Making adjustments to kiddo’s Minecraft server and am continuously grateful that the answer to “how did I do that?” is almost always found in the fish shell history.
The view this morning from the dashboard, while I was out running a couple of errands.
Last fall I wrote a bit about a Destiny 2 power level tracking tool I built using R. I’ve now converted it to a full-on Shiny app and solved some issues with the oauth2 flow that stumped me in my intermittent tinkering with it. I’m super satisfied to have been able to get this to work! Now that I have the authentication process figured out, I’m eager to also convert my armor profiling tool to use it. Look out!
You can check it out here: traveleR
Cappuccinos will continue until morale improves.
Yes, indeed, that’s brie, blue cheese and chicken on last night’s pizza. Yes, indeed, it was delicious.
Today I made improvements to some R code in my Destiny 2 hobby-coding-verse after learning how to much more cleanly deal with nested lists. I had previously used a solution using map() to apply a selector to each item in the list, but this was clunky, hard to remember, and became really hard to read with several levels of a nested list.
The far better solution is the unnest_auto function from {tidyr}, which I came upon when tinkering with the last.fm API data recently. Once I understood how it works, it’s so easy and satisfying! The key is to first make a named tibble.
> tibble(my_tibble = instanced)
# A tibble: 906 × 1
my_tibble
<named list>
1 <named list [10]>
2 <named list [10]>
3 <named list [10]>
4 <named list [10]>
5 <named list [12]>
6 <named list [10]>
7 <named list [10]>
8 <named list [10]>
9 <named list [9]>
10 <named list [9]>
# … with 896 more rowsThat nice tibble can be operated on by unnest_auto():
> tibble(my_tibble = instanced) %>% unnest_auto(my_tibble) %>%
select(itemLevel, breakerType)
Using `unnest_wider(my_tibble)`; elements have 8 names in common
# A tibble: 906 × 2
itemLevel breakerType
<int> <int>
1 132 NA
2 133 NA
3 133 NA
4 132 NA
5 133 3
6 132 NA
7 133 NA
8 133 NA
9 0 NA
10 0 NA
# … with 896 more rows(I selected just a couple of columns for readability there; if you don’t do that, you’ll receive all fields at the current list level, including additional nested lists if they exist). After figuring this out, I realized that I also needed to keep the names of each list element, because they constitute a unique ID for the element returned from the API query, and I banged my head a bit on trying to do that as a part of the unnest operation, before I backed up, recentered on the outcome I wanted to produce, and realized I could do it really cleanly using mutate()! The final code looks like this:
> tibble(my_tibble = instanced) %>% unnest_auto(my_tibble) %>%
select(itemLevel, breakerType) %>% mutate(id = names(instanced))
Using `unnest_wider(my_tibble)`; elements have 8 names in common
# A tibble: 906 × 3
itemLevel breakerType id
<int> <int> <chr>
1 132 NA 6917529338105913753
2 133 NA 6917529550016812142
3 133 NA 6917529281178546429
4 132 NA 6917529231127188610
5 133 3 6917529301504642848
6 132 NA 6917529234625200021
7 133 NA 6917529193313832065
8 133 NA 6917529182955737017
9 0 NA 6917529417667489181
10 0 NA 6917529490520758715
# … with 896 more rowsThe D2 API returns a ton of nested lists, so this simplified, accessible and effective tool is 100% getting a featured spot in my toolbox.
I got an espresso scale, so I can now be even more insufferable, with even greater precision, about my coffee.
It was still pretty chilly on this quick morning walk, but it was sure pretty out.
Yesterday my /now page surfaced an eight-year old post of some of my favorite photos from 2013. What a tide of memories those bring! Seems like a very, very long time ago.
