Joined December 2019
124 Photos and videos
10 Apr 2025
When you realize data.table is not just fast… but also lets you: - filter like a ninja - group like a boss - update in place like magic - chain like you're riding 🚲 downhill And all with [i, j, by]. Name a tidier feeling. I’ll wait. #rstats #datatable
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10 Apr 2025
I took a photo of a USAID nutrition pack. Nothing weird… until I looked closer. The image had hidden GPS data. Using Python, I pulled the coordinates and put a pin on the map. What I found was… strange. Should foreign aid be ending up here? 👇
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10 Apr 2025
I mapped the exact spot where the USAID nutrition pack was photographed. You’ll see the coordinates, the map, and how I did it in Python. This isn’t just about one photo — it’s about transparency. 📍 Full post & map here →
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8 Apr 2025
I found a USAID nutrition pack lying around and took a photo — just out of curiosity. Later, I checked the image’s metadata… It turns out that GPS coordinates were embedded in it. 👀 So I mapped it. See below for the location! 1/3
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8 Apr 2025
What I saw made me stop. It might provide some evidence and serious questions about how foreign aid is used — and where it ends up. 2/2
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8 Apr 2025
I broke down how to extract location data from any image — and revealed the exact location of this USAID pack. 🧭 Want to see where it was taken? 📍Check the map story here: marsja.se/how-to-extract-gps… Should it be there? Let me know! 3/3
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6 Apr 2025
Working with wide data? Want to sum several columns quickly — with or without grouping? Here’s how to do it cleanly using data.table in #RStats: 👇
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6 Apr 2025
3️⃣ Select columns using %in% Dynamically choose which columns to sum: rKopieraRedigeracols_to_sum <- c("var1", "var2") DT[, total := rowSums(.SD), .SDcols = names(DT) %in% cols_to_sum]
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29 Mar 2025
Filtering large datasets in R? You might be using dplyr::filter(), but there's a faster way. 🚀 Let's compare dplyr and data.table for big data filtering. 👇 #rstats #DataScience
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29 Mar 2025
To find out, we ran a benchmark comparing dplyr::filter() vs data.table on a 20-million-row dataset. We filtered rows where a > 0.5 and recorded the execution time. 🚀 Here's what happened...
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