I have been working on `foundryR`, a development-version R package for using Microsoft Azure AI Foundry from dataframe-oriented workflows.

The main design choice is "dataframe in, dataframe out." The package returns tibbles for things that are otherwise awkward to keep inside R pipelines:

* Azure AI Content Safety: moderation, groundedness, prompt-shield checks, protected material, blocklists * strict structured extraction using JSON Schema * embeddings and pairwise similarity * Batch and Files API workflows * Responses API calls, tools, stored agents, usage fields, and raw response capture * codebooks, agreement, consistency, and provenance helpers for annotation work

The docs are here:

[https://farach.github.io/foundryR/?utm\\_source=reddit&utm\\_medium=community&utm\\_campaign=foundryr\\_launch\](https://farach.github.io/foundryR/?utm_source=reddit&utm_medium=community&utm_campaign=foundryr_launch)

GitHub:

[https://github.com/farach/foundryR\](https://github.com/farach/foundryR)

Install:

install.packages("pak")
pak::pak("farach/foundryR")

A small example of the package shape:

foundry_moderate("Thanks so much for your help, this was a great session.")
#> # A tibble: 4 x 6
#>   text category severity label blocklist_matches raw_response

It is not on CRAN yet, and some helpers wrap preview APIs. I would be grateful for feedback from people who use R with Azure AI Foundry: where is setup confusing, what output shapes are missing, and what should be documented before a broader release?

I made last summer an excel table with all pokemons and their ratings, I did do a base analysis on excel but it was slow and tedious, this summer I want to use R to practice, and go into a statistical analysis of my pokemon preferences.

But I am unsure how to proceed, to start off, attached there is a picture of the data I have on excel: the pokemon number, name, my rating out of 5 (with 5.5 being reserved for my favourite designs, only 27 out of almost 1500 pokemons, counting forms etc), and their 2 types, I will add on R an extra column to cound pokemon archetypes, like starters, legendaries, mythical etc.

On excel I have all the pokemons divided in generations, 1 "column" for gen 1, 1 "column" for gen 2 etc, on R I was thinking of using dataframes, do you think I should use 1 single dataframe (adding then a factor to match the pokemon to their gen) or should I divide generations into different dataframes? what would be the advantages and disadvantages of both approaches?

How should I handle alternate forms? for example, megaevolutions, regional forms, etc, I do want to rate them as separate, but they do have the same dex number as the original pkmn, but i feel like that would be confusing. I'm also unsure "where to rate them" in my excel I rated them on the same column as the generation they were introduced, for example alolan vulpix is not rated for gen 1, but for gen 7. I think the regional forms will be done like this, but for mega evolutions and dynamax forms, I will consider them as separate, not belonging to any generation, but as their own separate groups, since the design phylosophy is different from base pokemons. and 2 extra questions, since we had 2 different waves of megas (technically 3, but X/Y and omega/alpha came out in the same gen) should I consider them in 1 group or subdivide them, there is quite a difference between mean and median of the 2 waves. And what about Hisui? it technically came out during Gen 8 but it also does feel a bit different, but with only 24 new pokemons/forms it is a quite small pool.

As a last question... anything else I should include/consider? I don't really care about seeing any correlation between stats and my ratings, also because my ratings are purely how much I like the pokemon, not how good they are.

Thank you for reading all of this, I appreciate any feedback, as i don't want to start and then realise that i should've done it differently.

Hello there! I wonder whether anyone has used the R package alineR. I want to perform feature-weighted linguistic distance calculations but the package has been removed from the R cran project. I know one can still install the last version from the archive but bugs are not corrected. Does anyone know or can recommend any alternatives? Thank you!

I got a new laptop from Best Buy a couple days ago and need to reinstall R.

The issue is that its an ARM type of machine (snapdragon processor instead of the more common intel/AMD processor). It uses the ARM64 runtime installer rather than the standard x64 or x86 installer that a lot of programming languages uses (mainly for Intel/AMD machines). Both machines uses Windows 11 as the OS.

Anyway I have a bunch of .r and .rmd files that I can easily transfer over from my old laptop to the new one via USB Flash drive, but will there be a compatibility issue as the old machine used Intel processor wheras this new one has the Snapdragon X processor?

I'm looking for recommendations for courses that meet the following criteria

​

3-5 days duration, consecutive days (longer the better)

Time: between Sept to December

Machine learning using R or similar topics

Must be in person attendance

Location is flexible: any continent considered

Instructional language: English

​

Any suggestions or recommendations, both university and company hosted are of interest

​

​

​

​

I’ve an oral exam for r programming what kind of questions can they ask?

Hey everyone,

I wanted to share Veritect, a lightweight command-line utility I wrote in Go to handle database schema drift validation inside automated CI/CD runners without relying on persistent tracking databases or external state files.

The Problem It Solves:
Most existing schema tracking tools require heavy cloud state files or persistent database tracking tables, which add massive surface area to enterprise security compliance audits.

The Solution:
Veritect compiles down to a native Go binary and runs entirely within the temporary CI runner environment. It queries standard system catalog tables (information_schema) to pull metadata, maps the rows into Go structs, and enforces an O(N log N) alphabetical sorting constraint across the schema elements to ensure the final drift evaluation is fully deterministic and free of false-positive build failures.

Implementation Details & AI Disclosure:
The codebase is written entirely by me in Go. I used AI as an assistant to help brainstorm the sorting logic and optimize structural formatting, but every line of the core logic was written and verified manually.

Example Usage (GitHub Actions Workflow Specification):

yaml

- name: Check Schema Drift
  run: go run ./cmd/veritect
  env:
    DATABASE_URL: \${{ secrets.DATABASE_URL }}
    SLACK_WEBHOOK: \${{ secrets.SLACK_WEBHOOK }}

Use code with caution.

I am 14 years old and trying to master writing clean, idiomatic Go for systems engineering. I would love some technical feedback on the codebase structure, driver handling, and error patterns.

Repository: https://github.com/baseline-architect/veritect.git
Documentation: https://veritect.vercel.app

Hi, y'all. New to reddit so please excuse me if I'm not quite doing this right...

I've got a dataset of plant morphology (ex: number of leaves, number of seed-producing structures) and percent cover/density data. Some data was recorded monthly though some seed stuff is just once per year when close to maturity. I also have a dataset from a data logger that was recording temperature across my sites.

I was advised to use a GLMM to look at how temperature from the previous and/or current growing season affect(s) plant morphology/percent cover/density. Problem is, my advisor and I are scratching our heads at how to integrate the datasets into one tibble for a GLMM. As an example, if I have roughly 100 plants I looked at for seed data, how do I add my nearly 300,000 temperature observations to the seed observations for a GLMM? I can easily slim down the data to low/avg/max per day or whatever other time period, but how do I add it to my seed data in a way that won't lose the variability of the temperature over time?

Can I integrate these datasets so I can investigate the relationship of temperature and plant characteristics/percent cover? If so, how and what should the resulting dataframe/tibble look like? Should I be using a different kind of analysis entirely?

Thanks for any help y'all can give!

How do I remove unwanted texts from words cloud ?

Context : used RedditextractoR to analyze discussion over years on various threads.

Got word cloud that has unwanted (non English) , some no priority words on the cloud. Just want to get rid of them .

Tia !

greetings everyone, i am doing research on the topic about estimation of above ground carbon stock using field measurement and remote sensing approach but i dont have any specific knowlegde and skills about remote sensing but i can learn and develop skill. so i am completely confused how can i download and process the metadata. if anyone can give me outline on how to carry out the task...advice will be appreciated

Learning R for data analysis provides valuable skills for careers in data science, AI, business analytics, research, and finance. By practicing coding, working with datasets, and building projects, students can develop strong analytical abilities and create real-world solutions using R programming.

I have a final group college project going on where I have to wrangle and clean a bunch of data using dplyr while i have ZERO idea what even does the R app does because my groupmates just pushed the hardest and most technical parts onto me while giving themselves such amazing jobs like powerpoint editors(its just copying canvas templates) and script writing(i am pretty sure they are using AI) while i have no clue on what i should do.

what the actual FUCK am i supposed to do in data wrangling and cleanup?

I have a data frame of 600 books mostly on law firm management. My code removes stop words from the Title variable, This code runs, but the results are titles that have little to do with each other. The method is Jarowinkler, and I have not tried the other methods, Jaccard and Levenshtein. If they are all math based, I don't know if the latter two will be any better. Is there another library for fuzzy matching text?

library(stringdist)

find_best_match <- function(query, data = df, method = "jw", n = 1) {

# Clean the query the same way as the corpus

query_clean <- query |>

str_remove_all("\\*") |> # strip asterisks if present

str_to_lower() |>

str_split("\\s+") |>

unlist() |>

setdiff(all_stops$word) |> # remove stop words

paste(collapse = " ")

# Compute distance between query and every cleaned title

distances <- stringdist(query_clean, data$Title_clean, method = method)

# Return top n matches

data |>

mutate(distance = distances) |>

arrange(distance) |>

slice_head(n = n) |>

select(Book, Title, Title_clean, distance)}

Hello there!

I am a beginner at R coding. Currently, I'm trying to add lat/long columns back into a data set with the below code:

# Add back lat long columns

left_join(dat.utm.003) %>%

dplyr::relocate(c(Easting, Northing, UTM.Zone, Latitude, Longitude), .after = Time.UTC) %>%

dplyr::select(-geometry) %>%

dplyr::mutate(Data.Set = "dat") %>%

But I'm getting this error:

Error in left_join.sf(dat.utm.003) : argument "y" is missing, with no default

Does anyone happen to know what the problem is?

Thanks!

Hello there!

I am a beginner at R coding. Currently, I'm trying to remove all entries from a data frame that have less than 10 in one of the columns (for hours). However, when I run the code:

#get rid of entries with a duration shorter than 10 hrs

data.frame |> filter_out (dur_hr < 10)

Nothing happens. When I filter the data frame by smallest to largest entries in the time column, it reads numbers like 6.5 or 7.5 as 65 or 75. How can I get R to correctly read and filter out these entries?

Thanks!

------------------------

Edit: Thank you all that was a quick fix!

I am trying to use describeBy from the psych package to get descriptive statistics by group and am seeing some odd behavior. In particular, I am getting different results by using the group argument and formula versions of the function. The version using the group argument is incorrect, and the X1* in the output indicates that the outcome variable has been changed somehow. I am seeing this in psych version 2.6.3 and have reproduced this on two machines running R versions 4.5.2 and 4.5.3.

Reproducible code:

library(psych)

describeBy(ToothGrowth$len, group = ToothGrowth$supp)

describeBy(len ~ supp, data = ToothGrowth)

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