Data Cleaning in R

Data Cleaning in R

Data Cleaning in R is the process to transform raw data into consistent data that can be easily analyzed. It is aimed at filtering the content of statistical statements based on the data as well as their reliability. Moreover, it influences the statistical statements based on the data and improves your data quality and overall productivity....

Purpose of Data Cleaning

The following are the various purposes of data cleaning in R:...

Overview of a typical data analysis chain

This section represents an overview of typical data analysis. Each rectangle in the figure represents data in a certain state while each arrow represents the activities needed to get from one state to the other. The first state (Raw data) is the data as it comes in. Raw data may lack headers, contain wrong data types, wrong category labels, unknown or unexpected character encoding, and so on. Once this preprocessing has taken place, data can be deemed Technically correct Data. That is, in this state data can be read into an R data. frame, with correct names, types, and labels, without further trouble. However, this does not mean that the values are error-free or complete. Consistent data is the stage where data is ready for statistical inference. It is the data that most statistical theories use as a starting point....

How to clean data in R

Here, this involves various steps, as from the initial raw data have to move toward the consistent and highly efficient data which is ready to be implemented as per the requirements and produces highly precise and accurate statistical results. The steps vary from data to data in this case the user should be aware of the date he/she is using for the results. As there are many characteristics and common symptoms of messy data which totally depend on the data used by the user for analysis....

Let’s Start the implementation of Data Cleaning in R

For this, we will use inbuilt datasets(air quality datasets) which are available in R....

Handling missing values in R

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Data Cleaning Operation

To handle the missing value we will check the columns of the datasets, if we found some missing data inside the columns then this generates the NA values as an output, which can be not good for every model. So let’s check it using mean() methods....