What sorts of things should be in .gitignore beyond the defaults? If it's something like a whole directory (say, Data), can that be done with something like regex or do the files need to be added individually?

After running usethis::use_course("rstd.io/wtf-debugging"), we get a directory of R scripts and a CSV file.

Suggestion: these files might want to be stored in a project so that it is self-contained.

The project-centric workflow covered is a huge is a pre-requisite to enable life in the ephemeral cloud, where compute resources are created and destroyed on demand.

It would be terrific to explore the gap between the content of this workshop and being productive in the cloud where naked machines need to be dressed with R and the R project before analysis can begin.

You have a set of tests: A, B, C, D
Each test has subscales: X, Y, Z
At each level of test, you want to score all subscales, so
A: X, Y, Z
B: X, Y, Z
. . . and so on
Jenny suggested that instead of thinking of this as an iteration problem, it's best to wrangle the data so that the two columns test, subscale are unnested into a single column

where: https://jennybc.github.io/wtf-2019-rsc/ under Schedule, Wednesday
current text: 2_2: Personal R Administration 1 of 2
suggested change: 2_2: Personal R Administration 2 of 2

I think this could be really helpful. I would have liked to spend more time on the different options that we could set here rather than making animals talk on start (although this is cute). What are the options that Jim, Jenny and other RStudio people set in their .Rprofile?

Create the Gitter room and replace the link placeholder in the README

At work, I'm working multiple reports that all rely on common data files that occasionally update. Originally, each report lived in its own project with its own Data folder. I then ran into the issue of not being able to easily track which projects had the most recent data. As a result, I migrated them all to one my_reports mega-project with a single Data folder. Now I have trouble organizing my files in a way that follows project-oriented a workflow. Is there a way to have both reliable data and a good file structure?

The pre-work recommended getting the latest versions of R (3.5+) and RStudio (with no minimum version mentioned). It seems that having the Terminal (available in 1.1+) is critical; can it be made clear that is the oldest viable version for this workshop?

I get that ideally we'd all be working with the latest versions, but for those of us stuck behind layers of IT bureaucracy it's not always so simple.

@jimhester or a TA:

In session 3, we will fork and clone this repo.
Then I want a change (see below).
Then we will add this as upstream remote and pull this change in.

Please remove this line (or wherever it has ended up):

https://github.com/jennybc/wtf-2019-rsc/blame/master/day2_3.md#L25

and include "closes #15" in the message (also good to demonstrate that!)

In this lesson we dove right into the weeds of R, and I wasn't clear on why we were learning this or how it would be useful in the larger context of the optimal workflow that this course is about.

Here's what I'm talking about:
https://rstd.io/wtf-2019-rsc

We're using GitHub Pages, in it simplest form, to create a landing page for the workshop, coupled with an rstd.io short link.

We want to be lightweight and nimble, because we always need to make live edits. We want minimal moving parts.

Not sure what the "best" theme is. But I know I'd rather get happy with a canned theme than do any Jekyll config.

I'll keep playing with this and am happy to receive suggestions.

The default theme looked bad, because the landing page had two headers. Possible solution:

https://github.community/t5/GitHub-Pages/Remove-Heading/td-p/4220

I have mixed feelings about getting into Jekyll config for this.

Update: I've switched to a different theme, where this bugs me less.

Would be nice to have a few .Rprofile .Renviron example files added in the slides

give examples about interactive vs non-interactive R mode would help too.

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Jim's slide 26 @jimhester

Install new version of R (lets say 3.5.0 in this example)

need fs and tidyverse here##

Create a new directory for the version of R

fs::dir_create("~/Library/R/3.5/library")

What is the role of the personal access token (PAT) in relation to the two authentication methods available on GitHub (SSH/HTTPS)? Is it only used to replace the password in HTTPS authentication? If so, what are the relevant differences between using SSH vs HTTPS with a PAT?

Do you think you all can touch on how/when to ease the transition from, for me at least, what usually starts out as a pretty free-flowing and chaotic EDA on a new project, to the type of scripted and controlled workflow that was covered yesterday? My research projects tend to be pretty unstructured at first, and even though I use version control, can get a bit bloated as I try new analyses. If when we settle on a workflow like that covered yesterday, what do we do with all the other code? Keep it in a repository, and start a new, smaller and refactored one?

You mentioned it a bit with the smell-test.R file, but I often end up with an entire smell test repo. Should these all be on branches?

Cheers

Is there a way to do this without rendering a .rmd? I found this code but couldn't figure out how to apply it:

tempDir <- tempfile()
dir.create(tempDir)
htmlFile <- file.path(tempDir, "index.html")
# (code to write some content to the file)
viewer <- getOption("viewer")
viewer(htmlFile)

The first bug was a type issue. Defensive programming would have made resolving this bug a non-issue.

@jimhester mentioned several options to consider: assertive, assertthat, checkmate, stopifnot, etc.

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