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spsanderson.com
#Calligraphy with #R and #RStats using #TidyDensity and #RandomWalker then plotting with #ggplot2

#GitHub github.com/spsanderson/...

#Coding #Programming #aRt
#Calligraphy with #R and #RStats using #TidyDensity and #RandomWalker then plotting with #ggplot2 #GitHub https://github.com/spsanderson/random_r_projects/blob/main/calligraphy_ggplot2.R #Coding #Programming #aRt #Calligraphy with #R and #RStats using #TidyDensity and #RandomWalker then plotting with #ggplot2 #GitHub https://github.com/spsanderson/random_r_projects/blob/main/calligraphy_ggplot2.R #Coding #Programming #aRt
September 17, 2025 at 2:16 AM
spsanderson.com
Same as yesterday, but this time, making a 2D Discrete Random Walk with my #R #Package #RandomWalker

#R #RStats #RProgramming #RandomWalks #Visual #ggplot2
A screenshot of R programming code written for generating 2D random walks, analyzing outliers, and plotting results. The code uses libraries such as RandomWalker, dplyr, ggplot2, and tidyr. It calculates confidence intervals, identifies outliers, and generates three types of plots: non-outliers, outliers, and highlighted outliers. The syntax is color-coded, with comments explaining each step. A scatterplot titled "2D Random Walks with Outliers Highlighted." It shows multiple colored random walk paths in a 2D space, with outliers prominently highlighted in bold red dashed lines. Non-outliers are displayed in lighter colors. Black dashed horizontal and vertical lines indicate threshold boundaries for outlier detection. The x-axis is labeled "Cumulative Sum X," and the y-axis is labeled "Cumulative Sum Y." A scatterplot titled "2D Random Walk Non-Outliers Only." It displays multiple colored linear paths representing random walks in a 2D space, constrained to non-outlier data points. The x-axis is labeled "Cumulative Sum X," and the y-axis is labeled "Cumulative Sum Y." The paths are laid out in a grid-like pattern with varied colors. A scatterplot titled "2D Random Walk Outliers Only." It showcases only the outlier random walk paths in a 2D space, displayed in various colors on a white background. Black dashed horizontal and vertical lines mark the threshold boundaries for outlier detection. The x-axis is labeled "Cumulative Sum X," and the y-axis is labeled "Cumulative Sum Y."
May 15, 2025 at 12:14 PM
spsanderson.com
Been working hard on my #R #Package #RandomWalker and I have added the ability to now generate 1D, 2D and 3D Random Walks.
This is still in dev if you want to install from there and try it.
#R #RStats #TimeSeries
November 11, 2024 at 3:33 PM
spsanderson.com
Next up on the list for my RandomWalker package is random_uniform_walk() which uses the runif() as it's base.

#R #RStats #RandomWalker #RandomWalks #tidy #ggplot2 #RProgramming #RCode
library(RandomWalker) library(ggplot2) library(dplyr) set.seed(123) unsampled_dat <- random_uniform_walk( .dimensions = 2, .num_walks = 1, .samp = FALSE ) set.seed(123) sampled_dat <- random_uniform_walk( .dimensions = 2, .num_walks = 1, .samp = TRUE ) # row_bind data and mutate a column called samp_ind that is set to sampled for # sampled data and unsampled for unsampled data dat <- unsampled_dat |> mutate(samp_ind = "unsampled") |> rbind( sampled_dat |> mutate(samp_ind = "sampled") ) dat |> ggplot(aes(x = x, y = y)) + facet_wrap(~ samp_ind, scales = "free") + geom_path(aes(color = step_number)) + geom_point(data = dat |> group_by(samp_ind) |> filter( step_number == min(step_number) | step_number == max(step_number) ), aes(color = step_number), size = 3) + scale_color_viridis_c(option = "plasma") + labs(title = "2D Random Uniform Walk", x = "X Position", y = "Y Position") + theme_minimal() + theme(legend.position = "none") + theme( axis.text.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.ticks.x = element_blank() ) library(RandomWalker) library(ggplot2) library(dplyr) set.seed(123) unsampled_dat <- random_uniform_walk( .dimensions = 2, .num_walks = 1, .samp = FALSE ) set.seed(123) sampled_dat <- random_uniform_walk( .dimensions = 2, .num_walks = 1, .samp = TRUE ) # row_bind data and mutate a column called samp_ind that is set to sampled for # sampled data and unsampled for unsampled data dat <- unsampled_dat |> mutate(samp_ind = "unsampled") |> rbind( sampled_dat |> mutate(samp_ind = "sampled") ) dat |> ggplot(aes(x = x, y = y)) + facet_wrap(~ samp_ind, scales = "free") + geom_path(aes(color = step_number)) + geom_point(data = dat |> group_by(samp_ind) |> filter( step_number == min(step_number) | step_number == max(step_number) ), aes(color = step_number), size = 3) + scale_color_viridis_c(option = "plasma") + labs(title = "2D Random Uniform Walk", x = "X Position", y = "Y Position") + theme_minimal() + theme(legend.position = "none") + theme( axis.text.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.ticks.x = element_blank() )
July 12, 2025 at 12:12 PM
desfontain.es
This piece about the UK liver transplant matching algorithm, by @randomwalker and @sayashk, is fantastic. Excellent example that algorithms that make real-world decisions aren't inherently problematic, but they can certainly be horrifyingly bad if not designed and tested carefully […]
Original post on hachyderm.io
hachyderm.io
November 12, 2024 at 5:37 PM
spsanderson.com
📊 Just released: RandomWalker 0.3.0! Now you can generate random walks in up to 3 dimensions. This is a must-read for R programmers looking to enhance their simulations.

Dive into the details: [https://www.spsanderson.com/steveondata/posts/2025-05-09/

#Blog #CRAN #RStats #TimeSeries #RandomWalker
May 9, 2025 at 12:47 PM
cranupdates.bsky.social
Updates on CRAN: allcontributors (0.2.2), clusterMI (1.2.2), metalite.ae (0.1.3), openCR (2.2.7), RandomWalker (0.2.0), Rlabkey (3.4.0), rStrava (1.3.2)
October 23, 2024 at 5:17 PM
spsanderson.com
Now I have added the t-distribution as well.

#R #RStats #RProgramming #RandomWaker #RCode #ggplot2 #tidydata
library(RandomWalker) library(ggplot2) library(dplyr) set.seed(123) unsampled_dat <- random_t_walk( .dimensions = 2, .num_walks = 1, .samp = FALSE ) set.seed(123) sampled_dat <- random_t_walk( .dimensions = 2, .num_walks = 1, .samp = TRUE ) # row_bind data and mutate a column called samp_ind that is set to sampled for # sampled data and unsampled for unsampled data dat <- unsampled_dat |> mutate(samp_ind = "unsampled") |> rbind( sampled_dat |> mutate(samp_ind = "sampled") ) dat |> ggplot(aes(x = x, y = y)) + facet_wrap(~ samp_ind, scales = "free") + geom_path(aes(color = step_number)) + geom_point(data = dat |> group_by(samp_ind) |> filter( step_number == min(step_number) | step_number == max(step_number) ), aes(color = step_number), size = 3) + scale_color_viridis_c(option = "plasma") + labs(title = "2D Random t-distribution Walk", x = "X Position", y = "Y Position") + theme_minimal() + theme(legend.position = "none") + theme( axis.text.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.ticks.x = element_blank() ) random_t_walk(.initial_value = 10) |> visualize_walks(.pluck = 2) library(RandomWalker) library(ggplot2) library(dplyr) set.seed(123) unsampled_dat <- random_t_walk( .dimensions = 2, .num_walks = 1, .samp = FALSE ) set.seed(123) sampled_dat <- random_t_walk( .dimensions = 2, .num_walks = 1, .samp = TRUE ) # row_bind data and mutate a column called samp_ind that is set to sampled for # sampled data and unsampled for unsampled data dat <- unsampled_dat |> mutate(samp_ind = "unsampled") |> rbind( sampled_dat |> mutate(samp_ind = "sampled") ) dat |> ggplot(aes(x = x, y = y)) + facet_wrap(~ samp_ind, scales = "free") + geom_path(aes(color = step_number)) + geom_point(data = dat |> group_by(samp_ind) |> filter( step_number == min(step_number) | step_number == max(step_number) ), aes(color = step_number), size = 3) + scale_color_viridis_c(option = "plasma") + labs(title = "2D Random t-distribution Walk", x = "X Position", y = "Y Position") + theme_minimal() + theme(legend.position = "none") + theme( axis.text.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.ticks.x = element_blank() ) random_t_walk(.initial_value = 10) |> visualize_walks(.pluck = 2) library(RandomWalker) library(ggplot2) library(dplyr) set.seed(123) unsampled_dat <- random_t_walk( .dimensions = 2, .num_walks = 1, .samp = FALSE ) set.seed(123) sampled_dat <- random_t_walk( .dimensions = 2, .num_walks = 1, .samp = TRUE ) # row_bind data and mutate a column called samp_ind that is set to sampled for # sampled data and unsampled for unsampled data dat <- unsampled_dat |> mutate(samp_ind = "unsampled") |> rbind( sampled_dat |> mutate(samp_ind = "sampled") ) dat |> ggplot(aes(x = x, y = y)) + facet_wrap(~ samp_ind, scales = "free") + geom_path(aes(color = step_number)) + geom_point(data = dat |> group_by(samp_ind) |> filter( step_number == min(step_number) | step_number == max(step_number) ), aes(color = step_number), size = 3) + scale_color_viridis_c(option = "plasma") + labs(title = "2D Random t-distribution Walk", x = "X Position", y = "Y Position") + theme_minimal() + theme(legend.position = "none") + theme( axis.text.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.ticks.x = element_blank() ) random_t_walk(.initial_value = 10) |> visualize_walks(.pluck = 2)
July 12, 2025 at 1:10 PM
spsanderson.com
Quick heads-up: no Linux post this Friday. Tomorrow’s blog spotlights RandomWalker 0.3.0 on CRAN—3-D walks, smart subsetting, confidence intervals, and step_number in place of x. Bigger upgrades on the horizon. Stay tuned!
#rstats #RandomWalker #DataScience #OpenSource #CRAN
May 8, 2025 at 12:14 PM
spsanderson.com
Looks like the newest version of #RandomWalker and #TidyDensity are the best yet as seen in the 30 post release downloads. I don't get tidyverse downloads but I'm proud of these works.

#R #RStats
Looks like the newest version of #RandomWalker and #TidyDensity are the best yet as seen in the 30 post release downloads. I don't get tidyverse downloads but I'm proud of these works. #R #RStats Looks like the newest version of #RandomWalker and #TidyDensity are the best yet as seen in the 30 post release downloads. I don't get tidyverse downloads but I'm proud of these works. #R #RStats
September 30, 2025 at 2:38 PM
spsanderson.com
I added another random walk to the dev version of my #R #Package #RandomWalker for the Wilcoxon Signed Rank using the #BaseR function rsignrank.

Take a look :)

#R #ggplot2 #tidydata #Random #stochastic #discrete
I added another random walk to the dev version of my #R #Package #RandomWalker for the Wilcoxon Signed Rank using the #BaseR function rsignrank. Take a look :) #R #ggplot2 #tidydata #Random #stochastic #discrete I added another random walk to the dev version of my #R #Package #RandomWalker for the Wilcoxon Signed Rank using the #BaseR function rsignrank. Take a look :) #R #ggplot2 #tidydata #Random #stochastic #discrete I added another random walk to the dev version of my #R #Package #RandomWalker for the Wilcoxon Signed Rank using the #BaseR function rsignrank. Take a look :) #R #ggplot2 #tidydata #Random #stochastic #discrete
July 15, 2025 at 3:06 PM
spsanderson.com
Added a the random_poisson_walk() to the #dev version of #RandomWalker

#Random #ggplot2 #tidydata #RandomWalks #Stochastic
Added a the random_poisson_walk() to the #dev version of #RandomWalker #Random #ggplot2 #tidydata #RandomWalks #Stochastic Added a the random_poisson_walk() to the #dev version of #RandomWalker #Random #ggplot2 #tidydata #RandomWalks #Stochastic Added a the random_poisson_walk() to the #dev version of #RandomWalker #Random #ggplot2 #tidydata #RandomWalks #Stochastic
July 15, 2025 at 7:01 PM
cranberriesfeed.bsky.social
CRAN updates: gauseR nLTT RandomWalker RcmdrPlugin.aRnova #rstats
May 6, 2025 at 1:02 PM
spsanderson.com
Just added the function random_f_walk() to the #Dev version of #RandomWalker

#R #RStats #GitHub #ggplot2 #tidydata #Random #FDistribution #F #Distribution
Just added the function random_f_walk() to the #Dev version of #RandomWalker #R #RStats #GitHub #ggplot2 #tidydata #Random #FDistribution #F #Distribution Just added the function random_f_walk() to the #Dev version of #RandomWalker #R #RStats #GitHub #ggplot2 #tidydata #Random #FDistribution #F #Distribution Just added the function random_f_walk() to the #Dev version of #RandomWalker #R #RStats #GitHub #ggplot2 #tidydata #Random #FDistribution #F #Distribution
July 19, 2025 at 2:19 PM
spsanderson.com
Excellent this means I can resubmit #RandomWalker
August 18, 2025 at 1:59 PM
spsanderson.com
I updated yesterdays script on #Calligraphy in #R with #ggplot2 I like this one a bit better. I used my #RandomWalker package to generate random walks for a normal Gaussian distribution and a Cauchy distribution.

#R #RStats #CodeArt #Art
library(RandomWalker) library(dplyr) library(ggplot2) library(patchwork) n <- 25 nw <- 3 x <- random_normal_walk(.num_walks = nw, .n = n, .samp = FALSE) |> select(cum_sum_y) y <- random_cauchy_walk(.num_walks = nw, .n = n, .samp = FALSE) |> select(y) xx <- predict(smooth.spline(x$cum_sum_y, spar = 0.005), seq(1, n, 0.01))$y[-1] yy <- predict(smooth.spline(y$y, spar = 0.005), seq(1, n, 0.01))$y[-1] x_fns <- get_attributes(x)$fns |> convert_snake_to_title_case() y_fns <- get_attributes(y)$fns |> convert_snake_to_title_case() df <- tibble( walk_number = paste0("Sim ", rep(1:nw, each = length(xx)/nw)) |> factor(levels = paste0("Sim ", 1:nw)), x = xx, y = yy ) p1 <- df |> ggplot(aes(color = walk_number)) + facet_wrap(~ walk_number, scales = "free") + geom_path( aes(x = x, y = y, lwd = c(0, diff(y))), show.legend = FALSE ) + scale_color_viridis_d(option = "viridis") + labs(caption = "Separate Random Walks") + theme_void() p2 <- df |> ggplot(aes(color = walk_number)) + geom_path( aes(x = x, y = y, lwd = c(0, diff(y))), show.legend = FALSE ) + scale_color_viridis_d(option = "viridis") + labs(caption = "All Random Walks Put Together by Factors") + theme_void() p3 <- df |> ggplot() + geom_path(aes(x = x, y = y, color = y, lwd = c(0, diff(y))), show.legend = FALSE) + scale_color_viridis_c(option = "plasma") + theme_void() (p1 + (p2 / p3)) + plot_annotation( title = "Caligraphy in ggplot2 using RandomWalker", subtitle = paste0(nw, " Random Walks with ", n, " Steps", "\n", "Using: ", x_fns, ", ", y_fns), caption = "All Walks Put Together Scale Color Plasma" ) library(RandomWalker) library(dplyr) library(ggplot2) library(patchwork) n <- 25 nw <- 3 x <- random_normal_walk(.num_walks = nw, .n = n, .samp = FALSE) |> select(cum_sum_y) y <- random_cauchy_walk(.num_walks = nw, .n = n, .samp = FALSE) |> select(y) xx <- predict(smooth.spline(x$cum_sum_y, spar = 0.005), seq(1, n, 0.01))$y[-1] yy <- predict(smooth.spline(y$y, spar = 0.005), seq(1, n, 0.01))$y[-1] x_fns <- get_attributes(x)$fns |> convert_snake_to_title_case() y_fns <- get_attributes(y)$fns |> convert_snake_to_title_case() df <- tibble( walk_number = paste0("Sim ", rep(1:nw, each = length(xx)/nw)) |> factor(levels = paste0("Sim ", 1:nw)), x = xx, y = yy ) p1 <- df |> ggplot(aes(color = walk_number)) + facet_wrap(~ walk_number, scales = "free") + geom_path( aes(x = x, y = y, lwd = c(0, diff(y))), show.legend = FALSE ) + scale_color_viridis_d(option = "viridis") + labs(caption = "Separate Random Walks") + theme_void() p2 <- df |> ggplot(aes(color = walk_number)) + geom_path( aes(x = x, y = y, lwd = c(0, diff(y))), show.legend = FALSE ) + scale_color_viridis_d(option = "viridis") + labs(caption = "All Random Walks Put Together by Factors") + theme_void() p3 <- df |> ggplot() + geom_path(aes(x = x, y = y, color = y, lwd = c(0, diff(y))), show.legend = FALSE) + scale_color_viridis_c(option = "plasma") + theme_void() (p1 + (p2 / p3)) + plot_annotation( title = "Caligraphy in ggplot2 using RandomWalker", subtitle = paste0(nw, " Random Walks with ", n, " Steps", "\n", "Using: ", x_fns, ", ", y_fns), caption = "All Walks Put Together Scale Color Plasma" )
September 11, 2025 at 2:05 PM
spsanderson.com
Last one for today, I just added random_hypergeometric_walk() to the #dev version of my #R #package #RandomWalker

this package relies on #dplyr #ggplot2 #stats #purrr

#R #RStats #tech #Coding
Last one for today, I just added random_hypergeometric_walk() to the #dev version of my #R #package #RandomWalker this package relies on #dplyr #ggplot2 #stats #purrr #R #RStats #tech #Coding Last one for today, I just added random_hypergeometric_walk() to the #dev version of my #R #package #RandomWalker this package relies on #dplyr #ggplot2 #stats #purrr #R #RStats #tech #Coding Last one for today, I just added random_hypergeometric_walk() to the #dev version of my #R #package #RandomWalker this package relies on #dplyr #ggplot2 #stats #purrr #R #RStats #tech #Coding
July 19, 2025 at 2:49 AM
spsanderson.com
Next up and last for today is the rweibull distribution from base R added to RandomWalker development.

#R #ggplot2 #TidyData #RandomWalker #RandomWalks #Random #Programming #Coding #RCode #RProgramming
library(RandomWalker) library(ggplot2) library(dplyr) set.seed(123) unsampled_dat <- random_weibull_walk( .dimensions = 2, .num_walks = 1, .samp = FALSE ) set.seed(123) sampled_dat <- random_weibull_walk( .dimensions = 2, .num_walks = 1, .samp = TRUE ) # row_bind data and mutate a column called samp_ind that is set to sampled for # sampled data and unsampled for unsampled data dat <- unsampled_dat |> mutate(samp_ind = "unsampled") |> rbind( sampled_dat |> mutate(samp_ind = "sampled") ) dat |> ggplot(aes(x = x, y = y)) + facet_wrap(~ samp_ind, scales = "free") + geom_path(aes(color = step_number)) + geom_point(data = dat |> group_by(samp_ind) |> filter( step_number == min(step_number) | step_number == max(step_number) ), aes(color = step_number), size = 3) + scale_color_viridis_c(option = "plasma") + labs(title = "2D Random Weibull Walk", x = "X Position", y = "Y Position") + theme_minimal() + theme(legend.position = "none") + theme( axis.text.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.ticks.x = element_blank() ) library(RandomWalker) library(ggplot2) library(dplyr) set.seed(123) unsampled_dat <- random_weibull_walk( .dimensions = 2, .num_walks = 1, .samp = FALSE ) set.seed(123) sampled_dat <- random_weibull_walk( .dimensions = 2, .num_walks = 1, .samp = TRUE ) # row_bind data and mutate a column called samp_ind that is set to sampled for # sampled data and unsampled for unsampled data dat <- unsampled_dat |> mutate(samp_ind = "unsampled") |> rbind( sampled_dat |> mutate(samp_ind = "sampled") ) dat |> ggplot(aes(x = x, y = y)) + facet_wrap(~ samp_ind, scales = "free") + geom_path(aes(color = step_number)) + geom_point(data = dat |> group_by(samp_ind) |> filter( step_number == min(step_number) | step_number == max(step_number) ), aes(color = step_number), size = 3) + scale_color_viridis_c(option = "plasma") + labs(title = "2D Random Weibull Walk", x = "X Position", y = "Y Position") + theme_minimal() + theme(legend.position = "none") + theme( axis.text.x = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.ticks.x = element_blank() )
July 10, 2025 at 3:15 PM
taloustaito.bsky.social
Millainen sijoittaja on #RandomWalker Jarkko Aho ja millaisella filosofialla hän valitsee uusia sijoituskohteita? Tarjolla on vinkkejä tavallisellekin piensijoittajalle. Entä jos saisi vaihtaa päivän ajaksi jonkun toisen sijoittajan saappaisiin?

Katso tallenne: youtu.be/2SNBpEM0boQ

#sijoittaminen
Millainen sijoittaja olet, Jarkko Aho alias Random Walker?
YouTube video by Taloustaito
youtu.be
March 21, 2025 at 8:42 AM
spsanderson.com
Updated #pkgdown sites

www.spsanderson.com/healthyR/

www.spsanderson.com/healthyR.ts/

www.spsanderson.com/healthyR.ai/

www.spsanderson.com/healthyR.data/

www.spsanderson.com/healthyverse/

www.spsanderson.com/TidyDensity/

www.spsanderson.com/tidyAML/

www.spsanderson.com/RandomWalker
Hospital Data Analysis Workflow Tools
Hospital data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include average length of stay, r...
www.spsanderson.com
November 5, 2025 at 2:08 AM
spsanderson.com
#ggplot2 #timeseries #RandomWalker #random
August 28, 2025 at 2:37 PM
spsanderson.com
Another #Random #Stochastic #Walk added to my #R #Package #RandomWalker dev version, this time it is random_multinomial_walk() As of now you can only generate a single trial but I'll work on it in the future, for now, let's just get it and move down the list.

#RStats #Code #tidydata #ggplot2
Another #Random #Stochastic #Walk added to my #R #Package #RandomWalker dev version, this time it is random_multinomial_walk(). As of now you can only generate a single trial but I'll work on it in the future, for now, let's just gt it into dev and move down the list. #RCode #RStats #RProgramming #Code #tidydata #ggplot2 Another #Random #Stochastic #Walk added to my #R #Package #RandomWalker dev version, this time it is random_multinomial_walk(). As of now you can only generate a single trial but I'll work on it in the future, for now, let's just gt it into dev and move down the list. #RCode #RStats #RProgramming #Code #tidydata #ggplot2 Another #Random #Stochastic #Walk added to my #R #Package #RandomWalker dev version, this time it is random_multinomial_walk(). As of now you can only generate a single trial but I'll work on it in the future, for now, let's just gt it into dev and move down the list. #RCode #RStats #RProgramming #Code #tidydata #ggplot2 Another #Random #Stochastic #Walk added to my #R #Package #RandomWalker dev version, this time it is random_multinomial_walk(). As of now you can only generate a single trial but I'll work on it in the future, for now, let's just gt it into dev and move down the list. #RCode #RStats #RProgramming #Code #tidydata #ggplot2
July 17, 2025 at 12:24 PM
spsanderson.com
random_binomial_walk() now in #Dev version of #RandomWalker on #GitHub almost done
random_binomial_walk() now in #Dev version of #RandomWalker on #GitHub almost done random_binomial_walk() now in #Dev version of #RandomWalker on #GitHub almost done random_binomial_walk() now in #Dev version of #RandomWalker on #GitHub almost done
July 19, 2025 at 3:12 PM
spsanderson.com
I'm choice 2, using group_nest and map as it retains the original column types and there is no need to rename, although if I put some effort into it I'm sure I could remove the need to enframe and rename. I just don't feel like doing that this morning.

#tidyr #purrr #dplyr #imap #map #RandomWalker
I'm choice 2, using group_nest and map as it retains the original column types and there is no need to rename, although if I put some effort into it I'm sure I could remove the need to enframe and rename. I just don't feel like doing that this morning. #tidyr #tidyverse #purrr #dplyr #imap #map #RandomWalker
October 7, 2025 at 11:56 AM
xor-archive.bsky.social
Really good look at crypto usability and how new metaphors—lock, key, seal, imprint—can help teach PGP https://freedom-to-tinker.com/blog/randomwalker/why-king-george-iii-can-encrypt/ ht @zararah
November 8, 2024 at 1:28 AM