Data Manipulation with R

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

Data Manipulation with R involves using powerful packages like dplyr and tidyr to clean, transform, and summarize data efficiently for analysis and visualization It enables handling complex datasets through intuitive, readable code.

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Course Details

Language: R

Duration: Approximately 1–2 weeks (with practical exercises).

Difficulty: medium

Category: Programming

Certificate: Yes

Requirements

Basics of R

Content

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

Content

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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