How to handle missing data and outliers in the R language?

In R language, dealing with missing data and outliers commonly involves using the following methods:

1. Dealing with missing data:

1. Remove missing data: You can use the na.omit() function to delete rows containing missing values, or use the complete.cases() function to filter out rows with missing values.
2. Fill in missing data: You can use the na.fill() function or na.locf() function to fill in missing values.

1. Handling outliers:

1. Remove outliers: You can use threshold-based methods, such as standard deviation or boxplot methods, to identify and remove outliers.
2. Replace outliers: outliers can be replaced with statistical measures such as the median or mean, or by using interpolation methods to estimate the values of outliers.
3. Convert outliers: Sometimes outliers may be due to incorrect data recording or exceptional circumstances, and appropriate transformations or adjustments can be made based on the specific situation.

In general, dealing with missing data and outliers requires selecting and implementing appropriate methods based on the specific situation to ensure the quality of data and accuracy of analysis results.