macOS (ARM)

DSCI 310 software stack install instructions for macOS.

Installation notes

If you have already installed Git, Latex, or any of the R or Python related packages please uninstall these and follow the instructions below to reinstall them (make sure to also remove any user configuration files and backup them if desired). In order to be able to support you effectively and minimize setup issues and software conflicts, we suggest you install the required software as specified below.

In all the sections below, if you are presented with the choice to download either a 64-bit (also called x64) or a 32-bit (also called x86) version of the application always choose the 64-bit version.

Once you have completed these installation instructions, make sure to follow the post-installation notes at the end to check that all software is setup correctly.

Web browser

In DSCI 310 we will be using many tools that work most reliably on Google Chrome and Firefox (including our online quiz software), so we recommend that you use one of these browsers.

Bash shell

Apple recently changed the Mac default shell in the Terminal to Zsh, however, we aim to teach with the same shell across all three operating systems we support, which is the Bash shell. Thus, we ask that you change the default shell in your Terminal to Bash by opening the Terminal (how to video) and typing:

chsh -s /bin/bash

You will have to quit all instances of open Terminals and then restart the Terminal for this to take effect.

Visual Studio Code

Installing

The open-source text editor Visual Studio Code (VS Code) is both a powerful text editor and a full-blown Python IDE, which we will use for more complex analysis. You can download and install the macOS version of VS Code from the VS code website https://code.visualstudio.com/download. Once the download is finished, click “Open with Archive utility”, and move the extracted VS Code application from “Downloads” to “Applications”. In addition to reading the getting started instructions, be sure to follow the “Launching from the command line” steps as well.

You can test that VS code is installed and can be opened from Terminal by restarting terminal and typing the following command:

code --version

you should see something like this if you were successful:

1.81.1
5763d909d5f12fe19f215cbfdd29a91c0fa9208a
arm64

Note: If you get an error message such as -bash: code: command not found, but you can see the VS Code application has been installed, then something went wrong with setting up the launch from the command line. Try following these instructions again, in particular you might want to try the described manual method of adding VS Code to your path.

GitHub

In DSCI 310 we will use the publicly available GitHub.com. If you do not already have an account, please sign up for one at GitHub.com

Sign up for a free account at GitHub.com if you don’t have one already.

Git

We will be using the command line version of Git as well as Git through RStudio and JupyterLab. Some of the Git commands we will use are only available since Git 2.23, so if your Git is older than this version, we ask you to update it using the Xcode command line tools (not all of Xcode), which includes Git.

Open Terminal and type the following command to install Xcode command line tools:

xcode-select --install

After installation, in terminal type the following to ask for the version:

git --version

you should see something like this (does not have to be the exact same version) if you were successful:

git version 2.39.2 (Apple Git-143)

Note: If you run into trouble, please see that Install Git Mac OS section from Happy Git and GitHub for the useR for additional help or strategies for Git installation.

Configuring Git user info

Next, we need to configure Git by telling it your name and email. To do this, type the following into the terminal (replacing Jane Doe and , with your name and email that you used to sign up for GitHub, respectively):

git config --global user.name "Jane Doe"
git config --global user.email janedoe@example.com

Note: To ensure that you haven’t made a typo in any of the above, you can view your global Git configurations by either opening the configuration file in a text editor (e.g. via the command code ~/.gitconfig) or by typing git config --list --global.

Setting VS Code as the default editor

To make programs run from the terminal (such as git) use vscode by default, we will modify ~/.bash_profile. First, open it using VS Code (this will also create the file if it doesn’t already exist):

code ~/.bash_profile

Note: If you see any existing lines in your ~/.bash_profile related to a previous Python or R installation, please remove these.

Now append the following lines to ~/.bash_profile:

# Set the default editor for programs launch from terminal
EDITOR="code --wait"
VISUAL=$EDITOR  # Use the same value as for "EDITOR" in the line above

Then save the file and exit VS Code.

Note: Most terminal programs will read the EDITOR environmental variable when determining which editor to use, but some read VISUAL, so we’re setting both to the same value.

In some cases, VScode is not set as the default text editor for git even after appending the two lines above, so to make sure it is registered properly, also run the following from your terminal:

git config --global core.editor "code --wait"

On MacOS, VScode sometimes reads a different configuration file than your other programs. To avoid this, open your ~/.bashrc file:

code ~/.bashrc

And append the following lines:

# Do NOT add anything to this file, use `~/.bash_profile` instead.
# The next line automatically loads your `~/.bash_profile`
# any time a program tries to read your `~/.bashrc` file.
if [ -f ~/.bash_profile ]; then . ~/.bash_profile; fi

The comment is a reminder to your future self who might open up this file a few months from now =)

Python, Conda, and JupyterLab

Python and Conda

We will be using Python for a large part of the program, and conda as our Python package manager. To install Python and the conda package manager, we will use the Miniconda platform (read more here).

Select the appropiate link:

Intel Mac: Miniconda MacOS Intel 64-bit pkg install can be downloaded here..

Mac M1 or higher: Miniconda MacOS Apple M1 64-bit pkg install for Python 3.x can be downloaded here.

Note: on August 24th, 2023 we observed an issue using the latest install link above for “Mac M1 or higher”. If you also observe this, then please visit https://docs.conda.io/en/latest/miniconda-other-installer-links.html#macos-installers and download and install the “Miniconda3 macOS Apple M1 64-bit pkg” installer from the latest (highest) version of Python that you can see listed on that page.

After installation, restart the terminal. If the installation was successful, you will see (base) prepending to your prompt string. To confirm that conda is working, you can ask it which version was installed:

conda --version

which should return something like this:

conda 23.5.2

Note: If you see zsh: command not found: conda, see the section on Bash{:target=“_self”} above to set your default Terminal shell to Bash as opposed to Zsh.

Next, type the following to ask for the version of Python:

python --version

Make sure it returns Python 3.11.0 or greater:

Python 3.11.4

Installing Python packages

conda installs Python packages from different online repositories which are called “channels”. A package needs to go through thorough testing before it is included in the default channel, which is good for stability, but also means that new versions will be delayed and fewer packages are available overall. There is a community-driven effort called the conda-forge (read more here), which provides more up to date packages. To enable us to access the most up to date version of the Python packages we are going to use, we will add the more up to date channel. To add the conda-forge channel by typing the following in the terminal:

conda config --add channels conda-forge

To install packages individually, we can now use the following command: conda install <package-name>. After running that command conda will show you the packages that will be downloaded, and you can press enter to proceed with the installation. If you want to answer yes by default and skip this confirmation step, you can replace conda install with conda install -y. Also note that we may occasionally need to install packages using pip, the standard Python package manager. The installation command is very similar to that of conda: pip install <package-name>.

Let’s try this out by installing a package that makes conda faster and changing the config to use this package by default:

conda install conda-libmamba-solver
conda config --set solver libmamba

In the next session we will use conda to install some of the key packages we will use in DSCI 310.

JupyterLab setup

JupyterLab is a coding environment that we will be using frequently throughout the course. The JupyterLab git extension facilitates using notebooks in JupyterLab together with Git & GitHub. The spellchecker helps us correcting typos in our writing. Install them via the following commands:

conda install jupyterlab jupyterlab-git jupyterlab-spellchecker

To test that your JupyterLab installation is functional, you can type jupyter lab into a terminal, which should open a new tab in your default browser with the JupyterLab interface. To exit out of JupyterLab you can click File -> Shutdown, or go to the terminal from which you launched JupyterLab and hold Ctrl while pressing c twice.

R, XQuartz, IRkernel, and RStudio

R is the second language that we will be using frequently in this course. We will use R both in Jupyter notebooks and in RStudio.

R

Go to https://cran.r-project.org/bin/macosx/ and download the latest version of R for Mac. Open the file and follow the installer instructions. Pay attention that you will have to install R-4.3.2-arm64.pkg if you are working with a Apple silicon (M1/M2) Mac and R-4.3.2-x86_64.pkg if you are working in n older Intel Mac.

After installation, open a new terminal window and type the following:

R --version

You should see something like this if you were successful:

R version 4.3.2 (2023-10-31) -- "Eye Holes"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under the terms of the
GNU General Public License versions 2 or 3.
For more information about these matters see
https://www.gnu.org/licenses/.

Note: Although it is possible to install R through conda, we highly recommend not doing so. In case you have already installed R using conda you can remove it by executing conda uninstall r-base.

XQuartz

Some R packages rely on the dependency XQuartz which no longer ships with the Mac OS, thus we need to install it separately. Download it from here: https://www.xquartz.org/ and follow the installation instructions.

RStudio

Download the macOS Desktop version (not Pro) of RStudio https://posit.co/download/rstudio-desktop/. Open the file and follow the installer instructions.

To see if you were successful, try opening RStudio by clicking on its icon (from Finder, Applications or Launchpad).

Now we are going to change RStudio’s Insert Pipe shortcut so that it inserts the new native pipe operator |>. Go to Tools > Global Options > Code > Editing and tick the following option:

Once the change is made you can try in the RStudio console Command + Shift + m to check if works.

Finally, let’s install a common R package that you used a lot in DSCI 100 by typing the following into the console inside RStudio:

install.packages("tidyverse")

IRkernel

The IRkernel package is needed to make R work in Jupyter notebooks. To enable this kernel in the notebooks, install by pasting the following command into the RStudio Console:

install.packages('IRkernel')

Next, open a terminal and type the following (you can’t use RStudio for this step since it doesn’t honor $PATH changes in ~/.bash_profile)

R -e "IRkernel::installspec()"

To see if you were successful, try running JupyterLab and check if you have a working R kernel. To launch JupyterLab, type the following in Terminal:

jupyter lab

A browser should have launched and you should see a page that looks like the screenshot below. Now click on “R” notebook (circled in red on the screenshot below) to launch an JupyterLab with an R kernel.

Sometimes a kernel loads, but doesn’t work as expected. To test whether your installation was done correctly now type library(tidyverse) in the code cell and click on the run button to run the cell. If your R kernel works you should see something like the image below:

To improve the experience of using R in JupyterLab, we will add keyboard shortcuts for inserting the common R operators <- and |>. Go to Settings -> Settings Editor. Then click JSON Settings Editor in the top right corner and click on Keyboard Shortcuts in the navigation panel to the left. You will see two panels, the right-most panel allows you to perform advanced modification of keyboards shortcuts in JupyterLab and it already contains quite a few shortcuts. We’re going to add two more shortcuts, by pasting a text snippet just before the first existing shortcut. Go ahead and create a new line just after the line that says "shortcuts": [ and paste the following:

        {
            "command": "apputils:run-first-enabled",
            "selector": "body",
            "keys": ["Alt -"],
            "args": {
                "commands": [
                    "console:replace-selection",
                    "fileeditor:replace-selection",
                    "notebook:replace-selection",
                ],
                "args": {"text": "<- "}
            }
        },
        {
            "command": "apputils:run-first-enabled",
            "selector": "body",
            "keys": ["Accel Shift M"],
            "args": {
                "commands": [
                    "console:replace-selection",
                    "fileeditor:replace-selection",
                    "notebook:replace-selection",
                ],
                "args": {"text": "|> "}
            }
        },

After you have pasted this text, hit the small floppy disk in the top right (or Ctrl + s) to save the settings. Here is a screenshot of what it looks like with the settings saved:

To check that the extension is working, open JupyterLab, launch an R notebook, and try inserting the operators by pressing Alt + - or Command + Shift + m, respectively. You could add any arbitrary text insertion command the same way, but this is all that we suggest for this course.

Quarto CLI

Quarto is an open-source scientific and technical publishing system that you can access from VSCode, Jupyter Lab, RStudio, or the terminal.

The RStudio version that you have downloaded is already equipped with the last version of Quarto. You can check this by opening a new document in File -> New File -> Quarto Document.

Quarto can be used outside RStudio as well, this is why we are going to install Quarto CLI. Please, download the last version of Quarto CLI for MacOs.

After the installation finishes, close all the terminals you may have open. Then, open a new one and try running this command:

quarto --version

If the installation was successful you will read the output:

1.3.450

LaTeX

We will install the lightest possible version of LaTeX and its necessary packages as possible so that we can render Jupyter notebooks and R Markdown documents to html and PDF. If you have previously installed LaTeX, please uninstall it before proceeding with these instructions.

First, run the following command to make sure that /usr/local/bin is writable:

sudo chown -R $(whoami):admin /usr/local/bin

Note: You might be asked to enter your password during installation.

Now open RStudio and run the following commands to install the tinytex package and setup tinytex:

install.packages('tinytex')
tinytex::install_tinytex()

You can check that the installation is working by opening a terminal and asking for the version of latex:

latex --version

You should see something like this if you were successful:

pdfTeX 3.141592653-2.6-1.40.25 (TeX Live 2023)
kpathsea version 6.3.5
Copyright 2023 Han The Thanh (pdfTeX) et al.
There is NO warranty.  Redistribution of this software is
covered by the terms of both the pdfTeX copyright and
the Lesser GNU General Public License.
For more information about these matters, see the file
named COPYING and the pdfTeX source.
Primary author of pdfTeX: Han The Thanh (pdfTeX) et al.
Compiled with libpng 1.6.39; using libpng 1.6.39
Compiled with zlib 1.2.13; using zlib 1.2.13
Compiled with xpdf version 4.04

The above is all we need to have LaTeX work with R Markdown documents, however for Jupyter we need to add several more packages. Do this by opening a terminal and copying the following there press enter:

tlmgr install eurosym \
  adjustbox \
  caption \
  collectbox \
  enumitem \
  environ \
  fp \
  jknapltx \
  ms \
  parskip \
  pdfcol \
  pgf \
  rsfs \
  soul \
  tcolorbox \
  titling \
  trimspaces \
  ucs \
  ulem \
  upquote \
  lwarp \
  oberdiek

To test that your latex installation is working with jupyter notebooks, launch jupyter lab from a terminal and open either a new notebook or the same one you used to test IRkernel above. Go to File -> Save and Export Notebook as... -> PDF. If the PDF file is created, your LaTeX environment is set up correctly.

WebPDF export

Jupyter recently added another way to export notebooks to PDF which does not require Latex and makes the exported PDF look similar to notebooks exported to HTML. This requires the pyppeteer package, which we can install as follows.

pip install "nbconvert[webpdf]"
playwright install chromium

Try this by going to File -> Export notebook as... -> Export Notebook to WebPDF.

Docker

You will use Docker to create reproducible, sharable and shippable computing environments for your analyses. For this you will need a Docker account. You can sign up for a free one here.

After signing-up and signing into the Docker Store, go here: https://store.docker.com/editions/community/docker-ce-desktop-mac and click on the button “Mac with Intel chip” or “Mac with Apple chip”. Then follow the installation instructions on that screen to install the stable version.

To test if Docker is working, after installation open the Docker app by clicking on its icon (from Finder, Applications or Launchpad). Next open Terminal and type the following:

docker run hello-world

you should see something like this if you were successful:

Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
1b930d010525: Pull complete 
Digest: sha256:451ce787d12369c5df2a32c85e5a03d52cbcef6eb3586dd03075f3034f10adcd
Status: Downloaded newer image for hello-world:latest

Hello from Docker!
This message shows that your installation appears to be working correctly.

To generate this message, Docker took the following steps:
 1. The Docker client contacted the Docker daemon.
 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
    (amd64)
 3. The Docker daemon created a new container from that image which runs the
    executable that produces the output you are currently reading.
 4. The Docker daemon streamed that output to the Docker client, which sent it
    to your terminal.

To try something more ambitious, you can run an Ubuntu container with:
 $ docker run -it ubuntu bash

Share images, automate workflows, and more with a free Docker ID:
 https://hub.docker.com/

For more examples and ideas, visit:
 https://docs.docker.com/get-started/

VS Code extensions (Optional!)

The real magic of VS Code is in the extensions that let you add languages, debuggers, and tools to your installation to support your specific workflow. Now that we have installed all our other Data Science tools, we can install the VS Code extensions that work really well with them. From within VS Code you can open up the Extension Marketplace (read more here) to browse and install extensions by clicking on the Extensions icon in the Activity Bar indicated in the figure below.

To install an extension, go to View -> Extensions or click in the icon as you can see in the image above. Then, search for the names of the ones you are interested in the search bar, click the extension you want, and click “Install”. There are extensions available to make almost any workflow or task you are interested in more efficient! Here we are interested in setting up VS Code as a Python IDE. To do this, search for and install the following extensions:

This video tutorial is an excellent introduction to using VS Code in Python.

Post-installation notes

You have completed the installation instructions, well done 🙌! We have created a script to help you check that your installation was successful, and to provide instructions for how you can troubleshoot any potential issues. To run this script, please execute the following command from your terminal.

bash <(curl -Ss https://raw.githubusercontent.com/UBC-DSCI/dsci-310-student/main/src/check_setup.sh)

The output from running the script will look something like this:

# DSCI 310 setup check 2024.1

If a program or package is marked as MISSING,
this means that you are missing the required version of that program or package.
Either it is not installed at all or the wrong version is installed.
The required version is indicated with a number and an asterisk (*),
e.g. 4.* means that all versions starting with 4 are accepted (4.0.1, 4.2.5, etc).

You can run the following commands to find out which version
of a program or package is installed (if any):


name_of_program --version  # For system programs
conda list  # For Python packages
R -q -e "as.data.frame(installed.packages()[,3])"  # For R packages


Checking program and package versions...

## Operating system
ProductName:            macOS
ProductVersion:         13.4
BuildVersion:           22F66

## System programs
OK        rstudio 2023.12.0+369
OK        R 4.3.2 (2023-10-31) -- "Eye Holes"
OK        python 3.11.6
OK        conda 23
OK        bash 3.2.57(1)-release (arm64-apple-darwin22)
OK        git 2.39.2 (Apple Git-143)
OK        make 3.81
OK        latex 3.141592653-2.6-1.40.25 (TeX Live 2023)
OK        tlmgr 5:21 +0200)
OK        docker 24.0.6, build ed223bc
OK        code 1.85.0

## Python packages
OK        nbconvert-core=7.8.0
OK        playwright=1.40.0
OK        jupyterlab=4.0.6
OK        jupyterlab-git=0.41.0
OK        jupyterlab-spellchecker=0.8.4
OK        jupyterlab PDF-generation was successful.
OK        jupyterlab WebPDF-generation was successful.
OK        jupyterlab HTML-generation was successful.

## R packages
OK        IRkernel=1.3.2
OK        tinytex=0.46
OK        rmarkdown PDF-generation was successful.
OK        rmarkdown HTML-generation was successful.

The above output has been saved to the file /Users/timberst/Documents/dsci-310/dsci-310-student/check-setup-310.log
together with system configuration details and any detailed error messages about PDF and HTML generation.
You can open this folder in your file browser by typing `open .` (without the surrounding backticks).

As you can see at the end of the output, a log file is saved in your current directory. We might ask you to upload this file if we need to troubleshoot your installation, so that we can help you more effectively. If any of your packages are marked as “MISSING” you will need to figure out what is wrong and possibly reinstall them. Once all packages are marked as “OK” we will ask you to submit this log file, so that we can confirm that your installation was successful. Details on where to submit will be provided later.

Note: In general you should be careful running scripts unless they come from a trusted source as in this case (just like how you should be careful when downloading and installing programs on your computer).

Attributions

© Copyright 2022 Tiffany Timbers.

Software licensed under the MIT License.

Attribution: website theme was derived from the tidymodels.org website theme (courtesy of Desirée De Leon).