| title | author | date |
|---|---|---|
Tutorial Setup |
Phillip Alday, Douglas Bates, Reinhold Kliegl |
February 17-19, 2020 |
A tutorial on Mixed Models in the Julia Programming Language will be held February 17-19, 2020 at the Zentrum fur interdisziplinare Forschung (ZiF) at the University of Bielefeld. In preparation for this tutorial, please follow these instructions to install Julia on the computer you will be using.
We assume that those participating in the tutorial are somewhat familiar with R and the RStudio IDE (integrated development environment).
The Julia download site provides binary downloads for most common operating systems. Ensure that the version you install is at least v1.3.1. Version 1.4.0-rc1 (release candidate 1) is also suitable if you want to be in the vanguard.
By itself the Julia binary provides a basic REPL (read-eval-print-loop), which is quite adequate for installing packages. (Installing and using IDE's for Julia is discussed below.) When you start Julia you are in julia mode, as indicated by the julia> prompt.
Typing certain characters as the first in an input line will change the mode of the REPL.
| Character | Prompt | Context |
|---|---|---|
? |
help?> |
Help mode - print help messages on functions, types, etc. |
] |
(v1.3) pkg> |
Pkg mode - install, list, remove, etc. packages |
; |
shell> |
Shell mode - execute a single shell command |
$ |
R> |
R mode - requires RCall package installed and active |
<backspace> |
julia> |
return to Julia mode |
A binary installation of Julia includes several packages in the standard library. They are listed and documented as part of the general documentation at https://docs.julialang.org. Most of the time a user does not need to be conscious of these packages but occasionally we will attach one explicitly.
For example
julia> using InteractiveUtils, Random, Statistics
julia> varinfo(Random) # list exported functions and types
name size summary
––––––––––––––– ––––––––––– ––––––––––––––––––––––––––
AbstractRNG 176 bytes DataType
MersenneTwister 232 bytes DataType
Random 478.102 KiB Module
RandomDevice 192 bytes DataType
bitrand 0 bytes typeof(Random.bitrand)
rand! 0 bytes typeof(Random.rand!)
randcycle 0 bytes typeof(Random.randcycle)
randcycle! 0 bytes typeof(Random.randcycle!)
randexp 0 bytes typeof(Random.randexp)
randexp! 0 bytes typeof(Random.randexp!)
randn! 0 bytes typeof(Random.randn!)
randperm 0 bytes typeof(Random.randperm)
randperm! 0 bytes typeof(Random.randperm!)
randstring 0 bytes typeof(Random.randstring)
randsubseq 0 bytes typeof(Random.randsubseq)
randsubseq! 0 bytes typeof(Random.randsubseq!)
shuffle 0 bytes typeof(Random.shuffle)
shuffle! 0 bytes typeof(Random.shuffle!)
julia> varinfo(Statistics)
name size summary
–––––––––– ––––––––––– ––––––––––––––––––––––––––––
Statistics 209.972 KiB Module
cor 0 bytes typeof(Statistics.cor)
cov 0 bytes typeof(Statistics.cov)
mean 0 bytes typeof(Statistics.mean)
mean! 0 bytes typeof(Statistics.mean!)
median 0 bytes typeof(Statistics.median)
median! 0 bytes typeof(Statistics.median!)
middle 0 bytes typeof(Statistics.middle)
quantile 0 bytes typeof(Statistics.quantile)
quantile! 0 bytes typeof(Statistics.quantile!)
std 0 bytes typeof(Statistics.std)
stdm 0 bytes typeof(Statistics.stdm)
var 0 bytes typeof(Statistics.var)
varm 0 bytes typeof(Statistics.varm)
A listing of registered Julia packages is available at https://pkg.julialang.org/. Note the panel on the left where you can search by name or filter by tags on the packages. Packages we will use include
| Name | Purpose |
|---|---|
| CSV | read and write comma-separated-value files |
| CategoricalArrays | factor-like objects |
| DataFrames | data tables with properties and capabilities like R's data.frame |
| DataFramesMeta | database-like operations on data tables |
| IJulia | Run Julia in Jupyter notebooks |
| MixedModels | fit and examine mixed-effects models |
| PooledArrays | light-weight version of categorical arrays |
| RCall | call R from Julia, including data transfers |
| RData | read data tables stored in .rda or .rds format |
| Tables | general data table structures - either row- or column-oriented |
| Weave | similar to the knitr package for R |
These packages must be added (similar to package.install in R) before they can be attached with using (similar to library or require in R). In the package REPL (accessed by typing ] as the first character of a line) just type add Tables, for example. Note that you must have R installed on your computer to be able to successfully install the RCall package in Julia.
This document was created from the file README.jmd in this repository using Weave as
using Weave
weave("README.jmd", doctype="pandoc")
Editing and running Julia code is supported in both VSCode and the Atom editor. They are IDEs similar to RStudio. The VSCode support is documented at https://www.julia-vscode.org. The Atom support is called Juno and is documented at https://junolab.github.io. At least on some systems VSCode recognizes default installations of Julia and automatically sets the necessary paths. We recommend to start with installations of Julia and VSCode.
Either of these environments is fine for this workshop.
Jupyter provides interactive notebooks in the browser. When the IJulia package is installed and built it installs a version of conda if it is not already available. The jupyter-notebook and jupyter-lab applications can be added through conda. If you haven't used Jupyter notebooks it is probably best to wait to install them at the workshop.