Billy Fung

Hadleyverse vs data.table

Billy Fung / 2017-08-23

Is the Hadleyverse the only option?

One of the major downsides to learning R is figuring out which packages to use. R comes with many standard packages, but then sometimes they aren’t the most intuitive way to handle your data, or they can be straight up bad. Many people often direct new learners to use the packages writtein by Hadley Wickham, which I will say are great packages.


Most of the packages deal with handling data in an intuitive way that makes your work easier to follow, and easier to read. One such package is dplyr, which is used to manipulate data, mainly data frames.

Example of filtering a dataframe for rows where a column is a specific value.

    starwars %>%
      filter(species == "Droid")

The beauty in dplyr lies in being able to chain multiple operations together, so you don’t need to create multiple dataframes. In base R, I found myself creating many tmp dataframes that I used to hold data as I was doing multiple operations on it.

    starwars %>%
      group_by(species) %>%
        n = n(),
        mass = mean(mass, na.rm = TRUE)
      ) %>%
      filter(n > 1)

But is there something faster?

One of the downsides of manipulating dataframes is that they aren’t the most nimble things to be moving around. I’ve been using dplyr and other Hadleyverse packages for quite awhile now, but lately I’ve been dealing with datasets that are much larger, like <5m rows. So the manipulation of data becomes much slower, and grouping operations start to lag.


This is a package that lets a data.table inherit from data.frame. Essentially somebody created this package to improve on memory usage, along with speed. There isn’t any piping or elegant usage of writing code that makes a datatable stand out, because that is a very subjective topic.

What it does do better is that I have found fread/fwrite to be way faster at reading/writing files. Much much faster compared to read_csv/write_csv.

With operations that modify the data, data.table can update by reference, this saves computation and memory cost in assigning the result back to a variable.

    DT[x > 0, y:= 'positive']

That simple code will update the data.table y column in-place. As table size grows, data.table will do that a lot quicker compared to dplyr and data.frame.

The dplyr and data.frame equivalent.

    new_DF <- DF %>%
        mutate(y = replace(y, which(x > 0), 'positive'))

So far I am still quite new to data.table, but I have found that I am enjoying using it because of the speed and memory gains, and I don’t find that I wish it had dplyr like syntax. Aggregating and joining is so much faster with large tables that I wish I found out about data.table earlier so then I could gain the intuitive for writing consistent code.

Every bit of optimisation counts when your datasets start growing, so learning the fastest way first will save you time in the long run.