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spsanderson.com
Now using the hai_data_scale() fns from healthyR.ai

Reference: www.spsanderson.com/healthyR.ai/...

#R #Rstats #tidydata #sttistics #data #normalization
Now using the hai_data_scale() fns from healthyR.ai Reference: https://www.spsanderson.com/healthyR.ai/reference/hai_data_scale.html #R #Rstats #tidydata #sttistics #data #normalization Now using the hai_data_scale() fns from healthyR.ai Reference: https://www.spsanderson.com/healthyR.ai/reference/hai_data_scale.html #R #Rstats #tidydata #sttistics #data #normalization Now using the hai_data_scale() fns from healthyR.ai Reference: https://www.spsanderson.com/healthyR.ai/reference/hai_data_scale.html #R #Rstats #tidydata #sttistics #data #normalization Now using the hai_data_scale() fns from healthyR.ai Reference: https://www.spsanderson.com/healthyR.ai/reference/hai_data_scale.html #R #Rstats #tidydata #sttistics #data #normalization
September 26, 2025 at 4:35 PM
spsanderson.com
My #R package TidyDensity has been updated!

News: www.spsanderson.com/TidyDensity/...

Post: www.spsanderson.com/steveondata/...

#R #RStats #TidyData #Distributions
Changelog
www.spsanderson.com
September 8, 2025 at 11:51 AM
spsanderson.com
Here is a simple script that will create a discrete walk of 100, 1000, 10000, and 100000 steps, I thought it was neat to see the difference in the 2D output.

For this I used the RandomWalker package developed by myself and Antti Rask 🦜

#R #RStats #RProgramming #ggplot2 #RandomWalker #Random #tidy
Here is a simple script that will create a discrete walk of 100, 1000, 10000, and 100000 steps, I thought it was neat to see the difference in the 2D output. For this I used the RandomWalker package developed by myself and Antti Rask 🦜 #R #RStats #RProgramming #ggplot2 #RandomWalker #Random #tidydata Here is a simple script that will create a discrete walk of 100, 1000, 10000, and 100000 steps, I thought it was neat to see the difference in the 2D output. For this I used the RandomWalker package developed by myself and Antti Rask 🦜 #R #RStats #RProgramming #ggplot2 #RandomWalker #Random #tidydata
September 1, 2025 at 1:17 AM
spsanderson.com
You can use the custom_walk() function in #RandomWalker to use distributions that are either not yet supported by the package or custom to you, remember to only return a single value from your function and let custom_walk() do the rest.

#R #RStats #RProgramming #Code #tidydata
You can use the custom_walk() function in #RandomWalker to use distributions that are either not yet supported by the package or custom to you, remember to only return a single value from your function and let custom_walk() do the rest. #R #RStats #RProgramming #Code #tidydata You can use the custom_walk() function in #RandomWalker to use distributions that are either not yet supported by the package or custom to you, remember to only return a single value from your function and let custom_walk() do the rest. #R #RStats #RProgramming #Code #tidydata You can use the custom_walk() function in #RandomWalker to use distributions that are either not yet supported by the package or custom to you, remember to only return a single value from your function and let custom_walk() do the rest. #R #RStats #RProgramming #Code #tidydata
August 26, 2025 at 1:24 PM
spsanderson.com
Antti Rask and I created the #RandomWalker package to generate time series of random variables. Inspired by my tinkering, we plan 100+ distributions. The next version will let users define custom random processes. We're proud and excited for future updates!

#ggplot2 #Tech #tidydata
Antti Rask 🦜and I have worked pretty hard on our #RandomWalker #RStats package 📦 and we are proud of it. The original idea came when I was looking at a time series and thought: "Well wouldn't it be nice to generate a somewhat comprehensive time series object of random variables?" I get very bored and often, sometimes it causes me to be up all night tinkering with something that either myself or at least one other person has found somewhat useful. This is exactly how RandomWalker came to be. I have every intent of bringing probably at least 100 different (discrete plus continuous) distributions to the package. The failure in that method is that it requires the end user to wait for me to make one, or it requires them to abandon ship and go rouge on their own. So yesterday or the day before I was sitting at my kitchen table thinking about a way to allow users to specify their own random generating process and how to pass it to a function that will generate the standard output that the rest of the functions output. This will be coming with the next version of RandomWalker which will also see visualization enhancements. Antti Rask 🦜and I have worked pretty hard on our #RandomWalker #RStats package 📦 and we are proud of it. The original idea came when I was looking at a time series and thought: "Well wouldn't it be nice to generate a somewhat comprehensive time series object of random variables?" I get very bored and often, sometimes it causes me to be up all night tinkering with something that either myself or at least one other person has found somewhat useful. This is exactly how RandomWalker came to be. I have every intent of bringing probably at least 100 different (discrete plus continuous) distributions to the package. The failure in that method is that it requires the end user to wait for me to make one, or it requires them to abandon ship and go rouge on their own. So yesterday or the day before I was sitting at my kitchen table thinking about a way to allow users to specify their own random generating process and how to pass it to a function that will generate the standard output that the rest of the functions output. This will be coming with the next version of RandomWalker which will also see visualization enhancements.
August 21, 2025 at 1:14 PM
spsanderson.com
Here are all of the available Continuous and Discrete Random Walks in the RandomWalker package :)

#R #RStats #RandomWalker #Random #tidydata
Here are all of the available Continuous and Discrete Random Walks in the RandomWalker package :) #R #RStats #RandomWalker #Random #tidydata Here are all of the available Continuous and Discrete Random Walks in the RandomWalker package :) #R #RStats #RandomWalker #Random #tidydata Here are all of the available Continuous and Discrete Random Walks in the RandomWalker package :) #R #RStats #RandomWalker #Random #tidydata Here are all of the available Continuous and Discrete Random Walks in the RandomWalker package :) #R #RStats #RandomWalker #Random #tidydata
August 12, 2025 at 2:48 PM
spsanderson.com
Prepping the first Major release of my #R #package #RandomWalker

github.com/spsanderson/...

#GitHub #RStats #ggiraf #ggplot2 #tidydata #random #code
Release RandomWalker 1.0.0 · Issue #235 · spsanderson/RandomWalker
Prepare for release: git pull Check current CRAN check results Polish NEWS urlchecker::url_check() devtools::build_readme() devtools::check(remote = TRUE, manual = TRUE) devtools::check_win_devel()...
github.com
August 2, 2025 at 11:30 AM
spsanderson.com
I am done with all the distributions for now for my #R #Package #RandomWalker the last one was a displacement walk.

#ggplot2 #tidydata #R #RStats #Random
I am done with all the distributions for now for my #R #Package #RandomWalker the last one was a displacement walk. #ggplot2 #tidydata #R #RStats #Random I am done with all the distributions for now for my #R #Package #RandomWalker the last one was a displacement walk. #ggplot2 #tidydata #R #RStats #Random I am done with all the distributions for now for my #R #Package #RandomWalker the last one was a displacement walk. #ggplot2 #tidydata #R #RStats #Random I am done with all the distributions for now for my #R #Package #RandomWalker the last one was a displacement walk. #ggplot2 #tidydata #R #RStats #Random
July 21, 2025 at 2:08 PM
spsanderson.com
In #dev on #GitHub is now random_chisquared_walk() I'm on a role, five more to go!

#RStats #R #ggplot2 #ggplot #tidydata
In #dev on #GitHub is now random_chisquared_walk() I'm on a role, five more to go! #RStats #R #ggplot2 #ggplot #tidydata In #dev on #GitHub is now random_chisquared_walk() I'm on a role, five more to go! #RStats #R #ggplot2 #ggplot #tidydata In #dev on #GitHub is now random_chisquared_walk() I'm on a role, five more to go! #RStats #R #ggplot2 #ggplot #tidydata
July 19, 2025 at 2:46 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
Just added random_geometric_walk() to the #dev version of my #R #package #RandomWalker

You can install it from #GitHub

#R #RStats #RCode #RProgramming #Random #Geometric #ggplot2 #tidydata
Just added random_geometric_walk() to the #dev version of my #R #package #RandomWalker You can install it from #GitHub #R #RStats #RCode #RProgramming #Random #Geometric #ggplot2 #tidydata Just added random_geometric_walk() to the #dev version of my #R #package #RandomWalker You can install it from #GitHub #R #RStats #RCode #RProgramming #Random #Geometric #ggplot2 #tidydata Just added random_geometric_walk() to the #dev version of my #R #package #RandomWalker You can install it from #GitHub #R #RStats #RCode #RProgramming #Random #Geometric #ggplot2 #tidydata
July 19, 2025 at 1:37 PM
spsanderson.com
Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Lognormal #Distribution with random_lognormal_walk()

See below :)

#ggplot2 #tidydata
Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Lognormal #Distribution with random_lognormal_walk() See below :) #ggplot2 #tidydata Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Lognormal #Distribution with random_lognormal_walk() See below :) #ggplot2 #tidydata Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Lognormal #Distribution with random_lognormal_walk() See below :) #ggplot2 #tidydata
July 18, 2025 at 4:54 PM
jhollist.bsky.social
I work with the best people... Gift from a colleague because I am sticking it out at work. I think my #rstats and #tidydata rants have made an impact!
July 18, 2025 at 2:12 PM
spsanderson.com
Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Logistic #Distribution with random_logistic_walk()

See below :)

#ggplot2 #tidydata
Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Logistic #Distribution with random_logistic_walk() See below :) #ggplot2 #tidydata Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Logistic #Distribution with random_logistic_walk() See below :) #ggplot2 #tidydata Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Logistic #Distribution with random_logistic_walk() See below :) #ggplot2 #tidydata
July 18, 2025 at 1:21 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
Another new #RandomWalk function for my #R #Package #RandomWalker this time for a #Discrete #Distribution of the Negative Binomial. This is in Dev.

#RandomWalk #Random #R #Rcode #RProgramming #ggplot2 #tidydata
Another new #RandomWalk function for my #R #Package #RandomWalker this time for a #Discrete #Distribution of the Negative Binomial. #RandomWalk #Random #R #Rcode #RProgramming #ggplot2 #tidydata Another new #RandomWalk function for my #R #Package #RandomWalker this time for a #Discrete #Distribution of the Negative Binomial. #RandomWalk #Random #R #Rcode #RProgramming #ggplot2 #tidydata Another new #RandomWalk function for my #R #Package #RandomWalker this time for a #Discrete #Distribution of the Negative Binomial. #RandomWalk #Random #R #Rcode #RProgramming #ggplot2 #tidydata Another new #RandomWalk function for my #R #Package #RandomWalker this time for a #Discrete #Distribution of the Negative Binomial. #RandomWalk #Random #R #Rcode #RProgramming #ggplot2 #tidydata
July 16, 2025 at 5:47 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
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
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
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
spsanderson.com
Want to visualize a 2D Random Walk of the Wilcox distribution in #R then the RandomWalker package developed by myself and Antti Rask can do that.

#R #RStats #RandomWalker #RandomWalk #Visualization #ggplot2 #TidyData
library(RandomWalker) library(ggplot2) random_wilcox_walk(.m = 1, .k = 2, .dimensions = 2) |> ggplot(aes(x = x, y = y)) + geom_path(aes(color = step_number)) + scale_color_viridis_c(option = "plasma") + labs( title = "2D Random Wilcox 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) random_wilcox_walk(.m = 1, .k = 2, .dimensions = 2) |> ggplot(aes(x = x, y = y)) + geom_path(aes(color = step_number)) + scale_color_viridis_c(option = "plasma") + labs( title = "2D Random Wilcox 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 12:42 PM
opensourcestories.org
The tidyverse has a rich history, which you can hear about in this conversation between Hadley Wickham and Tracy Teal. Learn about Hadley's early influences as well as how he has kept focus throughout the development of tidydata, tidyr, and more!

#OpenSource #R #DataScience
Focus and feedback in the tidyverse
Hadley Wickham takes Tracy Teal through the evolution of the tidyverse, starting with a box of rocks all the way through to a thriving community of data scientists. Join them on the Open Source Story…
buff.ly
January 23, 2025 at 12:43 AM
bergelsonlab.bsky.social
i didn't, thanks! we're lucky psych has an amazing stats series for grads taught by Patrick Mair but i am definitely always looking for awesome courses on data science, stats, tidydata, git, etc. for our folks at all levels! nice to e-meet you @mattblackwell.bsky.social !
January 5, 2024 at 5:03 PM
neuinfo.org
📢 Explore further the importance of tidy data! Check out this insightful blog by @memartone on #OpenData Commons for Traumatic Brain Injury (ODC-TBI). Don't miss it! 📚🔍
Read here:#DataManagement #TidyData #DataStandards #TBI

odc-tbi.org/about/odc_tbi_…
December 5, 2024 at 7:14 AM
gillessanmartin.bsky.social
📊 I gave a seminar on best practices for data collection

Learn which data / metadata to collect systematically & tips to avoid hours of data cleaning nightmare 😅(aka 80% of data scientist's job😭)
#DataCollection #DataScience #TidyData

📽️Watch now:

youtube.com/watch?v=zsyTlg…
Best practices for data collection
In this online training session you’ll learn: - Best practices for data collection and common mistakes to avoid - Which information should be collected? - What can be done with spreadsheets and CSV files? - Why metadata is important? - How to organize the datasets and metadata? You can access the pdf version of the presentation here: https://8e8ea45e-d3b1-4c0f-8009-56b8df6908d2.usrfiles.com/ugd/8e8ea4_f98a6a988b5942f99ff1a6082e927ce3.pdf This online session is organised by BeeLife European Beekeeping Coordination and the Walloon Agricultural Research Center (CRA-W) within the framework of the EUBP Platform project.
www.youtube.com
December 3, 2024 at 9:29 PM