Data Manipulation with R

Name: Data Manipulation with R
Price: 85 NPR
Availability: InStock

This course focuses on data manipulation with R, teaching you how to efficiently clean, transform, and organize data You’ll learn how to work with packages like dplyr and tidyr for tasks such as filtering, selecting, grouping, and summarizing data . The course covers techniques like reshaping data, handling missing values, and merging datasets. You’ll also explore the power of pipes (%>%) for more readable and streamlined code. By the end, you'll be able to manipulate and prepare data for analysis with ease, using R's robust data manipulation tools.

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Course Details

Language

Duration

1 Week

Difficulty:

medium

Requirements

Basics of R

Introduction to Data

3 Chapters - 0/3 Completed

What is Data Manipulation?

Introduction to dplyr and tidyr

Basic Data Operations in R

Filtering and Selecting Data with dplyr

4 Chapters - 0/4 Completed

Understanding Filtering and Selection in Data Manipulation

Using filter() to Extract Specific Rows

Using select() to Choose Specific Columns

Combining filter() and select()

Transforming Data with dplyr

5 Chapters - 0/5 Completed

Understanding Data Transformation

Using mutate() to Add or Modify Columns

Using arrange() to Sort Data

Summarizing Data with summarize()

Grouping Data with group_by()

Tidying Data with tidyr

6 Chapters - 0/6 Completed

Introduction to Data Tidying

Using pivot_longer() to Reshape Data

Using pivot_wider() to Spread Data

Using separate() to Split Columns

Using unite() to Combine Columns

Handling Missing Data with fill()

Handling Missing Data and Data Cleaning

5 Chapters - 0/5 Completed

Introduction to Missing Data

Detecting Missing Data

Handling Missing Data: Removal

Handling Missing Data: Imputation

Handling Missing Data: Forward and Backward Filling

Data Transformation with dplyr

7 Chapters - 0/7 Completed

Introduction to dplyr

Filtering Data with filter()

Selecting Columns with select()

Sorting Data with arrange()

Creating New Columns with mutate()

Summarizing Data with summarize()

Grouping Data with group_by()

Handling Missing Data with NA in R

7 Chapters - 0/7 Completed

Data Manipulation with R

Course Details

Requirements

Introduction to Data

What is Data Manipulation?

Introduction to dplyr and tidyr

Basic Data Operations in R

Filtering and Selecting Data with dplyr

Understanding Filtering and Selection in Data Manipulation

Using filter() to Extract Specific Rows

Using select() to Choose Specific Columns

Combining filter() and select()

Transforming Data with dplyr

Understanding Data Transformation

Using mutate() to Add or Modify Columns

Using arrange() to Sort Data

Summarizing Data with summarize()

Grouping Data with group_by()

Tidying Data with tidyr

Introduction to Data Tidying

Using pivot_longer() to Reshape Data

Using pivot_wider() to Spread Data

Using separate() to Split Columns

Using unite() to Combine Columns

Handling Missing Data with fill()

Handling Missing Data and Data Cleaning

Introduction to Missing Data

Detecting Missing Data

Handling Missing Data: Removal

Handling Missing Data: Imputation

Handling Missing Data: Forward and Backward Filling

Data Transformation with dplyr

Introduction to dplyr

Filtering Data with filter()

Selecting Columns with select()

Sorting Data with arrange()

Creating New Columns with mutate()

Summarizing Data with summarize()

Grouping Data with group_by()

Handling Missing Data with NA in R

Understanding Missing Data

Identifying Missing Data

Removing Missing Data

Imputing Missing Data

Advanced Imputation Techniques

Visualizing Missing Data

Final Project: Build a Complete Data Preprocessing Pipeline in R

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