Johns Hopkins University

Importing Data in the Tidyverse

This course is part of Tidyverse Skills for Data Science in R Specialization

Taught in English

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Carrie Wright, PhD
Shannon Ellis, PhD
Stephanie Hicks, PhD

Instructors: Carrie Wright, PhD

1,801 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.7

(43 reviews)

Beginner level

Recommended experience

15 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Describe different data formats

  • Apply Tidyverse functions to import data into R from external formats

  • Obtain data from a web API

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(43 reviews)

Beginner level

Recommended experience

15 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Tidyverse Skills for Data Science in R Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course

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.

What's included

15 readings1 quiz

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.

What's included

10 readings1 quiz

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.

What's included

11 readings1 quiz

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.

What's included

3 readings1 quiz

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.

What's included

11 readings2 ungraded labs

This project will give you the opportunity to read in data from multiple sources and conduct some simple operations on those data.

What's included

2 readings1 quiz

Instructors

Instructor ratings
4.3 (13 ratings)
Carrie Wright, PhD
Johns Hopkins University
7 Courses7,382 learners
Shannon Ellis, PhD
Johns Hopkins University
5 Courses5,744 learners
Stephanie Hicks, PhD
Johns Hopkins University
5 Courses5,744 learners

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