R代码非常慢

Question

Recently I have been working on some R scripts to do some reports. One of the tasks involved is to check if a value in a column matches any row of another dataframe. If this is true, then set a new column with logical TRUE/FALSE.

More specifically, I need help improving this code chunk:

for (i in 1:length(df1$Id)) {
  df1 <- within(df1, newCol <- df1$Id %in% df2$Id)
}
df1$newCol <- as.factor(df1$newCol)

The dataset has about 10k rows so it does not make sense to need 6 minutes (tested with proc.time() to execute it completely, which is what it is currently happening. Also, I need to do so other types of checking, so I really need to get this right.

What am I doing wrong there that is devouring time to accomplish?

Thank you for your help!

Answer 1

Your code is vectorized - there is no need for the for loop. In this case, you can tell because you don't even use i inside the loop . This means your loop is executing the exact same code for the exact same result 10k times. If you delete the for wrapper around your functional line

df1 <- within(df1, newCol <- df1$Id %in% df2$Id)

you should get ~10k times speed-up.

One other comment is that the point of within is to avoid re-typing a data frame's name inside. So you're missing the point by using df1$ inside within() , and your data frame name is so short that it is longer to type within() in this case. Your entire code could be simplified to one line:

df1$newCol = factor(df1$Id %in% df2$Id)

My last comment I'm making from a state of ignorance about your application, so take it with a grain of salt, but a binary variable is almost always nicer to have as boolean (TRUE/FALSE) or integer (1/0) than as a factor. It does depend what you're doing with it, but I would leave the factor() off until necessary.