R Programming - Advanced Course - Jakarta

Understand how work more efficiently in R

Build on your base knowledge of R. There are often many ways to achieve a result using R, learn how to run analysis more efficiently:

  • create your own functions
  • control structures
  • loops and loop alternatives
  • tidyverse piping syntax
  • create visualisations with ggplot2

You will work with your experienced trainer as you complete exercises throughout the day to cement your understanding of these concepts. The skills you learn on the day will empower you to build your own solutions in R.

R Programming - Advanced Course - Jakarta

Build on your base knowledge of the R language and learn how to run data analysis more efficiently. Remote Courses in Jakarta.

Rp 7.690K
10 max
1 day


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Frequently Asked Questions

Course Introduction

Our R courses were designed by Tamara Shatar, who holds a PhD in Agricultural Data Science. She focused her extensive experience and skills in modelling using machine learning, simulation and other techniques to create a course with depth and applicability.

The course is consistently well reviewed by students.

"The course was really good, the resources provided to help after the course are excellent. I was a bit overwhelmed at the beginning trying to learn a new language, but I was put at ease and went through the content at a pace that I could understand." - R Beginner Brisbane, Australia.

What is Remote Training?

We offer some of our more specialist courses in a remote format. At Nexacu, this means you will learn the same content as you would in our classrooms and the same trainer will deliver training but all students will log in from their own premises and device.

Why Learn R?

Rather than being a point and click tool, R is a language that is used by writing reusable scripts, enabling automation and repeatable workflows. Because it is a language, it offers a huge amount of flexibility in manipulating data and the ability to write new functions. Even without a background in programming, it is relatively easy to get up and running once you know the basics. 

R Programming Advanced Jakarta Course Details

Rp 7.690K
1 day
9am - 4pm ( approx )
Class Size (max)
Class Size (avg)
Reference Materials
CPD Hours
6 hours
Instructor-led training
Live Online

R Programming Course Outlines

Course Rating
R Programming Training Courses

4,72 5 R Programming Training Courses
4,72 out of 5
71528 Student Reviews

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What do I need to know to attend?

  • You should have completed our R Beginner and R Intermediate course or have basic familiarity with R.
  • You will not be expected to code unassisted but will learn more and participate more easily if you have at least a fundamental understanding of R syntax.
  • Basic understanding of statistics (mean, median, standard deviation, variance).

R Programming Advanced Jakarta Learning Outcomes

You will develop a better understanding of R and be able to:

  • write custom functions
  • write more concise code

  • use functionals, loops and other control structures
  • create graphics with ggplot2.

R Programming Advanced Jakarta Course Content

  • Working more efficiently in R
  • How to work more efficiently in R
  • Vectorization
  • Using better functions
  • Concise code
  • Reusable scripts
  • Custom functions
  • Loops and other control structures
  • Loop alternatives
  • Functions to reduce typing
  • Printing objects after creating
  • with and within
  • Inserting multiple quotation marks
  • Create your own functions
  • Why write your own functions?
  • Basics of functions
  • What is a function?
  • Creating your own functions
  • Syntax for writing your own function
  • More complex functions
  • Ellipses and further arguments
  • Scope
  • Loading your functions
  • Loops and control structures in R
  • If and if else
  • Loops for loops
  • Saving results from a loop
  • Improving your code
  • While loops
  • Repeat loops
  • Loop alternatives
  • Functionals
  • apply functions
  • split
  • map functions
  • map variants
  • Returning a vector
  • walk
  • Loop, apply or map?
  • Tidyverse piping syntax
  • Purpose
  • Using the pipe
  • Pipe variants
  • The tee operator
  • The exposition operator
  • Plotting with ggplot2
  • The grammar of graphics
  • Required components
  • Using ggplot()
  • Scatterplot
  • Line chart
  • ggplot2 resources

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