📝 "Apply Family Notebook"
Master R's "Apply" family functions with this quick guide.
👤 Meghan Harris
🔗 https://www.thetidytrekker.com/post/applyfamilynotes/apply-family.html
#rladies#rstats#oldiebutgoodie#r#rprogramming#baser#apply#lapply#sapply#functionalprogramming
Master R's "Apply" family functions with this quick guide.
👤 Meghan Harris
🔗 https://www.thetidytrekker.com/post/applyfamilynotes/apply-family.html
#rladies#rstats#oldiebutgoodie#r#rprogramming#baser#apply#lapply#sapply#functionalprogramming
November 8, 2026 at 1:56 PM
📝 "Apply Family Notebook"
Master R's "Apply" family functions with this quick guide.
👤 Meghan Harris
🔗 https://www.thetidytrekker.com/post/applyfamilynotes/apply-family.html
#rladies#rstats#oldiebutgoodie#r#rprogramming#baser#apply#lapply#sapply#functionalprogramming
Master R's "Apply" family functions with this quick guide.
👤 Meghan Harris
🔗 https://www.thetidytrekker.com/post/applyfamilynotes/apply-family.html
#rladies#rstats#oldiebutgoodie#r#rprogramming#baser#apply#lapply#sapply#functionalprogramming
No curso de Programação em R, traduzi as lições Olhando os Dados, lapply e sapply, vapply e tapply. Também corrigi o caminho dos dados que impedia três lições de abrir depois de instaladas.
September 17, 2026 at 3:27 PM
No curso de Programação em R, traduzi as lições Olhando os Dados, lapply e sapply, vapply e tapply. Também corrigi o caminho dos dados que impedia três lições de abrir depois de instaladas.
TIL what the heck grepl does in R. I suspect this is another embarrassing thing I have been coding “around” for years.
My previous horrors were grep(TRUE, …) because I didn’t know about which and lapply(as.list(), …) because I didn’t know about sapply.
Am I the only one who does this?
My previous horrors were grep(TRUE, …) because I didn’t know about which and lapply(as.list(), …) because I didn’t know about sapply.
Am I the only one who does this?
June 11, 2025 at 3:14 PM
TIL what the heck grepl does in R. I suspect this is another embarrassing thing I have been coding “around” for years.
My previous horrors were grep(TRUE, …) because I didn’t know about which and lapply(as.list(), …) because I didn’t know about sapply.
Am I the only one who does this?
My previous horrors were grep(TRUE, …) because I didn’t know about which and lapply(as.list(), …) because I didn’t know about sapply.
Am I the only one who does this?
Sad, sorry, sapply
Live, laugh, lapply
March 7, 2026 at 4:44 PM
Sad, sorry, sapply
RT @CMastication [blog & video] example of how to use plyr to address sapply() frustrations in #rstats http://ow.ly/L3QX
Cerebral Mastication - Cerebral Mastication - Something t...
ow.ly
December 1, 2024 at 10:56 PM
RT @CMastication [blog & video] example of how to use plyr to address sapply() frustrations in #rstats http://ow.ly/L3QX
do you want to know what i think about how the world should be run?
...no?
too bad. here's my sapply values
...no?
too bad. here's my sapply values
August 12, 2026 at 1:30 PM
do you want to know what i think about how the world should be run?
...no?
too bad. here's my sapply values
...no?
too bad. here's my sapply values
I only write the most elegant R.
August 14, 2023 at 4:02 PM
I only write the most elegant R.
Excuse my language / but sometimes only one ugly word works / Fuck you Trump. Irony is that the times I have been blessed to make love to a woman, which brings us back to the F word, ste the best memories of my life as I roll towards 79. Seems so contradictory for same word sapply in both cases
December 20, 2024 at 12:30 AM
Excuse my language / but sometimes only one ugly word works / Fuck you Trump. Irony is that the times I have been blessed to make love to a woman, which brings us back to the F word, ste the best memories of my life as I roll towards 79. Seems so contradictory for same word sapply in both cases
Cf sec. A in the supplement to the BMJ article, where you multiply death pdf for one population with survival for other.
R for Gompertz (u periods older):
gouts <- function(r0, a, u, x) gmort(r0,a,x)*gsurv(r0,a,x) * gsurv(gmort(r0,a,u), a, x)
sum(sapply(0:1000, gouts, r0=0.0005, a=0.005, u=100))
R for Gompertz (u periods older):
gouts <- function(r0, a, u, x) gmort(r0,a,x)*gsurv(r0,a,x) * gsurv(gmort(r0,a,u), a, x)
sum(sapply(0:1000, gouts, r0=0.0005, a=0.005, u=100))
August 21, 2025 at 8:37 AM
Cf sec. A in the supplement to the BMJ article, where you multiply death pdf for one population with survival for other.
R for Gompertz (u periods older):
gouts <- function(r0, a, u, x) gmort(r0,a,x)*gsurv(r0,a,x) * gsurv(gmort(r0,a,u), a, x)
sum(sapply(0:1000, gouts, r0=0.0005, a=0.005, u=100))
R for Gompertz (u periods older):
gouts <- function(r0, a, u, x) gmort(r0,a,x)*gsurv(r0,a,x) * gsurv(gmort(r0,a,u), a, x)
sum(sapply(0:1000, gouts, r0=0.0005, a=0.005, u=100))
Not fully equivalent but it gets the for-less job done 😄 (Blergh, I felt so dirty writing this code.)
December 17, 2024 at 2:20 PM
Not fully equivalent but it gets the for-less job done 😄 (Blergh, I felt so dirty writing this code.)
Find out what's taking up all your RAM in R:
sort( sapply(ls(),function(x){object.size(get(x))})) via https://stackoverflow.com/questions/1395270/determining-memory-usage-of-objects #rstats
sort( sapply(ls(),function(x){object.size(get(x))})) via https://stackoverflow.com/questions/1395270/determining-memory-usage-of-objects #rstats
May 23, 2025 at 3:34 PM
Find out what's taking up all your RAM in R:
sort( sapply(ls(),function(x){object.size(get(x))})) via https://stackoverflow.com/questions/1395270/determining-memory-usage-of-objects #rstats
sort( sapply(ls(),function(x){object.size(get(x))})) via https://stackoverflow.com/questions/1395270/determining-memory-usage-of-objects #rstats
you can speed up the R version by a *huge* factor by iterating only on items present in both groups.
![bench::mark(
a = {
g1 <- input$group1[input$group1 %in% input$group2] |> unique()
g2 <- input$group2[input$group2 %in% g1]
n <- sapply(g1, \(i) sum(i == g2))
sum(g1 * n)
},
b = {
sum(input$group1 * table(input$group2)[as.character(input$group1)], na.rm = TRUE)
}
)
# # A tibble: 2 × 13
# expression min median `itr/sec` mem_alloc `gc/sec` n_itr n_gc total_time
# <bch:expr> <bch:tm> <bch:> <dbl> <bch:byt> <dbl> <int> <dbl> <bch:tm>
# 1 a 122µs 129µs 7398. 123KB 37.5 3155 16 426ms
# 2 b 503µs 522µs 1843. 215KB 8.23 896 4 486ms
# # ℹ 4 more variables: result <list>, memory <list>, time <list>, gc <list>](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:x4zy26lhegouf3lf2zgjk4mz/bafkreihnuomcwe2g5x3qund2t5vvqugzso5buyo7fmtc57jou66upoersu)
December 1, 2024 at 9:58 PM
you can speed up the R version by a *huge* factor by iterating only on items present in both groups.
I can't sleep so I did the sapply values test for funsies.
August 28, 2024 at 4:31 AM
I can't sleep so I did the sapply values test for funsies.
Please read matloff.github.io/TidyverseSkeptic. Or note this quick point: Part of the tidyverse philosophy is not write loops; use functional programming (FP) instead, meaning replace loops with function calls, e.g. sapply in base-R. thread 1/
matloff.github.io
March 11, 2026 at 8:11 PM
Please read matloff.github.io/TidyverseSkeptic. Or note this quick point: Part of the tidyverse philosophy is not write loops; use functional programming (FP) instead, meaning replace loops with function calls, e.g. sapply in base-R. thread 1/
slidingWindow <- function(x,windowWidth,f,matTransp=TRUE) {
n <- length(x)
tmp <- sapply(1:(n-windowWidth+1),
function(j) { f(x[j:(j+windowWidth-1)]) })
if (is.matrix(tmp) && matTransp) return(t(tmp))
tmp
}
2/
December 8, 2024 at 5:43 AM
slidingWindow <- function(x,windowWidth,f,matTransp=TRUE) {
n <- length(x)
tmp <- sapply(1:(n-windowWidth+1),
function(j) { f(x[j:(j+windowWidth-1)]) })
if (is.matrix(tmp) && matTransp) return(t(tmp))
tmp
}
2/
this definitely brings up some bad, uh, let's call them memories, but the sapply political compass using 3 axes of economic/social/cultural as the main splitting points is honestly the least worst way of framing, since otherwise things like "pro lgbq+ anti gun" doesn't square well.
December 1, 2025 at 5:49 PM
this definitely brings up some bad, uh, let's call them memories, but the sapply political compass using 3 axes of economic/social/cultural as the main splitting points is honestly the least worst way of framing, since otherwise things like "pro lgbq+ anti gun" doesn't square well.
quickr recently gained support for OpenMP!
Just add a `declare(parallel())` above a `for` or `sapply()`!
github.com/t-kalinowski...
Just add a `declare(parallel())` above a `for` or `sapply()`!
github.com/t-kalinowski...
January 28, 2026 at 11:24 PM
quickr recently gained support for OpenMP!
Just add a `declare(parallel())` above a `for` or `sapply()`!
github.com/t-kalinowski...
Just add a `declare(parallel())` above a `for` or `sapply()`!
github.com/t-kalinowski...
Conquering R’s Apply Family: Your Guide to apply(), lapply(), sapply(), and tapply()
"Introduction
Welcome, fellow R warriors! Today, we delve into the heart of vectorized operations wit..."
https://www.r-bloggers.com/2024/02/conquering-rs-apply-family-your-guide-to-apply-lapply-sapply-and-tapply/
"Introduction
Welcome, fellow R warriors! Today, we delve into the heart of vectorized operations wit..."
https://www.r-bloggers.com/2024/02/conquering-rs-apply-family-your-guide-to-apply-lapply-sapply-and-tapply/
Conquering R’s Apply Family: Your Guide to apply(), lapply(), sapply(), and tapply()
Introduction
Welcome, fellow R warriors! Today, we delve into the heart of vectorized operations with R’s “apply” family: apply(), lapply(), sapply(), and tapply(). These functions are your secret wea...
www.r-bloggers.com
February 15, 2024 at 5:00 AM
Conquering R’s Apply Family: Your Guide to apply(), lapply(), sapply(), and tapply()
"Introduction
Welcome, fellow R warriors! Today, we delve into the heart of vectorized operations wit..."
https://www.r-bloggers.com/2024/02/conquering-rs-apply-family-your-guide-to-apply-lapply-sapply-and-tapply/
"Introduction
Welcome, fellow R warriors! Today, we delve into the heart of vectorized operations wit..."
https://www.r-bloggers.com/2024/02/conquering-rs-apply-family-your-guide-to-apply-lapply-sapply-and-tapply/
Darren's R Top Tips!
A simple way to avoid the failing of "table" in the previous tip would be to sum over a testable condition and use sapply over a range, effectively like a column of SUMIFs in Excel.
sapply(1:10, \(i) {sum(c(1,1,2,3,4,5,6,6,6,8,1,2)==i)})
A simple way to avoid the failing of "table" in the previous tip would be to sum over a testable condition and use sapply over a range, effectively like a column of SUMIFs in Excel.
sapply(1:10, \(i) {sum(c(1,1,2,3,4,5,6,6,6,8,1,2)==i)})
March 10, 2025 at 10:45 AM
Darren's R Top Tips!
A simple way to avoid the failing of "table" in the previous tip would be to sum over a testable condition and use sapply over a range, effectively like a column of SUMIFs in Excel.
sapply(1:10, \(i) {sum(c(1,1,2,3,4,5,6,6,6,8,1,2)==i)})
A simple way to avoid the failing of "table" in the previous tip would be to sum over a testable condition and use sapply over a range, effectively like a column of SUMIFs in Excel.
sapply(1:10, \(i) {sum(c(1,1,2,3,4,5,6,6,6,8,1,2)==i)})
Darren's R Top Tips!
Overlooked R function: replicate()
This is quite different to rep(), and is basically a wrapper for sapply, e.g.
replicate(3,list("a",2,3))
Note it produces here a matrix, not just a longer list or vector.
Overlooked R function: replicate()
This is quite different to rep(), and is basically a wrapper for sapply, e.g.
replicate(3,list("a",2,3))
Note it produces here a matrix, not just a longer list or vector.
March 5, 2025 at 10:45 AM
Darren's R Top Tips!
Overlooked R function: replicate()
This is quite different to rep(), and is basically a wrapper for sapply, e.g.
replicate(3,list("a",2,3))
Note it produces here a matrix, not just a longer list or vector.
Overlooked R function: replicate()
This is quite different to rep(), and is basically a wrapper for sapply, e.g.
replicate(3,list("a",2,3))
Note it produces here a matrix, not just a longer list or vector.
Darren's R Top Tips!
You don't need a for loop or sapply to vectorise a testable condition. Instead, just use the ifelse() function. E.g.
ifelse((1:10)%%2, "daddy", "chips")
You don't need a for loop or sapply to vectorise a testable condition. Instead, just use the ifelse() function. E.g.
ifelse((1:10)%%2, "daddy", "chips")
December 9, 2024 at 10:45 AM
Darren's R Top Tips!
You don't need a for loop or sapply to vectorise a testable condition. Instead, just use the ifelse() function. E.g.
ifelse((1:10)%%2, "daddy", "chips")
You don't need a for loop or sapply to vectorise a testable condition. Instead, just use the ifelse() function. E.g.
ifelse((1:10)%%2, "daddy", "chips")
Darren's R Top Tips!
R can vectorise a lot, but where it can't, you don't always need to use a for loop. The "sapply" function is great for applying a function to each member of a vector and putting the results into a vector, e.g.
sapply(1:10, function(i) i*i)
Here, i*i will do of course.
R can vectorise a lot, but where it can't, you don't always need to use a for loop. The "sapply" function is great for applying a function to each member of a vector and putting the results into a vector, e.g.
sapply(1:10, function(i) i*i)
Here, i*i will do of course.
December 4, 2024 at 10:45 AM
Darren's R Top Tips!
R can vectorise a lot, but where it can't, you don't always need to use a for loop. The "sapply" function is great for applying a function to each member of a vector and putting the results into a vector, e.g.
sapply(1:10, function(i) i*i)
Here, i*i will do of course.
R can vectorise a lot, but where it can't, you don't always need to use a for loop. The "sapply" function is great for applying a function to each member of a vector and putting the results into a vector, e.g.
sapply(1:10, function(i) i*i)
Here, i*i will do of course.
Yeah, it is true that the result deviates from the expectations of ordering variables/observations. If the matrix format that sapply outputs isn’t convenient you can use lapply instead, or you can just transpose it
May 22, 2025 at 12:31 AM
Yeah, it is true that the result deviates from the expectations of ordering variables/observations. If the matrix format that sapply outputs isn’t convenient you can use lapply instead, or you can just transpose it
I think this is because you are not supposed to use sapply on matrices. If you do it probably gets coerced into a data.frame, whose elements are the columns.
Anyways, not long ago I discovered asplit() which is pretty handy when needing the flexibility to work across either margin
Anyways, not long ago I discovered asplit() which is pretty handy when needing the flexibility to work across either margin
May 21, 2025 at 11:55 PM
I think this is because you are not supposed to use sapply on matrices. If you do it probably gets coerced into a data.frame, whose elements are the columns.
Anyways, not long ago I discovered asplit() which is pretty handy when needing the flexibility to work across either margin
Anyways, not long ago I discovered asplit() which is pretty handy when needing the flexibility to work across either margin