This course continues our gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:
Data Analysis with Tidyverse
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Data Analysis with Tidyverse
This course is part of Expressway to Data Science: R Programming and Tidyverse Specialization
Instructor: Jane Wall
Included with 
Gain insight into a topic and learn the fundamentals.
4.0
(10 reviews)
Beginner level
Recommended experience
Flexible schedule
Approx. 17 hours
Learn at your own pace
Prepare for a degree
What you'll learn
You will learn to identify and describe tidy data and transform a non-tidy data set to be tidy in R.
You will learn to analyze data between multiple related data tables.
You will be learn to apply regular expressions to detect patterns in strings and return matches and replace patterns with new values.
Skills you'll gain
Details to know
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Assessments
8 assignments
Taught in English
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This course is part of the Expressway to Data Science: R Programming and Tidyverse Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
When analyzing data, you will often be required to import data from CSV or txt files. In this module, you will learn how to import and parse data in base R and the readr library, a package in the Tidyverse. You will also be introduced to R projects, which help store and organize data files associated with an analysis.
What's included
7 videos2 assignments4 plugins
Data are stored in tabular forms and are often organized differently depending on its use. In this module, you will learn how to reorganize data to produce a "tidy" data set, where every variable is stored in its own column, every observation is stored in its own row, and each value is stored in a table cell.
What's included
6 videos1 reading1 assignment1 peer review2 ungraded labs1 plugin
Data analysis rarely involves a single data table and you will be required to combine multiple related tables to answer questions you are interested in. In this module, you will learn and practice mutating variables and filtering observations from relational data.
What's included
4 videos1 reading1 assignment1 peer review1 ungraded lab2 plugins
This module will introduce string manipulation in R. You will learn the basics of strings, including string creation, merging, and subsetting. Then, you will use regular expressions to describe and view patterns in strings.
What's included
11 videos1 reading3 assignments1 peer review2 ungraded labs2 plugins
In the last module of the course, you will use the forcats package in the tidyverse to work with categorical variables, variables that have discrete values. The forcats package introduces factors - data objects used to categorize the data in levels. You will practice creating and modifying factors.
What's included
6 videos2 readings1 assignment1 peer review1 ungraded lab3 plugins
Instructor
Offered by
Recommended if you're interested in Data Analysis
University of Illinois Urbana-Champaign
Rice University
LearnQuest
Prepare for a degree
Taking this course by University of Colorado Boulder may provide you with a preview of the topics, materials and instructors in a related degree program which can help you decide if the topic or university is right for you.
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