R Tutorial: Filtering rows in a data.table

Key Takeaways

in this lesson we will look at how to subset or filter rows from a data table using row numbers and logical expressions the general form of a data table is DT square brackets I comma J comma by it is read out loud as take DT filter rows and I then compute J grouped by bar we will look at the first argument I hear the functionality is similar to data frames but more convenient and enhanced rows can be filtered using row numbers as you would do in a data frame except the first argument is always interpreted as a row operation irrespective of whether or not you specify a comma for example to subset the third and the fourth rows you can use the code as shown here if you want to exclude a set of rows you can use the negative sign or the exclamation mark as shown here data table contains a few handy special symbols or variables that make many operations efficient one such symbol is dot n which is an integer vector of length 1 when used in the I argument it returns the total number of rows in the data table to get the last row for example you can simply do ba trips square bracket n similarly to get all but the last 10 rows you can specify the I argument as 1 colon dot n minus 10 since dot n is a total number of rows you're essentially creating a continuous sequence of integers from 1 to the required row number you can also construct expressions resulting in a logical vector in the I argument this returns only those rows that evaluate to true however note how we refer to the columns in the data table as if they are variables this is because within the scope of data table that is within the to square brackets columns are seen as if they are variables this avoids unnecessary petition of the dollar sign when referring to the column names while constructing expressions and therefore awards subtle and hard to try bugs also the resulting expression is much cleaner and easier to read here's another example in this example we filter four rows where start underscore terminal column is 58 and end and the score terminal column is not 65 again note the concise and clear syntax of data table as mentioned in the first video data table is fast it automatically creates an index or key of the columns used to subset the data so that any subsequent operations on that column are incredibly faster you can have a look at all such indeces using the indices function let's look at an example first we create a data table DT with ten million rows when we call in thesis on DT we see that the result is not thus at this point the data table has no indeces when we perform a subset operation for the first time we can see that it takes about 0.2 seconds now when we run in theses on DT again notice that X is an index so when we perform the same operation again it takes only about 0.002 seconds it's time for you to practice sub setting data table