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#datacleaning

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TechKeysX<p>Remove Duplicates &amp; Convert table into Range Part 28:<br>Use Remove Duplicates to delete repeated rows, and Convert to Range to turn a table back into a normal data set.<br><a href="https://mastodon.social/tags/ExcelTips" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ExcelTips</span></a> <a href="https://mastodon.social/tags/RemoveDuplicates" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RemoveDuplicates</span></a> <a href="https://mastodon.social/tags/ConvertToRange" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConvertToRange</span></a> <a href="https://mastodon.social/tags/ExcelTable" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ExcelTable</span></a> <a href="https://mastodon.social/tags/DataCleaning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataCleaning</span></a> <a href="https://mastodon.social/tags/SpreadsheetSkills" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpreadsheetSkills</span></a> <a href="https://mastodon.social/tags/ExcelBasics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ExcelBasics</span></a></p>
PabloTe<p>🧩 [R-TIP] – ¿Te gusta la magia? Acá va un truco con {janitor} 🧼</p><p>Con clean_names() podés limpiar todos los nombres de columnas de tu dataset en un paso:</p><p>✔️ minúsculas + guiones bajos<br>✔️ sin acentos ni símbolos raros<br>✔️ listo para usar con dplyr y ggplot2</p><p>🔗 <a href="https://sfirke.github.io/janitor/reference/clean_names.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">sfirke.github.io/janitor/refer</span><span class="invisible">ence/clean_names.html</span></a><br>✍️ Sam Firke</p><p><a href="https://mastodon.social/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://mastodon.social/tags/Rtips" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Rtips</span></a> <a href="https://mastodon.social/tags/DataCleaning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataCleaning</span></a> <a href="https://mastodon.social/tags/janitor" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>janitor</span></a> <a href="https://mastodon.social/tags/cienciadedatos" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cienciadedatos</span></a></p>
Linux Professional Institute<p>Need a <a href="https://fosstodon.org/tags/free" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>free</span></a> tool to handle messy <a href="https://fosstodon.org/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a>?</p><p><span class="h-card" translate="no"><a href="https://fosstodon.org/@OpenRefine" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>OpenRefine</span></a></span> runs locally and is trusted by journalists, researchers, &amp; contributors worldwide.</p><p>See how this <a href="https://fosstodon.org/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a> tool empowers a global community: <a href="https://lpi.org/0f1n" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">lpi.org/0f1n</span><span class="invisible"></span></a></p><p><a href="https://fosstodon.org/tags/LPI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LPI</span></a> <a href="https://fosstodon.org/tags/FOSS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>FOSS</span></a> <a href="https://fosstodon.org/tags/OpenRefine" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenRefine</span></a> <a href="https://fosstodon.org/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a> <a href="https://fosstodon.org/tags/datacleaning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datacleaning</span></a> <a href="https://fosstodon.org/tags/dataprivacy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataprivacy</span></a> <a href="https://fosstodon.org/tags/BSD" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BSD</span></a> <a href="https://fosstodon.org/tags/Wikidata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Wikidata</span></a> <span class="h-card" translate="no"><a href="https://wikis.world/@wikidata" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>wikidata</span></a></span></p>
Harald Klinke<p>The {emend} R package leverages large language models to clean and standardize data:<br>✔️ Fix typos<br>✔️ Map inconsistent entries<br>✔️ Translate + reorder factors<br>✔️ Clean addresses &amp; dates</p><p>📦 Install via CRAN: install.packages("emend")<br><a href="https://anuopensci.github.io/emend/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">anuopensci.github.io/emend/</span><span class="invisible"></span></a><br>⚠️ Reproducibility may vary across systems. Validate before use.<br><a href="https://det.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://det.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://det.social/tags/DataCleaning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataCleaning</span></a> <a href="https://det.social/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLM</span></a> <a href="https://det.social/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenSource</span></a> <a href="https://det.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a></p>
Harald Klinke<p>Need to clean messy data with typos, abbreviations, or inconsistent labels?<br>📦 The {emend} R package uses LLMs to standardize categories, fix addresses &amp; more – perfect for bioinformatics, business, or any data project.<br>🔗 <a href="https://cran.r-project.org/web/packages/emend/index.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">cran.r-project.org/web/package</span><span class="invisible">s/emend/index.html</span></a><br><a href="https://det.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://det.social/tags/DataCleaning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataCleaning</span></a> <a href="https://det.social/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLM</span></a> <a href="https://det.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://det.social/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenSource</span></a></p>

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