#tidyverse — Public Fediverse posts

Curator: @nrennie
https://DSLC.io welcomes you to week 19 of #TidyTuesday! We're exploring Twinned Cities!

📂 https://tidytues.day/2026/2026-05-12
📰 https://bothness.github.io/twin-cities/

Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md

#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds

Curator: @fosstodon.org/@nrennie
https://DSLC.io welcomes you to week 18 of #TidyTuesday! We're exploring Italian industrial production!

📁 https://tidytues.day/2026/2026-05-05
🗞️ https://seriestoriche.istat.it/fileadmin/documenti/14_History_Industry.pdf

Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md

#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds

Curator: @jonthegeek
https://DSLC.io welcomes you to week 17 of #TidyTuesday! We're exploring US Agricultural Tariffs!

📁 https://tidytues.day/2026/2026-04-28
📰 https://ers.usda.gov/sites/default/files/_laserfiche/publications/102980/ERR-304.pdf?v=13480

Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md

#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds

https://DSLC.io welcomes you to week 16 of #TidyTuesday! We're exploring Global Health Spending!

📁 https://tidytues.day/2026/2026-04-21
🗞️ https://data.one.org/analysis/out-of-pocket-health-burden

Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md

#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds

Wait! You can wrap pipelines in R in parentheses to directly use the last value?!?

#rlang #tidyverse #R

Curator: @jonthegeek
https://DSLC.io welcomes you to week 15 of #TidyTuesday! We're exploring Bird Sightings at Sea!

📁 https://tidytues.day/2026/2026-04-14
🗞️ https://obis.org/dataset/29ea15ed-8f76-40ca-bd14-58c62e10b2ef

Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md

#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds

#Day11 | Distributions – Physiscal | #30DayChartChallenge | Density Distribution of Temperatures in Central America, source: World Bank Climate Portal. Built with #RStats using #ggplot2, #tidyverse and #ggridges.

#Día6 | Comparaciones – RSF-Data Day | #30DayChartChallenge. Cambio en score de libertad de prensa 2024–2025 en paises de América. Creada usando R con #tidyverse, #ggtext, #scales, #rnaturalearth, #rnaturalearthdata y #patchwork.

#Día6 | Comparaciones – RSF-Data Day | #30DayChartChallenge. Cambio en score de libertad de prensa 2024–2025 en paises de América. Creada usando R con #tidyverse, #ggtext, #scales, #rnaturalearth, #rnaturalearthdata y #patchwork.

#Día6 | Comparaciones – RSF-Data Day | #30DayChartChallenge. Cambio en score de libertad de prensa 2024–2025 en paises de América. Creada usando R con #tidyverse, #ggtext, #scales, #rnaturalearth, #rnaturalearthdata y #patchwork.

#Día6 | Comparaciones – RSF-Data Day | #30DayChartChallenge. Cambio en score de libertad de prensa 2024–2025 en paises de América. Creada usando R con #tidyverse, #ggtext, #scales, #rnaturalearth, #rnaturalearthdata y #patchwork.

#Día6 | Comparaciones – RSF-Data Day | #30DayChartChallenge. Cambio en score de libertad de prensa 2024–2025 en paises de América. Creada usando R con #tidyverse, #ggtext, #scales, #rnaturalearth, #rnaturalearthdata y #patchwork.

Having gotten my head fully around R pipes, I feel I need to write an article about it before I forget the fiddly details:

|>, %>%, with(), lambdas, %$%, ...

there are a lot of clever tricks for edge cases! (The with() hack I should have figured out for myself though!)

Also, %$% -- the exposition pipe -- why didn't any of you tell me about this one!?!? You're slacking!

#rlang #datascience #stats #statistics #rprogramming #tidyverse #tidydata #Rpipes #pipes!

Ok, why do people keep doing this:

library(tidyverse)
library(lubridate)

when the first call automatically loads the package in the second call? Am I missing something here?

I see this **everywhere**!

#rlang #rlanguage #tidyverse #lubridate

[Перевод] Почему Python — не лучший язык для data science. Часть 2 — Python против R

Команда Python for Devs подготовила перевод статьи о том, почему Python — несмотря на свою популярность — не всегда идеален для Data Science. Автор показывает, как отсутствие нестандартной оценки выражений усложняет анализ данных, и сравнивает Python с R, где такие задачи решаются куда элегантнее.

https://habr.com/ru/articles/971372/

#python #datascience #tidyverse #pandas #векторизация #polars

Just learned today that pluck(.x, 1,2,1) is equivalent to .x[[1]][[2]][[1]] Source: book.rleripio.com/deflating-va... Very cool! #purrr #pluck #tidyverse

7  Deflating nominal values to...

Na #PythonCerrado2025, tivemos ontem um excelente tutorial do Lucas Marcondes Pavelski https://github.com/lucasmpavelski.

Aprendemos sobre #R, #tidyverse, #reticulate, várias ferramentas essenciais como #ggplot2 e #dplyr, vendo na prática como aplicá-las. Foco na ponte #Python <-> R.

Tudo novidade pra mim, vieram várias ideias interessantes de análises e plots.

#PythonCerrado

I was annoyed that there is no "expand_grid()" function in :python: #Python as in :rstats: #RStats #tidyverse

So I just published a small package on #PyPI !

Introducing polarsgrid
https://pypi.org/project/polarsgrid/

Using the excellent #polars 🐻‍❄️ package, easily create a table with product of factors:

from polarsgrid import expand_grid
expand_grid(a=[1, 2, 3], b=["x", "y"])

Yields all combinations of its inputs as a #DataFrame

It can also produce a #LazyFrame for streaming extra-big tables to disk

I was annoyed that there is no "expand_grid()" function in :python: #Python as in :rstats: #RStats #tidyverse

So I just published a small package on #PyPI !

Introducing polarsgrid
https://pypi.org/project/polarsgrid/

Using the excellent #polars 🐻‍❄️ package, easily create a table with product of factors:

from polarsgrid import expand_grid
expand_grid(a=[1, 2, 3], b=["x", "y"])

Yields all combinations of its inputs as a #DataFrame

It can also produce a #LazyFrame for streaming extra-big tables to disk

I was annoyed that there is no "expand_grid()" function in :python: #Python as in :rstats: #RStats #tidyverse

So I just published a small package on #PyPI !

Introducing polarsgrid
https://pypi.org/project/polarsgrid/

Using the excellent #polars 🐻‍❄️ package, easily create a table with product of factors:

from polarsgrid import expand_grid
expand_grid(a=[1, 2, 3], b=["x", "y"])

Yields all combinations of its inputs as a #DataFrame

It can also produce a #LazyFrame for streaming extra-big tables to disk

I was annoyed that there is no "expand_grid()" function in :python: #Python as in :rstats: #RStats #tidyverse

So I just published a small package on #PyPI !

Introducing polarsgrid
https://pypi.org/project/polarsgrid/

Using the excellent #polars 🐻‍❄️ package, easily create a table with product of factors:

from polarsgrid import expand_grid
expand_grid(a=[1, 2, 3], b=["x", "y"])

Yields all combinations of its inputs as a #DataFrame

It can also produce a #LazyFrame for streaming extra-big tables to disk

I was annoyed that there is no "expand_grid()" function in :python: #Python as in :rstats: #RStats #tidyverse

So I just published a small package on #PyPI !

Introducing polarsgrid
https://pypi.org/project/polarsgrid/

Using the excellent #polars 🐻‍❄️ package, easily create a table with product of factors:

from polarsgrid import expand_grid
expand_grid(a=[1, 2, 3], b=["x", "y"])

Yields all combinations of its inputs as a #DataFrame

It can also produce a #LazyFrame for streaming extra-big tables to disk

At first glance, bar charts might seem like a simple visualization type. But with a little creativity, they can be enhanced in countless ways to reveal deeper insights and make your data shine.

The attached visual highlights a variety of bar chart styles to inspire your work.

Take a look here for more details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #data #tidyverse #rstats #package #datasciencetraining

Что в чёрной коробочке? Выясняем самостоятельно, не привлекая внимания коллег

Всем привет, меня зовут Миша, и я разрабатываю платформу Яндекс Еды. Первые компоненты были написаны почти 10 лет назад (когда Еда ещё была стартапом Foodfox), и у нас накопилось много кода, который просто хорошо работает, а иногда даже «работает — не трогай». Но в процессе развития и устоявшиеся части системы нужно трогать, про что мои коллеги уже писали — как мы повышали версию PHP , пилили монолит и снимали нагрузку с БД . Наконец настал черёд рассказать про процессинг заказов доставки еды из кафе и ресторанов (а также продуктов из магазинов и многого другого). За годы эволюционного развития он значительно разросся, что стало заметно затруднять дальнейшее развитие — например, изменения, связанные с выходом на новые рынки, — а также влиять на надёжность. Поэтому мы решили вынести процессинг заказа в отдельный специализированный сервис. Чтобы определить, что выносить, а что оставлять, нужно было составить исчерпывающий и актуальный список процессов, которые происходят с заказом. И здесь мы столкнулись с вызовом: это знание распределено по многим людям и документам, поскольку на протяжении долгого времени в процессинг заказов вносили изменения многие команды. И перед нами встал вопрос — как собрать нужную информацию о системе с заметной долей легаси быстро, да так, чтобы информация была актуальна?

https://habr.com/ru/companies/yandex/articles/921122/

#process_mining #анализ_данных #tidyverse #триз #рефакторинг #duckdb #архитектура_по #яндекс_еда #монолит

Using dplyr and ggplot2 in R can significantly streamline your data analysis process, making it easier to work with complex data sets.

I have created a video tutorial in collaboration with Albert Rapp, where I demonstrate how to do this in practice: https://www.youtube.com/watch?v=EKISB0gnue4

#coding #datavisualization #rprogramming #dataviz #statisticalanalysis #package #datastructure #ggplot2 #bigdata #tidyverse

I recently discovered the tidyplots package in R, and it’s impressive how effortlessly it enables you to create beautiful, publication-ready plots.

The example visualizations shown here were created by the package author, Jan Broder Engler, and are featured on the tidyplots website: https://jbengler.github.io/tidyplots/

Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#statisticsclass #datavisualization #advancedanalytics #rprogramminglanguage #visualanalytics #package #tidyverse

I've talked about creating data.frames and tibbles before, but it is an important topic so I have covered it again. This time specifically from the perspective of creating them from vectors. Post: www.spsanderson.com/steveondata/... #R #RStats #tibble #dplyr #tidyverse #dataframe #baseR #blog

I've talked about creating data.frames and tibbles before, but it is an important topic so I have covered it again. This time specifically from the perspective of creating them from vectors. Post: www.spsanderson.com/steveondata/... #R #RStats #tibble #dplyr #tidyverse #dataframe #baseR #blog

I've talked about creating data.frames and tibbles before, but it is an important topic so I have covered it again. This time specifically from the perspective of creating them from vectors. Post: www.spsanderson.com/steveondata/... #R #RStats #tibble #dplyr #tidyverse #dataframe #baseR #blog

I've talked about creating data.frames and tibbles before, but it is an important topic so I have covered it again. This time specifically from the perspective of creating them from vectors. Post: www.spsanderson.com/steveondata/... #R #RStats #tibble #dplyr #tidyverse #dataframe #baseR #blog

I've talked about creating data.frames and tibbles before, but it is an important topic so I have covered it again. This time specifically from the perspective of creating them from vectors. Post: www.spsanderson.com/steveondata/... #R #RStats #tibble #dplyr #tidyverse #dataframe #baseR #blog

Make your plots more stylish and visually appealing! The ggthemes package offers a variety of pre-built themes that help you customize the look of your ggplot2 visualizations, drawing inspiration from popular design standards.

The visualization shown here is from the package website: https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

More: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datascienceeducation #coding #visualanalytics #tidyverse #ggplot2 #package

Creating publication-ready plots in R is easier than ever with ggpubr. This extension for ggplot2 simplifies the process of generating clean and professional graphics, especially for exploratory data analysis and reporting.

The attached visual, which I created using ggpubr, demonstrates its versatility.

Additional information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#bigdata #visualanalytics #tidyverse #programming #statisticalanalysis #datavisualization #package #data #ggplot2

If you are looking for data processors to get your data in line for the algo in question, then my #R #package { healthyR.ai } has you covered. These are based on using #tidymodels #parsnip from the #tidyverse www.spsanderson.com/healthyR.ai/... #RStats #Data #ModelData

Day 12 | Distributions – Data Day – Data.gov | #30DayChartChallenge. Visualization made with R using #sf, #tigris, #ggthemes, #patchwork, #tidyverse, #ggtext and #showtext . | Source: data.gov - https://catalog.data.gov/dataset/biodiversity-by-county-distribution-of-animals-plants-and-natural-communities

Day 15 | Relationships – Complicated | #30DayChartChallenge. Visualization made with R using #tidyverse, #ggtext and #showtext . | Source: google trends https://trends.google.com/trends/explore?date=all&q=Avril%20Lavigne%20Complicated&hl=en

Day 10 | Distributions / Multi – Modal | #30DayChartChallenge. Visualization made with R using #ggplot2, #tidyverse, #cowplot, #stars, #raster, #ggspatial and #sf. Data source: Sentinel-2 MSI (2024)

Day 7 | Distributions– Outliers | #30DayChartChallenge. Visualization made with R using #ggplot2, #tidyverse, #terra, #ggtext, #showtext y #sf. Data source: Sentinel-2 MSI (2019-2024)

Day 6 | Comparisons – Florence Nightingale (theme day) | #30DayChartChallenge. Visualization made with R using #tidyverse, #ggtext and #showtext. Data source: HDX - https://data.humdata.org/dataset/cod-ps-hnd.

@safest_integer

#rstats #datascience #graphics #tidyverse #visualization

Count in #tinytable and #rdatatable.

@safest_integer

#rstats #datascience #graphics #tidyverse #visualization

Count in #tinytable and #rdatatable.

@safest_integer

#rstats #datascience #graphics #tidyverse #visualization

Count in #tinytable and #rdatatable.

@safest_integer

#rstats #datascience #graphics #tidyverse #visualization

Count in #tinytable and #rdatatable.

@safest_integer

#rstats #datascience #graphics #tidyverse #visualization

Count in #tinytable and #rdatatable.

ggplot2 is the gold standard when it comes to data visualization.

The image in this post showcases examples of ggplot2 visualizations, demonstrating its versatility to create a wide range of plots with nearly limitless customization options.

Check out my online course, "Data Visualization in R Using ggplot2 & Friends," for a deeper dive into creating stunning plots with ggplot2.

More info: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#package #dataviz #statistical #tidyverse #pythondeveloperjobs

Day 2 | Comparisons – Slope | #30DayChartChallenge. Analysis develop with R using #ggplot2, #tidyverse, #ggpmisc, , #terra, #ggtext, #showtext y #sf. Data source: Sentinel-2 MSI (2019-2024)

gganimate is a powerful extension for ggplot2 that transforms static visualizations into dynamic animations. By adding a time dimension, it allows you to illustrate trends, changes, and patterns in your data more effectively.

The attached animated visualization, which I created with gganimate, showcases a ranked bar chart of the top 3 countries for each year based on inflation since 1980.

More information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #datavisualization #tidyverse #ggplot2

Understanding probability distributions is key to making informed decisions in statistics and data science. Probability distributions describe how the values of a variable are expected to behave, making them crucial for interpreting data and predicting outcomes.

The visualization shown in this post illustrates the distributions.

Further details: https://statisticsglobe.com/online-course-statistical-methods-r

#dataanalytic #tidyverse #rstats #datavisualization

Visualizing gene structures in R? gggenes, an extension of ggplot2, simplifies the process of creating clear and informative gene diagrams, making genomic data easier to interpret and share.

Visualization: https://cran.r-project.org/web/packages/gggenes/vignettes/introduction-to-gggenes.html

Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #datavisualization #dataanalytics #data #tidyverse #datascientists #ggplot2

Local regression is a non-parametric method for fitting smooth curves to data by applying multiple localized regressions. It is useful for uncovering non-linear relationships when the data’s exact form is unknown. Proper use of local regression can reveal trends in noisy data, but poor implementation might lead to misleading results.

Image: https://en.wikipedia.org/wiki/Local_regression#/media/File:Loess_curve.svg

More details: http://eepurl.com/gH6myT

#database #package #bigdata #businessanalyst #tidyverse #datavisualization #rprogramming