Filter functions are especially useful to filter out NA or missing values rows. Similar to selection functions, filters can be applied to all columns with no discrimination, or used with conditions to return just the registers that meet them. With characters, you can filter to include or exclude results based on exact or partial character matches, and also find registers that begin with, end up or contain certain terms.įilters with multiple conditions can be combined with AND, OR, and NOT conditions, and you can even specify to return values when only one condition out of two is met, but not doing it when both are met. This is a sub-setting procedure that allows you to pick specific values by column/s, a range of values, or even approximations with ranges of tolerance. When you don’t want to include all rows of a dataset in your analysis, you can use a filter function to return rows that match 1 or more conditions.

show only data that’s not date formatted).Īdditionally, you can use logical expressions on numeric data like selecting values that are above a certain threshold or contain an average value below a certain parameter. The other way around, you can select the negation of these conditions, and select only data that doesn’t meet one or more conditions(e.g. You can use conditional selections to show columns that meet certain criteria, like showing only columns that contain numeric data, or character data for example.

Pca use for binary columns