Course Summary
Learn how to import data into R using the tidyverse package, and how to manipulate and clean it to prepare it for analysis. This course covers a range of data import methods, including CSV, Excel, and JSON files.Key Learning Points
- Understand the importance of importing clean and well-structured data for analysis
- Learn how to use the readr, readxl, and jsonlite packages in R to import data
- Explore data cleaning and manipulation techniques using dplyr and tidyr
Related Topics for further study
Learning Outcomes
- Import data from CSV, Excel, and JSON files using R
- Apply data cleaning and manipulation techniques to prepare data for analysis
- Understand the importance of importing clean and structured data for analysis
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of R programming
- Familiarity with data formats such as CSV and Excel
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Wrangling with dplyr and tidyr
- Data Visualization with ggplot2
- Data Science Methodology
Related Education Paths
- Data Science Specialization
- Applied Data Science with R Specialization
- Statistics with R Specialization
Related Books
Description
Getting data into your statistical analysis system can be one of the most challenging parts of any data science project. Data must be imported and harmonized into a coherent format before any insights can be obtained. You will learn how to get data into R from commonly used formats and harmonizing different kinds of datasets from different sources. If you work in an organization where different departments collect data using different systems and different storage formats, then this course will provide essential tools for bringing those datasets together and making sense of the wealth of information in your organization.
Knowledge
- D​escribe different data formats
- A​pply Tidyverse functions to import data into R from external formats
- O​btain data from a web API
Outline
- Importing (and Exporting) Data in R
- About This Course
- Tibbles
- Creating a tibble
- Subsetting
- Spreadsheets
- Excel files
- Google Sheets
- CSVs
- Downloading CSV files
- Reading CSVs into R
- TSVs
- Reading TSVs Files into R
- Delimited Files
- Reading Delimited Files into R
- Exporting Data from R
- Importing and Exporting Data Quiz
- JSON, XML, and Databases
- JSON
- XML
- Databases
- Relational Data
- Relational Databases: SQL
- Connecting to Databases: RSQLite
- Working with Relational Data: dplyr & dbplyr
- Mutating Joins
- Filtering Joins
- How to Connect to a Database Online
- JSON, XML, and Databases Quiz
- Web Scraping and APIs
- Web Scraping
- SelectorGadget
- Web Scraping Example
- A final note: SelectorGadget
- API
- Getting Data: httr
- Example 1: GitHub’s API
- Example 2: Obtaining a CSV
- read_csv() from a URL
- API keys
- Getting Data from the Internet Quiz
- Foreign Formats, Images, and googledrive
- haven
- Images
- googledrive
- Foreign Formats, Images and googledrive Quiz
- Case Studies
- Case Study #1: Health Expenditures
- Healthcare Coverage Data
- Healthcare Spending Data
- New Case Study #2: Firearms
- Census Data
- Counted Data
- Suicide Data
- Brady Data
- Crime Data
- Land Area Data
- Unemployment Data
- Project: Importing Data into R
- Introduction and Background
- Datasets
- Importing Data into R Project
Summary of User Reviews
Learn to import and clean data using tidyverse in this comprehensive online course. Students have praised the course's practical approach and easy-to-follow lessons.Key Aspect Users Liked About This Course
Practical approachPros from User Reviews
- Clear and concise explanations
- Hands-on exercises that reinforce learning
- Great for beginners and intermediate learners
Cons from User Reviews
- Lack of advanced topics
- Some users found the pace too slow
- Limited interaction with instructors
Carrie Wright, PhD
4.6 Raiting
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