From here on use the dataframe with non-missing Iwage. filter (!is.na (lwage)) It is not a...

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From here on use the dataframe with non-missing Iwage. filter (!is.na (lwage)) It is not a bad idea to give the dataframe a new name at this point. 1c) (5 points) Consider the following variables: HRLYEARN AGE_12 EDUC SEX COWMAIN UNION Investigate each variable either from the drop-down arrow in the global environment or using the "head" or "glimpse" commands, e.g. glimpse(mydata$HRLYEARN) Which variables are continuous? Which variables are categorical (factors)? From here on use the dataframe with non-missing Iwage. filter (!is.na (lwage)) It is not a bad idea to give the dataframe a new name at this point. 1c) (5 points) Consider the following variables: HRLYEARN AGE_12 EDUC SEX COWMAIN UNION Investigate each variable either from the drop-down arrow in the global environment or using the "head" or "glimpse" commands, e.g. glimpse(mydata$HRLYEARN) Which variables are continuous? Which variables are categorical (factors)?

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To identify which variables are continuous and which variables are categorical factors in t... View the full answer