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

In data science work, we often need to `filter` or subset data. Frequently, we’ll want to analyze a subset of the data given to us based on some condition that we can check within the dataset itself. For example, in a student dataset, we want to look at average grades for students only in a particular year or a specific course. Then we'll need to `filter` the data to view the relevant records only. 



Filters in the tidyverse are applied similarly to `group_by` statements. In the example below, we use `filter` to subset student grade data contained in the attached `csv` files. The file `StudentInformation.csv` contains general information regarding students, while the file `GradeData-byCourse.csv` contains the students' grades (`Grade`) for each course (`CourseID`). 

#Load tidyverse libraries
library(ggplot2)
library(purrr)
library(tibble)
suppressPackageStartupMessages(library(dplyr))
library(tidyr)
library(stringr)
library(readr)
library(forcats)

#Load datasets directly to tibbles
VAR_StudentData <- read_csv("StudentInformation.csv",
                        col_names = TRUE,
                        skip = 0,
                        n_max = Inf,
                        show_col_types = FALSE)

VAR_GradeDataByCourse <- read_csv("GradeData-ByCourse.csv",
                            col_names = TRUE,
                            skip = 0,
                            n_max = Inf,
                            show_col_types = FALSE)

#Join the two data sets
VAR_CombinedStudentData <- VAR_StudentData %>%
                      full_join(VAR_GradeDataByCourse,
                            by = "StudentID", multiple = "all")
 
#Filter the combined data set to the MATH101 course
VAR_CombinedDataMath101 <- VAR_CombinedStudentData %>%
                      filter(CourseID == "MATH101")

#Join and filter the data sets in a single command
VAR_CombinedDataMath101Piped <- VAR_StudentData %>%
                      full_join(VAR_GradeDataByCourse,
                            by = "StudentID", multiple = "all") %>%
                      filter(CourseID == "MATH101")

#Output results
paste0("Multi-step combination of data")
VAR_CombinedDataMath101

paste0("Single step combination using pipe")
VAR_CombinedDataMath101Piped

* **Lines 25–31:** We add a single line here, the `filter` command. We check whether the `CourseID` is exactly `MATH101` using the `==` operator.
 
* **Lines 34–37:** We use the `pipe` operator to condense our code and remove the need to store results in an intermediate variable.

The choice between the two ways to write the code depends on the use case. If the combined dataset is never again referenced in our code, it may make the code slightly more readable to connect the lines through the `pipe` operator. If the combined dataset is referenced again, we can create it just once and reference it in multiple places later.



Learn to analyze data subsets using filters in the tidyverse.

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

Filtering Datasets

Using `filter`