Missing data in R I am trying to perform multiple regression on a dataset.
If I have a column in my dataset which has say 900 Positive and 1000 Negative and 600 missing data. What is the recommendation to deal with missing data here? If I replace with Mode, it would skew the data by a lot. Is it recommended to just remove the column altogether?
How about if I have column/columns with missing numeric data. Should I replace them with the mean of a column, or use regression imputation methods? What are the consequences? Which method is better and recommended?

You may definitely have a skew problem, the question is whether you will get more benefits. For better advice, it would be nice to see your data. Here I will list some options you have:

*

*1st: you can remove rows with missing values, which in your case seems to be the best option since 31% of your data is missing;


*2nd: you can input artificial data using any input method that you prefer (replace by 0,mean, median, prediction, deep learning), according to your data. This is resky in your case because you have a lot of missing data and can lead you to false insights. This method works better with small datasets;


*3rd: you can use an algorithm that is robust to missing values, such as k-NN, random forest, Naive Bayes etc.
Anyway, you don´t have a right answer for this problem. You'll have to analyse your data and make your own decision, there is no rule. Plus, it's free to try! Just run some tests and see which solution performs best for you.
