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@wronglang I also see that when I use rvest and the read_html() function it just goes on and on. so strange. maybe I need an RSelenium approach
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I'm trying to download pdf's of medical policy but having issues, anyone?
Do you need better performance than what the standard #tidyverse functions have? {collapse} might be worth a look: https://sebkrantz.github.io/collapse/ #rstats #optimization
sebkrantz.github.ioAdvanced and Fast Data Transformation in RA large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as units, integer64, xts/zoo, tibble, grouped_df, data.table, sf, and pseries/pdata.frame.
It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io/join (find the #chat-tidytuesday channel)!
RT @jonthegeek https://fosstodon.org/@jonthegeek/114336585969522119
Curator: @jonthegeek
https://DSLC.io welcomes you to week 15 of #TidyTuesday! We're exploring Base R Penguins!
https://tidytues.day/2025/2025-04-15
https://zenodo.org/records/14902740
Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md
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)
You can also use it with the #tidyverse as that is how it was designed and build off of. Mostly #parsnip #R #Programming
![library(tidyAML)
library(dplyr)
create_model_spec(
.parsnip_eng = list("lm","glm"),
.parsnip_fns = list("linear_reg")
) |>
pull(.model_spec)
[[1]]
Linear Regression Model Specification (regression)
Computational engine: lm
[[2]]
Linear Regression Model Specification (regression)
Computational engine: glm](https://media.mstdn.social/media_attachments/files/114/313/636/555/433/614/original/46d6b02551206089.png "library(tidyAML)
library(dplyr)
create_model_spec(
.parsnip_eng = list(\"lm\",\"glm\"),
.parsnip_fns = list(\"linear_reg\")
) |>
pull(.model_spec)
[[1]]
Linear Regression Model Specification (regression)
Computational engine: lm
[[2]]
Linear Regression Model Specification (regression)
Computational engine: glm")
It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io/join (find the #chat-tidytuesday channel)!
RT @jonthegeek https://fosstodon.org/@jonthegeek/114297010745251048
Curator: @jonthegeek
https://DSLC.io welcomes you to week 14 of #TidyTuesday! We're exploring Timely and Effective Care by US State!
https://tidytues.day/2025/2025-04-08
https://www.visualcapitalist.com/mapped-emergency-room-visit-times-by-state/
Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md
```
library(tidyverse)
crossing(x=0:10, y=x) |>
mutate(dx = rnorm(n(), 0, (y/20)^1.5),
dy = rnorm(n(), 0, (y/20)^1.5)) |>
ggplot() +
geom_tile(aes(x=x+dx, y=y+dy, fill=y), colour='black',
lwd=2, width=1, height=1, alpha=0.8, show.legend=FALSE) +
scale_fill_gradient(high='#9f025e', low='#f9c929') +
scale_y_reverse() + theme_void()
```
#rstats #tidyverse #generativeart
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.
"It is worth emphasizing that tinyplot requires only base R. It has zero recursive dependencies and we have been careful to keep its installation size down to a minimum."
That's my kind of package. Let's go Tinyverse!
New blog post on the Tidyverse blog: Learning the #tidyverse with the help of #ai tools #rstats
www.tidyverse.orgLearning the tidyverse with the help of AI toolsTips and recommendations for learning the tidyverse with AI tools.
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
It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io/join (find the #chat-tidytuesday channel)!
RT @jonthegeek https://fosstodon.org/@jonthegeek/114257310205493006
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
GIF
https://DSLC.io welcomes you to week 13 of #TidyTuesday! We're exploring Pokemon!
https://tidytues.day/2025/2025-04-01 https://medium.com/@hanahshih46/pokemon-data-visualization-and-analysis-with-r-60970c8e37f4
Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md
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
It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io/join (find the #chat-tidytuesday channel)!
RT @jonthegeek https://fosstodon.org/@jonthegeek/114217675218155198