Explore R for data science, from basics to machine learning. Learn data manipulation, visualization, version control, and workflow optimization for real-world challenges.

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Tidyverse

Embark on an exciting journey with R as your trusted ally for data science. This comprehensive course will equip you with essential skills to leverage R's power for data manipulation and analysis. The course is suitable even if you have limited R experience, empowering you to perform data science tasks effectively.

The journey starts with R fundamentals to lay a strong foundation. You’ll master the tidyverse to create powerful and readable code. Then, you will explore the import of various data sources, visualization, and best practices before gaining hands-on machine learning experience. Also, you’ll learn version control with Git and GitHub and to optimize your R code for efficient data science workflows.

At course completion, you'll emerge as a confident R data scientist, ready to tackle real-world challenges. You'll be well-equipped to advance your career in data science with R's extensive capabilities in your toolbelt.

Data Science in R: From Basics to Machine Learning

Without the ability to load external data sources, we need to hardcode our data into R using statements like the ones below:





#Store some survey data in a data frame object
VAR_DataFrame <- data.frame(
                                   Q1_Ans = c(1,4,3,5,1,2),
                                   Q2_Ans = c(5,3,2,2,5,1),
                                   Q3_Ans = c(TRUE,TRUE,FALSE,TRUE,FALSE,FALSE))

VAR_DataFrame #Print the resulting data frame

* **Lines 2–5:** We create a hardcoded data frame, using the `data.frame` function and explicitly coding values into that data frame.

While snippets like these are helpful in particular circumstances, most of the time, we'll deal with much more extensive, possibly dynamic, datasets, so hardcoding them into our scripts would ruin our efficiency. Most of the time in data science, we’ll pull these larger datasets from other sources—`csv` files, databases, and websites. Fortunately, R is well-suited to the task of dynamically loading data.

In base-R, the primary function to pull in data from a `csv` file is `read.csv()`. Say we have a `csv` file called `MySurveyData.csv`, which we can examine in the code window below.

This data would be frustrating to hardcode into our scripts. And if we re-ran the survey or added new questions to the study, updating our script would be very frustrating. Luckily, we can read the `csv` file directly into R. So, if our survey data is updated, we just need a new `csv` file to read. Now we can access some big datasets and get into more exciting analyses!

Learn how to pull data from a CSV file into our R environment, which lays the foundation for more complex database pulls.

Why R?

R Fundamentals

R Fundamentals Exercises

Readable Coding with tidyverse

Tidyverse Exercises

Importing More Data Sources

Data Visualization with ggplot2

Best Practices for Data Scientists

Statistical Analysis and Machine Learning with tidymodels

Exploring tidymodels through Exercises

Useful Libraries for Data Science

Git Integration

Getting The Most Out of R

Appendix

Credit Card Fraud Detection using the R Language

Loading External Data

The `read.csv` function