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**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · 3g

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

@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

#R #RStats #Web

**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · 3g

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

I'm trying to download pdf's of medical policy but having issues, anyone?

A screenshot of R code in a code editor. The code includes libraries being loaded (tidyverse and rvest), a URL definition for scraping data, and a block for reading and processing HTML using XPath. It also features error handling to display messages if the scraping fails, indicating possible issues like authentication requirements, automated scraping blocks, changes in XPath structure, or network connectivity problems. The background is a soft gradient, and the text is displayed in a monospaced font.

#Medical #Policy #Insurance

**Data Science** @datascience@genomic.social · 4g

Data Science @datascience@genomic.social

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.

**Jon Harmon** @jonthegeek@fosstodon.org · 6g

Jon Harmon @jonthegeek@fosstodon.org

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

TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.

Scatterplot titled 'Flipper and bill length' showing the relationship between flipper length (x-axis, in mm) and bill length (y-axis, in mm) for three penguin species at Palmer Station LTER. Adelie penguins are represented by orange circles with a slight positive trend line. Chinstrap penguins are shown with purple triangles, clustering higher on the bill length axis and a steeper trend line. Gentoo penguins are shown with dark green squares, positioned farther right along the flipper length axis, with their own upward trend line. A legend at the bottom right indicates the color and shape used for each species. The image is from the palmerpenguins R package.

#RStats #PyData #JuliaLang

**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · 10 apr

10 apr

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

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

**Jon Harmon** @jonthegeek@fosstodon.org · 7 apr

7 apr

Jon Harmon @jonthegeek@fosstodon.org

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

Infographic titled 'Emergency Room Visit Times by State' from the Visual Capitalist, showing a map of the U.S. with each state color-coded based on average ER visit times, ranging from 1 hour 50 minutes for North Dakota to 5 hours 14 minutes for the District of Colombia. At the top, diverse individuals are seated in a waiting room. The source is the U.S. Centers for Medicare & Medicaid Services, 2024. More details about the graphic are available at https://www.voronoiapp.com/healthcare/People-in-DC-Spend-Over-5-Hours-On-Average-In-the-Emergency-Room--3590

#RStats #PyData #JuliaLang

**safest_integer** @safest_integer@mastodon.social · 5 apr *

5 apr *

safest_integer @safest_integer@mastodon.social

"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!

https://github.com/grantmcdermott/tinyplot

#rstats
#datascience
#graphics
#tidyverse
#visualization

**Mine Çetinkaya-Rundel** @minecr@fosstodon.org · 4 apr

4 apr

Mine Çetinkaya-Rundel @minecr@fosstodon.org

New blog post on the Tidyverse blog: Learning the #tidyverse with the help of #ai tools #rstats

https://www.tidyverse.org/blog/2025/04/learn-tidyverse-ai/

www.tidyverse.orgLearning the tidyverse with the help of AI toolsTips and recommendations for learning the tidyverse with AI tools.

**Statistics Globe** @StatisticsGlobe@mastodon.social · 4 apr

4 apr

Statistics Globe @StatisticsGlobe@mastodon.social

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

**Statistics Globe** @StatisticsGlobe@mastodon.social · 1 apr

1 apr

Statistics Globe @StatisticsGlobe@mastodon.social

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

#datastructure #datavisualization #tidyverse

**Jon Harmon** @jonthegeek@fosstodon.org · 31 mar

31 mar

Jon Harmon @jonthegeek@fosstodon.org

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

Image shows faceted scatterplots of pokemon dataset. Attack is on the x-axis, and defense is on the y-axis. The primary pokemon color is indicated with the color of the points, and the charts are faceted by pokemon type. The breakdown highlights a somewhat linear relationship between attack and defense for most pokemon types. For the most part, they are correlated, with defense and attack increasing together. There is less variance between the two variables when type is "dragon", "electric", or "normal". Noticeable types that break this trend are "rock" type pokemon. In addition, there are a few outliers, such as a "bug" type with a near-zero attack and defense above 200 hp.

#RStats #PyData #JuliaLang

**Statistics Globe** @StatisticsGlobe@mastodon.social · 28 mar

28 mar

Statistics Globe @StatisticsGlobe@mastodon.social

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

**Statistics Globe** @StatisticsGlobe@mastodon.social · 25 mar

25 mar

Statistics Globe @StatisticsGlobe@mastodon.social

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

**Jon Harmon** @jonthegeek@fosstodon.org · 24 mar

24 mar

Jon Harmon @jonthegeek@fosstodon.org

https://DSLC.io welcomes you to week 12 of #TidyTuesday! We're exploring Text Data From Amazon's Annual Reports!

https://tidytues.day/2025/2025-03-25
https://gregoryvdvinne.github.io/Text-Mining-Amazon-Budgets.html

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

A scatter plot comparing word frequencies in annual reports from 2005–2013 and 2014–2023. Each orange dot represents a word, with its position indicating how often it appeared in the two time periods. A dashed diagonal line represents equal frequency across both periods. Some words, such as 'cash,' 'tax,' and 'billion,' appear more frequently in the later period. Others, such as 'stock' and 'million,' appear less often in the later period. A note explains that 2014–2023 word counts are adjusted to account for the extra year in that period.

#RStats #PyData #JuliaLang

**Stadia Maps** @stadiamaps@en.osm.town · 24 mar

24 mar

Stadia Maps @stadiamaps@en.osm.town

The mighty ggmap is a powerful R library for visualizing data geospatially.

Learn how to make great looking data with Stadia Maps.

https://docs.stadiamaps.com/tutorials/getting-started-in-r-with-ggmap/?utm_campaign=tutorial_start_ggmap&utm_source=mastodon&utm_medium=social

#RStats #DataViz #Geospatial #Tidyverse
https://docs.stadiamaps.com/tutorials/getting-started-in-r-with-ggmap/?utm_campaign=tutorial_start_ggmap&utm_source=mastodon&utm_medium=social

docs.stadiamaps.comQuickstart: ggmap - Stadia Maps DocumentationLearn how to use Stadia Maps basemap tiles in R with ggmap.

**Jon Harmon** @jonthegeek@fosstodon.org · 17 mar

17 mar

Jon Harmon @jonthegeek@fosstodon.org

Curator: @lydz_gibby
https://DSLC.io welcomes you to week 11 of #TidyTuesday! We're exploring Palm Trees!

https://tidytues.day/2025/2025-03-18
https://www.nature.com/articles/s41597-019-0189-0

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

Collage of 15 different palm trees showing variation in palm growth forms, steam and leaf variation, and fruit variation.

#RStats #PyData #JuliaLang

**Statistics Globe** @StatisticsGlobe@mastodon.social · 14 mar

14 mar

Statistics Globe @StatisticsGlobe@mastodon.social

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

**Eric R. Scott** @LeafyEricScott@fosstodon.org · 11 mar

11 mar

Eric R. Scott @LeafyEricScott@fosstodon.org

There are now so many "backends" for {dplyr}—duckdb with {duckplyr}, polars with {tidypolars}, various database engines with {dbplyr}, {data.table} with {dtplyr}. Is there a blog post or flow chart somewhere with pros and cons of each? Like, comparisons of memory requirements, speed, and how likely they are to "just work"?

#rstats #duckdb #dplyr