Efficient R Programming for Everyday Data Science
What you'll learn
- Learn how to improve your R programming skills and code efficiently
- Learn how to use powerful tidyverse tools to tackle everyday data science problems
- Learn how to manipulate data with dplyr
- Learn how to re-shape and re-organise data with tidyr
- Learn how to create advanced graphics using ggplot2
- Learn how to link code efficiently using the magrittr forward pipe (%>%)
- Realistic worked examples to illustrate the tools of the tidyverse
- Mini quizzes to test your knowledge of the tidyverse functions
- Familiarity with the RStudio interface
- Be able to install and load packages
- Basic knowledge of data structures in R (vectors, matrices, dataframes, numbers, text characters)
- Basic knowledge of common R operators (assignment, addition, subtraction, and, or, equals, not equals)
- Very basic awareness of a function call in R and the concept of arguments of a function
Take your R programming skills to the next level with this short course in data science using R's tidyverse packages! Learn to code efficiently and elegantly to tackle everyday data science challenges in business, finance, scientific research, engineering and more!
Do you feel you have a basic knowledge of R but don't yet have the tools or confidence to tackle everyday data science problems like plotting, summarising, sub-setting and merging data? Still turning to MS Excel to manipulate, format, and visualize data? Then look no further.
Aimed at beginners and intermediates who have a basic understanding of R, this course introduces some of the core tools of the tidyverse. It covers a step-by-step guide to the most important functions offered by some tidyverse packages, providing students with a comprehensive toolkit to address everyday data science tasks.
The course covers the following areas:
1) Data manipulation with dplyr (filtering, sorting, creating new variables, summarising data, joining data sets, selecting columns/rows)
2) Data reformatting with tidyr (gathering variables, spreading out variables, separating data in cells)
3) Data visualization with ggplot2 (scatterplots, boxplots, bar charts, line charts, panels)
4) Linking code efficiently using the magrittr forward pipe operator
After completing the course, you will be confident to use R for your everyday data science tasks!
Who this course is for:
- Beginners in R looking to explore the tools of the tidyverse
- Intermediate users of R looking to code more efficiently
Hi, my name is Adam and thank you for considering one of my courses.
As a brief introduction I am a PhD qualified scientist with a passion for R programming. In particular, I enjoy using R to create clear, concise and readable code, which can be applied to everyday data science tasks.
I have over 10 years experience working as a scientific professional in data-intensive fields such as biology, chemistry and statistics. I now use R on a daily basis to automate, visualise and model data.