![R code in the image:
```rstats
xdf[,c("handle", "name", "description")] |>
unique() |>
within({
handle <- sub("^at://", "@", handle)
md <- sprintf(
fmt = "- (`%s`) %s%s%s",
handle,
name,
ifelse(is.na(description), "", ": "),
ifelse(is.na(description), "", description)
)
}) |>
sort_by(~md) |>
getElement("md") |>
writeLines()
```](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:hgyzg2hn6zxpqokmp5c2xrdo/bafkreifr34id5gve6k4kkvhn7fkllae2xlkikcdh6gm4ts3qcsf4ax5w7a)
How do you use if, else, and switch in C++?
tpointtech.wordpress.com/2025/06/05/h...
#ifelsecpp #c++ifelse
tpointtech.wordpress.com/2025/06/05/h...
#ifelsecpp #c++ifelse
June 6, 2025 at 7:06 AM
How do you use if, else, and switch in C++?
tpointtech.wordpress.com/2025/06/05/h...
#ifelsecpp #c++ifelse
tpointtech.wordpress.com/2025/06/05/h...
#ifelsecpp #c++ifelse
In R, in many contexts, missing values throw an exception or pass through to result vector. Like:
x <- c(2,0,NA,3)
ifelse(x>1,1,0)
[1] 1 0 NA 1
x <- c(2,0,NA,3)
ifelse(x>1,1,0)
[1] 1 0 NA 1
December 18, 2025 at 11:05 AM
In R, in many contexts, missing values throw an exception or pass through to result vector. Like:
x <- c(2,0,NA,3)
ifelse(x>1,1,0)
[1] 1 0 NA 1
x <- c(2,0,NA,3)
ifelse(x>1,1,0)
[1] 1 0 NA 1
lol oh no
my first thought is, yours is worst-case quadratic if x is all NA, but linear if there are no NAs
so just throw outer() at it so it's always quadratic:
my first thought is, yours is worst-case quadratic if x is all NA, but linear if there are no NAs
so just throw outer() at it so it's always quadratic:
![> x = c("a","b","c","d",NA,"e")
> outer(is.na(x), x, ifelse, yes = "na")
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] "a" "b" "c" "d" NA "e"
[2,] "a" "b" "c" "d" NA "e"
[3,] "a" "b" "c" "d" NA "e"
[4,] "a" "b" "c" "d" NA "e"
[5,] "na" "na" "na" "na" "na" "na"
[6,] "a" "b" "c" "d" NA "e"
> diag(outer(is.na(x), x, ifelse, yes = "na"))
[1] "a" "b" "c" "d" "na" "e"](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:6fs5x6cofzyz4si2xhqiqj37/bafkreie373qo5pb57sau2hmojazwqjns2lafmpkdjfanfqldqf2376x3ci)
February 24, 2026 at 6:52 AM
lol oh no
my first thought is, yours is worst-case quadratic if x is all NA, but linear if there are no NAs
so just throw outer() at it so it's always quadratic:
my first thought is, yours is worst-case quadratic if x is all NA, but linear if there are no NAs
so just throw outer() at it so it's always quadratic:
My current Foundry struggle:
Old Dice Parser: X < Y && Z < A ? B : C
New Dice Parser: ifelse(and(lt(X, Y), lt(Z, A)), B, C)
These say the same thing, but I find the first one significantly more understandable than the latter v_v
Old Dice Parser: X < Y && Z < A ? B : C
New Dice Parser: ifelse(and(lt(X, Y), lt(Z, A)), B, C)
These say the same thing, but I find the first one significantly more understandable than the latter v_v
March 20, 2025 at 5:58 PM
My current Foundry struggle:
Old Dice Parser: X < Y && Z < A ? B : C
New Dice Parser: ifelse(and(lt(X, Y), lt(Z, A)), B, C)
These say the same thing, but I find the first one significantly more understandable than the latter v_v
Old Dice Parser: X < Y && Z < A ? B : C
New Dice Parser: ifelse(and(lt(X, Y), lt(Z, A)), B, C)
These say the same thing, but I find the first one significantly more understandable than the latter v_v
Day 5 of #30DayMapChallenge
Theme: A Journey
Response: Journey's Journey 2024 - The 73(!) venues being played by the band Journey in 2024. Concert details from setlist.fm/search?artist=3d6b507&upcoming=true&year=2024 with a bunch of additional getting venue coordinates as commitment to the bit.
Theme: A Journey
Response: Journey's Journey 2024 - The 73(!) venues being played by the band Journey in 2024. Concert details from setlist.fm/search?artist=3d6b507&upcoming=true&year=2024 with a bunch of additional getting venue coordinates as commitment to the bit.
November 4, 2024 at 6:19 PM
Day 5 of #30DayMapChallenge
Theme: A Journey
Response: Journey's Journey 2024 - The 73(!) venues being played by the band Journey in 2024. Concert details from setlist.fm/search?artist=3d6b507&upcoming=true&year=2024 with a bunch of additional getting venue coordinates as commitment to the bit.
Theme: A Journey
Response: Journey's Journey 2024 - The 73(!) venues being played by the band Journey in 2024. Concert details from setlist.fm/search?artist=3d6b507&upcoming=true&year=2024 with a bunch of additional getting venue coordinates as commitment to the bit.
FYI: For N=1000, I changed "after" to:
after = before + ifelse(before>=5, 0, sample(c(-1, 0, 1), sample_size, replace = TRUE)),
after = before + ifelse(before>=5, 0, sample(c(-1, 0, 1), sample_size, replace = TRUE)),
July 2, 2024 at 10:33 PM
FYI: For N=1000, I changed "after" to:
after = before + ifelse(before>=5, 0, sample(c(-1, 0, 1), sample_size, replace = TRUE)),
after = before + ifelse(before>=5, 0, sample(c(-1, 0, 1), sample_size, replace = TRUE)),
![R code in the image:
```rstats
xdf[,c("handle", "name", "description")] |>
unique() |>
within({
handle <- sub("^at://", "@", handle)
md <- sprintf(
fmt = "- (`%s`) %s%s%s",
handle,
name,
ifelse(is.na(description), "", ": "),
ifelse(is.na(description), "", description)
)
}) |>
sort_by(~md) |>
getElement("md") |>
writeLines()
```](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:twerjlabpqmvieyx3roydios/bafkreicyz7wkw2z5hqz33oadmqspfcii6275wvijszq7tfbu5ufvlyb5fu)
Here's my standard bit of code for pulling out the original categories for Germany but you can just delete stuff. Line 23 is the part you want, I think.
May 8, 2025 at 1:49 PM
Here's my standard bit of code for pulling out the original categories for Germany but you can just delete stuff. Line 23 is the part you want, I think.
Trying #adventofcode in R. Monstrous solution to part two of day 1:
x = readr::read_lines("input.txt")
xdir = substring(x,1,1)
xlen = as.numeric(substring(x,2))
xcum = cumsum(c(50,xlen*ifelse(xdir=="L",-1,1)))
sum(
0.5 * abs(diff(floor(xcum / 100))) +
0.5 * abs(diff(floor((xcum-1) / 100)))
)
x = readr::read_lines("input.txt")
xdir = substring(x,1,1)
xlen = as.numeric(substring(x,2))
xcum = cumsum(c(50,xlen*ifelse(xdir=="L",-1,1)))
sum(
0.5 * abs(diff(floor(xcum / 100))) +
0.5 * abs(diff(floor((xcum-1) / 100)))
)
December 1, 2025 at 6:58 PM
Trying #adventofcode in R. Monstrous solution to part two of day 1:
x = readr::read_lines("input.txt")
xdir = substring(x,1,1)
xlen = as.numeric(substring(x,2))
xcum = cumsum(c(50,xlen*ifelse(xdir=="L",-1,1)))
sum(
0.5 * abs(diff(floor(xcum / 100))) +
0.5 * abs(diff(floor((xcum-1) / 100)))
)
x = readr::read_lines("input.txt")
xdir = substring(x,1,1)
xlen = as.numeric(substring(x,2))
xcum = cumsum(c(50,xlen*ifelse(xdir=="L",-1,1)))
sum(
0.5 * abs(diff(floor(xcum / 100))) +
0.5 * abs(diff(floor((xcum-1) / 100)))
)
Day 6: the first #adventofcode puzzle I've done where the bulk of the puzzle was parsing the input data. I enjoyed this one a lot! #rstats
![Screenshot of the following code:
library("readr")
library("purrr")
library("dplyr")
options(scipen = 9999)
file <- "input06.txt"
orig <- readLines(file)
ncol <- length(strsplit(orig[[1]], "\\s+")[[1]])
## part 1
dat1 <- read_fwf(file, n_max = length(orig) - 1) |>
mutate(across(everything(), as.numeric))
ops <- tail(orig, 1) |>
strsplit("\\s+")
ops <- ops[[1]]
math <- function(x, op) {
init <- ifelse(op == "*", 1, 0)
Reduce(function(x, y) do.call(op, as.list(c(x, y))), x = x, init = init)
}
sum(mapply(math, dat1, ops))
## part 2
# read the data in one character per column
dat2 <- read_fwf(file, col_positions = fwf_widths(rep(1, max(nchar(orig)))))
dat2 <- dat2[-nrow(dat2), ]
# split into groups based on where there's a column of all NAs
seps <- map_dbl(dat2, ~ all(is.na(.x)))
splits <- cumsum(c(1, diff(seps) != 0))
split_problems <- split.default(dat2, splits) |>
keep(~ !all(is.na(.x)))
ceph_math <- function(x, op) {
vals <- as.list(x) |>
map(~ paste0(na.omit(.x), collapse = "")) |>
map_dbl(as.numeric)
math(vals, op)
}
sum(mapply(ceph_math, split_problems, ops))](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:o4tcgqoslmipb3j62vjhrq73/bafkreie3tl2styhiwyx6mxaseod37ttmugl7dt2zsqgtkbmsg5fffj3gle)
December 8, 2025 at 6:20 PM
Day 6: the first #adventofcode puzzle I've done where the bulk of the puzzle was parsing the input data. I enjoyed this one a lot! #rstats
A bit more tweaking- using ggpattern (and alpha to mute it on the non-Don't Knows) to make it a bit clearer than for these purposes not giving a definitive answer is considered a different thing, and tweaking the Trust level factor/colour order.
A bit of the key #rstats graph making code in alt text
A bit of the key #rstats graph making code in alt text
![prosci <- sav23 |>
mutate(trust = case_when(A11d == "9. Don’t know" ~ "Don't Know/\nDidn't Answer",
is.na(A11d) ~ "Don't Know/\nDidn't Answer",
TRUE ~ gsub(".*\\. ","",A11d))) |>
summarise(.by=c(mvpartyvote, trust),
weighted = sum(vwgt)) |>
group_by(mvpartyvote) |>
mutate(percentage= 100*weighted/sum(weighted),
percent= ifelse(trust == "Don't Know/\nDidn't Answer", percentage * -1, percentage),
party = ifelse(is.na(mvpartyvote),"unknown", gsub(".*\\. ","",mvpartyvote))) |>
ungroup()
partorder <- prosci |>
filter(trust %in% c("Trust a lot", "Trust somewhat")) |>
summarise(.by=party,
orderscore = sum(percent)) |>
arrange(desc(orderscore))
vpal <- viridis_pal(alpha = 1, begin = 0, end = .9, direction = -1, option = "C")(4)
vcol <- c("#EEEEEE",vpal)
factrst <- c("Don't Know/\nDidn't Answer", "Trust a lot", "Trust somewhat", "Don’t trust very much", "Don’t trust at all")
prosci |>
mutate(party = factor(party, levels=partorder$party),
trust = factor(trust, levels = factrst[c(1,5,4,3,2)])) |>
ggplot(aes(x=party,y=percent, fill=trust)) +
# geom_col(colour="black", linewidth=0.05) +
geom_col_pattern(aes(fill = trust,
pattern_alpha = trust),
colour="black",linewidth=0.05,
pattern = 'plasma',
pattern_fill = "grey0") +
theme_david() +
scale_fill_manual(name="Trust level", values=vcol) +
scale_pattern_alpha_manual(name="Trust level", values = c(.5,0,0,0,0)) +
scale_y_continuous(breaks=c(-15,0,25,50,75,100),
labels=c(15,0,25,50,75,100)) +
labs(title="NZES 2023: How much trust do you have in scientists? by party vote",
subtitle="In order of trust, most trusting on left. Weighted percent.",
y="Percent of voters", x="Party voted for",
caption="Source: 2023 NZES")+
theme(axis.line.y = element_blank(), axis.line.x = element_blank())](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:c25e6vi7ojpyitmoi3xpuvpf/bafkreieack4v2snbnw5gecbtgxf5f6g2iysdkmegvaj5dzxjwagr62ygsm)
November 3, 2025 at 10:33 PM
A bit more tweaking- using ggpattern (and alpha to mute it on the non-Don't Knows) to make it a bit clearer than for these purposes not giving a definitive answer is considered a different thing, and tweaking the Trust level factor/colour order.
A bit of the key #rstats graph making code in alt text
A bit of the key #rstats graph making code in alt text
![# a kind of nonlinear color-ramp
r <- scales::colour_ramp(c("grey10", "grey20", "grey40", "grey90", "white"))
ds <- data_spectrogram |>
filter(tier_name == "phones") |>
mutate(
# convert from 70db dynamic range to 55 db
db_norm = scales::squish((db - 20) / 55),
fill = r(1 - db_norm),
fill = ifelse(
annotation_num %in% range(annotation_num),
fill,
# tint and cycle through colors
fill |>
scales::col_mix("lightblue", amount = .4) |>
scales::col_shift(annotation_num * 60)
)
)
ggplot(ds) +
aes(x = time, y = frequency) +
geom_raster(aes(fill = fill)) +
theme_minimal() +
geom_text(
aes(label = text, x = (xmax + xmin) / 2),
data = data_tg |> filter(tier_name == "phones"),
y = 6200
) +
scale_fill_identity() +
labs(x = "time [s]", y = "frequency [Hz]")](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:tpgnsvgqsu5b752uud3wmtja/bafkreifxkvxnj74t5muenoznkli3xavrx5ngfmwphxe7kfs3j6d7w45lty)
# unpipe-y
age_bin <- age_months %/% 12
age_bin <- ifelse(age_bin > 7, 7, age_bin)
# pipe-y
age_bin <- (age_months %/% 12) |> pmin(7)
age_bin <- (a$age_months %/% 12) |>
scales::oob_squish(range = c(NA, 7))
age_bin <- age_months %/% 12
age_bin <- ifelse(age_bin > 7, 7, age_bin)
# pipe-y
age_bin <- (age_months %/% 12) |> pmin(7)
age_bin <- (a$age_months %/% 12) |>
scales::oob_squish(range = c(NA, 7))
April 5, 2024 at 3:31 PM
# unpipe-y
age_bin <- age_months %/% 12
age_bin <- ifelse(age_bin > 7, 7, age_bin)
# pipe-y
age_bin <- (age_months %/% 12) |> pmin(7)
age_bin <- (a$age_months %/% 12) |>
scales::oob_squish(range = c(NA, 7))
age_bin <- age_months %/% 12
age_bin <- ifelse(age_bin > 7, 7, age_bin)
# pipe-y
age_bin <- (age_months %/% 12) |> pmin(7)
age_bin <- (a$age_months %/% 12) |>
scales::oob_squish(range = c(NA, 7))
```
r = c('R' = 'LRL', 'L'='LRR')
x = 'RRRR'
for (i in 1:8) {
moves = strsplit(x, '')[[1]]
angles = c(); a = 0
for (m in moves) {
a = a + pi/2 * ifelse(m == "R", -1, 1)
angles = c(angles, a)
}
x = paste(r[strsplit(x, "")[[1]]], collapse = "")
plot(c(0 […]
[Original post on mastodon.social]
r = c('R' = 'LRL', 'L'='LRR')
x = 'RRRR'
for (i in 1:8) {
moves = strsplit(x, '')[[1]]
angles = c(); a = 0
for (m in moves) {
a = a + pi/2 * ifelse(m == "R", -1, 1)
angles = c(angles, a)
}
x = paste(r[strsplit(x, "")[[1]]], collapse = "")
plot(c(0 […]
[Original post on mastodon.social]
May 23, 2025 at 11:46 AM
```
r = c('R' = 'LRL', 'L'='LRR')
x = 'RRRR'
for (i in 1:8) {
moves = strsplit(x, '')[[1]]
angles = c(); a = 0
for (m in moves) {
a = a + pi/2 * ifelse(m == "R", -1, 1)
angles = c(angles, a)
}
x = paste(r[strsplit(x, "")[[1]]], collapse = "")
plot(c(0 […]
[Original post on mastodon.social]
r = c('R' = 'LRL', 'L'='LRR')
x = 'RRRR'
for (i in 1:8) {
moves = strsplit(x, '')[[1]]
angles = c(); a = 0
for (m in moves) {
a = a + pi/2 * ifelse(m == "R", -1, 1)
angles = c(angles, a)
}
x = paste(r[strsplit(x, "")[[1]]], collapse = "")
plot(c(0 […]
[Original post on mastodon.social]
I love R, but the behaviour of ifelse when you combine scalars and vectors is really unintuitive. E.g., Guess the result of the line behaviour.
ifelse(1 == 2, "Yes", c("Yes", "No"))
ifelse(1 == 2, "Yes", c("Yes", "No"))
February 23, 2026 at 8:02 PM
I love R, but the behaviour of ifelse when you combine scalars and vectors is really unintuitive. E.g., Guess the result of the line behaviour.
ifelse(1 == 2, "Yes", c("Yes", "No"))
ifelse(1 == 2, "Yes", c("Yes", "No"))
8/ Now reshape it:
library(tidyr); library(dplyr)
df_tidy <- df %>%
pivot_longer(-strain,
names_to=c("genotype","min","rep"),
names_pattern="(.*)_(\\d+)_?(\\d*)") %>%
mutate(genotype=ifelse(grepl("normal",genotype),"normal","mutant"),
rep=ifelse(rep=="","1",rep))
library(tidyr); library(dplyr)
df_tidy <- df %>%
pivot_longer(-strain,
names_to=c("genotype","min","rep"),
names_pattern="(.*)_(\\d+)_?(\\d*)") %>%
mutate(genotype=ifelse(grepl("normal",genotype),"normal","mutant"),
rep=ifelse(rep=="","1",rep))
December 23, 2025 at 2:45 PM
8/ Now reshape it:
library(tidyr); library(dplyr)
df_tidy <- df %>%
pivot_longer(-strain,
names_to=c("genotype","min","rep"),
names_pattern="(.*)_(\\d+)_?(\\d*)") %>%
mutate(genotype=ifelse(grepl("normal",genotype),"normal","mutant"),
rep=ifelse(rep=="","1",rep))
library(tidyr); library(dplyr)
df_tidy <- df %>%
pivot_longer(-strain,
names_to=c("genotype","min","rep"),
names_pattern="(.*)_(\\d+)_?(\\d*)") %>%
mutate(genotype=ifelse(grepl("normal",genotype),"normal","mutant"),
rep=ifelse(rep=="","1",rep))
8/ Now reshape it:
library(tidyr); library(dplyr)
df_tidy <- df %>%
pivot_longer(-strain,
names_to=c("genotype","min","rep"),
names_pattern="(.*)_(\\d+)_?(\\d*)") %>%
mutate(genotype=ifelse(grepl("normal",genotype),"normal","mutant"),
rep=ifelse(rep=="","1",rep))
library(tidyr); library(dplyr)
df_tidy <- df %>%
pivot_longer(-strain,
names_to=c("genotype","min","rep"),
names_pattern="(.*)_(\\d+)_?(\\d*)") %>%
mutate(genotype=ifelse(grepl("normal",genotype),"normal","mutant"),
rep=ifelse(rep=="","1",rep))
August 24, 2025 at 1:45 PM
8/ Now reshape it:
library(tidyr); library(dplyr)
df_tidy <- df %>%
pivot_longer(-strain,
names_to=c("genotype","min","rep"),
names_pattern="(.*)_(\\d+)_?(\\d*)") %>%
mutate(genotype=ifelse(grepl("normal",genotype),"normal","mutant"),
rep=ifelse(rep=="","1",rep))
library(tidyr); library(dplyr)
df_tidy <- df %>%
pivot_longer(-strain,
names_to=c("genotype","min","rep"),
names_pattern="(.*)_(\\d+)_?(\\d*)") %>%
mutate(genotype=ifelse(grepl("normal",genotype),"normal","mutant"),
rep=ifelse(rep=="","1",rep))
[3]
ft_dt$disp <- ifelse(
ft_dt$disp > 200,
cell_spec(ft_dt$disp, color = "red", bold = T),
cell_spec(ft_dt$disp, color = "green", italic = T)
)
ft_dt$hp <- color_bar("lightgreen")(ft_dt$hp)
ft_dt <- ft_dt[c("car", "mpg", "cyl", "disp", "hp")]
to be continued
ft_dt$disp <- ifelse(
ft_dt$disp > 200,
cell_spec(ft_dt$disp, color = "red", bold = T),
cell_spec(ft_dt$disp, color = "green", italic = T)
)
ft_dt$hp <- color_bar("lightgreen")(ft_dt$hp)
ft_dt <- ft_dt[c("car", "mpg", "cyl", "disp", "hp")]
to be continued
December 4, 2024 at 3:00 PM
[3]
ft_dt$disp <- ifelse(
ft_dt$disp > 200,
cell_spec(ft_dt$disp, color = "red", bold = T),
cell_spec(ft_dt$disp, color = "green", italic = T)
)
ft_dt$hp <- color_bar("lightgreen")(ft_dt$hp)
ft_dt <- ft_dt[c("car", "mpg", "cyl", "disp", "hp")]
to be continued
ft_dt$disp <- ifelse(
ft_dt$disp > 200,
cell_spec(ft_dt$disp, color = "red", bold = T),
cell_spec(ft_dt$disp, color = "green", italic = T)
)
ft_dt$hp <- color_bar("lightgreen")(ft_dt$hp)
ft_dt <- ft_dt[c("car", "mpg", "cyl", "disp", "hp")]
to be continued