base_mutate % as.ame () tbl % as_tibble () dt % as.ame () %>%. The following functions perform, in order, 1) adding a variable, 2)įiltering rows, and 3) summarizing data by group using baseįunctionality. Table, but note that an object has a size and an address on yourīelow, I will look at the behavior of data.table (compared to base R Using this address later on because we’ll be making copies of this data It is roughly 20 MB and has an address of 0x7fc4335f9600. d % factor, x = rnorm ( 1e6 ), y = runif ( 1e6 ) ) d We’ll use the following data table for this post. library ( bench ) # assess speed and memory library ( data.table ) # data.table for all of its stuff library ( dplyr ) # compare it to data.table library ( lobstr ) # assess the process of R functionsĪnd we’ll set a random number seed. My computer will use 4 threads (a form of parallelization). If you want the specifics, continue on :) Packagesįirst, we’ll use the following packages to further understand R,ĭata.table, and dplyr.
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