{"id":2785,"date":"2024-10-06T15:32:00","date_gmt":"2024-10-06T06:32:00","guid":{"rendered":"https:\/\/secondlife.lol\/?p=2785"},"modified":"2024-10-07T18:00:15","modified_gmt":"2024-10-07T09:00:15","slug":"r-%eb%8d%b0%ec%9d%b4%ed%84%b0%eb%b6%84%ec%84%9d-%ec%98%88%ec%a0%9c-%eb%b6%93%ea%bd%83-iris-%eb%8d%b0%ec%9d%b4%ed%84%b0%ec%85%8b%ec%9c%bc%eb%a1%9c-%eb%b0%b0%ec%9a%b0%eb%8a%94-%eb%b6%84%eb%a5%98","status":"publish","type":"post","link":"https:\/\/secondlife.lol\/en\/r-%eb%8d%b0%ec%9d%b4%ed%84%b0%eb%b6%84%ec%84%9d-%ec%98%88%ec%a0%9c-%eb%b6%93%ea%bd%83-iris-%eb%8d%b0%ec%9d%b4%ed%84%b0%ec%85%8b%ec%9c%bc%eb%a1%9c-%eb%b0%b0%ec%9a%b0%eb%8a%94-%eb%b6%84%eb%a5%98\/","title":{"rendered":"R Data Analysis Example: Learn classification and visualization with the Iris Iris dataset"},"content":{"rendered":"<p class=\"wp-block-paragraph\">When you start analyzing data, one of the first datasets you're likely to encounter is the iris <a href=\"https:\/\/www.google.com\/url?sa=t&amp;source=web&amp;rct=j&amp;opi=89978449&amp;url=https:\/\/www.kaggle.com\/datasets\/uciml\/iris&amp;ved=2ahUKEwjevYa3xOyIAxXzffUHHaSBGt8QFnoECD4QAQ&amp;usg=AOvVaw3UnPiUFbtR1WBkMC0ssiGi\" data-type=\"link\" data-id=\"https:\/\/www.google.com\/url?sa=t&amp;source=web&amp;rct=j&amp;opi=89978449&amp;url=https:\/\/www.kaggle.com\/datasets\/uciml\/iris&amp;ved=2ahUKEwjevYa3xOyIAxXzffUHHaSBGt8QFnoECD4QAQ&amp;usg=AOvVaw3UnPiUFbtR1WBkMC0ssiGi\" target=\"_blank\" rel=\"noopener\">Iris dataset<\/a>This dataset is a classic R data analysis example that is often used to learn the basics of data analysis. It addresses the problem of classifying which variety each sample belongs to, based on several characteristics of iris flowers (length and width of petals and sepals).<\/p>\n\n\n<style>.kb-image2785_23533e-f8 .kb-image-has-overlay:after{opacity:0.3;border-top-left-radius:5px;border-top-right-radius:5px;border-bottom-right-radius:5px;border-bottom-left-radius:5px;}.kb-image2785_23533e-f8 img.kb-img, .kb-image2785_23533e-f8 .kb-img img{border-top-left-radius:5px;border-top-right-radius:5px;border-bottom-right-radius:5px;border-bottom-left-radius:5px;box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}<\/style>\n<div class=\"wp-block-kadence-image kb-image2785_23533e-f8\"><figure class=\"aligncenter size-medium_large\"><img decoding=\"async\" width=\"768\" height=\"576\" src=\"https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/image-1-7-768x576.jpg\" alt=\"(R \ub370\uc774\ud130\ubd84\uc11d \uc608\uc81c - \ubd93\uaf43 Iris \ub370\uc774\ud130\uc14b \" class=\"kb-img wp-image-2941\" srcset=\"https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/image-1-7-768x576.jpg 768w, https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/image-1-7-300x225.jpg 300w, https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/image-1-7-600x450.jpg 600w, https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/image-1-7.jpg 1200w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/><figcaption>( R Data Analysis Example - Iris dataset )<\/figcaption><\/figure><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">In this post, you'll learn how to explore and visualize data using the Iris dataset as an R data analysis example, and create a simple classification model. Read on to learn important information about classification and visualization!<\/p>\n\n\n<style>.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap{padding-top:var(--global-kb-spacing-sm, 1.5rem);padding-right:var(--global-kb-spacing-sm, 1.5rem);padding-bottom:var(--global-kb-spacing-sm, 1.5rem);padding-left:var(--global-kb-spacing-sm, 1.5rem);border-top:3px solid var(--global-palette2, #2B6CB0);border-right:3px solid var(--global-palette2, #2B6CB0);border-bottom:3px solid var(--global-palette2, #2B6CB0);border-left:3px solid var(--global-palette2, #2B6CB0);border-top-left-radius:5px;border-top-right-radius:5px;border-bottom-right-radius:5px;border-bottom-left-radius:5px;box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title-wrap{padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title-wrap{color:var(--global-palette2, #2B6CB0);}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title{color:var(--global-palette2, #2B6CB0);font-size:28px;font-weight:regular;font-style:normal;}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap .kb-table-of-content-list{color:var(--global-palette1, #3182CE);line-height:2em;font-weight:regular;font-style:normal;margin-top:var(--global-kb-spacing-sm, 1.5rem);margin-right:0px;margin-bottom:0px;margin-left:0px;}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap .kb-table-of-content-list .kb-table-of-contents__entry:hover{color:var(--global-palette6, #718096);}@media all and (max-width: 1024px){.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap{border-top:3px solid var(--global-palette2, #2B6CB0);border-right:3px solid var(--global-palette2, #2B6CB0);border-bottom:3px solid var(--global-palette2, #2B6CB0);border-left:3px solid var(--global-palette2, #2B6CB0);}}@media all and (max-width: 1024px){.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title{font-size:28px;}}@media all and (max-width: 767px){.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap{border-top:3px solid var(--global-palette2, #2B6CB0);border-right:3px solid var(--global-palette2, #2B6CB0);border-bottom:3px solid var(--global-palette2, #2B6CB0);border-left:3px solid var(--global-palette2, #2B6CB0);}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title{font-size:28px;}}<\/style>\n\n\n<h2 class=\"wp-block-heading\">Iris dataset overview<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Iris dataset<\/strong>contains information about the morphology of irises and is useful for solving the problem of predicting iris varieties from the data. The dataset consists of 150 samples, each with four characteristics (sepal length, sepal width, petal length, and petal width). The <strong>Data analysis examples<\/strong>we'll see how to classify three iris varieties (Setosa, Versicolor, and Virginica) based on these characteristics.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Loading and exploring data<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Let's start by using R to load our Iris data, and do a little exploring. R has a <code>iris<\/code> The dataset is built-in, so no installation is required.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Loading data from R<\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>Import the # Iris Dataset\ndata(iris)\n\nCheck the # data structure\nstr(iris)\nsummary(iris)\nhead(iris)<\/code><\/pre>\n\n\n<style>.kb-image2785_344203-f1 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<figure class=\"wp-block-kadence-image kb-image2785_344203-f1 size-large\"><img decoding=\"async\" width=\"600\" height=\"176\" src=\"https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/Screenshot_20241001-1546052-600x176.jpg\" alt=\"R \ub370\uc774\ud130\ubd84\uc11d \uc608\uc81c - iris \ub370\uc774\ud130 \ud0d0\uc0c91\" class=\"kb-img wp-image-2791\" srcset=\"https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/Screenshot_20241001-1546052-600x176.jpg 600w, https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/Screenshot_20241001-1546052-300x88.jpg 300w, https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/Screenshot_20241001-1546052-768x225.jpg 768w, https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/Screenshot_20241001-1546052.jpg 812w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><code>iris<\/code> The dataset is organized into five columns, each containing the following variables<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>Sepal.Length<\/code>: calyx length<\/li>\n\n\n\n<li><code>Sepal.Width<\/code>: calyx-width<\/li>\n\n\n\n<li><code>Petal.Length<\/code>: petal length<\/li>\n\n\n\n<li><code>Petal.Width<\/code>: petal-width<\/li>\n\n\n\n<li><code>Species<\/code>: varieties of irises (Setosa, Versicolor, Virginica)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Data structure descriptions<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Through the above code <code>iris<\/code> You can see the structure of your data and explore summary statistics. For each attribute, you can see basic statistics like mean, median, minimum, and maximum to help you understand the overall distribution of your data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">R data analysis example data visualization<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Once you've explored your data, it's important to visualize it to visually understand the relationship between each attribute. R's <strong>ggplot2<\/strong> package makes it simple to perform visualizations. In this <strong>Data analysis examples<\/strong>shows the length and width of the petals and sepals as a scatter plot.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Drawing scatter plots in R<\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>Install and load # ggplot2\n# install.packages(\"ggplot2\") #ggplot2 If the package is not installed, install it.\nlibrary(ggplot2)\n\n# Visualize the length and width of petals and sepals\nggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Species)) +\n  geom_point(size = 3) +\n  labs(title = \"Distribution of varieties by petal length and width\", x = \"Petal Length\", y = \"Petal Width\")<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">When you run this code, you'll see a scatter plot that visualizes the distribution of iris varieties based on petal length and width. We've colored the varieties differently, so you can see at a glance how the data is visually distributed.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In this visualization, you can see that Setosa is clearly separated from the other two varieties, while Versicolor and Virginica have some overlap.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Build a K-Nearest Neighbor (K-NN) classification model<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Now you can create a simple <strong>Classification models<\/strong>This time, we'll use the K-Nearest Neighbor (K-NN) algorithm to predict the variety of irises. K-NN is a simple and intuitive algorithm that, given a new data point, classifies it by referring to the data of its K closest neighbors.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Building K-NN models in R<\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>Install and load the # prerequisite packages\ninstall.packages(\"class\")\nlibrary(class)\n\nSplit the # dataset into training and test sets\nset.seed(123)\nindex &lt;- sample(1:nrow(iris), 0.7 * nrow(iris))\ntrain_data &lt;- iris[index, ]\ntest_data &lt;- iris[-index, ]\n\nTrain and predict the # K-NN model\ntrain_labels &lt;- train_data$Species\ntest_labels &lt;- test_data$Species\nknn_pred &lt;- knn(train = train_data[, -5], test = test_data[, -5], cl = train_labels, k = 3)\n\nCheck the # prediction results\ntable(knn_pred, test_labels)\n\n# execution result data #\n            test_labels\nknn_pred setosa versicolor virginica\n  setosa 14 0 0\n  versicolor 0 17 0\n  virginica 0 1 13<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">The above code would be called <strong>K-NN algorithm<\/strong>to predict the iris varieties in the Iris dataset. After splitting the training and test data, it predicts the varieties on the test data and compares the results to the actual values.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Evaluate prediction results<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">One way to evaluate prediction results is to use the <strong>Confusion matrix<\/strong>The confusion matrix allows you to see at a glance how accurately the model predicted and how many samples were misclassified. In the code above <code>table()<\/code> function to compare predicted and actual values can output a confusion matrix, but it's hard to analyze its meaning right away.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">So when evaluating performance on a K-NN classification model, we typically use the <strong>Precision<\/strong>, <strong>Recall<\/strong>, <strong>F1 Score<\/strong> and more to specifically analyze the performance of your model. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><code>caret<\/code> package or <code>e1071<\/code> package makes it easy to calculate precision, recall, and F1 scores. Here, we'll use the <code>caret<\/code> package to get it, and we'll explain how to use it.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">1. install and load the caret package<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">First, create a <code>caret<\/code> Let's install and load the package.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Installing # on-demand packages\n# install.packages(\"caret\")\n\nlibrary(caret)<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">2. Generate confusion matrices and evaluate performance<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><code>table()<\/code> function to evaluate performance using a confusion matrix of your own creation, <code>caret<\/code> package's <code>confusionMatrix()<\/code> Functions allow you to automatically calculate precision, recall, F1 score, and more.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Generate a # confusion matrix and evaluate its performance\nconfusion_matrix &lt;- confusionMatrix(knn_pred, test_labels)\n\nPrint the # results\nprint(confusion_matrix)<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">When you run this code, you'll see that for each class, the <strong>Precision<\/strong>, <strong>Reproducibility<\/strong>, <strong>F1 Score<\/strong>as well as <strong>Accuracy<\/strong>to the end of the document.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">3. Interpret the results<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><code>confusionMatrix()<\/code> function returns a result containing various performance metrics. We'll describe the main metrics here:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Precision<\/strong>The percentage of data that actually belongs to a particular class out of the values predicted for that class.<\/li>\n\n\n\n<li><strong>Recall<\/strong>The percentage of data that actually belongs to that class that you predicted correctly.<\/li>\n\n\n\n<li><strong>F1 Score<\/strong>The harmonic mean of precision and recall, which allows for a balanced evaluation of both values.<\/li>\n\n\n\n<li><strong>Accuracy<\/strong>: The percentage of the total data that was correctly predicted.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">4. example results<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><code>confusionMatrix()<\/code> function will output something like this.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Confusion Matrix and Statistics\n\n            Reference\nPrediction setosa versicolor virginica\n  setosa 14 0 0\n  versicolor 0 17 0\n  virginica 0 1 13\n\nOverall Statistics\n\n               Accuracy : 0.9778\n                 95% ci : (0.8887, 0.9994)\n    No Information Rate : 0.3556\n    P-Value [Acc &gt; NIR] : &lt; 2e-16\n\n                  Kappa : 0.9662\n\nMcnemar&#039;s Test P-Value : NA\n\nStatistics by Class:\n\n                     Class: setosa Class: versicolor Class: virginica\nSensitivity 1.0000 0.9444 1.0000\nSpecificity 1.0000 1.0000 0.9722\nPos Pred Value 1.0000 1.0000 0.9286\nNeg Pred Value 1.0000 0.9667 1.0000\nPrevalence 0.2889 0.3556 0.3556\nDetection Rate 0.2889 0.3333 0.3556\nDetection Prevalence 0.2889 0.3333 0.3833\nBalanced Accuracy 1.0000 0.9722 0.9861<\/code><\/pre>\n\n\n\n<h5 class=\"wp-block-heading\">Interpretation of each key metric:<\/h5>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Sensitivity<\/strong>Recall. Indicates how well the predicted value matches the actual value.<\/li>\n\n\n\n<li><strong>Specificity<\/strong>: A metric for how well it predicted non-specific classes of data.<\/li>\n\n\n\n<li><strong>Pos Pred Value<\/strong>Precision, which is the percentage of predictions that are correct.<\/li>\n\n\n\n<li><strong>Neg Pred Value<\/strong>is the percentage of predictions that were correct.<\/li>\n\n\n\n<li><strong>Accuracy<\/strong>: The percentage of the total forecast that was correct.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">In this way, you can more specifically evaluate and analyze the performance of your model.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Common mistakes in data analysis and how to fix them<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This time <strong>Data analysis examples<\/strong>here are some tips to avoid common beginner mistakes.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Neglecting to preprocess data<\/strong>: Before you analyze your data, you must deal with missing values, outliers, etc. While Iris data is free of missing values, other datasets must take care of preprocessing.<\/li>\n\n\n\n<li><strong>Select the appropriate K value<\/strong>: In a K-NN model, the <code>k<\/code> It is important to choose a value that is appropriate. A K value that is too small can result in overfitting, while a K value that is too large can miss important patterns.<\/li>\n\n\n\n<li><strong>Not paying attention to data partitioning<\/strong>: When dividing training and test data, be sure to sample the data randomly, otherwise the model may be biased toward certain data.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">FAQs<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Q1: Where can I download the Iris dataset?<\/strong><br>A: Iris datasets are built into R by default and do not require a separate download. <code>data(iris)<\/code> command to load them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Q2: How do I set the K value in a K-NN model?<\/strong><br>A: The K value is typically set to an odd number, and we recommend using cross-validation to find the optimal K value. Depending on the size and distribution of your dataset, your K value may vary.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Q3: Can I try other classification algorithms?<\/strong><br>A: Absolutely! In addition to K-NN, you'll get hands-on experience with a variety of other classification algorithms, including logistic regression, decision trees, random forests, and more.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Organize<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In this post, we'll use the <strong>Data analysis examples<\/strong>to do some basic exploratory data analysis and visualization with the Iris dataset, and then build a classification model using the K-Nearest Neighbor (K-NN) algorithm. While the Iris dataset is small and simple, it is a very useful resource for learning and analyzing classification problems. We hope that this example has helped you understand the basic concepts of data analysis and has given you a springboard to more complex problems. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You can build on this example to create a <a href=\"https:\/\/secondlife.lol\/en\/data-analysis-example-titanic-survivor-prediction\/\" data-type=\"post\" data-id=\"2765\">Other datasets<\/a>for the challenge!<\/p>","protected":false},"excerpt":{"rendered":"<p>When you start analyzing data, one of the first datasets you encounter is...<\/p>","protected":false},"author":3,"featured_media":2943,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[6],"tags":[324,325,174,39,164],"class_list":["post-2785","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r-coding","tag-iris-","tag-r--","tag-r-","tag-rstudio","tag-164"],"taxonomy_info":{"category":[{"value":6,"label":"\uc54c(R)"}],"post_tag":[{"value":324,"label":"iris \ub370\uc774\ud130\uc14b"},{"value":325,"label":"R \ub370\uc774\ud130\ubd84\uc11d \uc608\uc81c"},{"value":174,"label":"R \ud504\ub85c\uadf8\ub798\ubc0d"},{"value":39,"label":"RStudio"},{"value":164,"label":"\ub370\uc774\ud130 \uc2dc\uac01\ud654"}]},"featured_image_src_large":["https:\/\/secondlife.lol\/wp-content\/uploads\/2024\/10\/R-\ub370\uc774\ud130\ubd84\uc11d-\uc608\uc81c-\ubd93\uaf43\ud3ec\uc2a4\ud2b8-\uc378\ub124\uc77c-600x600.webp",600,600,true],"author_info":{"display_name":"TERE","author_link":"https:\/\/secondlife.lol\/en\/author\/tere\/"},"comment_info":0,"category_info":[{"term_id":6,"name":"\uc54c(R)","slug":"r-coding","term_group":0,"term_taxonomy_id":6,"taxonomy":"category","description":"","parent":20,"count":61,"filter":"raw","cat_ID":6,"category_count":61,"category_description":"","cat_name":"\uc54c(R)","category_nicename":"r-coding","category_parent":20}],"tag_info":[{"term_id":324,"name":"iris \ub370\uc774\ud130\uc14b","slug":"iris-%eb%8d%b0%ec%9d%b4%ed%84%b0%ec%85%8b","term_group":0,"term_taxonomy_id":324,"taxonomy":"post_tag","description":"","parent":0,"count":1,"filter":"raw"},{"term_id":325,"name":"R \ub370\uc774\ud130\ubd84\uc11d \uc608\uc81c","slug":"r-%eb%8d%b0%ec%9d%b4%ed%84%b0%eb%b6%84%ec%84%9d-%ec%98%88%ec%a0%9c","term_group":0,"term_taxonomy_id":325,"taxonomy":"post_tag","description":"","parent":0,"count":1,"filter":"raw"},{"term_id":174,"name":"R \ud504\ub85c\uadf8\ub798\ubc0d","slug":"r-%ed%94%84%eb%a1%9c%ea%b7%b8%eb%9e%98%eb%b0%8d","term_group":0,"term_taxonomy_id":174,"taxonomy":"post_tag","description":"","parent":0,"count":15,"filter":"raw"},{"term_id":39,"name":"RStudio","slug":"rstudio","term_group":0,"term_taxonomy_id":39,"taxonomy":"post_tag","description":"","parent":0,"count":9,"filter":"raw"},{"term_id":164,"name":"\ub370\uc774\ud130 \uc2dc\uac01\ud654","slug":"%eb%8d%b0%ec%9d%b4%ed%84%b0-%ec%8b%9c%ea%b0%81%ed%99%94","term_group":0,"term_taxonomy_id":164,"taxonomy":"post_tag","description":"","parent":0,"count":52,"filter":"raw"}],"_links":{"self":[{"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/posts\/2785","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/comments?post=2785"}],"version-history":[{"count":7,"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/posts\/2785\/revisions"}],"predecessor-version":[{"id":2948,"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/posts\/2785\/revisions\/2948"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/media\/2943"}],"wp:attachment":[{"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/media?parent=2785"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/categories?post=2785"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/secondlife.lol\/en\/wp-json\/wp\/v2\/tags?post=2785"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}