R: Programming & Troubleshooting: 3-in-1

Course details

R is a high-level statistical language and is widely used among developers, statisticians, and data miners to develop statistical applications. While powerful and expressive, R is not without its pitfalls and novice developers may find themselves spending much of their time troubleshooting bugs and edge cases rather than getting on with the task at hand. This solution-based course will be your guide taking you through different programming aspects with R and solving common obstacles one may encounter while programming with R.

In this comprehensive 3-in-1 course, you will explore R programming language incisively. You will learn about the various programming concepts in R for developing applications and the difficulties faced while developing them. You will also know how to manipulate data frames and learn about troubleshooting problems and practical techniques for importing and exporting data. Taking a problem-solution approach, this learning path will upgrade your R skills and save time and effort when writing R.

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Learn R programming, starts off with explaining how to install R on your systems. You will then learn to work with powerful R tools and techniques. You will also learn some of the most powerful packages in R and data structures such as matrices, lists, and factors. Next, you will learn how to create vectors, handle variables, and perform other core functions. You will be able to tackle issues with data input/output and will learn to work with strings and dates. Moving ahead, you will learn more advanced concepts in R such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data manipulation.

The second course, Advanced Tools and Techniques Beyond Base R, introduces a number of recently developed R packages and paradigms, in particular the concept of tidy data and the Tidyverse collection of packages, which are rapidly becoming indispensable to R data analysts. You will learn how to efficiently process and analyze data in ways not possible with base R and produce high-quality statistical graphics. This course will finish with a taste of how functional programming and metaprogramming with R can simplify and speed up your data analysis code.

The third course, R Troubleshooting Solutions, begins with explaining you the common difficulties in manipulating data frames, followed by troubleshooting problems that arise from some of the more confusing building blocks of the core R language. Next, you will learn practical techniques for importing and exporting data. Finally, you will learn how to resolve common issues when drawing graphics with ggplot2.

By the end of this Learning Path, you will have all the required R skills that will save your time and effort when writing R.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

• Dr. David Wilkins has been writing R for over a decade. He is the author of a number of popular open-source R packages, two previous Packt Publishing courses on the R language, and over a dozen scientific publications involving R analyses. He holds a Bachelor's degree in Science and a PhD in molecular genetics. He has a particular passion for creating beautiful and informative statistical graphics, and enjoys teaching people to use R to find and express insights in their own datasets.