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Introduction to Coding with R

Duration: 1 Days (Classroom, Business Site or Remote)


£ 500 per person

*Discounts may apply, contact us.


R is a programming language and open-source software environment specifically designed for statistical analysis, data visualization, and data manipulation. It was created in the early 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and has since become one of the most widely used languages in the field of data science and statistics.

Key features and characteristics of R programming include:

1. **Statistical Analysis:** R provides a rich set of functions and libraries for performing various statistical analyses, from basic descriptive statistics to complex predictive modeling and hypothesis testing.

2. **Data Visualization:** R offers powerful tools for creating data visualizations, including scatter plots, bar charts, line graphs, heatmaps, and more. The most popular data visualization package in R is ggplot2.

3. **Data Manipulation:** R excels at data manipulation and transformation. Libraries like dplyr and tidyr allow users to easily filter, summarize, reshape, and clean data.

4. **Open Source:** R is an open-source language, meaning it is freely available and has a strong community of users and contributors. This has led to the creation of numerous packages and extensions that enhance its functionality.

5. **Reproducibility:** R makes it easy to document and reproduce analyses, which is essential in research and data-driven decision-making.

6. **Integration:** R can be integrated with other programming languages, databases, and data sources, making it versatile in working with diverse data formats and systems.

7. **Machine Learning:** R has a growing ecosystem of machine learning libraries, such as caret and randomForest, making it a popular choice for predictive modeling and machine learning tasks.

8. **Interactivity:** R provides interactive development environments like RStudio, which simplifies code development, debugging, and data visualization.

9. **Community and Support:** R has a vibrant and active community of users, which means there are plenty of resources, forums, and tutorials available for learning and problem-solving.

R is particularly favored by statisticians, data analysts, and researchers for its strong analytical capabilities. It is widely used in various fields, including academia, industry, and government, for tasks like data exploration, statistical analysis, data visualization, and building predictive models.

Prerequisites: None


Learn to code in a day using the R programming language to understand the fundamentals of statistical computing, develop logic and data structures, and build a primitive data visualization.  Throughout this introductory course, you will explore R language features, data types and recursive functions, as well as how to utilize packages to enhance the modelling within your data science notebooks.