In the alt text of the flower photo I took is my day 8 solution for #AdventOfCode in #rstats
It's igraph time, and while I don't know igraph well (there are probably more elegant uses out there) I know a minimum spanning tree problem when I read it (and I read it in part 2)
It's igraph time, and while I don't know igraph well (there are probably more elegant uses out there) I know a minimum spanning tree problem when I read it (and I read it in part 2)
![library(dplyr)
library(tidyr)
library(igraph)
shortlist <- 1000
coords <- as.matrix(read.csv("d8.txt", header=FALSE))
n <- nrow(coords)
dist_df <- data.frame(as.matrix(dist(coords))) |>
mutate(rws = 1:n) |>
pivot_longer(names_to="cls",values_to = "distance", -rws) |>
filter(distance > 0) |>
arrange(distance) |>
mutate(cls = as.numeric(gsub("X","",cls)))
dist_df$low <- apply(dist_df[,c("rws","cls")],MARGIN=1, min)
dist_df$high <- apply(dist_df[,c("rws","cls")],MARGIN=1, max)
selectfew <- dist_df |> select(low,high,weight=distance) |>
distinct() |> arrange()
g <- graph_from_data_frame(selectfew[1:shortlist,c("low","high")], directed = FALSE)
comp <- components(g)
prod(sort(comp$csize, decreasing = TRUE)[1:3])
g <- graph_from_data_frame(selectfew, directed = FALSE)
mst_g <- mst(g, weights = E(g)$weight)
lastone <- as_data_frame(mst_g) |>
mutate(fromto = paste0(from,"-",to)) |>
arrange(desc(weight)) |> slice(1)
x1 <- as.double(input$V1[as.numeric(lastone$from)])
x2 <- as.double(input$V1[as.numeric(lastone$to)])
x1 * x2](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:c25e6vi7ojpyitmoi3xpuvpf/bafkreicmakgc37lajxxud6vvsghms2fvqrfhi6rwmjxabvilmcbhueh2vy@jpeg)
December 8, 2025 at 9:23 AM
In the alt text of the flower photo I took is my day 8 solution for #AdventOfCode in #rstats
It's igraph time, and while I don't know igraph well (there are probably more elegant uses out there) I know a minimum spanning tree problem when I read it (and I read it in part 2)
It's igraph time, and while I don't know igraph well (there are probably more elegant uses out there) I know a minimum spanning tree problem when I read it (and I read it in part 2)
I do take the point, but I don't think it's entirely fair either to say that tidyverse and pandas are almost equivalent, and then contrast native Python negatively without also contrasting doing the same thing in R _without_ dplyr/tidyr - also external packages.
Love his book, mind.
Love his book, mind.
November 13, 2025 at 7:31 PM
I do take the point, but I don't think it's entirely fair either to say that tidyverse and pandas are almost equivalent, and then contrast native Python negatively without also contrasting doing the same thing in R _without_ dplyr/tidyr - also external packages.
Love his book, mind.
Love his book, mind.
For #30DayMapChallenge day 5: Earth, I wasn't particularly motivated until I remembered that one guy in Auckland (AoNZ will understand what I mean) so learned to process geojson- Earthquakes (+) over 3.5 Magnitude, south of -38.8 and where and how strongly they were felt. #rstats code in alt text
![library(readr)
library(curl)
library(geojsonio)
library(dplyr)
library(tidyr)
library(ggplot2)
library(mapdata)
# quakes > 3.5, south of -38.8 Lat
searcheq <- "https://quakesearch.geonet.org.nz/csv?bbox=165.4794,-49.4789,179.4093,-38.8&minmag=3.5&startdate=2025-09-06T0:00:00&enddate=2025-11-06T1:00:00"
nzeqs <- read_csv(searcheq)
write.csv(nzeqs,"nzeqs.csv")
dir.create("eqdata")
#as geojsonio seems to expect a suffix, I am making a local cache
for(public_id in nzeqs$publicid){
urlloc = paste0("https://api.geonet.org.nz/intensity?type=reported&publicID=", public_id)
local_url <- paste0("eqdata/", public_id, ".geojson")
handle <- new_handle()
curl_download(urlloc, local_url, handle = handle)
}
all20 <- list.files("eqdata", full.names = TRUE, pattern=".*geojson$")
extractdata <- function(x){
#Some had no felt reports, so using tryCatch
tryCatch(
{
felt_data <- geojson_read(x, what = "sp")
},
error = function(e) {
return(NULL)}
)
feltcoords <- as.data.frame(felt_data@coords) |> rename(long=1,lat=2)
feltcoords$mmi <- felt_data@data[["mmi"]]
feltcoords$mmicount <- felt_data@data[["count"]]
return(feltcoords)
}
allfelts <- bind_rows(lapply(all20,extractdata))
expanded_felts <- allfelts |>
uncount(weights = mmicount) |>
arrange(mmi)
nzoutline <- map_data("nzHires")
ggplot() +
geom_polygon(data=nzoutline, aes(x=long, y=lat, group = group), colour="white", fill="white")+
geom_jitter(data=expanded_felts, aes(x=long,y=lat, colour=mmi))+
geom_point(data=nzeqs, aes(x=longitude,y=latitude), pch="+", size=8)+
annotate("text", y=-38.6,x=170, label="-38.8")+
scale_colour_viridis_c(name="Felt\nIntensity")+
geom_hline(yintercept = -38.8)+
coord_quickmap(xlim=c(166,178), ylim=c(-47.5,-34.5)) +
labs(title = "#30DayMapChallenge 2025 Day 05: Earth",
subtitle = "Felt reports of earthquakes(+) > 3.5, south of -38.8 in the last two months",
caption = "Source: Geonet") +
theme_void(paper="grey")+
theme(legend.position = c(0.8, 0.2))](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:c25e6vi7ojpyitmoi3xpuvpf/bafkreifxn5y4enweiua7lg7725ibb3dompyggtutddwn6zei5zq3cdfp4y@jpeg)
November 6, 2025 at 2:20 AM
For #30DayMapChallenge day 5: Earth, I wasn't particularly motivated until I remembered that one guy in Auckland (AoNZ will understand what I mean) so learned to process geojson- Earthquakes (+) over 3.5 Magnitude, south of -38.8 and where and how strongly they were felt. #rstats code in alt text
Going out on a limb here but the most exquisitely designed deeply domain specific functions in the #rstats #tidyverse are probably the tidyr::separate_ family.
separate_wider_regex() is my main. The patterns arg! Beautiful. The debug mode! So sympathetic to the user’s whole process.
separate_wider_regex() is my main. The patterns arg! Beautiful. The debug mode! So sympathetic to the user’s whole process.
November 4, 2025 at 6:35 AM
Going out on a limb here but the most exquisitely designed deeply domain specific functions in the #rstats #tidyverse are probably the tidyr::separate_ family.
separate_wider_regex() is my main. The patterns arg! Beautiful. The debug mode! So sympathetic to the user’s whole process.
separate_wider_regex() is my main. The patterns arg! Beautiful. The debug mode! So sympathetic to the user’s whole process.
There's a lot of good #rstats advice here. I try to use the pkg::function method. Though it can become tedious. For example if you're using dplyr and you want to use dplyr or tidyr functions within commands they can get unwieldy. Like dplyr::select(dplyr::allof(xx)).
There's a few rules I've adopted for data analysis this year that have helped me out a lot.
October 18, 2025 at 5:10 PM
There's a lot of good #rstats advice here. I try to use the pkg::function method. Though it can become tedious. For example if you're using dplyr and you want to use dplyr or tidyr functions within commands they can get unwieldy. Like dplyr::select(dplyr::allof(xx)).
#rstats
Just discovered something weird:
na.omit() and tidyr::drop_na() give different rownames when you select rows in their result.
Does anyone have an idea why this is so?
Just discovered something weird:
na.omit() and tidyr::drop_na() give different rownames when you select rows in their result.
Does anyone have an idea why this is so?
October 16, 2025 at 12:08 AM
#rstats
Just discovered something weird:
na.omit() and tidyr::drop_na() give different rownames when you select rows in their result.
Does anyone have an idea why this is so?
Just discovered something weird:
na.omit() and tidyr::drop_na() give different rownames when you select rows in their result.
Does anyone have an idea why this is so?
R
{tidyverse}
%>%
<-
{palmerpenguins}
tidyr::pivot_longer()
dplyr::group_by()
Quarto
purrr::map()
dplyr::mutate()
VS Code!
{tidyverse}
%>%
<-
{palmerpenguins}
tidyr::pivot_longer()
dplyr::group_by()
Quarto
purrr::map()
dplyr::mutate()
VS Code!
October 10, 2025 at 4:36 AM
R
{tidyverse}
%>%
<-
{palmerpenguins}
tidyr::pivot_longer()
dplyr::group_by()
Quarto
purrr::map()
dplyr::mutate()
VS Code!
{tidyverse}
%>%
<-
{palmerpenguins}
tidyr::pivot_longer()
dplyr::group_by()
Quarto
purrr::map()
dplyr::mutate()
VS Code!
The {tidylog} #RStats 📦"provides feedback about dplyr and tidyr operations. It provides simple wrapper functions for almost all dplyr and tidyr functions, such as filter, mutate, select, full_join, and group_by. By Benjamin Elbers
github.com/elbersb/tidy...
github.com/elbersb/tidy...
December 10, 2024 at 8:40 PM
The {tidylog} #RStats 📦"provides feedback about dplyr and tidyr operations. It provides simple wrapper functions for almost all dplyr and tidyr functions, such as filter, mutate, select, full_join, and group_by. By Benjamin Elbers
github.com/elbersb/tidy...
github.com/elbersb/tidy...
Great new pivoting methods #pivot_wider and #pivot_longer are coming to #tidyr package!
https://tidyr.tidyverse.org/dev/articles/pivot.html
But is it really wider? 😉
@hadleywickham #rstats #tidyverse
https://tidyr.tidyverse.org/dev/articles/pivot.html
But is it really wider? 😉
@hadleywickham #rstats #tidyverse
November 16, 2024 at 12:53 PM
Great new pivoting methods #pivot_wider and #pivot_longer are coming to #tidyr package!
https://tidyr.tidyverse.org/dev/articles/pivot.html
But is it really wider? 😉
@hadleywickham #rstats #tidyverse
https://tidyr.tidyverse.org/dev/articles/pivot.html
But is it really wider? 😉
@hadleywickham #rstats #tidyverse
Plot some variables against many others with tidyr and ggplot2 <a href="http://drsimonj.svbtle.com/plot-some-variables-against-many-others" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">http://drsimonj.svbtle.com/plot-some-variables-against-many-others via @drsimonj
Plot some variables against many others with tidyr and gg...
Want to see how some of your variables relate to many oth...
drsimonj.svbtle.com
November 9, 2024 at 8:44 PM
Plot some variables against many others with tidyr and ggplot2 <a href="http://drsimonj.svbtle.com/plot-some-variables-against-many-others" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">http://drsimonj.svbtle.com/plot-some-variables-against-many-others via @drsimonj
Is your longitudinal data currently in long format, but you need it in wide format, or vice versa? No worries! It is easy to restructure your data in R using the {tidyr} package.
#rstats #edresearch
You can find more examples here: github.com/Cghlewis/dat...
#rstats #edresearch
You can find more examples here: github.com/Cghlewis/dat...
December 7, 2023 at 2:07 PM
Is your longitudinal data currently in long format, but you need it in wide format, or vice versa? No worries! It is easy to restructure your data in R using the {tidyr} package.
#rstats #edresearch
You can find more examples here: github.com/Cghlewis/dat...
#rstats #edresearch
You can find more examples here: github.com/Cghlewis/dat...
El paquete {reshape2} fue abandonado (superseded) hace años, igual que las funciones gather() y spread() de {tidyr} 🙁 Parece que es una guía un tanto desactualizada
January 2, 2025 at 2:21 PM
El paquete {reshape2} fue abandonado (superseded) hace años, igual que las funciones gather() y spread() de {tidyr} 🙁 Parece que es una guía un tanto desactualizada
Same as yesterday, but this time, making a 2D Discrete Random Walk with my #R #Package #RandomWalker
#R #RStats #RProgramming #RandomWalks #Visual #ggplot2
#R #RStats #RProgramming #RandomWalks #Visual #ggplot2
May 15, 2025 at 12:14 PM
Same as yesterday, but this time, making a 2D Discrete Random Walk with my #R #Package #RandomWalker
#R #RStats #RProgramming #RandomWalks #Visual #ggplot2
#R #RStats #RProgramming #RandomWalks #Visual #ggplot2
That used to be more the case, but Hadley Wickham developed an ecosystem called "The Tidyverse" or "TidyR", made up of packages designed to make R a full-featured data science language.
November 30, 2023 at 8:13 AM
That used to be more the case, but Hadley Wickham developed an ecosystem called "The Tidyverse" or "TidyR", made up of packages designed to make R a full-featured data science language.
I love how I have never used Stata and can fully relate to this because every language has a confusing form for the reshape function
For me, it would be
?tidyr::pivot_wider() and
?tidyr::pivot_longer()
For me, it would be
?tidyr::pivot_wider() and
?tidyr::pivot_longer()
October 25, 2023 at 5:24 AM
I love how I have never used Stata and can fully relate to this because every language has a confusing form for the reshape function
For me, it would be
?tidyr::pivot_wider() and
?tidyr::pivot_longer()
For me, it would be
?tidyr::pivot_wider() and
?tidyr::pivot_longer()
I agree, but also I feel like dplyr/tidyr just creates such a dependency rats nest and is more of a headache than it's worth, particularly in the context of package writing.
November 15, 2024 at 12:27 AM
I agree, but also I feel like dplyr/tidyr just creates such a dependency rats nest and is more of a headache than it's worth, particularly in the context of package writing.
November 5, 2025 at 3:27 PM
Thinking of expanding on this… like a “here’s how you’d do it in a dplyr/tidyr” and “this is how to do it in arquero” notebook.
(similar to how Allison Horst translated observable plot concepts from a ggplot lens).
(similar to how Allison Horst translated observable plot concepts from a ggplot lens).
July 20, 2024 at 6:34 PM
Thinking of expanding on this… like a “here’s how you’d do it in a dplyr/tidyr” and “this is how to do it in arquero” notebook.
(similar to how Allison Horst translated observable plot concepts from a ggplot lens).
(similar to how Allison Horst translated observable plot concepts from a ggplot lens).
A good post! <3 DuckDB
I would maybe quibble that it's worth adding a footnote about the packages you need, specifically RE: `dbplyr` (which will power a lot of what you need to translate your dplyr/tidyr/etc code). That's only so someone who tries it out isn't blindsided by installation issues.
I would maybe quibble that it's worth adding a footnote about the packages you need, specifically RE: `dbplyr` (which will power a lot of what you need to translate your dplyr/tidyr/etc code). That's only so someone who tries it out isn't blindsided by installation issues.
May 1, 2025 at 7:16 PM
A good post! <3 DuckDB
I would maybe quibble that it's worth adding a footnote about the packages you need, specifically RE: `dbplyr` (which will power a lot of what you need to translate your dplyr/tidyr/etc code). That's only so someone who tries it out isn't blindsided by installation issues.
I would maybe quibble that it's worth adding a footnote about the packages you need, specifically RE: `dbplyr` (which will power a lot of what you need to translate your dplyr/tidyr/etc code). That's only so someone who tries it out isn't blindsided by installation issues.
Don't forget that you can also pass tidyselect helpers!
If you have a pattern of columns, say address_1, address_2, ..., but you don't know how many, you can `unite("address", starts_with("address_"), sep = " ")` which is difficult to do otherwise without breaking your flow.
<3 tidyr
If you have a pattern of columns, say address_1, address_2, ..., but you don't know how many, you can `unite("address", starts_with("address_"), sep = " ")` which is difficult to do otherwise without breaking your flow.
<3 tidyr
September 24, 2025 at 2:26 PM
Don't forget that you can also pass tidyselect helpers!
If you have a pattern of columns, say address_1, address_2, ..., but you don't know how many, you can `unite("address", starts_with("address_"), sep = " ")` which is difficult to do otherwise without breaking your flow.
<3 tidyr
If you have a pattern of columns, say address_1, address_2, ..., but you don't know how many, you can `unite("address", starts_with("address_"), sep = " ")` which is difficult to do otherwise without breaking your flow.
<3 tidyr
The operation with many names: pivot_longer (tidyr), unpivot (polars), melt (pandas), fold (arquero). Here's a fork using arquero: observablehq.com/d/0b847c3547...
Boring Plot Doc pt. 1
I like Observable Plot a lot. It's super powerful and makes a lot of charts faster to write. But there are some pretty tough challenges to making basic plots that I think aren't very well-documented. ...
observablehq.com
December 15, 2024 at 2:33 AM
The operation with many names: pivot_longer (tidyr), unpivot (polars), melt (pandas), fold (arquero). Here's a fork using arquero: observablehq.com/d/0b847c3547...