John Hopkins University - Wrangling Data in the Tidyverse
- Offered byCoursera
Wrangling Data in the Tidyverse at Coursera Overview
Duration | 14 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Wrangling Data in the Tidyverse at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Wrangling Data in the Tidyverse at Coursera Course details
- Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This course addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively. The key goal in data wrangling is transforming non-tidy data into tidy data.
- This course covers many of the critical details about handling tidy and non-tidy data in R such as converting from wide to long formats, manipulating tables with the dplyr package, understanding different R data types, processing text data with regular expressions, and conducting basic exploratory data analyses. Investing the time to learn these data wrangling techniques will make your analyses more efficient, more reproducible, and more understandable to your data science team.
- In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
Wrangling Data in the Tidyverse at Coursera Curriculum
Wrangling Data in the Tidyverse
About This Course
Tidy Data Review
Reshaping Data
Wide Data
Long Data
Reshaping Data
Data Wrangling
R Packages
The Pipe Operator
Filtering Data
Reordering
Creating New Columns
Separating Columns
Merging Columns
Cleaning Column Names
Combining Data Across Data Frames
Grouping Data
Summarizing Data
Operations Across Columns
Reshaping Data Quiz
Data Wrangling Quiz
Working With Factors, Dates, and Times
Working with Factors
Factor Review
Manually Changing the Labels of Factor Levels: fct_releve()
Keeping the Order of the Factor Levels: fct_inorder()
Advanced Factoring
Re-ordering Factor Levels by Frequency: fct_infreq()
Reversing Order Levels: fct_rev()
Re-ordering Factor Levels by Another Variable: fct_reorder()
Combining Several Levels into One: fct_recode()
Converting Numeric Levels to factors: ifelse() + factor()
Dates and Times Basics
Creating Dates and Date-Time Objects
Working with Dates
Time Spans
Working With Factors Quiz
Working With Dates Quiz
Working With Strings and Text and Functional Programming
Working with Strings
stringr
String Basics
Regular Expressions
glue
Tidy Text Format
Sentiment Analysis
Word and Document Frequency
Functional Programming
For Loops vs. Functionals
map Functions
Multiple Vectors
Anonymous Functions
Working With Strings Quiz
Functional Programming Quiz
Exploratory Data Analysis
General Principles of EDA
Case Studies
Case Studies
Healthcare Coverage Data
Healthcare Spending Data
Join the Data
Census Data
Violent Crime
Brady Scores
The Counted Fatal Shootings
Unemployment Data
Population Density: 2015
Firearm Ownership
Important information before you start the project
Wrangling Data in the Tidyverse Course Project
Wrangling Data in the Tidyverse at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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