Introduction to R Programming and Tidyverse
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Introduction to R Programming and Tidyverse
This course is part of Expressway to Data Science: R Programming and Tidyverse Specialization
Instructor: Jane Wall
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What you'll learn
By successfully completing this course, you will be able to write functions in R.
You will be able to analyze and visualize a data set.
You will be able to use RMarkdown to share documents in reports with the global R community.
Skills you'll gain
- Plot (Graphics)
- Data Analysis Software
- Data Validation
- Statistical Visualization
- Statistical Programming
- Code Reusability
- Data Visualization Software
- Aesthetics
- Data Analysis
- Data Visualization
- Programming Principles
- Data Transformation
- Software Installation
- Tidyverse (R Package)
- Data Manipulation
- Statistical Reporting
Tools you'll learn
Details to know
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There are 4 modules in this course
This course is a gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:
• you want to do data analysis but don’t know programming • you know programming but aren’t familiar with R • you know some R programming but want to learn the tidyverse verbs You will learn to do data visualization and analysis in a reproducible manner and use functions that allow your code to be easily read and understood. You will use RMarkdown to create nice documents and reports that execute your code freshly every time it’s run and that capture your thoughts about the data along the way. This course has been designed for learners from non-STEM backgrounds to help prepare them for more advanced data science courses by providing an introduction to programming and to the R language. I am excited for you to join me on the journey! The course logo was created using images of stickers from the RStudio shop. Please visit https://swag.rstudio.com/s/shop.
In the first module of this course, you will install and configure R and RStudio. You will review the fundamentals of R and reproducibility, install R packages required for the course, and input basic commands using the RStudio console. Finally, you will create an RMarkdown document - the deliverable for this module.
What's included
5 videos7 readings2 assignments1 peer review1 ungraded lab6 plugins
5 videos•Total 25 minutes
- Course Introduction•4 minutes
- Configuring RStudio•4 minutes
- Installing R Packages•6 minutes
- RMarkdown Overview•3 minutes
- Creating an RMarkdown Document•9 minutes
7 readings•Total 61 minutes
- Course Updates and Accessibility Support•1 minute
- Welcome and Course Organization•10 minutes
- R Resources•10 minutes
- Installing R and RStudio•10 minutes
- Fundamentals of R Summary•10 minutes
- Reproducible Research - Why and How•10 minutes
- Helpful Tips and Resources on RMarkdown•10 minutes
2 assignments•Total 30 minutes
- R Fundamentals•15 minutes
- RMarkdown•15 minutes
1 peer review•Total 60 minutes
- RMarkdown•60 minutes
1 ungraded lab•Total 60 minutes
- RMarkdown Lab•60 minutes
6 plugins•Total 171 minutes
- R for Data Science - Introduction•1 minute
- RStudio - What and Why•15 minutes
- Hands-On Programming with R - Chapter 2•60 minutes
- R for Data Science - Chapter 4•30 minutes
- Reproducible Research•20 minutes
- R for Data Science - RMarkdown•45 minutes
In this module, we will explore functions in R. You will review the syntax of functions and best practices of function creation. You will also practice writing functions with default arguments and argument validation.
What's included
10 videos7 readings2 assignments1 peer review4 ungraded labs
10 videos•Total 50 minutes
- Our First Function•10 minutes
- Naming a Function•3 minutes
- Function Inputs•4 minutes
- A Change in Function Requirements•5 minutes
- Conditional Execution•8 minutes
- Multiple Conditions•6 minutes
- For Loops•1 minute
- Checking Inputs•5 minutes
- Function Output•3 minutes
- Introduction to Pipes•6 minutes
7 readings•Total 37 minutes
- Resources•2 minutes
- Writing a Function•10 minutes
- Statement Conditions•5 minutes
- The Switch Statement•5 minutes
- Using the Stop Function•5 minutes
- Return Specific Values•5 minutes
- Using Pipes•5 minutes
2 assignments•Total 30 minutes
- Functions•15 minutes
- Input Checking and Outputs•15 minutes
1 peer review•Total 60 minutes
- Functions•60 minutes
4 ungraded labs•Total 240 minutes
- Practice Problems: Set 1•60 minutes
- Practice Problems: Set 2•60 minutes
- Practice Problems: Set 3•60 minutes
- Functions•60 minutes
In this module, you will be introduced to ggplot2 - an R package for data visualization. You will explore the different grammatical elements and aesthetic mappings (layers) that are essential to visualize data in ggplot2.
What's included
7 videos2 readings1 assignment1 peer review1 ungraded lab
7 videos•Total 32 minutes
- Introduction to ggplot2•5 minutes
- Aesthetics•4 minutes
- Geometric Objects•5 minutes
- Statistical Transformations•6 minutes
- Position Adjustments•5 minutes
- Facets•3 minutes
- Coordinate Systems•4 minutes
2 readings•Total 20 minutes
- Resources•10 minutes
- Using ggplot2•10 minutes
1 assignment•Total 30 minutes
- Layers•30 minutes
1 peer review•Total 60 minutes
- Data Visualization•60 minutes
1 ungraded lab•Total 60 minutes
- Data Visualization•60 minutes
In the final module of this course, you will be introduced to data analysis using dplyr. You will learn and practice with the many dplyr verbs including select, filter, arrange, mutate, group_by, and summarize.
What's included
14 videos3 readings2 assignments1 peer review3 ungraded labs
14 videos•Total 79 minutes
- Introduction to dplyr•6 minutes
- Chaining Functions•4 minutes
- Selecting Variables (Columns)•7 minutes
- Conditionally Selecting Rows•6 minutes
- Selecting Rows by Location•7 minutes
- Arrange Rows by Value•4 minutes
- Renaming Data•4 minutes
- Distinct, Mutate, and Transmute•5 minutes
- Rename, Relocate, and Summarize•5 minutes
- Summary Functions•8 minutes
- Counting Observations•6 minutes
- Grouping Variables: Part 1•6 minutes
- Grouping Variables: Part 2•6 minutes
- Grouping Variables: Part 3•6 minutes
3 readings•Total 22 minutes
- Main dplyr Functions•10 minutes
- R and NA•2 minutes
- Grouping datasets•10 minutes
2 assignments•Total 35 minutes
- dplyr Verbs•5 minutes
- Select, Filter, and Arrange•30 minutes
1 peer review•Total 60 minutes
- Data Analysis•60 minutes
3 ungraded labs•Total 180 minutes
- Practice Problems: Set 4•60 minutes
- Practice Problems: Set 5•60 minutes
- Data Analysis•60 minutes
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Reviewed on Sep 11, 2023
Thanks! so much. This course helped me understand the excellent features for data analysis using R programming. This will certainly help me in my data science career path.
Reviewed on Nov 1, 2022
Very good course for first time R learners. Challenging but doable with some determination and attention to detail.
Reviewed on Feb 1, 2024
Helpful content! However, the sequence of practice questions and lecture contents are not fully aligned.
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