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.
This course is part of the Tidyverse Skills for Data Science in R Specialization
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Tidyverse Skills for Data Science in R SpecializationJohns Hopkins UniversityAbout this Course
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Course 2 of 5 in the
Beginner Level
F​amiliarity with the R programming language
Approx. 15 hours to complete
English
Subtitles: English
What you will learn
D​escribe different data formats
A​pply Tidyverse functions to import data into R from external formats
O​btain data from a web API
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 5 in the
Beginner Level
F​amiliarity with the R programming language
Approx. 15 hours to complete
English
Subtitles: English
Offered by
Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Syllabus - What you will learn from this course
3 hours to complete
Importing (and Exporting) Data in R
A basic data type in the tidyverse is the tibble. Tibbles store tabular data and are a modern take on the standard R data frame. They have many user-friendly features that are an improvement over standard data frames when doing interactive data analysis. The remainder of this module covers tabular data in spreadsheet formats like Excel, CSV, TSV, and other delimited files.
3 hours to complete
15 readings
3 hours to complete
JSON, XML, and Databases
Data can come in non-tabular formats, especially unstructured data or data that otherwise would not fit into a table. JSON and XML are common formats for storing arbitrarily structured data and this module covers the packages used to read in those data formats. In addition, relational databases are common for storing very large collections of tables where you do not need to read in the entire dataset at once. There are many relational database formats and we will cover the SQLite format, which is a compact and simple to use format.
3 hours to complete
10 readings
2 hours to complete
Web Scraping and APIs
Reading in data from various Internet sources can be a useful way to build analyses that need to be regularly updated. The rvest and httr packages are useful for connecting to web sites, web APIs and other online sources of data.
2 hours to complete
11 readings
2 hours to complete
Foreign Formats, Images, and googledrive
Working with others in a data science project often involves reading output or data produced using other statistical analysis packages or other software. This module covers packages for reading in these foreign formats, as well as images and data from Google Drive.
2 hours to complete
3 readings
4 hours to complete
Case Studies
Now we will demonstrate how to import data using our case study examples. When working through the steps of the case studies, you can use either RStudio on your own computer or Coursera lab spaces provided for each case study.
4 hours to complete
11 readings
1 hour to complete
Project: Importing Data into R
This project will give you the opportunity to read in data from multiple sources and conduct some simple operations on those data.
1 hour to complete
2 readings
Reviews
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- 4 stars24.24%
- 3 stars6.06%
TOP REVIEWS FROM IMPORTING DATA IN THE TIDYVERSE
by FCJan 28, 2021
Excellent tutorial for importing data into the tidyverse environment
by VMMar 27, 2021
Great for beginners. Clearly explained, and easy to follow.
About the Tidyverse Skills for Data Science in R Specialization
This Specialization is intended for data scientists with some familiarity with the R programming language who are seeking to do data science using the Tidyverse family of packages. Through 5 courses, you will cover importing, wrangling, visualizing, and modeling data using the powerful Tidyverse framework. The Tidyverse packages provide a simple but powerful approach to data science which scales from the most basic analyses to massive data deployments. This course covers the entire life cycle of a data science project and presents specific tidy tools for each stage.
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