I have a large dataframe that has many rows and columns, and I would like to remove the rows for which at least 1 column is NA / NaN. Below is a small example of the dataframe I am working with:
team_id athlete_id GP tm_STL tm_TOV player_WS
1 13304 75047 1 2 8 NaN
2 13304 75048 1 2 8 0.28563827
3 13304 75049 1 2 8 NaN
4 13304 75050 1 2 8 NaN
5 13304 75053 1 2 8 0.03861989
6 13304 75060 1 2 8 -0.15530707
...albeit a bad example because all of the NaNs show up in the last column in this case. i am familiar with the approach of which(is.na(df$column_name))
for getting the rows with NA values from an individual column, but again want to do something like this for rows where at least 1 column in a row of a dataframe has an NA value.
Thanks!