R Data Cleaning And Preprocessing

R Data Cleaning And Preprocessing

R: Data Cleaning And Preprocessing
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hours | 136 MB
Genre: eLearning | Language: English
This Learning Path will guide you how to clean and prepare your raw data in R once it is loaded, so that you may get started with analyzing it and gaining insights.

This Learning Path will guide you how to clean and prepare your raw data in R once it is loaded, so that you may get started with analyzing it and gaining insights.

We begin this Learning Path by learning to identify these column headers and converting the values present within them to appropriate data types, for example, converting values in columns titled Date and Name to the date and character data types respectively. We will then work more extensively with the date format. Next we'll be learning how to add new records, removing certain columns and rows from the data set, and extracting partial records. Further, we'll learn to combine multiple datasets and sort them, transform data from long to wide format and vice versa. Finally, we shall cover the detection of missing values within a dataset, and imputing their values using R.

By the end of this Learning Path, you would have become a master at cleaning and filtering datasets in R, such that you would be able to move on to analyzing and visualizing the data.




R Data Cleaning And Preprocessing


Download link:



Links are Interchangeable - No Password - Single Extraction
R Data Cleaning And Preprocessing

R: Data Cleaning And Preprocessing
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hours | 136 MB
Genre: eLearning | Language: English
This Learning Path will guide you how to clean and prepare your raw data in R once it is loaded, so that you may get started with analyzing it and gaining insights.

This Learning Path will guide you how to clean and prepare your raw data in R once it is loaded, so that you may get started with analyzing it and gaining insights.

We begin this Learning Path by learning to identify these column headers and converting the values present within them to appropriate data types, for example, converting values in columns titled Date and Name to the date and character data types respectively. We will then work more extensively with the date format. Next we'll be learning how to add new records, removing certain columns and rows from the data set, and extracting partial records. Further, we'll learn to combine multiple datasets and sort them, transform data from long to wide format and vice versa. Finally, we shall cover the detection of missing values within a dataset, and imputing their values using R.

By the end of this Learning Path, you would have become a master at cleaning and filtering datasets in R, such that you would be able to move on to analyzing and visualizing the data.




R Data Cleaning And Preprocessing


Download link:


Links are Interchangeable - No Password - Single Extraction
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