I have been tinkering with lots of different programming languages (see here and here) over the last few years. Scheme is the only language so far that I have enjoyed enough to write a decent amount of code. Elixir first caught my eye back in April 2020, but I've only recently tried to write more than 'hello world' with it. So far, I think it is great and I'm excited to learn more. I haven't previously been a fan of code notebooks, but I think Livebook is amazing.

I'm using Elixir on Ubuntu. There are lots of different ways to install Elixir on Linux. I opted to first install Erlang with sudo apt install erlang. Then I installed the Elixir precompiled package for the version of Erlang that I just installed (get Erlang/OTP version by running erl -s halt). I moved the unzipped folder to my home directory and added the following line to .zshrc: export PATH="$PATH:/home/username/elixir-otp-25/bin".

I installed Livebook by running mix escript.install hex livebook in the terminal and added the following line to .zshrc: export PATH="$PATH:/home/username/.mix/escripts". After running source .zshrc, I was able to launch Livebook with livebook server.

As with learning Scheme, I like to first try to recreate examples that I've written in other programming languages. Below, I've written Elixir code that corresponds to the Scheme examples in this blog post based on the Texas housing dataset that is included as part of the ggplot2 package for R. I wrote that post to try out my dataframe library for Scheme, but below I will focus on the comparison with R. I will highlight snippets of code in this post, but the full notebook is here.

One of the great features of Livebook is that the user-friendly GUI elements create code that is then easy to edit. It dramatically lowers the learning curve for beginners. At the top of every notebook is a setup block that includes an Add package button that allows for searching of available packages and generates the following code.

  {:kino, "~> 0.9.4"},
  {:kino_explorer, "~> 0.1.8"},
  {:kino_vega_lite, "~> 0.1.8"},
  {:req, "~> 0.3.10"}

Kino is behind the magic of Livebook. KinoExplorer and KinoVegaLite provide nice tables for viewing dataframes and data visualizations, respectively. Those packages include Explorer and VegaLite as dependencies so they do not need to be installed separately. I'm using req to get data directly from a URL.

Explorer is a dataframe library for Elixir. I love the choice to mostly follow dplyr, which is my favorite R package. We require these packages because they require compilation and include aliases with as:.

require Explorer.DataFrame, as: DF
require Explorer.Series, as: Series

In R, read.csv allows for reading directly from a URL. This Elixir code composes well, though. It didn't occur to me at first (but should have) that I should be searching for how to do a GET request. load_csv is used here because there is already a representation of the CSV in memory. To read a file from disk, use from_csv. The ! in load_csv! indicates that a problem with the file will raise an exception, which is arguably the preferred behavior for an interactive use case like this notebook.

txhousing =
  |> DF.load_csv!()

This block of code is almost exactly the same as what you would write in dplyr. I love that Elixir has a pipe operator (and it even uses the same characters as the pipe operator in base R!). It looks a little weird that pipe operators are placed at the beginning of lines after many years of using pipes in R.

df_agg_year =
  |> DF.group_by("year")
  |> DF.summarise(
    avg_sales: mean(sales),
    avg_volume: mean(volume),
    avg_median: mean(median)

In group_by, the column month can be written as a string ("month") or an atom (:month) but it can't be bare (month). In summarise and arrange, the column names must be bare, not a string or atom. Without reading the documentation or source code, I would guess that summarise and arrange are macros and group_by is not.

df_agg_month =
  |> DF.group_by(:month)
  |> DF.summarise(
    avg_sales: mean(sales),
    avg_volume: mean(volume),
    avg_median: mean(median)
  |> DF.arrange(month)

One of the convenient features of dplyr::mutate is that expressions can perform calculations on columns that were created within that mutate. Here we need to move that last expression to its own DF.mutate.

|> DF.group_by(["city", "year"])
|> DF.mutate(
  total_sales: sum(sales),
  total_volume: sum(volume)
# need a new mutate when working with newly calculated column
|> DF.mutate(prop_sales: sales / total_sales)