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Channel Posts
In the next month or so, I'm going to "close" the channel. I won't be posting anymore. If you are looking for a good channel I suggest you follow https://t.me/ramikrispinds
| 2 | I'm not yet back to writing my blog because I'm not feeling it at the moment, still on break. Here, though is a basic usage article for using my #RandomWalker #RStats library.
https://www.spsanderson.com/RandomWalker/articles/basic-concepts.html | 0 |
| 3 | Here is today's #R #Blog #Post on using the unname() function.
Post: https://www.spsanderson.com/steveondata/posts/2025-12-01/ | 0 |
| 4 |  +1Here I am using my #RandomWalker #RStats package to graph possible animal foraging paths. Many things can be done with some imagination :)
Reference Link: https://www.spsanderson.com/RandomWalker/articles/multi-dimensional-walks.html#use-cases | 0 |
| 5 | Got some documentation vignettes going up. Starting with #RandomWalker
Home Wiki: https://www.spsanderson.com/RandomWalker/articles/home.html
#RStats #R #Random | 0 |
| 6 | Today's blog post is about for loops with a range in R
Post: https://www.spsanderson.com/steveondata/posts/2025-11-17/
#R #RStats #ForLoop #Range | 0 |
| 7 | I most likely will not post this week, I’ll see how my back is next week | 0 |
| 8 | Today's Python article, remember, I'm learning so be kind or rewind :)
Post: https://www.spsanderson.com/steveondata/posts/2025-11-06/
#Python #TextMessage #Email #Gmail #Twillio #Text | 0 |
| 9 | Loops in R? Got you covered. Here is today's post regarding nested for loops.
Post: https://www.spsanderson.com/steveondata/posts/2025-11-03/ | 0 |
| 10 | Here is this weeks R post, it deals with using ollama for RAG.
Post: https://www.spsanderson.com/steveondata/posts/2025-10-29/ | 0 |
| 11 | For today's article here are:
Audio Overview: https://app.dotadda.io/teams/ab732481-52f3-4388-896c-23d34e828b35/dots/c381844d-1ce0-4501-a099-7e1772a17f8d
Video Overview: https://app.dotadda.io/teams/ab732481-52f3-4388-896c-23d34e828b35/dots/000c92f3-e2f2-4299-8e64-7c7bab1b9ac5
Overviews generated by Gemini from my article. | 0 |
| 12 | Today's Python post is about time, datetime, and schedule.
import time
# Measure how long code takes to run
start_time = time.time()
# Your code here
end_time = time.time()
print(f"Execution time: {end_time - start_time} seconds")
# Pause execution for 3 seconds
time.sleep(3)
print("This prints after 3 seconds!")
Post: https://www.spsanderson.com/steveondata/posts/2025-10-23/ | 0 |
| 13 | My new course with LinkedIn for Learning - Build with AI: SQL Agents with Large Language Models is out! 🚀
Here is what it covers 👇🏼
https://open.substack.com/pub/ramikrispin/p/new-course-build-sql-ai-agent-from?r=1x99er&utm_medium=ios | 0 |
| 14 | Today's blog post is a simple one, but still important to your data. Looking at dropping NA values.
Post: https://www.spsanderson.com/steveondata/posts/2025-10-20/ | 0 |
| 15 |  Looking at the AIC of my global package downloads using all available util_*_aic functions in my TidyDensity package
#R #RStats | 0 |
| 16 | I came across a problem yesterday where I wanted to combine .md files from sub directories and place the combined file in each respective directory, so I wrote this:
# Libraries ----
library(tidyverse)
# Directory ----
## Make a list of input directories ----
base_path <- "C:/file/path/"
input_dirs <- list.dirs(base_path)[-1]
input_dir_tbl <- tibble(
input_dir = input_dirs
) |>
mutate(output_dir = paste0(input_dir, "/", basename(input_dir), "_combined_files.md"))
input_dir_tbl |>
group_split(input_dir) |>
imap(
.f = function(obj, id){
input_dir = obj$input_dir
output_dir = obj$output_dir
# Check if the directory exists
if (!dir.exists(input_dir)) {
stop(paste("Error: Directory", input_dir, "does not exist."))
}
# List all .md files in the directory ----
cat("Searching for .md files in:", input_dir, "\n")
md_files <- list.files(path = input_dir, pattern = "\\.md$", full.names = TRUE)
# Check if any .md files were found
if (length(md_files) == 0) {
stop("No .md files found in the specified directory.")
}
cat("Found", length(md_files), ".md files:\n")
for (file in md_files) {
cat("-", basename(file), "\n")
}
# Read and combine the contents of all .md files
cat("\nReading and combining files...\n")
combined_content <- character(0)
for (file in md_files) {
cat("Processing:", basename(file), "\n")
# Add a header separator for each file (optional)
file_header <- paste("\n<!-- Content from:", basename(file), "-->\n")
combined_content <- c(combined_content, file_header)
# Read the file content
file_content <- readLines(file, warn = FALSE)
combined_content <- c(combined_content, file_content)
# Add some spacing between files
combined_content <- c(combined_content, "\n")
}
# Write the combined content to the output file
cat("Writing combined content to:", output_dir, "\n")
writeLines(combined_content, output_dir)
cat("Successfully combined", length(md_files), ".md files into", output_dir, "\n")
cat("Total lines written:", length(combined_content), "\n")
}
)
Link: https://github.com/spsanderson/random_r_projects/blob/main/combine_md_files.R | 0 |
| 17 | I forgot to post my latest Python blog post. So here it is:
Post: https://www.spsanderson.com/steveondata/posts/2025-10-01/
Here is a link to audio and video both generated by NotebookLM from Google/Gemini, look on September 30
Dots: https://app.dotadda.io/teams/ab732481-52f3-4388-896c-23d34e828b35/dots?date=2025-09-08×pan=month
#Python | 0 |
| 18 | I had shown how to use different imputation methods with healthyR.ai and so now I made a blogpost using that and hai_scale_data()
Post: https://www.spsanderson.com/steveondata/posts/2025-09-29/ | 0 |
| 19 |  +3Visualizing a few different method of imputation using my healthyR.ai package. I'm using the hai_impute_data() function.
Reference: https://www.spsanderson.com/healthyR.ai/reference/hai_data_impute.html | 0 |
| 20 | TL;DR: Today I learned how to use Python to work with PDF and Word documents. I shared some simple code and tips for beginners. 🐍📄📝
In today's article, I talk about how you can use Python to handle PDF and Word files. I tried out some beginner-friendly code to pull text from PDFs, combine files, and even make new Word documents. For example, I used the PyPDF2 library to read text from a PDF file:
import PyPDF2
with open('document.pdf', 'rb') as pdf:
reader = PyPDF2.PdfFileReader(pdf)
text = reader.getPage(0).extractText()
print(text)
I also used python-docx to create a Word document with just a few lines of code:
from docx import Document
doc = Document()
doc.add_heading('Hello, Python!', 0)
doc.add_paragraph('This was created automatically.')
doc.save('hello.docx')
I want to mention that I am still learning as I write this series. There might be mistakes or things I could do better. If you notice anything or have advice, please let me know. I appreciate any feedback and hope these examples help you get started with Python and document automation.
Thanks for reading. If you have questions or suggestions, feel free to share. #Python #Automation #LearningTogether
🔗 Read more: https://www.spsanderson.com/steveondata/posts/2025-09-24/
My main source of learning: https://automatetheboringstuff.com/2e/chapter15/ | 0 |
