualization.
Why make data-driven decisions?
A range of different industries have adopted R to make sense of their data. From customer segmentation, to demand forecasting, R can be used to improve operations by uncovering patterns within data, using a range of statistical methods, including sophisticated machine-learning techniques.
What is Remote Training?
Remote training at Nexacu, means our team of experienced trainers will deliver your training live online. With remote learning students can access our usual classroom training courses via video conferencing, ask questions, participate in discussion and share their screen with the trainer if they need help at any point. Students have the same level of participation and access to the trainer as they would in classroom training sessions.
R Programming Basics Bangkok Course Details
Data Analytics Course Outlines
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R Basics
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R Beginner
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R Intermediate
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Machine Learning in R
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R Programming Certification
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Machine Learning in R Certification
Skills Test
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What do I need to know to attend?
R Programming Basics Bangkok Learning Outcomes
You will understand and be able to:
- use R data types and objects
- write basic syntax
- create and manipulate objects
- use functions
- create basic data visualisations
R Programming Basics Bangkok Course Content
- Introduction
- Introduction to R
- Base R and contributed packages
- Download and installation of base R
- Installing R packages
- The RStudio IDE
- Download and installation
- Overview of the RStudio environment
- The main panes
- Working directory
- Workspace
- Projects
- Create a project
- Using R as a Calculator
- Executing commands from the command line and the source pane
- Arithmetic operators
- Relational operators
- Logical operators
- Creating Objects
- Objects in R
- Assignment operators
- Naming rules
- Basics of R Syntax
- Creating objects
- Viewing objects in RStudio
- Viewing objects in the console
- Data Types and Classes
- Basic data types
- Data structures in R
- Vectors
- Factors
- Matrices
- Arrays
- Lists
- Data frames
- Tibbles
- Which data structure should I use?
- Changing data types
- Implicit coercion
- Explicit coercion
- Naming parts of data objects
- Column names
- Row names
- Dimension names
- Names
- Accessing Data within Data Structures
- Referring to data by position
- Referring to data by name
- Replace parts of an object
- Replace names
- Replace values
- Add to a data object
- Add elements to vectors
- Add rows or columns
- Add by position
- Add by name
- Removing data from a data object
- Remove elements from vectors
- Remove rows or columns from matrices
- Remove rows or columns from data frames and lists
- Evaluation in R
- Vector arithmetic
- Order of operations
- Vector recycling
- Vectorised operations
- Applying functions to elements of data structures
- Using Functions
- What is a function?
- Syntax for using functions in R
- Arguments
- Getting help with a function
- Overview of help documentation in R
- Basic statistical summary functions
- Masking of functions
- Explicitly specifying the package name when calling a function
- Package: conflicted
- Importing Data
- Importing data in RStudio
- Importing data from text files (csv)
- Exporting Data
- Export data to text file
- Basic Data Visualisation
- The plot function
- Add reference lines
- Add text
- Add a legend
- Exporting plots
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