tj mahr 🤘

@tjmahr.com

data scientist studying how kids learn to speak, dad, jump roper, bayesian, tjmahr.com, Madison, WI

$> cli::cli_bullets(c("!" = "Watch out for {x}")) ! Watch out for too many spaces > cli::cli_bullets(c("!" = "Watch out for {.val {x}}")) ! Watch out for "too many spaces" > cli::cli_bullets(c("!" = "Watch out for {.q {x}}")) ! Watch out for "too many spaces"$

count of how many times the kids says say "goodbye" (about 85 / 1500)

Screenshot of the RStudio console printing out toggle pairs:

ace.Editor.prototype.$toggleWordPairs
(23) [Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2), Array(2)]
0
:
(2) ['first', 'last']
1
:
(2) ['true', 'false']
2
:
(2) ['yes', 'no']
3
:
(2) ['width', 'height']
4
:
(2) ['top', 'bottom']
5
:
(2) ['right', 'left']
6
:
(2) ['on', 'off']
7
:
(2) ['x', 'y']
8
:
(2) ['get', 'set']
9
:
(2) ['max', 'min']
10
:
(2) ['horizontal', 'vertical']
11
:
(2) ['show', 'hide']
12
:
(2) ['add', 'remove']
13
:
(2) ['up', 'down']
14
:
(2) ['before', 'after']
15
:
(2) ['even', 'odd']
16
:
(2) ['in', 'out']
17
:
(2) ['inside', 'outside']
18
:
(2) ['next', 'previous']
19
:
(2) ['increase', 'decrease']
20
:
(2) ['attach', 'detach']
21
:
(2) ['&&', '||']
22
:
(2) ['==', '!=']
length
:
23
[[Prototype]]
:
Array(0)

my setup showing that Ctrl+Shift+Up for Expand selection masks Increment Number

Workout that alternates between jumping rope, goblet squats and kettlebell swings

Luce and Pisoni (1998) looked at similarity more broadly by studying phonological neighborhoods (also called similarity neighborhoods). Indeed, their neighborhood activation model boils down to an equation of the form:
probability(target recognition) =
similarity (target word) • frequency(target word)
= similarity(neighbor) • frequency(neighbor)
competitiveness of target
total competitiveness of neighborhood
(1)
where "similarity" is some measure of the phonetic similarity of a candidate word to the spoken target and where the similarity neighborhood is the set of words that are similar to the target (which includes the target word itself). The number of words in this neighborhood is called the neighborhood density. This equation implies, holding all other things equal, that higher frequency words should be more recognizable than lower frequency ones, words with more neighbors (denser neighborhoods) should be less recognizable than words with fewer neighbors (sparser neighborhoods), and words with high-frequency neighbors should be less recognizable than words with low-frequency neighbors. Because both frequency and neighborhood density contribute to the denominator in the equation, this number is best thought of as the overall "competitiveness" of a neighborhood.

What defines a similarity neighborhood? Luce and Pisoni (1998) based their similarities on phoneme confusion probabilities based on an earlier experiment. A simpler and more common approach is to define a word's neighbors as the words that differ by the addition, substitution, or deletion of a single phoneme (more formally, a Levenshtein distance or string-edit distance of 1). For example, the 20-plus-word neighborhood for sting would include additions such as "st(r)ing" and "s(itting," substitutions such as "s(wing" and "st(u)ng," and deletions such as "sOing" or "Oting."

Ceremony by galaxie 500, day wave, chromatics, and Xiu Xiu

i asked codex:

i want a function trunc_kable(x, row_limit) that modifies the output of knitr::kable(). If the number of rows of output in the table exceeds row_limit by more than 10%, the middle n - row_limit rows have their content replaced with [...n-row_limit...] if that string fits, otherwise use [...] or ` if they fit.

it:

Worked for 3m 56s

screenshot of some yaml showing aligned key-value pairs

Total: 0
5750
Description
[TAS] Can You Beat Mike Tyson's Punch-Out!!
With Only Gut Punches (Obsoleted)
674
Likes
35,676
Views
2024
Jul 24
Important update! I'm not sure if anyone will see this but I'll leave this here just in case anyone does come along. Anyways, the TAS has been obsoleted and additional face/star punches can be taken out from the TAS. I've made an updated TAS file but frankly don't know what to do with it, since I don't think many people will be interested seeing as they've seen most the fights and understood the basic concept. Regardless, thanks to FartButt87 for correcting a couple basic and stupid oversights that I missed due to lack of research. Oh well.

YouTube thumbnail

Why AI is
Like a Horse
18:43
:
Why Al is like a (Clever Hans) Horse - Computerphile
Computerphile • 2.3K views • 1 hour ago

screenshot of the README highlighting four files that misnamed or misplaced

We need to identify that the ab-01/day02/ab-01_01* has inconsistent time values and that ab-02/day02/[yellow] is unexpected and 02/day03/[green] is unexpected:

# fs::dir_tree("./inst/demo-3")
./inst/demo-3
├── ab-01
│ ├── day01
│ │ ├── ab-01_01_green.txt
│ │ └── ab-01_01_red.txt
│ ├── day02
│ │ ├── ab-01_01_green.txt <---- wrong time value
│ │ └── ab-01_01_red.txt <---- wrong time value
│ └── day03
│ └── ab-01_03_yellow.txt
└── ab-02
├── day01
│ ├── ab-02_01_green.txt
│ └── ab-02_01_red.txt
├── day02
│ ├── ab-02_02_green.txt
│ ├── ab-02_02_red.txt
│ └── ab-02_02_yellow.txt <---- can't go here
└── day03
└── ab-02_03_green.txt <---- can't go here

Adjective
spavined (comparative more spavined, superlative most spavined)
1. (farriery, veterinary medicine) Of a horse: having spavin ("a disease of horses caused by a bony swelling which develops in a leg due to inflammation"). [synonym, antonym,
coordinate term A] [quotations V]
Synonym: (archaic) spavindy
Antonym: unspavined
Coordinate term: foundered
2. (by extension) Of a person: lame due to a leg disease. [synonyms, antonym, coordinate term A]
Synonyms: (archaic) halt, halting, limping
Antonym: unspavined
Coordinate term: crippled
3. (figurative) Of a person or a thing: old, worn-out; also, obsolete. [quotations V]
I'm a spavined old warrior, and I don't have much time left in this world, but I still have a few tricks to teach these whippersnappers.

Shark from Toy Story wearing Woody’s hat

(SQUEAKS) Look, I'm Woody!
Howdy, howdy, howdy!

The back of an excavator looking like a face

The values of R and ESS require reliable estimates of variances and autocorrelations (in addition to the existence of these quantities; see our Cauchy examples in Section 5.l), which can only occur if the chains have enough independent replicates. In particular, we only recommend relying on the R estimate to make decisions about the quality of the chain if each of the split chains has an average ESS estimate of at least 50. In our minimum recommended setup of four parallel chains, the total ESS should be at least 400 before we expect R to be useful.

Keep honking! I’m listening to the spiritual sound of ecstatic black metal by the band Agriculture

screenshot of R console which throws a nice error

> file_rename_impl(path, path_new, .overwrite = FALSE, .dry_run = TRUE)
Warning message:
Cannot safely rename files
✖ 2 files have naming collisions
• Ensure that files do not rename to the same destination
✖ Detected chained renames:
f -> g -> h
• Rename files in separate steps to avoid order-dependent file renames.

Rename plan:
a1 -> a2
b1 -> b2
✖ (c1, cc1) -> c2 (naming collision)
✖ f -> g (chained rename)
g -> h

The Runaway Bunny hiding in a crocus in a garden

Screwworms were once endemic to the US, but were eradicated in the 1960s amid a concerted effort to annihilate their population. This is done with aerial bombings of sterile male flies, which is the most effective weapon against the parasites.
The mass release of dud studs elbows out fertile males, preventing them from mating with females, which generally only mate once.

$rstudio error message that includes c:\users\jenkins in the message$

If any of you wants to know what's wrong with this garbage, it's based on pre set theory incorrect axioms used to prove theorems put into set theory as definitions. They claim one of these axioms is that which applies to the finite applies to the applicable infinite, but obviously it's does/doesn't, so obviously false. Another is not being able at all for no other apparent reason to prove something true proves it false.
Also in the diagonal argument there are 2 infinities, one exponential of the other AND equal to or less than the other, they use the diagonal argument to prove exponential of infinity is not equal to the same so only exponential. Both obviously can't be true
If you try to get out of this by rewriting the argument that not being able to take the diagonal disproved the point, then yes exponential of infinity doesn't equal the same infinity, and +2 and x2 and 12, contradicting what they claim.
Yet I'm banned from editing Wikipedia over this stuff ...

screenshot of my rstudio build pane with a makefile that creates a function to announce a task, report the script being run and runs that script in a separate r session and then uses that function on several tasks.

$``` r as_grp_glyph <- function(xs) { xs <- as.character(xs) class(xs) <- c("grp_glyph", "character") xs } type_sum.grp_glyph <- function(x) I("") add_group_glyph <- function(data, glyph_col = "g", by = NULL) { data |> dplyr::mutate( "{glyph_col}" := dplyr::case_when( dplyr::n() == 1 ~ "•", dplyr::row_number() == 1 ~ "┌", dplyr::row_number() == dplyr::n() ~ "└", TRUE ~ "│" ) |> as_grp_glyph() ) |> relocate(dplyr::one_of(glyph_col)) } library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union mtcars |> group_by(cyl, gear, carb) |> arrange(.by_group = TRUE) |> add_group_glyph() #> # A tibble: 32 × 12 #> # Groups: cyl, gear, carb [12] #> g mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 • 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1 #> 2 ┌ 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 3 │ 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1 #> 4 │ 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1 #> 5 └ 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1 #> 6 ┌ 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 7 │ 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 8 │ 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2 #> 9 └ 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 #> 10 ┌ 26 4 120. 91 4.43 2.14 16.7 0 1 5 2 #> # ℹ 22 more rows ``` <sup>Created on 2026-05-22 with [reprex v2.1.1](https://reprex.tidyverse.org)</sup>$

$tibble printed with box-drawing characters in a column to make start and end of a groups. function used (vibecoded): # assumes the data is grouped and sorted already add_group_glyph <- function(data, glyph_col = "g") { data |> mutate( "{glyph_col}" := case_when( dplyr::n() == 1 ~ "•", dplyr::row_number() == 1 ~ "┌", dplyr::row_number() == dplyr::n() ~ "└", TRUE ~ "│" ) ) }$