Cleaning Data In R with Tidyverse and Data.table
Cleaning Data In R with Tidyverse and Data.table, available at $59.99, has an average rating of 4.6, with 37 lectures, based on 580 reviews, and has 2801 subscribers.
You will learn about Convert raw and dirty data into clean data Understand how clean data looks and how to achieve it Use the R Tidyverse packages to clean data Handle missing values in R Detect outliers Filter and query tables Select a proper class for your data Clean various classes of data (numeric, string, categorical, integer, …) This course is ideal for individuals who are Anybody working with R will benefit from this course since data cleaning is an integral part of any form of analysis It is particularly useful for Anybody working with R will benefit from this course since data cleaning is an integral part of any form of analysis.
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Summary
Title: Cleaning Data In R with Tidyverse and Data.table
Price: $59.99
Average Rating: 4.6
Number of Lectures: 37
Number of Published Lectures: 37
Number of Curriculum Items: 37
Number of Published Curriculum Objects: 37
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- Convert raw and dirty data into clean data
- Understand how clean data looks and how to achieve it
- Use the R Tidyverse packages to clean data
- Handle missing values in R
- Detect outliers
- Filter and query tables
- Select a proper class for your data
- Clean various classes of data (numeric, string, categorical, integer, …)
Who Should Attend
- Anybody working with R will benefit from this course since data cleaning is an integral part of any form of analysis
Target Audiences
- Anybody working with R will benefit from this course since data cleaning is an integral part of any form of analysis
Welcome to this course on Data Cleaning in R with Tidyverse, Dplyr, Data.table, Tidyr and many more packages!
You may already know this problem: Your data is not properly cleaned before the analysis so the results are corrupted or you can not even perform the analysis.
To be brief: you can not escape the initial cleaning part of data science. No matter which data you use or which analysis you want to perform, data cleaning will be a part of the process. Therefore it is a wise decision to invest your time to properly learn how to do this.
Now as you can imagine, there are many things that can go wrong in raw data. Therefore a wide array of tools and functions is required to tackle all these issues. As always in data science, R has a solution ready for any scenario that might arise. Outlier detection, missing data imputation, column splits and unions, character manipulations, class conversions and much more – all of this is available in R.
And on top of that there are several ways in how you can do all of these things. That means you always have an alternative if you prefer that one. No matter if you like simple tools or complex machine learning algorithms to clean your data, R has it.
Now we do understand that it is overwhelming to identify the right R tools and to use them effectively when you just start out. But that is where we will help you. In this course you will see which R tools are the most efficient ones and how you can use them.
You will learn about the tidyverse package system – a collection of packages which works together as a team to produce clean data. This system helps you in the whole data cleaning process starting from data import right until the data query process. It is a very popular toolbox which is absolutely worth it.
To filter and query datasets you will use tools like data.table, tibble and dplyr.
You will learn how to identify outliers and how to replace missing data. We even use machine learning algorithms to do these things.
And to make sure that you can use and implement these tools in your daily work there is a data cleaning project at the end of the course. In this project you get an assignment which you can solve on your own, based on the material you learned in the course. So you have plenty of opportunity to test, train and refine your data cleaning skills.
As always you get the R scripts as text to copy into your RStudio instance. And on course completion you will get a course certificate from Udemy.
R-Tutorials Team
Course Curriculum
Chapter 1: Introduction
Lecture 1: Intro
Lecture 2: Why Clean and Tidy Data Is Necessary for a Successful Analysis
Lecture 3: Why to Choose R for Data Cleaning
Lecture 4: How to Easily Import Data into R
Lecture 5: Best Table Types in R
Lecture 6: Script Course Intro
Chapter 2: Handling Missing Values And Detecting Outliers
Lecture 1: Welcome to: Missing Data Handling and Outlier Detection
Lecture 2: Introduction to Missing Data Handling
Lecture 3: Script Missing Values and Outliers
Lecture 4: Simple Methods for Missing Data Handling
Lecture 5: Machine Learning Learning for Missing Data Imputation
Lecture 6: Statistical Outliers
Lecture 7: Detecting Outliers in Univariate Datasets
Lecture 8: Detecting Outliers in Multivariate Datasets
Chapter 3: The Tidyverse Toolbox For Efficient Data Cleaning
Lecture 1: Welcome to: The Tidyverse
Lecture 2: What Is the Tidyverse?
Lecture 3: Script Tidyverse
Lecture 4: Using the Pipe Operator
Lecture 5: Exploring the Tibble
Lecture 6: Tidy Data as the Underlying Principle of the Tidyverse
Lecture 7: Changing Table Formats
Lecture 8: How to split Columns
Lecture 9: Converting from Long to Wide Format
Lecture 10: String Manipulations with Stringr
Chapter 4: Subsetting, Filtering And Queries With data.frame, data,table And tibble
Lecture 1: Welcome to: Query Systems in R
Lecture 2: Script Queries
Lecture 3: Filtering and Querying – General Background
Lecture 4: Using dplyr for Queries
Lecture 5: Queries with 'data.table'
Chapter 5: Course Project – Apply What You Learned On Real World Data
Lecture 1: Welcome to: The Great Course Project
Lecture 2: Project Data – Get the Data here!
Lecture 3: Project Assignment
Lecture 4: Script Course Project
Lecture 5: Solution: Data Import
Lecture 6: Solution: Data Cleaning
Lecture 7: Solution: Querying
Lecture 8: Course Summary
Instructors
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R-Tutorials Training
Data Science Education
Rating Distribution
- 1 stars: 6 votes
- 2 stars: 9 votes
- 3 stars: 81 votes
- 4 stars: 237 votes
- 5 stars: 247 votes
Frequently Asked Questions
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