Data Cleaning in R – remove outliers in R

Data cleaning is an important step in the data analysis process, and one of the tasks is often identifying and removing outliers. Outliers are data points that are significantly different from the rest of the data, and they can occur for a variety of reasons, such as measurement errors or data entry errors. These outliers can cause problems with the analysis and lead to inaccurate or unreliable results.

In R, there are several ways to remove outliers. One common method is to use the boxplot() function, which creates a box and whisker plot to visualize the data and identify outliers. You can then use the subset() function to remove the outliers from the dataset.

Another common approach is to use the z-score method, which calculates the z-score for each data point and removes data points that have a z-score that is greater than a certain threshold. The z-score is a measure of how far a data point is from the mean of the dataset.

A third approach is to use the interquartile range (IQR) method, which calculates the difference between the first and third quartile and removes data points that are outside the range of 1.5 times the IQR.

In summary, Data cleaning is an important step in the data analysis process, and one of the tasks is often identifying and removing outliers. In R, there are several ways to remove outliers: using the boxplot() function, using the z-score method, and using the interquartile range (IQR) method. The boxplot() function creates a box and whisker plot to visualize the data and identify outliers, the z-score method calculates the z-score for each data point and removes data points that have a z-score that is greater than a certain threshold and the IQR method calculates the difference between the first and third quartile and removes data points that are outside the range of 1.5 times the IQR. Choosing the right method depends on the nature of the data and the goals of the analysis.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Data Cleaning in R – remove outliers in R.

Data Cleaning in R – remove outliers in R

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