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Data Tidying and Importing with R

Ce cours n'est pas disponible en Français (France)

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Data Tidying and Importing with R

Ce cours fait partie de Spécialisation "Data Science with R"

Instructeurs : Dr. Elijah Meyer

Inclus avec

3 modules

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12 avis

niveau Débutant

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1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

12 avis

niveau Débutant

Aucune connaissance prérequise

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Apply tidy data principles to manipulate and restructure data (e.g., subsetting, adding columns, and transforming data between wide and long formats)
Develop and implement code to join data sets and perform basic web scraping to collect data
Apply data structures such as wide and long formats, using code to convert between these formats as part of data preparation and analysis

Compétences que vous acquerrez

Catégorie : Data Transformation
Catégorie : Data Wrangling
Catégorie : Data Preprocessing
Catégorie : Exploratory Data Analysis
Catégorie : Data Cleansing
Catégorie : Data Manipulation
Catégorie : Statistical Programming
Catégorie : Data Ethics
Catégorie : Web Scraping
Catégorie : Data Pipelines
Catégorie : Tidyverse (R Package)
Catégorie : Data Integration
Catégorie : Data Collection

Outils que vous découvrirez

Catégorie : R (Software)
Catégorie : R Programming

Détails à connaître

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3 devoirs

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Ce cours fait partie de la Spécialisation "Data Science with R"

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Il y a 3 modules dans ce cours

Build confidence working with messy, real-world data. In this course, you’ll learn how to import, clean, and organize data in R so that it’s ready for analysis, visualization, or modeling.

Using dplyr, tidyr, and other Tidyverse tools, you’ll practice joining datasets, reshaping data, and creating efficient data pipelines that support reproducible work. You’ll also explore how to responsibly collect and scrape data from online sources, including ethical and legal considerations. By the end of this course, you’ll know how to transform raw datasets into structured, tidy formats and you’ll understand how responsible data handling and documentation are essential to high-quality, ethical data science.

Détails du module

Tidy datasets have a specific structure: each variable is a column, and each observation is a row. In this module, we use functional verbs from the dplyr package in R to transform data into a ready-to-use tidy data format. Additionally, we use functional verbs to manipulate data frames.

Inclus

6 vidéos12 lectures1 devoir2 sujets de discussion1 plugin

6 vidéosTotal 73 minutes

Welcome2 minutes
Tidy Data4 minutes
Tidying Data7 minutes
Code Along :: Country Populations Over Time24 minutes
Joining Data10 minutes
Code Along :: Continent Populations25 minutes

12 lecturesTotal 115 minutes

Course Overview10 minutes
Meet Your Instructors10 minutes
Get Ready to Compute with R and RStudio!10 minutes
Discussion Guidelines10 minutes
Report a problem with the course5 minutes
JSS :: Tidy Data10 minutes
R4DS :: Chp 5 - Data Tidying (Sections 5.3 and 5.4)10 minutes
Code Along :: Country Populations Over Time - Companion10 minutes
Code Along :: Country Populations Over Time - Companion (Complete)10 minutes
R4DS :: Chp 19.1 - 19.4 - Joins10 minutes
Code Along :: Continent Populations - Companion10 minutes
Code Along :: Continent Populations - Companion (Complete)10 minutes

1 devoirTotal 60 minutes

Tidy Data Quiz60 minutes

2 sujets de discussionTotal 20 minutes

Course Introductions10 minutes
Tidy Basketball Reflection (Optional)10 minutes

1 pluginTotal 15 minutes

Tidy Basketball15 minutes

A column in our data set can be stored as many different types, such as numbers or characters. These different data types inform how R treats the data, and whether certain functions are compatible to use with certain types of data. In this module, we discuss more in detail, the different data types classified by R, data classes, as well as how to recode variables in a data set to be different types, classes, or take on different values.

Inclus

6 vidéos13 lectures1 devoir1 sujet de discussion1 plugin

6 vidéosTotal 77 minutes

Data Types11 minutes
Code Along :: That's My Type8 minutes
Data Classes7 minutes
Code Along :: Halving CO2 Emissions19 minutes
Importing Data16 minutes
Code Along :: Importing and Recoding14 minutes

13 lecturesTotal 130 minutes

R4DS :: Chp 12.1 - 12.4 - Logical Vectors10 minutes
R4DS :: Chp 13 - Numbers10 minutes
R4DS :: Chp 14.1 - 14.3 - Strings10 minutes
Code Along :: That's My Type - Companion10 minutes
Code Along :: That's My Type - Companion (Complete)10 minutes
R4DS :: Chp 16 - Factors10 minutes
R4DS :: Chp 17 - Dates and Times10 minutes
Code Along :: Halving CO2 Emissions - Companion10 minutes
Code Along :: Halving CO2 Emissions - Companion (Complete)10 minutes
R4DS :: Chp 7 - Data Import10 minutes
R4DS :: Chp 20 - Spreadsheets10 minutes
Code Along :: Importing and Recoding - Companion10 minutes
Code Along :: Importing and Recoding - Companion (Complete)10 minutes

1 devoirTotal 60 minutes

Importing + Recoding Data Quiz60 minutes

1 sujet de discussionTotal 10 minutes

Nobel Prize Winners & Sales Data Reflection (Optional)10 minutes

1 pluginTotal 15 minutes

Nobel Prize Winners & Sales Data15 minutes

Web scraping is the process of extracting this information automatically and transforming it into a structured dataset. In this module, we go over how to perform basic web scraping in R to make an abundance of data online more easily accessible.

Inclus

4 vidéos6 lectures1 devoir2 sujets de discussion1 plugin

4 vidéosTotal 55 minutes

Web Scraping10 minutes
Web Scraping Considerations5 minutes
Code Along :: Scraping an eCommerce Page20 minutes
Code Along :: Scraping many eCommerce Pages20 minutes

6 lecturesTotal 60 minutes

Code Along :: Scraping an eCommerce Page - Companion (Complete)10 minutes
R4DS :: Chp 25.1 - 25.2 - Functions10 minutes
R4DS :: Chp 26 - Iteration (Optional)10 minutes
Code Along :: Scraping Many eCommerce Pages - Companion (Complete)10 minutes
Final Course Project (Optional)10 minutes
Share your learning experience10 minutes

1 devoirTotal 60 minutes

Web Scraping and Programming Quiz60 minutes

2 sujets de discussionTotal 20 minutes

IMDB + Web Scraping Reflection (Optional)10 minutes
Final Project Reflection (Optional)10 minutes

1 pluginTotal 15 minutes

IMDB + Web Scraping15 minutes

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