{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPpA83DZn7z0sTr93hd8nnb"},"kernelspec":{"name":"ir","display_name":"R"},"language_info":{"name":"R"}},"cells":[{"cell_type":"markdown","source":["# Exploratory Data Analysis Tutorial for OMOP Dataset Extract"],"metadata":{"id":"zK1xJOsU01lS"}},{"cell_type":"markdown","source":["**Exploratory Data Analysis** (EDA) is the process of analyzing and summarizing the main characteristics of a dataset, often using visual methods. It’s an essential step in data analysis that helps to understand the data's structure, detect patterns, identify anomalies, test hypotheses, and determine relationships among variables."],"metadata":{"id":"A0sW6eE205bG"}},{"cell_type":"markdown","source":["### Setting up the enviroment"],"metadata":{"id":"yjMf-A0w1oAu"}},{"cell_type":"code","source":["library(dplyr)    # Data Manipulation\n","library(tidyr)    # Data Cleaning\n","library(ggplot2)  # Data Visualization\n","library(readr)    # Reading CSV files\n","library(forcats)  # for factor reordering\n","library(reshape2) # for melting correlation matrix"],"metadata":{"id":"HXS8Uwte11Qe","executionInfo":{"status":"ok","timestamp":1731293301274,"user_tz":300,"elapsed":141,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}}},"execution_count":93,"outputs":[]},{"cell_type":"markdown","source":["### Loading the dataset\n","The dataset that we will be using in this tutorial is the list of all patients suffering from COVID-19 in the OMOP database and their demographic data."],"metadata":{"id":"tflv4D0--ouy"}},{"cell_type":"code","source":["# Replace '/content/COVID-19-BMI.csv' with the actual path to your dataset\n","df <- read_csv('/content/COVID-19-BMI.csv')\n","\n","# Display the first few rows of the dataset to get a glimpse of its content\n","head(df)"],"metadata":{"id":"Yqk1uE841rfS","colab":{"base_uri":"https://localhost:8080/","height":436},"executionInfo":{"status":"ok","timestamp":1731292183335,"user_tz":300,"elapsed":403,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"0a9346bd-f234-4f9d-9ace-587701018d59"},"execution_count":46,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[1mRows: \u001b[22m\u001b[34m88166\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m9\u001b[39m\n","\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n","\u001b[1mDelimiter:\u001b[22m \",\"\n","\u001b[31mchr\u001b[39m  (4): gender, race, height, bmi\n","\u001b[32mdbl\u001b[39m  (4): person_id, condition_concept_id, age, weight\n","\u001b[34mdate\u001b[39m (1): condition_start_date\n","\n","\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n","\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"]},{"output_type":"display_data","data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 6 × 9</caption>\n","<thead>\n","\t<tr><th scope=col>person_id</th><th scope=col>gender</th><th scope=col>race</th><th scope=col>condition_concept_id</th><th scope=col>condition_start_date</th><th scope=col>age</th><th scope=col>weight</th><th scope=col>height</th><th scope=col>bmi</th></tr>\n","\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>1</td><td>F</td><td>white</td><td>37311061</td><td>2020-03-11</td><td>62</td><td>71.3</td><td>160.8</td><td>27.6</td></tr>\n","\t<tr><td>2</td><td>F</td><td>white</td><td>37311061</td><td>2020-03-02</td><td>75</td><td>87.7</td><td>169.5</td><td>30.5</td></tr>\n","\t<tr><td>3</td><td>M</td><td>white</td><td>37311061</td><td>2020-03-14</td><td>52</td><td>91.9</td><td>177.2</td><td>29.4</td></tr>\n","\t<tr><td>5</td><td>F</td><td>white</td><td>37311061</td><td>2020-03-10</td><td>32</td><td>74.4</td><td>158.1</td><td>29.8</td></tr>\n","\t<tr><td>6</td><td>F</td><td>white</td><td>37311061</td><td>2020-02-12</td><td>44</td><td>71.0</td><td>NULL </td><td>NULL</td></tr>\n","\t<tr><td>7</td><td>F</td><td>black</td><td>37311061</td><td>2020-03-09</td><td>62</td><td>81.2</td><td>164.0</td><td>30.2</td></tr>\n","</tbody>\n","</table>\n"],"text/markdown":"\nA tibble: 6 × 9\n\n| person_id &lt;dbl&gt; | gender &lt;chr&gt; | race &lt;chr&gt; | condition_concept_id &lt;dbl&gt; | condition_start_date &lt;date&gt; | age &lt;dbl&gt; | weight &lt;dbl&gt; | height &lt;chr&gt; | bmi &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|\n| 1 | F | white | 37311061 | 2020-03-11 | 62 | 71.3 | 160.8 | 27.6 |\n| 2 | F | white | 37311061 | 2020-03-02 | 75 | 87.7 | 169.5 | 30.5 |\n| 3 | M | white | 37311061 | 2020-03-14 | 52 | 91.9 | 177.2 | 29.4 |\n| 5 | F | white | 37311061 | 2020-03-10 | 32 | 74.4 | 158.1 | 29.8 |\n| 6 | F | white | 37311061 | 2020-02-12 | 44 | 71.0 | NULL  | NULL |\n| 7 | F | black | 37311061 | 2020-03-09 | 62 | 81.2 | 164.0 | 30.2 |\n\n","text/latex":"A tibble: 6 × 9\n\\begin{tabular}{lllllllll}\n person\\_id & gender & race & condition\\_concept\\_id & condition\\_start\\_date & age & weight & height & bmi\\\\\n <dbl> & <chr> & <chr> & <dbl> & <date> & <dbl> & <dbl> & <chr> & <chr>\\\\\n\\hline\n\t 1 & F & white & 37311061 & 2020-03-11 & 62 & 71.3 & 160.8 & 27.6\\\\\n\t 2 & F & white & 37311061 & 2020-03-02 & 75 & 87.7 & 169.5 & 30.5\\\\\n\t 3 & M & white & 37311061 & 2020-03-14 & 52 & 91.9 & 177.2 & 29.4\\\\\n\t 5 & F & white & 37311061 & 2020-03-10 & 32 & 74.4 & 158.1 & 29.8\\\\\n\t 6 & F & white & 37311061 & 2020-02-12 & 44 & 71.0 & NULL  & NULL\\\\\n\t 7 & F & black & 37311061 & 2020-03-09 & 62 & 81.2 & 164.0 & 30.2\\\\\n\\end{tabular}\n","text/plain":["  person_id gender race  condition_concept_id condition_start_date age weight\n","1 1         F      white 37311061             2020-03-11           62  71.3  \n","2 2         F      white 37311061             2020-03-02           75  87.7  \n","3 3         M      white 37311061             2020-03-14           52  91.9  \n","4 5         F      white 37311061             2020-03-10           32  74.4  \n","5 6         F      white 37311061             2020-02-12           44  71.0  \n","6 7         F      black 37311061             2020-03-09           62  81.2  \n","  height bmi \n","1 160.8  27.6\n","2 169.5  30.5\n","3 177.2  29.4\n","4 158.1  29.8\n","5 NULL   NULL\n","6 164.0  30.2"]},"metadata":{}}]},{"cell_type":"code","source":["# Find the number of rows and columns of your dataset\n","dim(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"7VEsrEcU6CBD","executionInfo":{"status":"ok","timestamp":1731292184764,"user_tz":300,"elapsed":293,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"d69878e0-7ac9-419d-eb32-a617b16171df"},"execution_count":47,"outputs":[{"output_type":"display_data","data":{"text/html":["<style>\n",".list-inline {list-style: none; margin:0; padding: 0}\n",".list-inline>li {display: inline-block}\n",".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n","</style>\n","<ol class=list-inline><li>88166</li><li>9</li></ol>\n"],"text/markdown":"1. 88166\n2. 9\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 88166\n\\item 9\n\\end{enumerate*}\n","text/plain":["[1] 88166     9"]},"metadata":{}}]},{"cell_type":"code","source":["# Provides a concise summary of the DataFrame, including data types and non-null counts.\n","str(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ghvDCzB36Hcv","executionInfo":{"status":"ok","timestamp":1731292185455,"user_tz":300,"elapsed":124,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"9f95d744-8a0c-4821-99cf-0620ee4a074b"},"execution_count":48,"outputs":[{"output_type":"stream","name":"stdout","text":["spc_tbl_ [88,166 × 9] (S3: spec_tbl_df/tbl_df/tbl/data.frame)\n"," $ person_id           : num [1:88166] 1 2 3 5 6 7 9 10 11 13 ...\n"," $ gender              : chr [1:88166] \"F\" \"F\" \"M\" \"F\" ...\n"," $ race                : chr [1:88166] \"white\" \"white\" \"white\" \"white\" ...\n"," $ condition_concept_id: num [1:88166] 37311061 37311061 37311061 37311061 37311061 ...\n"," $ condition_start_date: Date[1:88166], format: \"2020-03-11\" \"2020-03-02\" ...\n"," $ age                 : num [1:88166] 62 75 52 32 44 62 48 22 35 12 ...\n"," $ weight              : num [1:88166] 71.3 87.7 91.9 74.4 71 81.2 74.1 58.9 83.6 36.2 ...\n"," $ height              : chr [1:88166] \"160.8\" \"169.5\" \"177.2\" \"158.1\" ...\n"," $ bmi                 : chr [1:88166] \"27.6\" \"30.5\" \"29.4\" \"29.8\" ...\n"," - attr(*, \"spec\")=\n","  .. cols(\n","  ..   person_id = \u001b[32mcol_double()\u001b[39m,\n","  ..   gender = \u001b[31mcol_character()\u001b[39m,\n","  ..   race = \u001b[31mcol_character()\u001b[39m,\n","  ..   condition_concept_id = \u001b[32mcol_double()\u001b[39m,\n","  ..   condition_start_date = \u001b[34mcol_date(format = \"\")\u001b[39m,\n","  ..   age = \u001b[32mcol_double()\u001b[39m,\n","  ..   weight = \u001b[32mcol_double()\u001b[39m,\n","  ..   height = \u001b[31mcol_character()\u001b[39m,\n","  ..   bmi = \u001b[31mcol_character()\u001b[39m\n","  .. )\n"," - attr(*, \"problems\")=<externalptr> \n"]}]},{"cell_type":"markdown","source":["### Handling missing values\n","Handling missing values involves identifying and addressing gaps in the dataset by either imputing values, removing affected records, or using algorithms that can handle missing data to maintain data quality and accuracy during analysis."],"metadata":{"id":"zBk9kast6O17"}},{"cell_type":"code","source":["# Find count of null values in all the columns\n","sapply(df, function(x) sum(as.character(x) == 'NULL'))\n","# Replace all occurrences of \"NULL\" with NA in the data frame\n","df <- df %>% mutate(across(where(is.character), ~na_if(., \"NULL\")))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"pc8q8hd86Pvy","executionInfo":{"status":"ok","timestamp":1731292503764,"user_tz":300,"elapsed":776,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"3cd2697c-357c-4db2-85d5-7a7d3c1d255b"},"execution_count":59,"outputs":[{"output_type":"display_data","data":{"text/html":["<style>\n",".dl-inline {width: auto; margin:0; padding: 0}\n",".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n","</style><dl class=dl-inline><dt>person_id</dt><dd>0</dd><dt>gender</dt><dd>0</dd><dt>race</dt><dd>0</dd><dt>condition_concept_id</dt><dd>0</dd><dt>condition_start_date</dt><dd>0</dd><dt>age</dt><dd>0</dd><dt>weight</dt><dd>0</dd><dt>height</dt><dd>13758</dd><dt>bmi</dt><dd>15814</dd></dl>\n"],"text/markdown":"person_id\n:   0gender\n:   0race\n:   0condition_concept_id\n:   0condition_start_date\n:   0age\n:   0weight\n:   0height\n:   13758bmi\n:   15814\n\n","text/latex":"\\begin{description*}\n\\item[person\\textbackslash{}\\_id] 0\n\\item[gender] 0\n\\item[race] 0\n\\item[condition\\textbackslash{}\\_concept\\textbackslash{}\\_id] 0\n\\item[condition\\textbackslash{}\\_start\\textbackslash{}\\_date] 0\n\\item[age] 0\n\\item[weight] 0\n\\item[height] 13758\n\\item[bmi] 15814\n\\end{description*}\n","text/plain":["           person_id               gender                 race \n","                   0                    0                    0 \n","condition_concept_id condition_start_date                  age \n","                   0                    0                    0 \n","              weight               height                  bmi \n","                   0                13758                15814 "]},"metadata":{}}]},{"cell_type":"code","source":["# Drop columns that have all values as null\n","df <- df %>% select(where(~ any(!is.na(.))))"],"metadata":{"id":"772yviO06lyr","executionInfo":{"status":"ok","timestamp":1731292507238,"user_tz":300,"elapsed":221,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}}},"execution_count":60,"outputs":[]},{"cell_type":"code","source":["# Since we need both height and weight values to calculate the BMI value,\n","# we will remove all the records that have height missing\n","df <- df %>% drop_na(height)\n","dim(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"VPbtlMM_6qAR","executionInfo":{"status":"ok","timestamp":1731292515462,"user_tz":300,"elapsed":242,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"6deeacb7-42dd-4efb-d9c0-04ce07f8b65c"},"execution_count":62,"outputs":[{"output_type":"display_data","data":{"text/html":["<style>\n",".list-inline {list-style: none; margin:0; padding: 0}\n",".list-inline>li {display: inline-block}\n",".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n","</style>\n","<ol class=list-inline><li>74408</li><li>9</li></ol>\n"],"text/markdown":"1. 74408\n2. 9\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 74408\n\\item 9\n\\end{enumerate*}\n","text/plain":["[1] 74408     9"]},"metadata":{}}]},{"cell_type":"code","source":["# Replace the null values in the BMI column using the BMI formula\n","df$bmi <- ifelse(is.na(df$bmi), round(df$weight / ((as.numeric(df$height) / 100) ^ 2), 1), df$bmi)"],"metadata":{"id":"5t6M2ybT6OEo","executionInfo":{"status":"ok","timestamp":1731292688552,"user_tz":300,"elapsed":149,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}}},"execution_count":64,"outputs":[]},{"cell_type":"code","source":["sapply(df, function(x) sum(is.na(x)))\n","sapply(df, function(x) sum(as.character(x) == 'NULL'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":51},"id":"TXIYXhkXFJXf","executionInfo":{"status":"ok","timestamp":1731292721219,"user_tz":300,"elapsed":617,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"4fc04722-4f8a-468c-cbb1-5a15dbe14cb9"},"execution_count":67,"outputs":[{"output_type":"display_data","data":{"text/html":["<style>\n",".dl-inline {width: auto; margin:0; padding: 0}\n",".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n","</style><dl class=dl-inline><dt>person_id</dt><dd>0</dd><dt>gender</dt><dd>0</dd><dt>race</dt><dd>0</dd><dt>condition_concept_id</dt><dd>0</dd><dt>condition_start_date</dt><dd>0</dd><dt>age</dt><dd>0</dd><dt>weight</dt><dd>0</dd><dt>height</dt><dd>0</dd><dt>bmi</dt><dd>0</dd></dl>\n"],"text/markdown":"person_id\n:   0gender\n:   0race\n:   0condition_concept_id\n:   0condition_start_date\n:   0age\n:   0weight\n:   0height\n:   0bmi\n:   0\n\n","text/latex":"\\begin{description*}\n\\item[person\\textbackslash{}\\_id] 0\n\\item[gender] 0\n\\item[race] 0\n\\item[condition\\textbackslash{}\\_concept\\textbackslash{}\\_id] 0\n\\item[condition\\textbackslash{}\\_start\\textbackslash{}\\_date] 0\n\\item[age] 0\n\\item[weight] 0\n\\item[height] 0\n\\item[bmi] 0\n\\end{description*}\n","text/plain":["           person_id               gender                 race \n","                   0                    0                    0 \n","condition_concept_id condition_start_date                  age \n","                   0                    0                    0 \n","              weight               height                  bmi \n","                   0                    0                    0 "]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["<style>\n",".dl-inline {width: auto; margin:0; padding: 0}\n",".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n","</style><dl class=dl-inline><dt>person_id</dt><dd>0</dd><dt>gender</dt><dd>0</dd><dt>race</dt><dd>0</dd><dt>condition_concept_id</dt><dd>0</dd><dt>condition_start_date</dt><dd>0</dd><dt>age</dt><dd>0</dd><dt>weight</dt><dd>0</dd><dt>height</dt><dd>0</dd><dt>bmi</dt><dd>0</dd></dl>\n"],"text/markdown":"person_id\n:   0gender\n:   0race\n:   0condition_concept_id\n:   0condition_start_date\n:   0age\n:   0weight\n:   0height\n:   0bmi\n:   0\n\n","text/latex":"\\begin{description*}\n\\item[person\\textbackslash{}\\_id] 0\n\\item[gender] 0\n\\item[race] 0\n\\item[condition\\textbackslash{}\\_concept\\textbackslash{}\\_id] 0\n\\item[condition\\textbackslash{}\\_start\\textbackslash{}\\_date] 0\n\\item[age] 0\n\\item[weight] 0\n\\item[height] 0\n\\item[bmi] 0\n\\end{description*}\n","text/plain":["           person_id               gender                 race \n","                   0                    0                    0 \n","condition_concept_id condition_start_date                  age \n","                   0                    0                    0 \n","              weight               height                  bmi \n","                   0                    0                    0 "]},"metadata":{}}]},{"cell_type":"markdown","source":["No records are dropped since there are no null values in the dataset."],"metadata":{"id":"G8inZmZoFqy8"}},{"cell_type":"code","source":["# Statistical summary for all numerical columns in the dataset\n","df$height <- as.numeric(df$height) #change height column to numeric format\n","df$bmi <- as.numeric(df$bmi) #change bmi column to numeric format\n","summary(select_if(df, is.numeric))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255},"id":"NZ4BVio9Fqj1","executionInfo":{"status":"ok","timestamp":1731292845295,"user_tz":300,"elapsed":305,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"9a39e744-d390-4d04-fadd-2f076c9e386d"},"execution_count":69,"outputs":[{"output_type":"display_data","data":{"text/plain":["   person_id      condition_concept_id      age             weight      \n"," Min.   :     1   Min.   :37311061     Min.   :  0.00   Min.   :  1.80  \n"," 1st Qu.: 31087   1st Qu.:37311061     1st Qu.: 20.00   1st Qu.: 64.10  \n"," Median : 62212   Median :37311061     Median : 45.00   Median : 77.30  \n"," Mean   : 62182   Mean   :37311061     Mean   : 42.89   Mean   : 71.42  \n"," 3rd Qu.: 93303   3rd Qu.:37311061     3rd Qu.: 62.00   3rd Qu.: 87.00  \n"," Max.   :124150   Max.   :37311061     Max.   :111.00   Max.   :181.20  \n","     height           bmi       \n"," Min.   : 45.1   Min.   : 6.00  \n"," 1st Qu.:157.6   1st Qu.:23.60  \n"," Median :166.4   Median :27.80  \n"," Mean   :160.5   Mean   :26.21  \n"," 3rd Qu.:174.8   3rd Qu.:29.40  \n"," Max.   :198.7   Max.   :60.10  "]},"metadata":{}}]},{"cell_type":"markdown","source":[],"metadata":{"id":"TVNw7nrFFqX2"}},{"cell_type":"markdown","source":["### Data Visualization\n","\n","Data visualization is the practice of transforming data into visual formats, such as charts, graphs, and maps, to make information more accessible and understandable. It is crucial in data analysis because it helps identify trends, patterns, and insights at a glance, facilitating clearer communication and more informed decision-making."],"metadata":{"id":"iV9uyVkIGLWh"}},{"cell_type":"code","source":["# 1. Distribution of Age\n","ggplot(df, aes(x = age)) +\n","  geom_histogram(aes(y = ..density..), bins = 30, fill = \"skyblue\", color = \"black\", binwidth = 5) +\n","  geom_density(alpha = 0.2) +\n","  labs(title = \"Distribution of Age\", x = \"Age\", y = \"Density\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"zW9k7iviFkC5","executionInfo":{"status":"ok","timestamp":1731293006243,"user_tz":300,"elapsed":739,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"38358c03-8d9a-4f7e-b1fa-828b6b0f8db0"},"execution_count":76,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nAE+g2AEAAADwBIodAAAAA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68fX1Nev3\nXCyWDI6ddXbLusxTR7hXePxnCr8x7oN3gyOoH4yOv02DF7uamhpra+t7Q9ja2lZXV3d3zL0a\nGhrs7OxaW1v/9Kc/EdHWrVv/9re/ffHFF1ZWVtyAysrK2NjYtvFLly599tln9fUd6YtCoVAo\nFKxTmAoLCwvWEUyFUqlUKpWsU5gKS0tLg86fn5/v7u7u5eXV7mfv/fehiQud8oezW9YlH9rl\nNjCIiKytrR0dHVmHMqC23/ZgbW3NOoKpEM7fI9oO75Qyxhq7rjTobrVsW1vbzZs3t324fPny\nRYsWnTt3LiYmhntFJpMNHz68bYCbm1tLS0vX5zc0kUgklUq1Wq1Go2GdhT2RSCQWi/FWEJFY\nLJZIJBqNpuM/tALBLZw16FtRU1Nz586dcePGPej3Q2trq+GOrl/9goc49ffJOnPU2XMAEbW2\ntprULz09MsIPhrngfmO0trbqdDrWWdiTSCRarVYgb4VOp5PL5Q/6rMGLnZ2dXU1NjU6na6tu\n1dXV9vb23R3TAUtLS2dn5/Ly8rZXrK2tP//887YPGxoaOjj/Z3xSqdTOzq65ubm+vp51Fvbk\ncrlcLq+rq2MdhD25XG5jY9Pc3NzQ0MA6C3sKhUIsFhv0rUhOTiYiLy+vB/1+MKMzdkQUOuUP\ncev/WZp/nUzvl54eKZVKrVbb1NTEOgh73Nn9hoYGtVrNOgt7KpVKrVYL5K2QSCQdFDuD3zzh\n5+fX0tLStkiupqamqKho0KBB3R1zr4KCgv/+979t/5huamoqKyvr06ePYb4DAOAnftwS2yZ0\nyh+IqCQ3g3UQAGDJ4GfsHBwcIiMjP/vssxdffFEul2/cuNHX1zcwMJCIjh492tTUNGPGjA7G\nVFZWajQa7t/N3Dk5lUrl4OBw/vz51tbWxx57TKPRbN68WaVSRUVFGfp7AQA+4Vmxc/bycw8I\nuZOVyjoIALBkjDV2L7744vr161esWKHRaIKCgt566y3ukmtycnJNTc2MGTM6GPPaa6+VlpZy\n8yxevJiInnnmmYcffvj999//+uuvX375ZZlM5u/v/9FHHwln1SQA6AXPih0RhU7+Ax4aCyBw\nxih2SqXy5Zdf/v3rr732WqdjNm7c2O6cPj4+77//vr4SAoAA5ebmymSyfv36sQ6iN6FTZh/8\n9/+xTgEALOGBDQAgUHl5eZ6enjKZjHUQvbF17Wvr2pd+WbgCAAKEYgcAQlRaWlpbW+vj48M6\niJ71GTCIiBITE1kHAQA2UOwAQIj4t8CO4+LjT0Tnz59nHQQA2ECxAwAhysnJIbzcl8AAACAA\nSURBVD4WOwsrFRHl5uYWFRWxzgIADKDYAYAQ5eXlER+LHUen0/3000+sUwAAAyh2ACBEXLHj\n3xo7jkgk2rt3L+sUAMAAih0ACFFubq5SqXRzc2MdxCACAgKuXLly69Yt1kEAwNhQ7ABAcLRa\n7Y0bN3x8fNqeT80zw4cP1+l0+/fvZx0EAIwNxQ4ABOfmzZvNzc18XWBHRMOHDxeJRFhmByBA\nKHYAIDjcXife3t6sgxiKg4PD0KFDExMTS0pKWGcBAKNCsQMAweHrJnb3mj59ularPXjwIOsg\nAGBUKHYAIDgCKXZEdODAAdZBAMCoUOwAQHD4vYkdx9vbe9CgQWfOnKmqqmKdBQCMB8UOAAQn\nLy/P3t7ewcGBdRDDmjZtWktLy7Fjx1gHAQDjQbEDAGFRq9VFRUV83Zr4XtOmTSMiLLMDEBQU\nOwAQloKCAo1Gw+/rsJzBgwf369cvLi6uubmZdRYAMBIUOwAQFu7OCSGcsSOi2NjY+vr6s2fP\nsg4CAEaCYgcAwsLvp8TeZ/LkyUR0+PBh1kEAwEhQ7ABAWARV7EaNGmVtbX348GGdTsc6CwAY\nA4odAAgL7x87cS+5XD5u3Lhbt26lp6ezzgIAxoBiBwDCkpeX5+LiYmNjwzqIkcTGxhLRkSNH\nWAcBAGNAsQMAAWlqaiouLhbIdVhOTEyMRCJBsQMQCCnrAAAAvZWdna3RaLoyMjc3V6vVOjo6\nZmZmdjq4uLi419HYc3R0HDJkyOXLl8vLy52cnFjHAQDDQrEDALM3efLk2traro/fv3///v37\nDZfH1MTExFy8eDEuLu7RRx9lnQUADAvFDgD4QKGyCZ74UKfD7mSn3Uq/6jt8jL1bv04H190t\nyzxzVB/pGJs4ceLKlStR7ACEAMUOAPjA2sn1kXf/3emwnStevJV+dcarH7oOGNTp4BtJCfwo\ndiEhIX369ImPj29tbZVK8WsfgM9w8wQACEh5Ya5ILHboJ4i9TtqIRKLx48dXVVVdvnyZdRYA\nMCz80w3079ChQzk5OV0cLJFIpFJp159lqVAonnnmmZ5GA6G7W5hn6+Ius1CwDmJsMTExW7du\njYuLGzFiBOssAGBAKHagf9u3b//pp58MNLmdnR2KHfRMU211XUW57/CxrIMwMHbsWKlUevz4\n8TfeeIN1FgAwIBQ7MJTZb32qtHXo1pe0tqgv/PBlwdVEIpJIZZrWFiKydXUf9/RLKkcXItr7\nj9eptavn9gDuU16YR0TOnr6sgzBga2s7dOjQixcvYtMTAH5DsQND8R810da1b9fHtzQ1fvnH\nRwquXuwXPGT6K+/3D42ou1t65POPLu3+/tTm/y79cp+tq/vhNe+31qDYQQ9xxc5JkMWOiCZM\nmJCQkBAfHz937lzWWQDAUHDzBJgEnVa7/Z3nC65eDBo//bmNez3DhotEImsn10feWT3hmVcq\nbxdt/duzOq2WdUwwb+WFuUTk2E9Aj52414QJE4jo+PHjrIMAgAHhjB2YhLNb16ce+8kzNOKx\nj9ZJ5Rb3firmT38rzs1Mjz9wdss6VvGAH8oLcknAZ+wGDx7s4OBw4sQJrVYrFuNf9QD8hD/b\nwF5JTsbhNR8obR3mf/zVfa2OM+uNT5S2DkfX/kOr7dJjowDaVV6QK5HKHPr2Zx2EDbFYHB0d\nXV5enpaWxjoLABgKih0wptNqd33w/1rVzbPe+MTGuU+7Y6wdXaKf+rO6ob6xptrI8YBPygvz\nHDw8xRLhXqkYP348EcXHx7MOAgCGgmIHjF3eu7Xw2sVB0VNCYh7uYNjIuU9b2Ts21dXodDqj\nZQM+qb1b2lxfK9gFdpxx48YR0YkTJxjnAACDQbEDlprr6w7/9wOZwnLGays7HilXWkU9/pxO\nq1Wr1cbJBjwj8AV2HDc3t4CAgISEhMbGRtZZAMAgUOyApVOb1tRVlI954k/27p0/kX347CdI\nJFKr1ThpBz1QfiOHBF/siCg6OlqtVl+4cIF1EAAwCBQ7YKbubtmZ79da2TuOffL5roxXOTpb\nWCo1Gs25c+cMnQ34p7woj4ic+wu92I0dO5aITp06xToIABgEih0wc3LTGnVjw/glr1hYWXfx\nSxQqGyLatm2bIXMBP+FSLCcqKkomk508eZJ1EAAwCBQ7YKP2bmnCzm9snPuMmLOo618lU1iK\nxeKDBw9ipR10V3lBrlxpZf2AO6+FQ6VShYeHp6WlVVRUsM4CAPqHYgdsnN78WUtTY/TTL7W7\ncV0HZDJZdXU1ds+HbtFqWu/evOHU30ckErHOwl50dLRWqz19+jTrIACgfyh2wEBDdWXC/zap\nHJ0jZi/s7tfKZDIi2r17twFyAW9V3i7StKidPQewDmISuGV2KHYAvIRiBwyc/+FLdUP9qPlL\nZRaK7n6tVCp1d3c/evRoS0uLIbIBL5Xhlth7DBkyRKlUotgB8BKKHRhbS3PT+R82KlQ2I+c+\n3bMZYmJiampqsF8DdF15IXfnBM7YERHJ5fKRI0fm5eXdvHmTdRYA0DMUOzC2Kz9tq6+8O/yR\nJ7lbXHtg8uTJRHT48GG95gI+4zaxc/ZCsfvZ6NGjiejMmTOsgwCAnqHYgVHptNqzW9ZJpLKo\nx57p8SRjxoyxtLQ8cuSIHoMBv5UV5BCRk+A3sWuDZXYAfIViB0aVdTau7EZOSMzDtq59ezyJ\nQqEYO3Zsfn5+bm6uHrMBj5UX5No497GwUrEOYiqCg4Pt7OxQ7AD4B8UOjOrMd18QUdTjz/Vy\nHu5Z5thkFbqiub6utrzECddh7yGRSCIjI+/cuZOXl8c6CwDok5R1ABCQ4pyMvEtnPMOG9wse\n0supoqOjiejUqVOLFy/WRzTgs7KCHJ1OJ5CHiTXWVhNRSkoKtzFQB/r06UNEX331VUxMTNfn\nj4yMlMvlvUkIAAaFYgfGc3bLOp1ON3r+st5P5efn5+7ufubMGY1GI5FIej8h8FjZjWwiEsgZ\nu+LraUS0cuXKLo5ft27dunXruj7/1atX3d3de5IMAIwCxQ6MpL6q4urB/9n18QgcP1UvE44d\nO3bbtm1JSUnDhg3Ty4TAV7/cEuvHOojxDI6d5djPu9NhZ75fKxKJRs1f2pU5U+P2cRUZAEwZ\nih0YSeL/Nrc0N0U+ulgs0c9PHVfszpw5g2IHHSu9kU1ELkIqduHT5wWM6fwCa1lBTuqxn8Km\nPuLi49/54BvZKHYApg83T4AxaDWtCTu/liksh83q9jPEHiQqKoqIsE0xdKrsRo7MQmHn5sE6\niMnxGTaKiPIunWUdBAD0BsUOjCE1bl91ye0hDz2qtLXX15x9+/bt169fYmKiRqPR15zAP1qt\n5m5hnpOnr0iMX3f38xk6iojyLqPYAfAHftOBMZzbtkEkEkU+ukS/00ZFRdXW1qampup3WuCT\nyttFrepmZzxMrD0uPv4qB6f8y+d0Oh3rLACgHyh2YHA305MLkhN9Isa4+gbod+bIyEgiOn/+\nvH6nBT4py88mImdvAS2w6zqRSOQzdFRdRXlpXhbrLACgHyh2YHDntqwnolHze7sp8e9xxS4h\nIUHvMwNvlHMPE8MZuwfwHhpFuBoLwCModmBYteUl147ucfTwChjdjU1Qu8jHx8fJySkxMVHv\nMwNvlOZfJyIX74Gsg5go7v6J/MvnWAcBAP1AsQPDurDja02LOvKxZw20dH3YsGGlpaWFhYWG\nmBx4oDT/ukgsdhbG7sQ94Ow9EMvsAPgExQ4MqFWtTty12UKpGjbzcQMdgtvE7uLFiwaaH8xd\nWX6OnZuHTGHJOoiJEolE3kOj6irKy/Kvs84CAHqAYgcGlHxwZ93dsmGzFlhYWRvoEBEREUR0\n6dIlA80PZq3ubllDdYWgtibugZ+X2WE3OwBeQLEDg9Hpzny/ViyWRD3+rOEOEh4eLpPJcMYO\n2sUtsHPGArsOcbvZYZkdAD+g2IGh5CddKMnJCBw/1aGvp+GOYmlpGRgYmJ6e3tTUZLijgJnC\nnRNd4eLjb2XvmH8F2wYB8AGKHRhK4v82E9HohX8y9IHCw8NbWlpSUlIMfSAwO2Uodl0gEom8\nwiNr75aW3chhnQUAegvFDgwlP+lC/8ERnqERhj5QeHg4ESUnJxv6QGB2SrE7cdf4DI0iovwr\nuBoLYPZQ7MBgdLqxi543wnHCwsKIKCkpyQjHAvNSmpdl7eiixycU8xV3/wSW2QHwAIod6F9j\nYyMROXh4BUZPNcLh/P39lUolztjBfRprqmrKil18/FkHMQN9BgxS2tpjmR0AD6DYgf7l5OQQ\n0Yg5iwy0KfF9JBJJSEhITk5OdXW1EQ4H5qIkL4uIXH1R7DonEos9w0ZUl9yuuFXAOgsA9AqK\nHehZeXl5UVEREQVPfMhoBw0PD9fpdLh/Au5VlnediHDGrot8sJsdAC+g2IGerV27VqPREJFE\nKjPaQUNDQ4no6tWrRjsimL6SvExCsesyLLMD4AcUO9CnysrKL7/8Ui6XG/m4XLG7du2akY8L\npqwkN4tQ7LrMzT9YYW2LG2MBzB2KHejT2rVr6+rqfH19jXxcX19fKysrFDu4V2n+dZWjs5Wd\nA+sg5kEslniFDa+8XVR1p4h1FgDoORQ70JuKiooNGzY4ODh4eXkZ+dBisTg4ODgvL6+2ttbI\nhwbT1FRbXVN6x9UngHUQc+I9JJKI8nA1FsCcodiB3nz22We1tbUvvPCCVCo1/tFDQ0O1Wm1q\naqrxDw0mqDg3k4j6+A1iHcSc/LzMDpueAJgzFDvQj7Kyso0bNzo5OS1ZsoRJgMGDBxPun4Bf\nlORkEBHO2HVL30GhcqUV7p8AMGsodqAf//rXvxoaGv7yl78olUomAbhih2V2wCnJySQi1wEo\ndt0glki9QoffLcqvKb3DOgsA9BCKHehBYWHh5s2b+/Xr99RTT7HK4OfnZ2Fhga3sgFOcmyES\niVx9cSm2e7yGRBGW2QGYMxQ70IMPPvhArVYvX77c+BudtJFKpYGBgTk5OU1NTawygOkoycmw\nc/OwsFKxDmJmfLCbHYCZQ7GD3kpKStq9e3dQUNC8efPYJgkODm5tbU1PT2cbA5irKStuqK7E\n6boe8AgKkykscf8EgPlCsYNe0el0b7/9tk6nW7FihdgoT4btQEhICBHhxlgozskgoj5+gayD\nmB+JTN4/ZFh5QU7t3VLWWQCgJ1DsoFf27NmTkJAQExMzbtw41lkoODiYiLDMDkqy04nI1Rd3\nTvSE99AonU6Hk3YAZgrFDnquoaFhxYoVMpnsvffeY52FiCgoKEgikaDYwZ3raUTkNjCIdRCz\nhGV2AGYNxQ567tNPP71169Zzzz03YMAA1lmIiJRKpa+vb0ZGhkajYZ0FWLqTnSaVy529TOLH\n0uz0CxkqlVug2AGYKRQ76KGsrKy1a9e6ubm9+uqrrLP8KigoqKGhIT8/n3UQYEbToi67kePi\n4y+WMHgCCg9I5Rb9QoaW5mXVV95lnQUAug3FDnpCq9W+8sorarX6ww8/VKlMaEeJoKAgIkpL\nS2MdBJgpu5GjaVH38cN12J7zwTI7ALOFYgc9sXnz5sTExNjY2BkzZrDO8hvc/RO4MVbI7lxP\nJSyw6x3uobF5l86yDgIA3YZiB91269at9957z9ra+h//+AfrLPfjztih2AnZnex0InLDXie9\n0H9whFQuz7+MYgdgflDsoHt0Ot0rr7xSW1v7zjvveHh4sI5zvz59+jg5OeFSrJDdyUolIlyK\n7Q2ZhcIjaEhJbmZ9VQXrLADQPSh20D2bN28+fvz4mDFjFi1axDpL+4KDg+/cuVNRgb+QBOp2\nZoqta18re0fWQcybz7BROp3uxhXcGwtgZgRx15hIJJLJZKxT/EoikXD/a1KpuiI3N/fdd9+1\ntrb+/PPPO3gsrKEfQdHx+xYSEnLixInMzMzo6GiDxtA7qVRKRGKx2Ox+MAxBIpH04E9u1Z2i\nhuqKQWFTDJRKOHyGRh3f8GnepbNBEx6693WZTMb257NnPxi8xP2mNce/SgxBLBYL563o+C9Z\nQRQ7sVhsYWHBOsWv2v40mlSqTrW2ti5durS+vn79+vUdb1xn6GLX8fsWFhZGRJmZmbGxsQaN\noXfc+8bVO+D+/u7i4LaRtzNTiMg9IMRQsQSjf+hwqVz++/sn5HI5219cEolELBZ3/WeDx7jf\nFTKZjDtZIHBtZ0xYB2FPEH+FaDSahoYG1il+JZVK5XK5Wq2ur69nnaUbPvzww0uXLs2YMWP2\n7Nl1dXUdjGxtbTVoko6PzpXOpKSkjoeZILlczv1gmNSPKysKhUIsFnfxrdDpdNx/3M5KISJ3\nfxS73uKW2RUkJ9RXVVjZObS9Xl9fz/ZPllKp1Gq1TU1NDDOYCKVSKZVKm5qa1Go16yzsqVQq\ntVotkLdCIpEoFIoHfRZr7KBLTp48+Z///MfDw+PTTz9lnaUTfn5+crkcN8YKE87Y6RG3zA6P\noAAwL4I4Ywft2rhx45o1a7oyUqPRlJWVcf9KHjduXKfjKysrexuuF2QymZ+f3/Xr11taWgSy\n3gLa3M5KsbJzsOtjcvdrmyOfYaO4ZXbBEx/qfDQAmAYUO+Gqqam5ffu20tZeKu9kxUx9VaVW\nq1VYWatFUnWLttOZm5qb9ZSxh4KDg9PS0nJycgYNGsQ2CRhT7d3S6pLbfpHjWQfhCW43u7xL\nZ1gHAYBuQLETurnv/TdgTEc3Gfz0yRvntm4IHDd14aeburhg+aMpg2tK7+gpYE+0bVOMYico\nt9KTicgjMIx1EJ6QWSj6hQy7ceV8XUW5ysGJdRwA6BKssYOOXN677dzWDc5eA+a9/5kZ3YYW\nGBhIROnp6ayDgFHdRLHTNyyzAzA7KHbwQAVXL+5e+f8srKyf+NdmCytr1nG6gXtiLJ4/ITQ3\n05KIqC+Knf74DB1FRLgaC2BGcCkW2ld5q/C7//ektlWz4JN1zl5+rON0j6Ojo6urK4qd0NxK\nT7Z2crV1dWcdhD/6Dx4ms1DkXkSxAzAbOGMH7Wisqfrmpfl1FeVT/7IiYEwM6zg9ERgYWFpa\nWlZWxjoIGIm2tbWuohzXYfVLKrfoPzii7EZ2TVkx6ywA0CUodnC/VnXzt/9vUWle1sh5i0cv\nWMY6Tg/haqzQtKibiMgjKJx1EL7xHT6GiH7/CAoAME0odvAbWq1m29+W5l8+Nyh6yozlK1nH\n6TncPyE0rc1NRNQvZCjrIHzjM2w0EeVePM06CAB0CYod/Eqn0/34/itp8fs9w4Y//tF6sdiM\nH7rH7XiCM3bC0dLcJBKL++GMnb55BIXJlVZ5WGYHYCZQ7OBnOp1u3ydvXNqzxc0/+Kl/b5Ep\nLFkn6pUBAwbI5XIUO+FobW5y9hygsLZlHYRvJFKZ95DIilsFLU2NrLMAQOdQ7ICISKfT7f/0\n7XPbNjp7+S3+bAcP/naUyWT+/v7Z2dkCeSa0wGk0Gp1O1y9kCOsg/OQ7bDQR1VWWsw4CAJ1D\nsQPS6XR7//H62S3rnDx9n1m3izdbzAcFBanV6uzsbNZBwOC0Wi0R9QsZxjoIP3H3T9RXoNgB\nmAEUO6HTaTU73nnhwvavXHz8n9uwx8a5D+tEeoNldsKh0WiIqD+KnWG4DQxW2trXVd5lHQQA\nOodiJ3THN65K2r/dPWDwcxv2WDu5so6jTyh2wqHRaERiseuAANZB+EkkFnsPHcXddwwAJg7F\nTrjq6+uJ6GZakk/E6Oc27Layd2SdSM+4YocdT3ivuLhYq9XKLBRmfR+3iRswfAzrCADQJXik\nmEClpaV98803ROQzNOrpNT9I5XLWifTPwcHBzc0tNTWVdRAwrMTERCKSWZj3fdwmznf4WNYR\nAKBLcMZOiPbt2zdt2rSamhoiGvPEn3jZ6jhBQUHl5eUlJSWsg4ABJSQkEJFUoWAdhM+cvQbI\nLBT0y30qAGCyUOyERaPRfPjhh4sXL9bpdDNnziQiEolYhzIg7sFiOGnHb1yx42oHGI6VgxNh\nbQOAyUOxE5DKysrHH3989erVHh4e+/fv5x66xW9csUtJSWEdBAylpqYmNTVVIpGIRPhtZljc\nRkgXLlxgHQQAOoJfhUKRkZERGxsbHx8fHR197NixkJAQ1omMATfG8l5iYqJGo5FIcNuEwans\nUewAzACKnSAcPnx42rRpN27ceP7553/44QcHBwfWiYzEx8dHqVTiUiyPnTt3johQ7IxAaqEg\noqtXrzY24tliAKYLxY7/vv7660WLFrW0tHz++ecrVqwQ1F+BYrE4KCgoLy+voaGBdRYwiHPn\nzkkkEkH9VLOlVqtx0g7AlKHY8dzq1auXL19ua2v7448/zp07l3UcBoKDg7VaLVZ881JdXd3V\nq1eDg4NFvL4HyNScPHmSdQQAeCDsY2fqzp8///rrr/fsa8vLy0tLS2Uymb29/auvvvr7z/Y6\nnRngltmlpKQMG4bnTfHNhQsXWltbR48enZeXxzqLUEilUhQ7AFOGYmfqampq0tPTxRKpuJsX\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DZx2XLWWcCwuGKXfvIwih2AcaDYmYf6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06YMOHEiROCHx1uz2AwCCG0Wq3lZVBQkEqlOn36NAcG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\nAAAUgmAHAACgEAQ7AAAAhSDYAQAAKMQ/8G4kOXGbj3AAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"code","source":["# 2. Distribution of Weight\n","ggplot(df, aes(x = weight)) +\n","  geom_histogram(aes(y = ..density..), bins = 30, fill = \"green\", color = \"black\", binwidth = 5) +\n","  geom_density(alpha = 0.2) +\n","  labs(title = \"Distribution of Weight\", x = \"Weight (kg)\", y = \"Density\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"Plb26eF_GdHz","executionInfo":{"status":"ok","timestamp":1731293069389,"user_tz":300,"elapsed":701,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"a8e885ca-c328-48fa-c504-8ac138b1205f"},"execution_count":82,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nliz54Q9/2NzcbL1SVVV1/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QKHYADDgy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\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Fy8eFGSpBEjRvj6+pqd+7333mvfvr1d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\nTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"code","source":["# 3. Gender Distribution\n","gender_count <- df %>% count(gender)\n","ggplot(gender_count, aes(x = gender, y = n, fill = gender)) +\n","  geom_bar(stat = \"identity\") +\n","  scale_fill_manual(values = c(\"skyblue\", \"lightpink\")) +\n","  labs(title = \"Gender Distribution\", x = \"Gender\", y = \"Number of Patients\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"c1f-54oEGesj","executionInfo":{"status":"ok","timestamp":1731293086893,"user_tz":300,"elapsed":655,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"82c03021-38ab-472e-ace8-d7ccc1f5fcd8"},"execution_count":83,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nkZHRq1ev+N3s7Oz8/PwVK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2F1100dq1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\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4. Race Distribution\n","race_count <- df %>% count(race)\n","ggplot(race_count, aes(x = fct_reorder(race, n, .desc = TRUE), y = n, fill = race)) +\n","  geom_bar(stat = \"identity\", width = 0.7) +  # Increase width to 0.7\n","  labs(title = \"Race Distribution\", x = \"Race\", y = \"Number of Patients\") +\n","  theme(axis.text.x = element_text(angle = 45, hjust = 1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"L2RqGiwDHAE4","executionInfo":{"status":"ok","timestamp":1731293213322,"user_tz":300,"elapsed":291,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"8a948b8a-f5f2-4581-c01e-0b1ab7b78f63"},"execution_count":89,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94\nAAAgAElEQVR4nOzdd2AU1d7/8TNbkuymU5PQpYcSmhBqEAgYwr2hqCAK6A2aPICAShREAVHQ\nK10RERBFvAKKtEux0ESlSAnSBA1cIJQACaSRbLKb7O+P+d198gQMC2wJZ9+vv3bOzpz5zrA7\nfDJnZlaxWq0CAAAADz6NuwsAAACAYxDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRB\nsAMAAJAEwU4248ePVxRl4cKFTu329ddfVxRl/vz5jl3LrSsCAAD289Bgd/jwYeV2jEZjo0aN\nRo4cmZKS4u4a/79bS/Xz83vooYcee+yxzz77LC8vr9T8wcHBtWrV8vf3t38Vy5cv37BhQ9nz\n3EO397Z2560IAADpKZ75yxOHDx9u2bKln59fbGysrdFqtV65cuXIkSM3btwwGAwbNmzo0aOH\nG4tU3Vpqbm7umTNnTp48abVaq1Wr9umnn0ZHR9/PKkJDQ2NjY5csWWL/Iq+//vq0adM++OCD\nUaNG3c+q723tAADgtnTuLsCdqlatunLlylKNeXl548aN++ijj+Lj4//zn/9oNOXipOatpZ47\nd27atGmLFy+OjY3duHFjz549763nM2fOpKWlOaLGB2/tAABIplyklnLFaDTOnz8/ICDg/Pnz\nR48etbVnZ2dPmDChcePGBoPB29u7fv36SUlJ2dnZJZctLi5esGDBww8/7Ofn5+/v37179127\ndpWcwWq1LlmypH379v7+/gaDoXHjxm+88cbNmzfvoc5atWotWrTovffeM5vN8fHx+fn5avut\n16itXr26W7duFSpU8PLyCgsLi4mJ2bJli/rWY489VrduXSHEJ598oihKp06dhBATJ05UFGXD\nhg0LFiyoVq1aUFDQbbsVQmg0mh07dkRFRQUEBPj5+XXq1Gnr1q22d0eNGqUoymeffVZykb17\n9yqK0qdPn79a+60rMpvN8+bNe/jhh/39/X18fOrVqzdq1KhLly7ZZnjjjTfUgo8fP96vX78q\nVar4+Pi0aNFixYoV97BjAQB4cBHsbkOj0VSpUkUIUVBQoLaYzeY+ffq8++67vr6+o0aN+p//\n+R+tVjtz5swePXoUFRXZFhw4cODIkSPz8vKGDRsWFxf366+/RkVFLV++3DbD0KFDn3vuuXPn\nziUkJLz00ktBQUFvv/12p06dcnJy7q3UcePGtWjR4sKFC6tXr77tDIsXL3788cePHTv2xBNP\njB8/PiYm5tdff42NjVWrevbZZ5955hkhRGRk5Jw5c8aMGSOE8PLyEkL8+OOPSUlJUVFRTzzx\nxF+t/fDhwzExMUaj8bnnnuvRo8fu3btjYmJ+/PFHO4u/7dpLKS4ujouLGzt2bG5ubnx8/Cuv\nvNKgQYMPP/ywTZs2586dU+dRC05OTu7YsWNeXt7TTz/do0eP3377bfDgwdu3b7ezGAAAZGD1\nSMnJyUKIunXr3vbdU6dOaTQavV6fmZmptnzzzTdCiMjISIvForYUFBQ0atRICLFhwwa1RT0/\nFBMTY5vn5MmTRqPR19c3JyfHarWuWrVKCNG6devs7Gx1huLiYvUatfHjx99bqVar9d133xVC\nPP300+rkq6++KoT46KOP1MlmzZoJIVJSUmzzp6am+vv7R0ZGqpNff/21ECI+Pt42w/Tp04UQ\ngYGB3333na2xVLcTJ04UQmg0mvXr19vmmTFjhhCiY8eO6uTIkSOFEJ9++mnJavfs2SOEiI2N\n/au1l1rRokWLhBDt27c3mUy2eV5//XUhxBNPPKFOvvPOO0IILy+v5cuX2+YZN26cEGLYsGF/\ntd8AAJAPZ+z+jytXrqxduzY2Nra4uPj5558PDAxU21u1arVmzZoPPvhAq9WqLV5eXnFxcUKI\nI0eOqC2ffvqpEOK1116zzdOwYcNp06YlJiZevXpVCLF48WIhxDvvvGO75VNRlLfeekuv1y9b\ntuyeaw4PDxdCnD9//rbvZmZmKori6+tra6levXp6eroasG5LURQhROPGje943V7btm3//ve/\n2yZHjRrl4+Oze/fu69ev39UmlEHdM2+88Ya3t7etMSkpycvLa926dbYBaCHEww8//PTTT9sm\nH3/8cSHEH3/84ahKAAAo/zw62J0+fbrUk0RCQkL69++fkpISHx8/e/Zs25y1a9fu169fmzZt\nhBA5OTlpaWlpaWlGo1EIYcsWv/zyixCidevWJVcxduzYmTNnPvTQQ0KIvXv3CiE6dOhQcoag\noKCmTZtevnz5r5LZHfn5+alV3fbdv/3tb1ar9ZFHHlm6dKntNgV17LJs7du3v+M86lVxNj4+\nPo0aNbJaradOnbpz3XawWq0HDx4Ut+y0gICAhg0bFhYWHj9+3NYYGRlZcp7g4GBR4l8HAABP\n4NF3xZZ63MnFixd//vnnRo0arVmzpnHjxqVmXrdu3cyZMw8ePGgymW7t6ubNmzdv3vTx8TEY\nDLddV35+fm5urvhvDrvVxYsXa9aseQ9bkZGRIYSoUKHCbd+dO3duUVHR0qVL4+PjhRDh4eF9\n+vRJTEysU6dO2d1Wrlz5jqsODQ0t1aKWcePGDXsqv6Pc3FyTyeTl5WU7dVqqvPT0dFtLSEhI\nyRnU845Wj3yaDwDAY3l0sCv1DJH8/PymTZuePHny3LlzpYLdokWLEhIS/P39ExMT27ZtGxgY\nqNFo1q1b9/HHH6szqE9FMZvNVqtVjRSlqI2KokyaNOm2xZTKJfb79ddfhRDqBX+30uv1Cxcu\nnDx58oYNG7Zs2bJ9+/b33ntv7ty5y5cvL+OuCHXBO67aNuhso26mo54RU0Y4Ky4uts0AAABU\nHh3sSjEYDB9++GFMTExCQsKxY8dK/vjB1KlThRAbN27s0qWLrbHkZWoGg8Hf3z8nJycjI6NS\npUq3du7j4xMYGJiVlTVy5Eh7TobZqbCw8MsvvxRC9O7du4zZQkNDExISEhISTCbTZ5999sIL\nLyQkJMTFxZW8cO0elDxhpip5+vC2sezy5cv29+/n52c0GvPy8jIzM9Wnrthcu3ZN2HdaEQAA\nz+HR19jd6tFHH3388cfPnz+v3pupKigouHjxop+fX8lUZ7Vav/3225LLqlfglXyQmxDinXfe\nUZ8DIoRo166dEKLUk+2EEPdzq8G4ceMuXrzYtGnTmJiY285w7ty5klnKx8cnMTGxQ4cOmZmZ\nZ86cuef1qvbt21dysqCgQL2hWD196OPjI24Zlt2/f/9drULdq+r1izbXr18/deqUwWBo0qTJ\nvVUOAICUCHalzZ07NyAgYOHChbYE5u3tXaFChdzc3NTUVLXFarVOnTpVvd0hMzNTbRw2bJgQ\nYubMmbYHDp89e3bGjBl79uxRB3bVq9ymTJminm1S/fTTT1WrVlVv4bwrFy5ceOqppz744AOj\n0fj555/fdlDyt99+q1279tNPP11YWGhrzMnJOXPmjFarVZ/Vp8Yv9Uzb3dq2bZuaWVWLFy/O\nz89/5JFHAgIChBDqLSPq42DUGX7//Xf11mCbO65d3WnTp08vuQnTp0+3WCxPPfXUfZ5xBABA\nMgzFlhYWFvbWW2+NGTMmPj7+yJEj6s0QzzzzzOzZs7t3766mt40bN964cWPZsmW9evVauXJl\njRo1nnrqqSFDhqxevXrjxo1NmjSJiYm5efPmunXrcnJyFi9erN6h+cQTT6xbt27FihUtW7Yc\nOHCgv7//sWPHNmzYYDAYkpKSyq4qLS3tscceU18XFhampqYeOXKkuLi4WrVqX331VcuWLW+7\nVERExODBg7/88svGjRvHxMRUrFgxPT1906ZNFy5cGDNmTMWKFYUQjRs3VhRl06ZN8fHxXl5e\nH330kT17yWKxCCHi4+NjYmL69ev30EMP/f77719//bW3t/e0adPUeQYMGDB+/Pgff/yxY8eO\nkZGRly9f3rhx4+TJk5OSktQr5OxZ+5AhQ9asWbN+/frWrVvHxMTo9fp9+/Zt27atQYMG6gP8\nAADA/3LXA/Tcq+yn/hYVFalPLXn55ZfVlvz8/IkTJ9atW9fb27tGjRojRoxIT0+3Wq3PPPOM\nr69vSEjIkSNHrFar2WyeNWtW8+bNDQaDr69vly5dtm/fXqrnxYsXqz8pptPpqlevPnTo0N9/\n//2OpZak0WgqV67cvXv3Dz74IC8vr9T8pR7wW1RU9OGHH3bo0KFSpUparTYwMLBz585Lly4t\nLi62LfLuu+9WqlTJ29u7VatW1v8+73fGjBlldDt27FghxNdff71jx44uXbr4+fn5+vpGRUX9\n9NNPJZc6evRot27djEajn59fu3bt1q1bp56t7Nq161+tvdSK1L06d+7cVq1aGY1Gb2/vRo0a\nTZgw4caNG7YZblvwn3/+KYSIiIgoY98CACAZxcrzIAAAAKTANXYAAACSINgBAABIgmAHAAAg\nCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0A\nAIAkCHYAAACS0Lm7ADdIT093dwlOERwcrNFoMjIy3F2IRzAajUajMSsry2w2u7sW+el0uqCg\nIJPJlJub6+5aPAIHE1fy9fU1GAyyHkz0en1gYKC7q/AsnLEDAACQBMEOAABAEgQ7AAAASRDs\nAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAk\nQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMA\nAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATB\nDgAAQBIEOwAAAEkQ7AAAACShc3cBUrFM8XHj2q+JfCGEEO6sQTfF5Ma1AwDg4ThjBwAAIAmC\nHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACA\nJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYA\nAACSINgBAABIgmAHAAAgCZ1rVrN58+a1a9dmZGRUq1Zt6NChDz/8sBAiNzd30aJFR44cMZvN\nDRs2TExMrFKligPbAQAAPIorztht27Zt1apVCQkJCxcu7NGjx+LFi/Py8oQQc+fOvXr16uTJ\nk2fMmGE0GqdOnVpcXOzAdgAAAI/iimC3atWqYcOGtWnTpkqVKnFxcYsWLTIajenp6fv373/+\n+efr1KkTFhaWmJh48eLFo0ePOqrdBdsFAABQrjh9KDYjIyMtLU0IMXr06MuXL9eqVWv48OGN\nGjX6888/9Xp9nTp11Nn8/PyqV69+6tSpvLw8h7RHREQ4e9MAAADKFVcEOyHE1q1bX3nllcDA\nwJUrV7755psLFy7Mzs729/dXFMU2Z2BgYFZWVmBgoEPaS9bQv3//zMxM9XW3bt0mTpzopI29\nIm46qecHRcWKFd1dgouoH7mAgAB3F+JBfHx8vL293V2FR1A/3p7zdXYvuQ8mRUVF7i7B47jo\n5omBAwdWr15dCPGPf/xjx44dBw4cEP/9NN/KUe02fn5+ts+WwWDgCjzn8Zx9q9FoFEWxWq1W\nq9XdtchPURR1b3vOB8y9tFqt8KSvs3vJfTCRcqPKOacHuwoVKgghfH191UmtVluhQoUbN27U\nqFEjOzvbarXaYllWVlZwcHBQUJBD2kvW8Pnnn5ecTE9Pd9rm+jit5wfDjRs33F2CixiNRqPR\nmJOTYzab3V2L/HQ6XVBQUEFBQW5urrtr8QjBwcEajcZzvs7u5evrazAYZD2Y6PX6wMBAd1fh\nWZx+80SFChWCg4NPnjypThYWFl67dq1q1ar169c3m82nT59W27Ozs1NTUxs3buyodmdvFwAA\nQHmjnTJlilNXoChKUVHR6tWrH3roIZ1O99lnn129ejUhIcHf3//cuXM7duxo2LBhXl7eggUL\nfH19n3rqKaPR6JD2MsZn1YetOEPxThcNbZdbmq4Wd5fgInq9Xq/XFxQUMFzlAhqNxsfHx2Kx\nFBYWursWj2AwGBRFyc/Pd3chHsHLy0vig4lWq/Xx8fSxLBdTXDD+XVxc/MUXX2zdujU3N7dh\nw4YjRoyoUaOGECIvL2/RokXJyclFRUVNmjRJTExUh1Ad1f5XnDcUa5ni6R9f3RSTu0twEXUo\nNisrS8rRk/JGHYo1mUwMxbqGOhSr3voGZ1OHYmU9mDAU63quCHblDcHOeQh2cAaCnYsR7FyJ\nYAfH4rdiAQAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJ\nEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAA\nACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGw\nAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQ\nBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4A\nAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIE\nOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAA\nSRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwA\nAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRB\nsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAA\nkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEO\nAABAEgQ7AAAASRDsAAAAJEGwAwAAkITO3QW4gVardVLPFif1++Bw3r4tbzQajRBCq9UWFxe7\nuxb5qXtbURTP+YC5l6IowpO+zu6l7m2NRiPlDle/vHAlTwx2/v7+Tuq5QJic1PODwnn7trxR\nj1YGg8HHx8fdtchP/Z9Pr9d7zgfMvRRFURSFve0a6sHEaDRarVZ31+J4/Onrep4Y7DIzM53W\nt6f/H+/MfVu+GI1Go9GYm5trNpvdXYv8dDpdUFBQYWFhbm6uu2vxCMHBwRqNxnO+zu7l6+tr\nMBhkPZjo9XovLy93V+FZOEcKAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQI\ndgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAA\nkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgB\nAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiC\nYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAA\nIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYId\nAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAk\nCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAA\nAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDY\nAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABI\ngmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcA\nACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAktC5cmXbtm2bN2/ea6+9\nFhkZKYTIzc1dtGjRkSNHzGZzw4YNExMTq1Sp4sB2AAAAj+K6M3aZmZnLli3z8vKytcydO/fq\n1auTJ0+eMWOG0WicOnVqcXGxA9sBAAA8iuuC3cKFC7t27Wo0GtXJ9PT0/fv3P//883Xq1AkL\nC0tMTLx48eLRo0cd1e6y7QIAACgnXDQUu2fPntOnT48dO3bnzp1qy59//qnX6+vUqaNO+vn5\nVa9e/dSpU3l5eQ5pj4iIsK39jz/+KCoqUl8HBgbawqXDWZzU74NDp3Pp4L4baTQaIYRWq7Va\nre6uRX5arVYIodFoPOcD5l6KoghP+jq7l7q3ZT2YqIdKuJIrvre5ubkLFy588cUXfXx8bI3Z\n2dn+/v7qB1oVGBiYlZUVGBjokPaSBYwaNer69evq6169ek2bNs3h26hKE7lO6vlBERQU5O4S\nXMrPz8/dJXgQLy+vktdywNk87evsXrIeTCwWzni4miuC3SeffNKqVasWLVqUai+ZxpzRbtO7\nd++8vDz1dXh4uMlkKnt+3DPP2bc6nU6n0xUWFnJBpwsoiuLt7V1UVGQ2m91di0fw9vYWQhQU\nFLi7EI8g/cGEU78u5vTdffjw4UOHDs2fP79Ue1BQUHZ2ttVqtcWyrKys4OBgR7WXXNfYsWNL\nTqanpzt8M//L586zSC0311POWRqNRp1Ol5+fT9RwAZ1O5+3tbTabPecD5l56vV6j0bC3XcPX\n11fig4lery85WAcXcHqw++GHH27evJmYmKhO5ubmzpkzp0WLFgkJCWaz+fTp0/Xq1RNCZGdn\np6amNm7cODQ01CHtzt4uAACA8sbpwS4xMfHZZ5+1Tb744otDhw5t165dQEBA+/btP/zww9Gj\nR3t5eS1ZsqRu3brh4eGKojik3dnbBQAAUN4oLr4NZ+jQoSNGjFAfUJyXl7do0aLk5OSioqIm\nTZokJiaqQ6iOav8rzhuKtUzx9BPOuimeco2d0Wg0Go1ZWVlSjp6UNzqdLigoyGQyMTjoGsHB\nwRqNJiMjw92FeARfX1+DwSDrwUSv1wcGBrq7Cs/i6mBXHhDsnIdgB2cg2LkYwc6VCHZwLB4w\nAwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAHiQdOrUqUuXLhs3bqxRo0aHDh3U\nxpUrV7Zt29ZoNAYEBLRp02blypUlF/nhhx+ioqL8/f1DQkKeeOKJlJQU21s//vhjdHR0QECA\n0Whs1arV0qVLXboxjkawAwAADxJvb++srKykpKQJEyZMnDhRCLFq1aonn3yyevXqX3/99YoV\nKypXrvzkk09u2rRJnf+HH37o1auXj4/PwoULp0+ffvDgwS5duqSlpQkhtm3b1r1798LCwi+/\n/HL9+vXt2rWLj4+fNWuWOzfv/vDTvAAA4EGiKMqRI0fWrFnTr18/teXMmTPdunVbuXKll5eX\nEKJz584VK1ZcsWJFbGysEOK1116rXbv2pk2bdDqdEKJp06adO3f+6quvRo8enZSUVKdOnS1b\nthiNRiFEdHT0pUuX3nzzzZEjRz6gv3LLGTsAAPCA8fLy6tOnj21ywoQJ27ZtU1OdECIgICAk\nJOT8+fNCiIyMjAMHDsTExKipTgjRtm3bgoKC0aNHX716NTk5OTY2VqPRmP6rd+/eOTk5R48e\ndf1GOYS9wS4vL+/y5cvq6/z8/M8++2zWrFlnzpxxWmEAAAC3V6lSJb1eb5vMzs6eNGlSs2bN\nAgMDdTqdTqe7cOFCcXGxEEJNL1WqVLm1k0uXLgkh5s2bZyghMTFRCHHhwgUXbYmj2TUUe/Lk\nyaioqBdffHH8+PEWi6VLly4HDhwQQrz11ls7duxo2bKlk4sEAAD4XyVTnRDib3/72y+//PLq\nq68++uijQUFBiqL06tVLfUuj0Qgh1JB3W//4xz+ee+65Uo316tVzdMkuYlewmzhxYtWqVR9/\n/HEhxMqVKw8cOLBgwYKePXsOHTp0+vTpX3/9tZOLBAAAuL2UlJRdu3Y999xz06ZNU1ssFsv1\n69fr1KkjhKhRo4YQIjU1teQi586dMxqNNWvWFEIUFRVFRka6vGpnsWso9ueffx4/fnzdunWF\nEGvWrGnatOn//M//1K1bd+TIkfv27XNyhQAAAH/JbDYLIapXr25r+eijj0wmU1FRkRDC39+/\nWbNmGzduzMnJUd89efJk7dq1FyxYUKFChbZt265bty4zM9O27Oeff/76669bLBbXboTD2BXs\nMjMzQ0NDhRBFRUU7d+7s3bu32l65cuUrV644sToAAIAy1atXr0aNGosWLdqwYcMvv/wybty4\nNWvWdO3a9fjx4zt27Lh58+Y777yTkZERHR29YsWKxYsXx8XFValSJSEhQQjx3nvv5eXlRUVF\nff75599///0bb7wxfPjwixcv2u60eODYFeyqVq2q3iexffv2GzduPProo2p7ampqxYoVnVgd\nAABAmfR6/Zo1a2rWrPnkk08OGDAgNzd3/fr1L7/8sre394ABAy5evBgbG/vvf/9bUZThw4e/\n/vrrTZo0+fnnn0NCQoQQUVFR27dvr1q16qhRo/r06fPVV19NmzZt8eLF7t6me6dYrdY7zjR8\n+PBNmzY988wzK1as0Ol0p06d0mq1V69ejY6ObtCgwQN3jV16erqTerZMeSCfeeNAuikmd5fg\nIkaj0Wg0ZmVlqUMAcCqdThcUFGQymXJzc91di0cIDg7WaDQZGRnuLsQj+Pr6GrFkJ9cAACAA\nSURBVAwGWQ8mer0+MDDQ3VV4FrvONL711lvHjx9/9913K1Wq9O9//1ur1QohRo8efe7cueXL\nlzu5QgAAANjFrmAXGhq6Z8+e7Oxsg8Fgu8F43Lhx8+bNq1q1qjPLAwAAgL3susauTZs2v//+\ne0BAQMnHxrRp0+bnn38ODw93Wm0AAAC4C3YFu4MHD968ebNUo8ViOX78+OnTp51QFQAAAO7a\nHYZiFUVRXzz88MO3naFVq1YOrggAAAD35A7B7vDhwz/++OOYMWPi4uIqVapU8i1FUcLCwm79\nFQ4AAAC4xR2CXURERERExObNm2fMmFG/fn3X1AQAAIB7YNddsd9++62z6wAAAMB9suvmiatX\nrz7zzDPVqlXTarXKLZxdIgAAAOxh1xm7UaNGrV27NioqKjo6+sH99TQAAAC52ZXStm/fvnr1\n6ri4OGdXAwAAgHtmV7DLz8/v0KGDs0sBAAAom5N+8L3Uoz8eXHYFu9atWx8/frxr165OLgYA\nAOAO/GdMdWyHOUmTHNuhG9l188ScOXNeffXVPXv2OLsaAAAA3DO7ztiNGTPm8uXLHTp0MBqN\nlStXLvXu2bNnHV8XAAAA7pJdwU6j0TRo0KBBgwbOrgYAAAD3zK5gt2vXLmfXAQAAgPtk1zV2\nKpPJtH///rVr16o3pFgsFqdVBQAAgLtmb7CbNWtWlSpV2rZt279//5SUFCHE5MmTn332WeId\nAABAOWFXsFu8ePG4ceMeeeSRhQsX2hobNmz4xRdfzJkzx2m1AQAAPEgsFouiKFu3bnVXAXYF\nu/nz5ycmJq5fv37YsGG2xqFDhyYlJS1ZssRptQEAADxItFrtjh07Wrdu7a4C7Ap2f/zxx4AB\nA25t79q163/+8x9HlwQAAPBAUhSla9euwcHB7irArmAXEBBgMplubc/KyjIYDI4uCQAAoBw5\nduxYz549K1SoEBQU1KtXL/VmAyHEZ5991rhxY4PBEBISMmLECJPJVHIo9rZLFRcXK4qyYsWK\nXr16hYeH16pVa9myZQ4s1a5g17x585kzZ+bn55dsvH79+tSpUyMjIx1YDQAAQHnz2GOPhYaG\npqamnj9/3t/fX70y7cyZM//4xz/mz5+fm5u7e/fuPXv2lLrx4LZLaTQarVY7a9as5cuXnzhx\nYtKkSSNGjLh586ajSrXrOXYTJ07s0aNH8+bNY2NjhRCLFy9euHDh2rVr8/PzS95OAQAAIJ89\ne/Z4e3sbjUYhxODBgwcNGmS1WjMzM61Wa4UKFbRa7UMPPXTgwAGtVlvyaSG3XUpRFCHEkCFD\nqlSpIoTo3r17Xl7e2bNnmzRp4pBS7Tpj17Vr1++++87f33/evHlCiKVLly5btqxRo0Y//PBD\nx44dHVIHAABA+ZScnNynT5+QkJCQkJD4+Hiz2VxUVNSyZcuEhIS2bdt27NhxypQpZ86csWcp\n9a2aNWuqL3x8fIQQpQZF74e9z7Hr3r37oUOHrly5kpycnJycfP369X379kVFRTmqDgAAgHIo\nJSWld+/e0dHRZ8+eTUtL++yzz9R2RVEWLlz4559/PvXUU7/++mt4ePiqVavuuJRtWSdVexe/\nPCGEqFKlSosWLVq0aOHG2z0AAABc5sCBAxaLZdy4cerZtb1796rtFovl2rVrtWvXHjFixObN\nmxMSEhYsWHDHpZytrGDXqFGjd955R31RBtcUCgAA4Hq1a9cuKirau3dvQUHBihUrdu/eLYS4\ndOnS559/3qpVq4MHDxYXF6elpR0/frx+/fp3XMrZ1ZZ180RQUJD6NJOgoCBn1wEAAFAORUZG\nJiUlxcXFKYrSr1+/devWRUdHR0REHDx4cPjw4f369bty5UrFihUfffTRmTNn3nGp5ORkp1ar\nWK1Wp66gHEpPT3dSz5YpPk7q+UGhm3Kb5x1KyWg0Go3GrKwss9ns7lrkp9PpgoKCTCZTbm6u\nu2vxCMHBwRqNJiMjw92FeARfX1+DwSDrwUSv1wcGBjq2z/T0dP8ZUx3bZ07SpEqVKjm2T3ex\n6xq7Nm3a/P7777e2f/PNN+Hh4Y4uCQAAAPfCrmB38ODBWx+dZ7FYjh8/fvr0aSdUBQAAgLt2\nhwcU227Hffjhh287Q6tWrRxcEQAAAO7JHYLd4cOHf/zxxzFjxsTFxZUaflYUJSws7LnnnnNm\neQAAALDXHYJdRERERETE5s2bZ8yYUfImXlVubu7ly5edVhsAAADugl3X2H377be3pjohxL59\n+yIjIx1dEgAAAO7FHc7Y2WzatGnFihXnz58vLi5WW4qKio4fP+7t7e202gAAAHAX7Ap2K1eu\nfPLJJ3U6XUhIyIULF8LCwq5fv24ymR555JFx48Y5u0QAAACbnKRJ7i6h/LJrKHbmzJmPPvro\n9evXU1NTtVrtd999l5OT8/7771ut1s6dOzu7RAAAANjDrmD3xx9/jBo1yt/fX520Wq06ne6F\nF15o0aLFhAkTnFkeAAAA7GXXUKzZbNZqteprX1/fzMxM9fWAAQMGDhw4f/58Z1UHAADwf337\nfWXHdvhoz2uO7dCN7Dpj17hx408++aSwsFAIUaNGje+++05tv379elZWlhOrAwAAgN3sOmP3\n0ksvDRky5MaNG1u3bu3fv//06dOvXr1avXr1RYsWRUREOLtEAAAA2MOuYPf000/rdLqzZ88K\nIcaPH793797FixcLIWrUqDFv3jyn1gcAAAA72fscu0GDBqkvjEbj999/n5KSYjab69Wrp9fr\nnVYbAAAA7sKdg53JZDpy5IjZbG7atGlgYKDaWK9ePScXBgAAgLtzh5sn5s2bV6VKlXbt2nXq\n1Kly5cojR44sKChwTWUAAAC4K2UFuzVr1owdO7ZixYovvfTS66+/3q5duwULFvBTEwAAwHNY\nLBZFUb799ls72++tN0cpayh27ty5tWvXPnr0qJ+fn9oSHx//8ccfT5s2LSAgwEkFAQAA4N6U\ndcYuOTl5yJAhtlQnhEhMTDSbzUePHnV+YQAAALg7ZQW73Nzc6tWrl2xRJ3Nzc51bFAAAQHly\n7ty5zp07GwyGxo0br1+/vtS7x44d69mzZ4UKFYKCgnr16pWSkqK2X7hwoV+/fn5+fiEhISNG\njMjLyyu5lNlsjo6O7t27t8VicVSdd7h5QqP5PzMoiiKEsFqtjlo9AABA+Td79ux33333ypUr\nAwcOfPzxx8+dO1fy3cceeyw0NDQ1NfX8+fP+/v7Dhg1T2/v376/X6//888+ffvpp165dr7zy\nSsmlhg8ffvPmzdWrV+t09j5+7o4c1hEAAICshgwZ0rFjRyHEhAkTZsyYsWXLluHDh9ve3bNn\nj7e3t9FoFEIMHjx40KBBVqv1t99+279//4oVK0JDQ4UQy5cvv3Tpkm2RN95448CBAz/99JO6\nlKPcIdidOXNm7969tsnr168LIU6ePBkUFGRrjIyMdGBBAAAA5U2jRo3UF97e3mFhYampqSXf\nTU5Ofvvtt0+cOCGEKCgoMJvNRUVFKSkpiqLUqVNHnadly5YtW7ZUR12XLl369ddf79y5s0KF\nCo6t8w7B7p133nnnnXdKNb744oslJxmZBQAAcvPx8bG91mg03t7etsmUlJTevXtPnjx58+bN\nPj4+69ev79u3ryjzArb9+/f37Nlz3Lhxu3fvduyPeJUV7CZPnuzANQEAADygTp061adPHyFE\nYWHhpUuXatSoYXvrwIEDFotl3LhxakSzDXXWq1fParX+/vvvTZs2FUL8+uuvv/76a2JiohBi\n/vz5HTp0iIiIeO2112bMmOHAOssKdlOmTHHgmgAAAB5QS5cu7dmzZ8OGDWfNmmWxWP7+97/b\n3qpdu3ZRUdHevXvbtm27Zs2a3bt3CyEuXboUERHRrl27l19+eeHChWazOSEhoX379uoiWq02\nODj4iy++6N69e3R0dM+ePR1V5x3uigUAAPBkZrNZCPHqq68mJCQEBQUtX758zZo1FStWtM0Q\nGRmZlJQUFxcXFha2bdu2devWtW7dOiIi4uzZs//+978NBkPTpk07derUtm3bUifnunTp8uqr\nrw4dOvTq1auOqlbxwCvk0tPTndSzZYrPnWeSmm6Kyd0luIjRaDQajVlZWeoXHk6l0+mCgoJM\nJhMP0XSN4OBgjUaTkZHh7kI8gq+vr8FgkPVgotfrAwMDHdtnenr6t99Xdmyfj/a8VqlSJcf2\n6S6csQMAAJAEwQ4AAEASZQW7CxcuqL99cfbs2cLCQleVBAAAgHtRVrCrX7/+9u3bhRB16tQ5\ncuSIq0oCAADAvSjrcSeKonz11VfqZY+//fabyXT76+I7derklNIAAABwN8oKdv369Vu+fPny\n5cuFECV/EK0UD7yvFgAAoBwqK9gtW7Zs8ODB6enpzzzzzOTJk2vXru2qqgAAAHDXygp2Op0u\nNjZWCLF8+fLBgwc3aNDAVVUBAADc3qM9r7m7hPKrrGBns3XrViFERkbG3r17L126pNFoqlev\n3qFDB39/fyeXBwAAAHvZFeyKi4tfeeWV999/v+RzsX19fSdPnpyUlOS02gAAAEqrfOx9x3Z4\nrelox3boRnYFu1mzZs2aNatfv359+vQJDQ0tLi6+ePHimjVrXnnllapVqw4dOtTZVQIAAOCO\n7Ap2n3766UsvvTRr1qySjc8//3xCQsK8efMIdgAAAOWBXT8pdubMGfUuilLi4uJ+//13R5cE\nAACAe2FXsNPpdOpvi5ViNpu1Wq2jSwIAAMC9sCvYtWzZcvbs2aV+LtZkMi1YsKBNmzbOKQwA\nAAB3x65r7CZMmNCnT5/69ev37t27WrVqVqs1NTV106ZNaWlp3333nbNLBAAAgD3sCna9e/de\ns2bNhAkTFi5caGts1qzZ4sWLe/To4bTaAAAAcBfsCnZCiL59+/bt2/fSpUsXL15UFKVGjRpV\nq1Z1amUAAAAPNIvFotfrf/jhB5edCLM32KnCwsLCwsKcVAoAAIAEtm/fHhAQ0KZNG61Wu2PH\njoiICJet2q6bJwAAAGCn2bNnHzhwQAihKErXrl2Dg4NdtmqCHQAAwF8qLi5WFGXFihW9evUK\nDw+vVavWsmXL1LeOHTvWs2fPChUqBAUF9erVKyUlRQjRrVu3zZs3jx07tnXr1haLRVGUrVu3\nRkZGjhw50tbnzp07tVrtxYsX09LSBg0aFBYW5uvrGxUVdejQofuslmAHAADwlzQajVarnTVr\n1vLly0+cODFp0qQRI0bcvHlTCPHYY4+FhoampqaeP3/e399/2LBhQojt27fXrFlz7ty5Bw8e\ntHUyePDgtWvXFhcXq5NfffXVI488Uq1atb59+wohjh49mp6e3rlz55iYmPz8/Pup9u6usZOD\nj4+Pk3rOdVK/Dw7n7dvyRqfTCSG8vLx4RrcLaDQaIYRWq/WcD5h7KYoiPOnr7F7qMUTWg4n6\n5ZXDkCFDqlSpIoTo3r17Xl7e2bNnmzRpsmfPHm9vb6PRKIQYPHjwoEGDrFar+g0qZeDAgS+9\n9NIvv/zSuXPnoqKib7755p///OehQ4f27du3du3aihUrCiGmTp364YcfbtiwYeDAgfdcp13B\nrkOHDq+//nrv3r3veTXlym33uINYndbzg8GZ+7Z8UbdUURTP2WQ3Ym+7BXvbNfh4Pyhq1qyp\nvlD/5lHPqyUnJ7/99tsnTpwQQhQUFJjN5qKiIvUv/1KqVq3arVu31atXd+7ceefOnTk5OQMG\nDNi0aZMQotRtqWfOnLmfOu0KdqmpqSdPnpQm2N3nSc4yefofuM7ct+WLoih6vV79Gru7Fvnp\ndDqDwWCxWDznA+ZePj4+iqKwt11Do9FIfDDR6/XuLsFhbk3eKSkpvXv3njx58ubNm318fNav\nX6+Oq/6VwYMHv/HGG3Pnzl21alVcXJy/v7/BYBBC5OfnO/AEuV3nSD/88MMlS5asW7dOyo8d\nAADA3Tpw4IDFYhk3bpway/bu3Vv2/P37909PT9+zZ8+aNWuGDBkihKhfv74Q4vDhw7Z57vN0\nnbAz2M2cOVOn0/Xr18/Pz69atWq1/6/7rAAAAOCBU7t27aKior179xYUFKxYsWL37t1CiEuX\nLgkhjEZjSkpKZmZmyfkDAgJiY2MnTZqk0Wh69uwphAgPD+/WrdvLL798/vx5s9n80UcfNWvW\nTO3hntk1FFtcXFy5cuXu3bvfz5oAAACkERkZmZSUFBcXpyhKv3791q1bFx0dHRERkZycnJCQ\nMGHChFWrVv3nP/8puchTTz3Vv3//F154wXYd3r/+9a8xY8Y0b968uLi4WbNmW7Zsuc9fglCs\nVo+73j89Pd1JPVumePo1dropJneX4CJGo9FoNGZlZXF9ggvodLqgoCCTyZSby63nrhAcHKzR\naDIyMtxdiEfw9fU1GAyyHkz0en1gYKBj+0xPT6987H3H9nmt6ehKlSo5tk93uYv7kE0m0/79\n+9euXasGI4vF4rSqAAAAcNfsDXazZs2qUqVK27Zt+/fvrz5YefLkyc8++yzxDgAAoJywK9gt\nXrx43LhxjzzyyMKFC22NDRs2/OKLL+bMmeO02gAAAHAX7Ap28+fPT0xMXL9+vfpbGaqhQ4cm\nJSUtWbLEabUBAADgLtgV7P74448BAwbc2t61a9dSt3sAAADAXewKdgEBASbTbe52zMrKUh+a\nDAAAALezK9g1b9585syZpX5e5vr161OnTo2MjHROYQAAALg7dj2geOLEiT169GjevHlsbKwQ\nYvHixQsXLly7dm1+fn7J2ykAAACc7VrT0e4uofyy64xd165dv/vuO39//3nz5gkhli5dumzZ\nskaNGv3www8dO3Z0coUAAACwi11n7IQQ3bt3P3To0NWrV9WfMKtVq1ZwcLAzCwMAALiNyr/4\nOrbDax1vOrZDN7I32Akhzp8/f/DgwWvXrmk0mtTU1IcffjgkJMR5lQEAAOCu2BXsbty4MWTI\nkE2bNpVs1Gg0gwYNWrRoka+vg4MzAAAA7oFdwW706NGbNm0aMGBAnz591LN0aWlp33333YoV\nK/z8/D7++GMnFwkAAIA7syvYbdy4ccyYMXPnzi3Z+Mwzz9SrV++jjz4i2AEAAJQHdt0VW1BQ\n8Mgjj9zaHhUVVerhdgAAAHAXu4Jd69at//jjj1vbU1JSWrVq5eiSAAAAcC/sGoqdN2/e448/\nXrdu3b/97W96vV4IUVxcvG3btjlz5nz55ZdOrhAAAAB2KSvYNWrUSH2hKEphYeGAAQO8vb3D\nwsI0Gk1aWtrNmzerV6/+wgsv7N692yWlAgAAlFM6nW716tV9+/Z1cxllvFepUiXb64oVK9aq\nVcs2qd4bW1xcXFBQ4LziAAAAyq3t27cHBAS0adPG3YX8r7KC3c8//+yyOgAAAB4ss2fP7tOn\nT7kKdnbdPGGTk5OTeQsnVQYAAFAeXLly5cknnwwLCzMajR07dvzll1+EEN26ddu8efPYsWNb\nt26tzpaRkdGrVy8fH5+QkJDly5erjWlpaYMGDQoLC/P19Y2Kijp06JAQoqioSFGUJUuW1KlT\n59lnn3VgqXYFuzNnzvTp08fPzy8gICD4Fg6sBgAAoLyJi4u7cePG4cOH09PTIyMje/funZ6e\nvn379po1a86dO/fgwYPqbO+///6kSZOuXbsWHx+fmJiYm5srhFCvujt69Gh6enrnzp1jYmLy\n8/O1Wq1Wq/3444+/+eab999/34Gl2nVXbHx8fHJyct++fUNDQ7VarQNXDwAAUJ4lJyfv27fv\nxIkTVapUEUK8/fbbH3/88ZYtW4YMGVJqzsGDB3fs2FEIER8fP3369LNnzxYWFu7bt2/t2rUV\nK1YUQkydOvXDDz/csGHDwIEDhRB9+/Z1+GPj7Ap2+/fv//777zt06ODYdQMAAJRzp0+f1mg0\ntkeFGAyGWrVqnT179tY569evr74wGo1CCJPJlJKSIoQICwsrOduZM2fUF/Xq1XN4tXYFO19f\n39q1azt83QAAAA+c4uLiwsLCW9s1mtJXuBkMBiFEfn6+j4/PrfN7e3s7vDa7rrEbMmTI0qVL\nHb5uAACAcq5+/frFxcUnTpxQJ2/evHnu3Dnbybk7LiuEOHz4sK3FdrrOSew6Yzd9+vTY2Nhv\nv/22ffv26iBxSePHj3dCYQAAAO4XERHRoUOHpKSk5cuXe3t7v/rqq/7+/uotEUajMSUlJTMz\nMygo6LbLhoeHd+vW7eWXX16xYkVoaOiSJUvGjRv3559/lhqcdSC7gt3s2bO3bt0qhFDv7y2F\nYAcAACS2YsWK0aNHh4eHFxcXt23b9qeffgoICBBCJCQkTJgwYdWqVampqX+17L/+9a8xY8Y0\nb968uLi4WbNmW7ZscV6qE0IoVqv1jjOFhYV16NDhxRdfDAkJufWu2Afu8rv09HQn9WyZcpsR\ndI+im2JydwkuYjQajUZjVlaW2Wx2dy3y0+l0QUFBJpNJfXYAnC04OFij0WRkZLi7EI/g6+tr\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AAhCDsAAAAhCDsAAAAhHLQeAPjP7d7j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\nACAEYQcAACAEYQcAACDEfwEwstPXkdTbiQAAAABJRU5ErkJggg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"code","source":["# 5. Boxplot of Age by Gender\n","ggplot(df, aes(x = gender, y = age, fill = gender)) +\n","  geom_boxplot() +\n","  scale_fill_manual(values = c(\"skyblue\", \"lightpink\")) +\n","  labs(title = \"Age Distribution by Gender\", x = \"Gender\", y = \"Age\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"YufT5pypHB8y","executionInfo":{"status":"ok","timestamp":1731293100742,"user_tz":300,"elapsed":877,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"08073b88-4829-47ca-97cb-b153db66bd10"},"execution_count":85,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nfOihh8aMGRP/VLa4o48++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Fixe/9dZb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\n5ZRT2rZtm7zhAABouM0Iu4QePXpMnjx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6. Correlation heatmap between numerical features (age, weight, height, BMI)\n","correlation_matrix <- cor(select(df, age, weight, height, bmi), use = \"complete.obs\") #compute the correlation matrix\n","melted_correlation <- as.data.frame(as.table(correlation_matrix)) #convert the correlation matrix to a data frame suitable for ggplot\n","melted_correlation$Var1 <- factor(melted_correlation$Var1, levels = c(\"age\", \"weight\", \"height\", \"bmi\")) #set the desired order for the y-axis and x-axis\n","melted_correlation$Var2 <- factor(melted_correlation$Var2, levels = c(\"bmi\", \"height\", \"weight\", \"age\")) #same as above\n","ggplot(melted_correlation, aes(x = Var1, y = Var2, fill = Freq)) +\n","  geom_tile() +\n","  geom_text(aes(label = round(Freq, 2)), color = \"white\") +\n","  scale_fill_gradient2(low = \"skyblue\", high = \"blue\", mid = \"white\", midpoint = 0) +\n","  labs(title = \"Correlation Heatmap between Age, Weight, Height, and BMI\") +\n","  theme(axis.text.y = element_text(angle = 0, hjust = 1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"pIFieITDHDaq","executionInfo":{"status":"ok","timestamp":1731293724958,"user_tz":300,"elapsed":453,"user":{"displayName":"Yuqi Su","userId":"03184251790933787012"}},"outputId":"45add86c-8d9e-4ee2-f817-dae3b7d50b45"},"execution_count":104,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nBuPqgau7yo9WZmamvYLB4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S5V1AqUaw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\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X369PHx8YmNjXVOy65ZsyYuLk5SQEDA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\nBDsAAABDEOwAAAAMQbADAAAwxP8HofEt7qVhzJ8AAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]}]}