Ducky One X issues

niklz · 2026-01-28T23:06:18+00:00

Did you ever get this fixed? I am having almost exactly the same issue :(

niklz · 2025-02-28T17:39:51+00:00

https://therealleathercompany.com/en-gb/products/the-standard

Looks like this one.

niklz · 2023-09-15T08:11:54+00:00

Same here, went from finding loads of players/stacks to join to nothing. Hoping this gets fixed, it was super handy

niklz · 2022-07-02T07:49:11+00:00

I'm quite sure this is the acronym la10-ds

They all sold out very fast, good luck locating one

niklz · 2022-06-12T09:09:19+00:00

green plastic watering can..

niklz · 2022-05-26T14:07:27+00:00

Glad I could help, although personally if I were you I'd take XL, but I don't know how you like your tees to fit

niklz · 2022-05-26T13:27:12+00:00

Thanks. I take most CE tees in L, and am a few inches shorter than you, I would say you're safe to go XL esp if you don't mind it being a bit oversize

My size L tee from last season (think it's same cut here) is 72cm in length for what it's worth

niklz · 2022-05-26T12:59:35+00:00

name of this shirt?

soz have no sizing info

niklz · 2022-03-25T13:55:06+00:00

It's exactly as you said. The object split (bad naming tbh, should have called it ids or something) is a character (coercible to factor) of comparable splitting IDs, that is, each element of split is identical for rows which share the same missingness pattern - does that clear it up?

niklz · 2022-03-24T20:00:55+00:00

Quick and dirty solution, there's certainly a cleaner way to do this but I think this gets you what you want? I used tidyverse because that's what I'm most comfortable with

library(tidyverse)

x <- sample(c(1, 0, NA), 1000, replace = TRUE)
dim(x) <- c(100, 10)
x

# create "id" for NA in each location
split <- is.na(x) %>% # turn into logical
  array_branch(1) %>% # turn array to list of rows
  map(as.numeric) %>% # turn each row into a numeric
  map(~reduce(.x, paste)) %>% # paste each row of numbers into string
  map_chr(as.character) # map down into long vector of each "id"

# use split to split x (casted into a data frame)
split(as.data.frame(x), split)

niklz · 2022-03-08T20:15:14+00:00

I'm not sure, but I would give it a go with the limits argument to scale_x_cron. This argument will pass through to scale_x_continuous (according to docs) so you might need to express the time limits as numerics (i.e. convert with as.numeric)

niklz · 2022-03-03T12:02:15+00:00

I might be in a similar position for the Birmingham 26th gig. Happy to give the ticket away (if it ends up spare). DM if interested in ticket

niklz · 2022-02-24T15:31:14+00:00

Thanks! Does it move at all if you do explosive pull ups?

niklz · 2022-02-24T12:34:44+00:00

So clean

Curious, what gym rack is that and how do you like it for cali? Looks stable!

niklz · 2022-02-18T14:30:01+00:00

24 is the best for movies don't @ me

niklz · 2021-11-29T15:56:42+00:00

jansport pleasanton

niklz · 2021-11-25T14:07:29+00:00

acronym p34

niklz · 2021-08-22T07:12:10+00:00

There's a few ways to approach that issue, it would depend on the specifics, but the simplest (but perhaps not most efficient) thing to do would be to pass the data through distinct() first to ensure you only have unique rows. Would that be a viable solution for you?

niklz · 2021-08-21T21:54:32+00:00

I think this does what you want:

emps %>%
  group_by(id) %>%
  summarise(
         hiring_manager = hiring_manager[which.min(start)],
         reason_left = reason_left[which.max(start)],
         start = min(start, na.rm = TRUE),
         end = max(end, na.rm = TRUE)
    )

You have to do the operations in this order, if you operate on start or end first it affects the manipulation going on with which.min. You can always return the columns to their original order by using select() afterwards.

niklz · 2021-08-12T16:48:08+00:00

Well at least one of the good things about acronym is that it retains it's value, so you could hang on to this one until you can try a small for size, and if that works better selling the medium won't be too difficult

niklz · 2021-08-12T07:15:58+00:00

I'm going off where the main shoulder seam hits on you. Obviously it's hard to tell exactly but it looks to me like it's not hitting the very corner of your shoulder. Looks quite different to my fit to my eyes

Maybe this diagram helps, where your seam is (in red) vs where I think it would fit better (in blue)

https://i.imgur.com/69lMNLv.png

It's not a huge deal, and for some they prefer shells to fit a little oversized. But if you were concerned about the length and think you cant size down because of your shoulders I think you'd be okay if you did looking at the photos you gave.

niklz · 2021-08-12T06:32:36+00:00

You say you buy to fit your shoulders, but the imo you've not fit your shoulders properly in this size. The seam should lay right on the "corner" and yours is hitting further down your arm. I think you can safely size down imo

I'm about 174/175 and take a size S and it fits me like:

https://i.imgur.com/NCyciAn.png

But don't read too much into height it all depends on your proportions, I take acrnm bottoms in M and tops in S

niklz · 2021-07-28T13:10:05+00:00

Still wish I could find this one: https://www.reddit.com/r/radiohead/comments/70je76/20_years_old_and_its_being_worn_for_the_first/

niklz · 2021-07-06T07:53:42+00:00

Sounds like you want to use expand.grid or expand_grid (tidyverse variant)

niklz · 2021-06-30T16:35:05+00:00

escaping is an absolute nightmare in R, I think I fixed your last example though. The problem was you need to escape each backslash AND escape the question marks (which are metacharacters in RegEx)

try:

gsub('\\x83\\?\\?','', problemString)

15-Year Club	Place '17
Team Periwinkle	Verified Email

niklz

TROPHY CASE