Introduction

In this presentation I am going to cover a few tips and tricks that you can use in order to be an effective R programmer.

Outline

• Brief introduction of my learning journey in R.

• Tools and tricks for effective learning of R.

• What to expect in the near future.

• Further Learning resources.

My learning journey

I was first introduced to R while undertaking my undergraduate studies about 13 years ago. At that time I was still new to the world of computer programming, I only knew how to play around with Microsoft Office tools like Excel. R appeared to be difficult because of the syntax and lack of a consistent coding style.

Later on after graduation, I continued to explore R on my own, that is when I came across books written by Hadley Wickham (Chief Scientist at Posit). His books especially R for data science completely changed my programming style. For the first time I began seeing how R code can be made with style and how the tidyverse way of programming really simplifies many data manipulation tasks.

Tools and tricks for effective learning of R

• Practice Practice Practice.

• Attend online R programming forums. Follow R in pharma on Youtube (https://www.youtube.com/@RinPharma).

• Make good use of an IDE (eg Rstudio, VS code, Positron).

• Learn shortcuts of typing common commands in your IDE. eg know the shortcut for typing the pipe operator.(Cntrl + Shift + M)

• Learn the tidyverse - this will really simplify your data manipulation tasks.

• Learn how to get help whenever stuck. (how to access help pages, how to access R documentation etc).

• Learn about version control using git and github - a very common workflow with data scence and data engineering teams.

• Learn about reproducibility - you start by exploring the renv package.

What to expect in the near future

• Handling of large data sets with the help of data.table, arrow among other R packages that can be used within R without requiring database connections.

• Python might also come to the table, as its equally a scripting language, so take some time to learn it as well. (Its heavily used in data science and data engineering domains).

• Development of dynamic TLFs with the use of Rshiny. Invest some time to learn about Rshiny app development (a good book to get you started is: https://mastering-shiny.org/)

Further Learning resources

• Hands Programming with R (https://rstudio-education.github.io/hopr/)

• R For Data Science (https://r4ds.hadley.nz/)

• R Packages(https://r-pkgs.org/)

• Advanced R (https://adv-r.hadley.nz/)

• Building Reproducible analytical pipelines with R (https://raps-with-r.dev/)