publish tableau post

update date and deleted credit card fraud section.
lsinks · Jan 1, 2024 · cb6b02c · cb6b02c
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diff --git a/docs/listings.json b/docs/listings.json
@@ -2,6 +2,7 @@
   {
     "listing": "/posts.html",
     "items": [
+      "/posts/2024-01-01-tableau/tableau.html",
       "/posts/2023-11-01-website-building/website.html",
       "/posts/2023-09-08-web-scraping-tombstone/web-scraping.html",
       "/posts/2023-09-05-TidyTuesday-Labor/labor.html",

diff --git a/docs/posts.html b/docs/posts.html
diff --git a/docs/posts.xml b/docs/posts.xml
diff --git a/...ending-Learning Tableau/scatterplot_1.png → ...osts/2024-01-01-tableau/scatterplot_1.png b/...ending-Learning Tableau/scatterplot_1.png → ...osts/2024-01-01-tableau/scatterplot_1.png
diff --git a/...ending-Learning Tableau/scatterplot_2.png → ...osts/2024-01-01-tableau/scatterplot_2.png b/...ending-Learning Tableau/scatterplot_2.png → ...osts/2024-01-01-tableau/scatterplot_2.png
diff --git a/docs/posts/2024-01-01-tableau/tableau.html b/docs/posts/2024-01-01-tableau/tableau.html
diff --git a/docs/posts/pending-cc-fraud-geography/fraud_geo_right.html b/docs/posts/pending-cc-fraud-geography/fraud_geo_right.html
@@ -263,7 +263,7 @@ <h1 class="title">Credit Card Fraud: A Tidymodels Tutorial</h1>
     <div>
     <div class="quarto-title-meta-heading">Modified</div>
     <div class="quarto-title-meta-contents">
-      <p class="date-modified">December 28, 2023</p>
+      <p class="date-modified">January 1, 2024</p>
     </div>
   </div>
 

diff --git a/docs/search.json b/docs/search.json
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
@@ -2,7 +2,7 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
   <url>
     <loc>https://lsinks.github.io/posts.html</loc>
-    <lastmod>2023-12-28T19:18:03.858Z</lastmod>
+    <lastmod>2024-01-01T21:08:33.928Z</lastmod>
   </url>
   <url>
     <loc>https://lsinks.github.io/posts/2023-11-01-website-building/website.html</loc>
@@ -160,4 +160,8 @@
     <loc>https://lsinks.github.io/posts/2023-09-08-web-scraping-tombstone/web-scraping.html</loc>
     <lastmod>2023-11-03T22:48:05.404Z</lastmod>
   </url>
+  <url>
+    <loc>https://lsinks.github.io/posts/2024-01-01-tableau/tableau.html</loc>
+    <lastmod>2024-01-01T21:09:43.637Z</lastmod>
+  </url>
 </urlset>
diff --git a/posts/2024-01-01-tableau/scatterplot_1.png b/posts/2024-01-01-tableau/scatterplot_1.png
diff --git a/posts/2024-01-01-tableau/scatterplot_2.png b/posts/2024-01-01-tableau/scatterplot_2.png
diff --git a/posts/pending-Learning Tableau/tableau.qmd → posts/2024-01-01-tableau/tableau.qmd b/posts/pending-Learning Tableau/tableau.qmd → posts/2024-01-01-tableau/tableau.qmd
@@ -1,17 +1,16 @@
 ---
 title: "Some Lessons from Learning Tableau"
-description: "Making an animated graph of global temperature change over time with gganimate."
-twitter-card:
-  image: "thumbnail.gif"
-date: 07-17-2023
+description: "A few quirks of Tableau that might not be obvious to those coming from an R/ Matlab/ Python background"
+
+date: 01-01-2024
 categories: [Tableau] # self-defined categories
 citation:
-  url: https://lsinks.github.io/posts/2023-07-11-tidytuesday-temps/temperatures
-image: "thumbnail.png"
+  url: https://lsinks.github.io/posts/2024-01-01-tableau/tableau
+image: "scatterplot_1.png"
 format: 
   html:
     page-layout: full
-draft: true # setting this to `true` will prevent your post from appearing on your listing page until you're ready!
+draft: false # setting this to `true` will prevent your post from appearing on your listing page until you're ready!
 ---
 
 Tableau is one of the most common business analytics programs out there. I first played with Tableau in the Google Data Analytics Certificate program in January 2023. At the time, Tableau struck me as analogous to Excel, a more visual point-and-click way to make graphs than R, Python, or other coding/scripting languages.
@@ -144,14 +143,6 @@ Other things, like [having text labels appear at 45 degrees](https://community.t
 
 Tableau is known for creating gorgeous dashboards to convey high-level information. It is also surprisingly good for exploratory data analysis for more technical data analytics or data science projects. As long as you know what Tableau is doing, you can find interactions between the data that might not be visible from two-dimensional static plots you generate in R or Python. You can easily filter and group things on the fly and have additional data pop up in tooltips.
 
-I wanted to repeat the credit card fraud exploratory data analysis in Tableau (and also Python). I worked with this dataset extensively while learning R's Tidymodels framework. I had made various visualizations, so I thought repeating them in the new languages would be a good exercise.
-
-The geographic data in the dataset always seemed odd, but I couldn't really put my finger on it based on my R analysis. I brushed it off since the credit card dataset is synthetic, so I assumed it probably arose from a coding choice/ assumption made by the person who generated the dataset.
-
-Tableau allows you to put a ridiculous amount of data on a single visualization and change things dynamically. My dashboards are hideous because I packed so much in them and should never be presented to an executive, but they allowed me to play around with my data and sharply define the weirdness.
-
-Any guesses as to what the issue is?
-
 # Conclusion
 
 Tableau is a powerful business analytics tool. I think it is relatively easy to learn as long as you aren't coming in with incorrect assumptions about how it should be working. The way Tableau works conflicts with the paradigm that many users of R/Tableau/Matlab might have about how things should work. I struggled unnecessarily because I assumed certain things were true based on my experiences. Hopefully, you can avoid the mistakes I made.
diff --git a/posts/pending-Learning Tableau/credit_card_fraud.csv b/posts/pending-Learning Tableau/credit_card_fraud.csv
diff --git a/posts/pending-Learning Tableau/trying_embeds.qmd b/posts/pending-Learning Tableau/trying_embeds.qmd
diff --git a/posts/pending-cc-fraud-geography/fraud_geo_right.qmd b/posts/pending-cc-fraud-geography/fraud_geo_right.qmd
@@ -931,3 +931,11 @@ Not only are there no duplicate merchant locations for specific users...
 ```
 
 But there are no duplicate merchant locations over the entire dataset.
+
+I wanted to repeat the credit card fraud exploratory data analysis in Tableau (and also Python). I worked with this dataset extensively while learning R's Tidymodels framework. I had made various visualizations, so I thought repeating them in the new languages would be a good exercise.
+
+The geographic data in the dataset always seemed odd, but I couldn't really put my finger on it based on my R analysis. I brushed it off since the credit card dataset is synthetic, so I assumed it probably arose from a coding choice/ assumption made by the person who generated the dataset.
+
+Tableau allows you to put a ridiculous amount of data on a single visualization and change things dynamically. My dashboards are hideous because I packed so much in them and should never be presented to an executive, but they allowed me to play around with my data and sharply define the weirdness.
+
+Any guesses as to what the issue is?