I have a large CSV with the results of a medical survey from different locations (the location is a factor present in the data). As some analyses are specific to a location and for convenience, I'd like to extract subframes with the rows only from those locations. It happens that the location is the very first field so yes, I could do it by sorting the CSV rows, but I'd like to learn how to do it in R as I'm sure I'll need this for other columns.
So, in a nutshell, the question is: given a data frame foo, how can I create another data frame bar which only contains the rows from foo where foo$location = 'there'?
Here are the two main approaches. I prefer this one for its readability:
bar <- subset(foo, location == "there")
Note that you can string together many conditionals with & and | to create complex subsets.
The second is the indexing approach. You can index rows in R with either numeric, or boolean slices. foo$location == "there" returns a vector of T and F values that is the same length as the rows of foo. You can do this to return only rows where the condition returns true.
foo[foo$location == "there", ]
Just to extend the answer above you can also index your columns rather than specifying the column names which can also be useful depending on what you're doing. Given that your location is the first field it would look like this:
bar <- foo[foo[ ,1] == "there", ]
This is useful because you can perform operations on your column value, like looping over specific columns (and you can do the same by indexing row numbers too).
This is also useful if you need to perform some operation on more than one column because you can then specify a range of columns:
foo[foo[ ,c(1:N)], ]
Or specific columns, as you would expect.
foo[foo[ ,c(1,5,9)], ]
Source: Stackoverflow.com