Investigating EHR data from PIC-SURE - R.ipynb

{
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   "cell_type": "markdown",
   "id": "aad58f25",
   "metadata": {},
   "source": [
    "# Investigating EHR data from PIC-SURE\n",
    "\n",
    "The purpose of this notebook is to help researchers get started with EHR analysis in python using clinical data exported from PIC-SURE. \n",
    "\n",
    "### About the data\n",
    "This example code uses a synthetically generated dataset from Synthea, an open-source patient population simulation. \n",
    "The dataset has been formatted to mirror what a data export from PIC-SURE may look like.  \n",
    "It includes all age groups and is not limited to pediatric patients. \n",
    "\n",
    "\n",
    "\n",
    "Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079\n",
    "\n",
    "###  Installing packages\n",
    "The following code installs and loads packages needed for basic data management and manipulation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7af50932",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Updating HTML index of packages in '.Library'\n",
      "\n",
      "Making 'packages.html' ...\n",
      " done\n",
      "\n",
      "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.4     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.5\n",
      "\u001b[32m✔\u001b[39m \u001b[34mforcats  \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34mstringr  \u001b[39m 1.5.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2  \u001b[39m 3.5.1     \u001b[32m✔\u001b[39m \u001b[34mtibble   \u001b[39m 3.2.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3     \u001b[32m✔\u001b[39m \u001b[34mtidyr    \u001b[39m 1.3.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mpurrr    \u001b[39m 1.0.2     \n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n"
     ]
    }
   ],
   "source": [
    "options(repos = c(CRAN = \"https://cloud.r-project.org/\"))\n",
    "\n",
    "#install.packages('tidyverse')\n",
    "install.packages('lubridate')\n",
    "\n",
    "library(tidyverse)\n",
    "library(lubridate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "070e734f",
   "metadata": {},
   "source": [
    "## Loading the data\n",
    "The following code downloads the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46169833",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mOne or more parsing issues, call `problems()` on your data frame for details,\n",
      "e.g.:\n",
      "  dat <- vroom(...)\n",
      "  problems(dat)”\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m3737903\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m5\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  (3): PATIENT_NUM, CONCEPT_PATH, TVAL_CHAR\n",
      "\u001b[32mdbl\u001b[39m  (1): NVAL_NUM\n",
      "\u001b[34mdttm\u001b[39m (1): TIMESTAMP\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"
     ]
    }
   ],
   "source": [
    "# The data is stored on github:\n",
    "url <- \"https://github.com/hms-dbmi/pic-sure-public-datasets/raw/main/synthea_10k_picsure_format.csv.zip\"\n",
    "\n",
    "# Temporary file to store the ZIP\n",
    "temp_zip <- tempfile(fileext = \".zip\")\n",
    "GET(url, write_disk(temp_zip, overwrite = TRUE))\n",
    "files <- unzip(temp_zip, list = TRUE)\n",
    "\n",
    "# Extract and read the CSV file\n",
    "csv_file <- files$Name[1]\n",
    "data <- read_csv(unz(temp_zip, csv_file))\n",
    "\n",
    "# Clean up temporary files\n",
    "unlink(temp_zip)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b5758b",
   "metadata": {},
   "source": [
    "## Taking a first look at the dataset\n",
    "Let's use the `head()` function to take a first look at the dataset. \n",
    "You'll see 5 columns:\n",
    "* PATIENT_NUM: Represents a unique patient identifier\n",
    "* CONCEPT_PATH: Represents the clinical variable for a given observation (row)\n",
    "* TVAL_CHAR: The text / character value of the clinical variable, if applicable\n",
    "* NVAL_NUM: The numerical value of the clinical variable, if applicable\n",
    "* TIMESTAMP: The date and time associated to a given observation (row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b0df5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PATIENT_NUM</th><th scope=col>CONCEPT_PATH</th><th scope=col>TVAL_CHAR</th><th scope=col>NVAL_NUM</th><th scope=col>TIMESTAMP</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&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;dttm&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>55f11536-c06f-87bc-971e-7a39b232ecd3</td><td>\\ACT Demographics\\Sex\\     </td><td>Female</td><td>NA</td><td>2024-07-01 16:25:50</td></tr>\n",
       "\t<tr><td>55f11536-c06f-87bc-971e-7a39b232ecd3</td><td>\\ACT Demographics\\Race\\    </td><td>White </td><td>NA</td><td>2024-07-01 16:25:50</td></tr>\n",
       "\t<tr><td>55f11536-c06f-87bc-971e-7a39b232ecd3</td><td>\\ACT Demographics\\Hispanic\\</td><td>No    </td><td>NA</td><td>2024-07-01 16:25:50</td></tr>\n",
       "\t<tr><td>6225749a-9311-ab4e-d2a3-d9b1f919866d</td><td>\\ACT Demographics\\Sex\\     </td><td>Male  </td><td>NA</td><td>2024-07-01 16:25:50</td></tr>\n",
       "\t<tr><td>6225749a-9311-ab4e-d2a3-d9b1f919866d</td><td>\\ACT Demographics\\Race\\    </td><td>White </td><td>NA</td><td>2024-07-01 16:25:50</td></tr>\n",
       "\t<tr><td>6225749a-9311-ab4e-d2a3-d9b1f919866d</td><td>\\ACT Demographics\\Hispanic\\</td><td>No    </td><td>NA</td><td>2024-07-01 16:25:50</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " PATIENT\\_NUM & CONCEPT\\_PATH & TVAL\\_CHAR & NVAL\\_NUM & TIMESTAMP\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dttm>\\\\\n",
       "\\hline\n",
       "\t 55f11536-c06f-87bc-971e-7a39b232ecd3 & \\textbackslash{}ACT Demographics\\textbackslash{}Sex\\textbackslash{}      & Female & NA & 2024-07-01 16:25:50\\\\\n",
       "\t 55f11536-c06f-87bc-971e-7a39b232ecd3 & \\textbackslash{}ACT Demographics\\textbackslash{}Race\\textbackslash{}     & White  & NA & 2024-07-01 16:25:50\\\\\n",
       "\t 55f11536-c06f-87bc-971e-7a39b232ecd3 & \\textbackslash{}ACT Demographics\\textbackslash{}Hispanic\\textbackslash{} & No     & NA & 2024-07-01 16:25:50\\\\\n",
       "\t 6225749a-9311-ab4e-d2a3-d9b1f919866d & \\textbackslash{}ACT Demographics\\textbackslash{}Sex\\textbackslash{}      & Male   & NA & 2024-07-01 16:25:50\\\\\n",
       "\t 6225749a-9311-ab4e-d2a3-d9b1f919866d & \\textbackslash{}ACT Demographics\\textbackslash{}Race\\textbackslash{}     & White  & NA & 2024-07-01 16:25:50\\\\\n",
       "\t 6225749a-9311-ab4e-d2a3-d9b1f919866d & \\textbackslash{}ACT Demographics\\textbackslash{}Hispanic\\textbackslash{} & No     & NA & 2024-07-01 16:25:50\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 5\n",
       "\n",
       "| PATIENT_NUM &lt;chr&gt; | CONCEPT_PATH &lt;chr&gt; | TVAL_CHAR &lt;chr&gt; | NVAL_NUM &lt;dbl&gt; | TIMESTAMP &lt;dttm&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 55f11536-c06f-87bc-971e-7a39b232ecd3 | \\ACT Demographics\\Sex\\      | Female | NA | 2024-07-01 16:25:50 |\n",
       "| 55f11536-c06f-87bc-971e-7a39b232ecd3 | \\ACT Demographics\\Race\\     | White  | NA | 2024-07-01 16:25:50 |\n",
       "| 55f11536-c06f-87bc-971e-7a39b232ecd3 | \\ACT Demographics\\Hispanic\\ | No     | NA | 2024-07-01 16:25:50 |\n",
       "| 6225749a-9311-ab4e-d2a3-d9b1f919866d | \\ACT Demographics\\Sex\\      | Male   | NA | 2024-07-01 16:25:50 |\n",
       "| 6225749a-9311-ab4e-d2a3-d9b1f919866d | \\ACT Demographics\\Race\\     | White  | NA | 2024-07-01 16:25:50 |\n",
       "| 6225749a-9311-ab4e-d2a3-d9b1f919866d | \\ACT Demographics\\Hispanic\\ | No     | NA | 2024-07-01 16:25:50 |\n",
       "\n"
      ],
      "text/plain": [
       "  PATIENT_NUM                          CONCEPT_PATH                   TVAL_CHAR\n",
       "1 55f11536-c06f-87bc-971e-7a39b232ecd3 \\\\ACT Demographics\\\\Sex\\\\      Female   \n",
       "2 55f11536-c06f-87bc-971e-7a39b232ecd3 \\\\ACT Demographics\\\\Race\\\\     White    \n",
       "3 55f11536-c06f-87bc-971e-7a39b232ecd3 \\\\ACT Demographics\\\\Hispanic\\\\ No       \n",
       "4 6225749a-9311-ab4e-d2a3-d9b1f919866d \\\\ACT Demographics\\\\Sex\\\\      Male     \n",
       "5 6225749a-9311-ab4e-d2a3-d9b1f919866d \\\\ACT Demographics\\\\Race\\\\     White    \n",
       "6 6225749a-9311-ab4e-d2a3-d9b1f919866d \\\\ACT Demographics\\\\Hispanic\\\\ No       \n",
       "  NVAL_NUM TIMESTAMP          \n",
       "1 NA       2024-07-01 16:25:50\n",
       "2 NA       2024-07-01 16:25:50\n",
       "3 NA       2024-07-01 16:25:50\n",
       "4 NA       2024-07-01 16:25:50\n",
       "5 NA       2024-07-01 16:25:50\n",
       "6 NA       2024-07-01 16:25:50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631fb73f",
   "metadata": {},
   "source": [
    "The EHR data represented in this dataset may be very large, making it difficult to explore manually. \n",
    "\n",
    "You'll notice that the `CONCEPT_PATH` column consists of a hierarchy that narrows down to a certain clinical variable. \n",
    "\n",
    "Let's take a look at the highest level of the `CONCEPT_PATH` to see what kind of data we have. We can consider this to be the `VARIABLE_TYPE`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "158b7675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    ACT Demographics ACT Diagnosis ICD-10 ACT Lab Test Results \n",
       "               48222                96705              2788447 \n",
       "     ACT Medications   ACT Procedures CPT \n",
       "              748721                55808 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Isolate the variable type using regular expressions, and create a new column for variable type\n",
    "data$VARIABLE_TYPE = str_extract(data$CONCEPT_PATH, \"(?<=\\\\\\\\)[^\\\\\\\\]+(?=\\\\\\\\)\")\n",
    "# Count how many observations are associated with each variable type\n",
    "table(data$VARIABLE_TYPE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d830848f",
   "metadata": {},
   "source": [
    "As seen above, this dataset contains the following data types: \n",
    "- ACT Lab Test Results (Labs)\n",
    "- ACT Medications\n",
    "- ACT Diagnosis ICD-10 (Diagnoses)\n",
    "- ACT Procedures CPT (Procedures)\n",
    "- ACT Demographics\n",
    "\n",
    "\n",
    "#### Working with patient demographics from the dataset\n",
    "Here we will explore, organize and visualize the demographic data available in our dataset. This includes age, sex and race.\n",
    "\n",
    "This dataset should contain about 10,000 synthetic patient records. Let's verify this by checking the count of unique patient IDs present in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ed75f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "11644"
      ],
      "text/latex": [
       "11644"
      ],
      "text/markdown": [
       "11644"
      ],
      "text/plain": [
       "[1] 11644"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_distinct(data$PATIENT_NUM)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d34716e",
   "metadata": {},
   "source": [
    "Let's also check what date ranges are represented in our dataset. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce061bde",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mRemoved 5628 rows containing non-finite outside the scale range (`stat_bin()`).”\n"
     ]
    },
    {
     "data": {
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y6dTk+bNu2YY47ZZDfmtddeG4vFBg8enEwmFy5cuPvuu3/ta1/LNk2cOPHll1++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\n97yD3aeE7QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracting date from timestamp column\n",
    "data <- data %>%\n",
    "  mutate(TIMESTAMP = ymd_hms(TIMESTAMP, quiet = TRUE), # Convert to datetime\n",
    "         DATE = as.Date(TIMESTAMP)) # Extract date\n",
    "\n",
    "# Create a histogram of dates\n",
    "ggplot(data, aes(x = DATE)) +\n",
    "  geom_histogram(bins = 30, fill = \"skyblue\", color = \"black\") +\n",
    "  labs(x = \"Date\", y = \"Frequency\", title = \"Histogram of Dates\") +\n",
    "  theme_minimal() +\n",
    "  theme(axis.text.x = element_text(angle = 45, hjust = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d8942ca",
   "metadata": {},
   "source": [
    "The majority of our dataset is from the last 15 years, as expected. \n",
    "\n",
    "We also see data stretching far back to 1920; perhaps this is associated with a patient's age. Depending on your research question, you may want to investigate those outliers. \n",
    "\n",
    "## Exploring Demographic Data\n",
    "Here we will explore, organize and visualize the demographic data available in our dataset. This includes age, sex and race.\n",
    "\n",
    "Let's create a new dataframe called `demo_df` that will only include the demographics information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ae2d11b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new dataframe including only the demographics data:\n",
    "\n",
    "demo_df <- data %>%\n",
    "  filter(VARIABLE_TYPE == 'ACT Demographics')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14312ca7",
   "metadata": {},
   "source": [
    "Let's first take a look at the **sex** breakdown. \n",
    "\n",
    "We can find the Sex related observations by searching for this value in the CONCEPT_PATH column. Then, we take the sex related values (found in the TVAL_CHAR column) and transform them into a new column, \"sex\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8d7f67b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PATIENT_NUM</th><th scope=col>sex</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>55f11536-c06f-87bc-971e-7a39b232ecd3</td><td>Female</td></tr>\n",
       "\t<tr><td>6225749a-9311-ab4e-d2a3-d9b1f919866d</td><td>Male  </td></tr>\n",
       "\t<tr><td>fe16b3d3-424a-86ce-00df-cd1386d7279f</td><td>Male  </td></tr>\n",
       "\t<tr><td>2840480c-a43b-f0eb-916d-66aca598a621</td><td>Female</td></tr>\n",
       "\t<tr><td>6d316c1c-7ce7-7120-7f88-b9f0531ddb6f</td><td>Female</td></tr>\n",
       "\t<tr><td>cd3fca00-6a53-51dc-4b02-968e533952aa</td><td>Male  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 2\n",
       "\\begin{tabular}{ll}\n",
       " PATIENT\\_NUM & sex\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 55f11536-c06f-87bc-971e-7a39b232ecd3 & Female\\\\\n",
       "\t 6225749a-9311-ab4e-d2a3-d9b1f919866d & Male  \\\\\n",
       "\t fe16b3d3-424a-86ce-00df-cd1386d7279f & Male  \\\\\n",
       "\t 2840480c-a43b-f0eb-916d-66aca598a621 & Female\\\\\n",
       "\t 6d316c1c-7ce7-7120-7f88-b9f0531ddb6f & Female\\\\\n",
       "\t cd3fca00-6a53-51dc-4b02-968e533952aa & Male  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 2\n",
       "\n",
       "| PATIENT_NUM &lt;chr&gt; | sex &lt;chr&gt; |\n",
       "|---|---|\n",
       "| 55f11536-c06f-87bc-971e-7a39b232ecd3 | Female |\n",
       "| 6225749a-9311-ab4e-d2a3-d9b1f919866d | Male   |\n",
       "| fe16b3d3-424a-86ce-00df-cd1386d7279f | Male   |\n",
       "| 2840480c-a43b-f0eb-916d-66aca598a621 | Female |\n",
       "| 6d316c1c-7ce7-7120-7f88-b9f0531ddb6f | Female |\n",
       "| cd3fca00-6a53-51dc-4b02-968e533952aa | Male   |\n",
       "\n"
      ],
      "text/plain": [
       "  PATIENT_NUM                          sex   \n",
       "1 55f11536-c06f-87bc-971e-7a39b232ecd3 Female\n",
       "2 6225749a-9311-ab4e-d2a3-d9b1f919866d Male  \n",
       "3 fe16b3d3-424a-86ce-00df-cd1386d7279f Male  \n",
       "4 2840480c-a43b-f0eb-916d-66aca598a621 Female\n",
       "5 6d316c1c-7ce7-7120-7f88-b9f0531ddb6f Female\n",
       "6 cd3fca00-6a53-51dc-4b02-968e533952aa Male  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# isolating and showing sex data:\n",
    "\n",
    "sex_df <- demo_df %>%\n",
    "  filter(grepl(\"Sex\", CONCEPT_PATH)) %>%\n",
    "  select(PATIENT_NUM, TVAL_CHAR)\n",
    "\n",
    "# renaming the column title to \"sex\":\n",
    "sex_df<- sex_df %>%\n",
    "  rename(sex=TVAL_CHAR)\n",
    "\n",
    "head(sex_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c993b7",
   "metadata": {},
   "source": [
    "Creating a table to show the sex distribution of the dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eaf77e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>count</th><th scope=col>percentage</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Female</td><td>5747</td><td>49.35589</td></tr>\n",
       "\t<tr><td>Male  </td><td>5897</td><td>50.64411</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 3\n",
       "\\begin{tabular}{lll}\n",
       " sex & count & percentage\\\\\n",
       " <chr> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Female & 5747 & 49.35589\\\\\n",
       "\t Male   & 5897 & 50.64411\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 3\n",
       "\n",
       "| sex &lt;chr&gt; | count &lt;int&gt; | percentage &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| Female | 5747 | 49.35589 |\n",
       "| Male   | 5897 | 50.64411 |\n",
       "\n"
      ],
      "text/plain": [
       "  sex    count percentage\n",
       "1 Female 5747  49.35589  \n",
       "2 Male   5897  50.64411  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grouping by sex and calculating the percentage of each:\n",
    "sex_count<- sex_df %>%\n",
    "  group_by(sex) %>%\n",
    "  summarise(count=n()) %>%\n",
    "  mutate(percentage = (count / sum(count)) * 100)\n",
    "\n",
    "sex_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5879da04",
   "metadata": {},
   "source": [
    "Now let's look at the **age** distribution of our patients. \n",
    "\n",
    "Below, we isolate the age data by searching the CONCEPT_PATH for Age. Then, we take the age related values (found in the NVAL_NUM column) and transform them into a new column, \"age\". \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e481fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isolating and showing age data + renaming column title to \"age\":\n",
    "age_df<- demo_df %>%\n",
    "   filter(grepl(\"Age\", CONCEPT_PATH)) %>%\n",
    "  select(PATIENT_NUM, NVAL_NUM) %>%\n",
    "  rename(age=NVAL_NUM) %>%\n",
    "  arrange(PATIENT_NUM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f043fa",
   "metadata": {},
   "source": [
    "We can now use this age dataframe to plot the age distribution in our data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f1db4d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RtaeTAYtNlsxlVus9lsNpsRnRtaeSgU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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a histogram, demonstrating the age distribution:\n",
    "\n",
    "ggplot(data = age_df) +\n",
    "  geom_histogram(aes(x=age), binwidth = 1, fill = \"blue\", color = \"black\") +\n",
    "  theme_bw() +\n",
    "  scale_x_continuous(n.breaks=18)+\n",
    "  labs(x = \"Age\", y = \"Frequency\", title = \"Age Distribution\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a22365fe",
   "metadata": {},
   "source": [
    "We can also group our patients into age groups and create a table detailing how many patients are in each age group. \n",
    "\n",
    "We will group our patients into the following groups: \n",
    "* under 18\n",
    "* 18-65\n",
    "* 65+\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05d18d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>age_group</th><th scope=col>count</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td><span style=white-space:pre-wrap>&lt;18  </span></td><td>2213</td></tr>\n",
       "\t<tr><td>18-65</td><td>7413</td></tr>\n",
       "\t<tr><td>65+  </td><td>2018</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 2\n",
       "\\begin{tabular}{ll}\n",
       " age\\_group & count\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t <18   & 2213\\\\\n",
       "\t 18-65 & 7413\\\\\n",
       "\t 65+   & 2018\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 2\n",
       "\n",
       "| age_group &lt;chr&gt; | count &lt;int&gt; |\n",
       "|---|---|\n",
       "| &lt;18   | 2213 |\n",
       "| 18-65 | 7413 |\n",
       "| 65+   | 2018 |\n",
       "\n"
      ],
      "text/plain": [
       "  age_group count\n",
       "1 <18       2213 \n",
       "2 18-65     7413 \n",
       "3 65+       2018 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grouping by age (<18 , 18-65, 65+)\n",
    "\n",
    "age_groups<- age_df %>%\n",
    "  mutate(age_group= case_when(age < 18 ~ \"<18\",\n",
    "                    age >= 18 & age <65 ~ \"18-65\",\n",
    "                   age >=65 ~ \"65+\"))\n",
    "\n",
    "# calculating number of patients in each group\n",
    "\n",
    "age_group_sum<- age_groups %>%\n",
    "  group_by(age_group)%>%\n",
    "  summarize(count=n()) %>%\n",
    "  arrange(factor(age_group, levels = c(\"<18\", \"18-65\", \"65+\")))\n",
    "\n",
    "age_group_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06881add",
   "metadata": {},
   "source": [
    "Finally, let's investigate the **race** distribution. \n",
    "\n",
    "Similarly to above, we isolate the race data by searching the CONCEPT_PATH for Race. Then, we take the race related values (found in the TVAL_CHAR column) and transform them into a new column, \"race\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d324eb01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isolating race data + renaming column title to \"race\":\n",
    "race_df<- demo_df %>%\n",
    "  filter(grepl(\"Race\", CONCEPT_PATH)) %>%\n",
    "   select(PATIENT_NUM, TVAL_CHAR)%>%\n",
    "  rename(race=TVAL_CHAR)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22793211",
   "metadata": {},
   "source": [
    "creating a table of the race distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00238376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>race</th><th scope=col>count</th><th scope=col>percentage</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>White                                    </td><td>9648</td><td>82.86</td></tr>\n",
       "\t<tr><td>Black or African American                </td><td> 918</td><td> 7.88</td></tr>\n",
       "\t<tr><td>Asian                                    </td><td> 786</td><td> 6.75</td></tr>\n",
       "\t<tr><td>Native Hawaiian or Other Pacific Islander</td><td> 118</td><td> 1.01</td></tr>\n",
       "\t<tr><td>No Information                           </td><td> 113</td><td> 0.97</td></tr>\n",
       "\t<tr><td>American Indian or Alaska Native         </td><td>  61</td><td> 0.52</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " race & count & percentage\\\\\n",
       " <fct> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t White                                     & 9648 & 82.86\\\\\n",
       "\t Black or African American                 &  918 &  7.88\\\\\n",
       "\t Asian                                     &  786 &  6.75\\\\\n",
       "\t Native Hawaiian or Other Pacific Islander &  118 &  1.01\\\\\n",
       "\t No Information                            &  113 &  0.97\\\\\n",
       "\t American Indian or Alaska Native          &   61 &  0.52\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| race &lt;fct&gt; | count &lt;int&gt; | percentage &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| White                                     | 9648 | 82.86 |\n",
       "| Black or African American                 |  918 |  7.88 |\n",
       "| Asian                                     |  786 |  6.75 |\n",
       "| Native Hawaiian or Other Pacific Islander |  118 |  1.01 |\n",
       "| No Information                            |  113 |  0.97 |\n",
       "| American Indian or Alaska Native          |   61 |  0.52 |\n",
       "\n"
      ],
      "text/plain": [
       "  race                                      count percentage\n",
       "1 White                                     9648  82.86     \n",
       "2 Black or African American                  918   7.88     \n",
       "3 Asian                                      786   6.75     \n",
       "4 Native Hawaiian or Other Pacific Islander  118   1.01     \n",
       "5 No Information                             113   0.97     \n",
       "6 American Indian or Alaska Native            61   0.52     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grouping by race and calculating the percentage of each:\n",
    "\n",
    "race_count <- race_df %>%\n",
    "  group_by(race) %>%\n",
    "  summarise(count = n()) %>%\n",
    "  mutate(percentage = round((count / sum(count)) * 100,2)) %>%\n",
    "  mutate(race = fct_reorder(race, -percentage)) %>%\n",
    "  arrange(-percentage)\n",
    "\n",
    "race_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17078836",
   "metadata": {},
   "source": [
    "We will plot these results in a bar plot. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "69d1933f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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LhwYfz48RVP9g8PD3/ssce8I9zstmQ7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\no5eXl6enZ1ZWlkajcXR0lLdC4DER7AAz0t3dvWPHjnHjxkm7r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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a bar plot representing race distribution:\n",
    "\n",
    "ggplot(data= race_count) +\n",
    "  geom_bar(aes(x=race, y=percentage, fill= race ), stat = \"identity\")+\n",
    "  theme_bw()+\n",
    "  theme(axis.text.x = element_text(angle = 45, hjust = 1))+\n",
    "  theme(legend.position = \"none\")+\n",
    "  labs(x=\"Race\",y=\"Percentage\", title=\"Participants Race Distribution\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95ce3be",
   "metadata": {},
   "source": [
    "The way our initial dataset is formatted can be difficult to view multiple variables about a given patient at the same time. \n",
    "\n",
    "Below, we create one table presenting all demographic data (age, sex, race) by patient ID. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ddf928fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PATIENT_NUM</th><th scope=col>age</th><th scope=col>sex</th><th scope=col>race</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&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>000073ef-dea1-7599-2afc-5ff34bf99b34</td><td>34</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>000c0c49-3f93-823c-3932-249adbeaad1c</td><td>26</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>000fae46-91b6-a373-1134-296eb55705e1</td><td>69</td><td>Male  </td><td>White</td></tr>\n",
       "\t<tr><td>0011d179-6a33-440a-0e24-8dce63a492c1</td><td>29</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>0015fb36-0cad-3a48-87d9-a0f61c12c80d</td><td>66</td><td>Male  </td><td>White</td></tr>\n",
       "\t<tr><td>001ac6f0-8c11-79fc-8521-b004f687d67f</td><td>55</td><td>Female</td><td>White</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " PATIENT\\_NUM & age & sex & race\\\\\n",
       " <chr> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 000073ef-dea1-7599-2afc-5ff34bf99b34 & 34 & Female & White\\\\\n",
       "\t 000c0c49-3f93-823c-3932-249adbeaad1c & 26 & Female & White\\\\\n",
       "\t 000fae46-91b6-a373-1134-296eb55705e1 & 69 & Male   & White\\\\\n",
       "\t 0011d179-6a33-440a-0e24-8dce63a492c1 & 29 & Female & White\\\\\n",
       "\t 0015fb36-0cad-3a48-87d9-a0f61c12c80d & 66 & Male   & White\\\\\n",
       "\t 001ac6f0-8c11-79fc-8521-b004f687d67f & 55 & Female & White\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 4\n",
       "\n",
       "| PATIENT_NUM &lt;chr&gt; | age &lt;dbl&gt; | sex &lt;chr&gt; | race &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 000073ef-dea1-7599-2afc-5ff34bf99b34 | 34 | Female | White |\n",
       "| 000c0c49-3f93-823c-3932-249adbeaad1c | 26 | Female | White |\n",
       "| 000fae46-91b6-a373-1134-296eb55705e1 | 69 | Male   | White |\n",
       "| 0011d179-6a33-440a-0e24-8dce63a492c1 | 29 | Female | White |\n",
       "| 0015fb36-0cad-3a48-87d9-a0f61c12c80d | 66 | Male   | White |\n",
       "| 001ac6f0-8c11-79fc-8521-b004f687d67f | 55 | Female | White |\n",
       "\n"
      ],
      "text/plain": [
       "  PATIENT_NUM                          age sex    race \n",
       "1 000073ef-dea1-7599-2afc-5ff34bf99b34 34  Female White\n",
       "2 000c0c49-3f93-823c-3932-249adbeaad1c 26  Female White\n",
       "3 000fae46-91b6-a373-1134-296eb55705e1 69  Male   White\n",
       "4 0011d179-6a33-440a-0e24-8dce63a492c1 29  Female White\n",
       "5 0015fb36-0cad-3a48-87d9-a0f61c12c80d 66  Male   White\n",
       "6 001ac6f0-8c11-79fc-8521-b004f687d67f 55  Female White"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#merging all columns to show age, sex, race in one table:\n",
    "\n",
    "demographics_joined<- age_df %>%\n",
    "  left_join(sex_df, by=\"PATIENT_NUM\") %>%\n",
    "  left_join(race_df, by= \"PATIENT_NUM\")\n",
    "\n",
    "head(demographics_joined)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbecd0f",
   "metadata": {},
   "source": [
    "## Investigating ICD codes: Epilepsy case study\n",
    "We can isolate the diagnosis related information by searching for 'ACT Diagnosis' in the CONCEPT_PATH column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c9272027",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a new data frame including only diagnosis data:\n",
    "\n",
    "diagnosis_icd10 <- data %>%\n",
    "  filter(grepl(\"\\\\ACT Diagnosis ICD-10\", CONCEPT_PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e34f39",
   "metadata": {},
   "source": [
    "### In this case study, we will investigate epilepsy as the disease of interest. \n",
    "\n",
    "Epilapsy is represented by  ICD group code \"G40\", which includes the following and their sub-groups:\n",
    "* G40.0: Localization-related (focal) (partial) idiopathic epilepsy and epileptic syndromes with seizures of localized onset.\n",
    "* G40.1: Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with simple partial seizures.\n",
    "* G40.2: Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with complex partial seizures.\n",
    "* G40.3: Generalized idiopathic epilepsy and epileptic syndromes\n",
    "* G40.A: Absence epileptic syndrome.\n",
    "* G40.B: Juvenile myoclonic epilepsy [impulsive petit mal].\n",
    "* G40.C: Lafora progressive myoclonus epilepsy, intractable.\n",
    "* G40.4: Other generalized epilepsy and epileptic syndromes.\n",
    "* G40.5: Epileptic seizures related to external causes.\n",
    "* G40.8: Other epilepsy and recurrent seizures.\n",
    "* G40.9: Epilepsy, unspecified.\n",
    "\n",
    "*A full list of ICD-10 codes can be found at https://www.icd10data.com/ICD10CM/Codes*\n",
    "\n",
    "### Isolating the patient cohort\n",
    "\n",
    "First, we create a new data-frame that only includes patients with epilepsy.\n",
    "\n",
    "We create a variable called `icd_code_of_interest` and set it to `G40`.\n",
    "\n",
    "(you can replace the ICD code \"G40\" with the ICD code of your choice if you are interested in investigating a different disease group!):\n",
    "\n",
    "You can see a preview of this epilepsy focused dataset below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2ece5d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PATIENT_NUM</th><th scope=col>CONCEPT_PATH</th><th scope=col>icd_10_code</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&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>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td><td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td><td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td></tr>\n",
       "\t<tr><td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td><td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td><td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td></tr>\n",
       "\t<tr><td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td><td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td><td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td></tr>\n",
       "\t<tr><td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td><td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td><td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td></tr>\n",
       "\t<tr><td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td><td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td><td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td></tr>\n",
       "\t<tr><td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td><td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures                                                                            </td><td>G40.9 Epilepsy, unspecified                                               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " PATIENT\\_NUM & CONCEPT\\_PATH & icd\\_10\\_code\\\\\n",
       " <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t e6ca8432-abc9-e3d4-4e5d-47f972f07181 & \\textbackslash{}ACT Diagnosis ICD-10\\textbackslash{}G00-G99 Diseases of the nervous system (G00-G99)\\textbackslash{}G40-G47 Episodic and paroxysmal disorders (G40-G47)\\textbackslash{}G40 Epilepsy and recurrent seizures\\textbackslash{}G40.9 Epilepsy, unspecified\\textbackslash{}G40.90 Epilepsy, unspecified, not intractable & G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\\\\\n",
       "\t e6ca8432-abc9-e3d4-4e5d-47f972f07181 & \\textbackslash{}ACT Diagnosis ICD-10\\textbackslash{}G00-G99 Diseases of the nervous system (G00-G99)\\textbackslash{}G40-G47 Episodic and paroxysmal disorders (G40-G47)\\textbackslash{}G40 Epilepsy and recurrent seizures\\textbackslash{}G40.9 Epilepsy, unspecified\\textbackslash{}G40.90 Epilepsy, unspecified, not intractable & G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\\\\\n",
       "\t e6ca8432-abc9-e3d4-4e5d-47f972f07181 & \\textbackslash{}ACT Diagnosis ICD-10\\textbackslash{}G00-G99 Diseases of the nervous system (G00-G99)\\textbackslash{}G40-G47 Episodic and paroxysmal disorders (G40-G47)\\textbackslash{}G40 Epilepsy and recurrent seizures\\textbackslash{}G40.9 Epilepsy, unspecified\\textbackslash{}G40.90 Epilepsy, unspecified, not intractable & G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\\\\\n",
       "\t e6ca8432-abc9-e3d4-4e5d-47f972f07181 & \\textbackslash{}ACT Diagnosis ICD-10\\textbackslash{}G00-G99 Diseases of the nervous system (G00-G99)\\textbackslash{}G40-G47 Episodic and paroxysmal disorders (G40-G47)\\textbackslash{}G40 Epilepsy and recurrent seizures\\textbackslash{}G40.9 Epilepsy, unspecified\\textbackslash{}G40.90 Epilepsy, unspecified, not intractable & G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\\\\\n",
       "\t e6ca8432-abc9-e3d4-4e5d-47f972f07181 & \\textbackslash{}ACT Diagnosis ICD-10\\textbackslash{}G00-G99 Diseases of the nervous system (G00-G99)\\textbackslash{}G40-G47 Episodic and paroxysmal disorders (G40-G47)\\textbackslash{}G40 Epilepsy and recurrent seizures\\textbackslash{}G40.9 Epilepsy, unspecified\\textbackslash{}G40.90 Epilepsy, unspecified, not intractable & G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\\\\\n",
       "\t e6ca8432-abc9-e3d4-4e5d-47f972f07181 & \\textbackslash{}ACT Diagnosis ICD-10\\textbackslash{}G00-G99 Diseases of the nervous system (G00-G99)\\textbackslash{}G40-G47 Episodic and paroxysmal disorders (G40-G47)\\textbackslash{}G40 Epilepsy and recurrent seizures                                                                             & G40.9 Epilepsy, unspecified                                               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 6 × 3\n",
       "\n",
       "| PATIENT_NUM &lt;chr&gt; | CONCEPT_PATH &lt;chr&gt; | icd_10_code &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| e6ca8432-abc9-e3d4-4e5d-47f972f07181 | \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable | G40.909 Epilepsy, unspecified, not intractable, without status epilepticus |\n",
       "| e6ca8432-abc9-e3d4-4e5d-47f972f07181 | \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable | G40.909 Epilepsy, unspecified, not intractable, without status epilepticus |\n",
       "| e6ca8432-abc9-e3d4-4e5d-47f972f07181 | \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable | G40.909 Epilepsy, unspecified, not intractable, without status epilepticus |\n",
       "| e6ca8432-abc9-e3d4-4e5d-47f972f07181 | \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable | G40.909 Epilepsy, unspecified, not intractable, without status epilepticus |\n",
       "| e6ca8432-abc9-e3d4-4e5d-47f972f07181 | \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable | G40.909 Epilepsy, unspecified, not intractable, without status epilepticus |\n",
       "| e6ca8432-abc9-e3d4-4e5d-47f972f07181 | \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures                                                                             | G40.9 Epilepsy, unspecified                                                |\n",
       "\n"
      ],
      "text/plain": [
       "  PATIENT_NUM                         \n",
       "1 e6ca8432-abc9-e3d4-4e5d-47f972f07181\n",
       "2 e6ca8432-abc9-e3d4-4e5d-47f972f07181\n",
       "3 e6ca8432-abc9-e3d4-4e5d-47f972f07181\n",
       "4 e6ca8432-abc9-e3d4-4e5d-47f972f07181\n",
       "5 e6ca8432-abc9-e3d4-4e5d-47f972f07181\n",
       "6 e6ca8432-abc9-e3d4-4e5d-47f972f07181\n",
       "  CONCEPT_PATH                                                                                                                                                                                                                                  \n",
       "1 \\\\ACT Diagnosis ICD-10\\\\G00-G99 Diseases of the nervous system (G00-G99)\\\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\\\G40 Epilepsy and recurrent seizures\\\\G40.9 Epilepsy, unspecified\\\\G40.90 Epilepsy, unspecified, not intractable\n",
       "2 \\\\ACT Diagnosis ICD-10\\\\G00-G99 Diseases of the nervous system (G00-G99)\\\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\\\G40 Epilepsy and recurrent seizures\\\\G40.9 Epilepsy, unspecified\\\\G40.90 Epilepsy, unspecified, not intractable\n",
       "3 \\\\ACT Diagnosis ICD-10\\\\G00-G99 Diseases of the nervous system (G00-G99)\\\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\\\G40 Epilepsy and recurrent seizures\\\\G40.9 Epilepsy, unspecified\\\\G40.90 Epilepsy, unspecified, not intractable\n",
       "4 \\\\ACT Diagnosis ICD-10\\\\G00-G99 Diseases of the nervous system (G00-G99)\\\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\\\G40 Epilepsy and recurrent seizures\\\\G40.9 Epilepsy, unspecified\\\\G40.90 Epilepsy, unspecified, not intractable\n",
       "5 \\\\ACT Diagnosis ICD-10\\\\G00-G99 Diseases of the nervous system (G00-G99)\\\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\\\G40 Epilepsy and recurrent seizures\\\\G40.9 Epilepsy, unspecified\\\\G40.90 Epilepsy, unspecified, not intractable\n",
       "6 \\\\ACT Diagnosis ICD-10\\\\G00-G99 Diseases of the nervous system (G00-G99)\\\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\\\G40 Epilepsy and recurrent seizures                                                                            \n",
       "  icd_10_code                                                               \n",
       "1 G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\n",
       "2 G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\n",
       "3 G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\n",
       "4 G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\n",
       "5 G40.909 Epilepsy, unspecified, not intractable, without status epilepticus\n",
       "6 G40.9 Epilepsy, unspecified                                               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filtering to include only patients diagnosed with epilepsy and recurrent seizures (ICD GROUP CODE G40):\n",
    "icd_code_of_interest = 'G40'\n",
    "\n",
    "epilepsy_icd10<- diagnosis_icd10 %>%\n",
    "  filter(grepl(icd_code_of_interest, TVAL_CHAR)) %>%\n",
    "  select(PATIENT_NUM, CONCEPT_PATH,TVAL_CHAR) %>%\n",
    "  rename(icd_10_code= TVAL_CHAR) %>%\n",
    "  group_by(PATIENT_NUM)\n",
    "\n",
    "# creating a final list of patient_ids with epilepsy (G40 group code), removing any duplicate entries for same patient id :\n",
    "\n",
    "epilepsy_icd10_patient_ids<- epilepsy_icd10 %>%\n",
    "  select(PATIENT_NUM) %>%\n",
    "  distinct() \n",
    "\n",
    "head(epilepsy_icd10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a513a12",
   "metadata": {},
   "source": [
    "Let's also isolate the demographic data for patients who have been diagnosed with epilepsy, and see how much of our dataset they represent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "efdd5ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PATIENT_NUM</th><th scope=col>age</th><th scope=col>sex</th><th scope=col>race</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&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>000fae46-91b6-a373-1134-296eb55705e1</td><td>69</td><td>Male  </td><td>White</td></tr>\n",
       "\t<tr><td>001ac6f0-8c11-79fc-8521-b004f687d67f</td><td>55</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>00444938-6d64-989b-b975-519e92b26171</td><td> 2</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>0223b8f5-ddf1-be38-64c5-4b69c5f311f4</td><td>47</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>0277b5f2-d7ed-0784-bcc6-39fa409f04fc</td><td>23</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>028184d4-b458-043c-189d-2f5b229db856</td><td>44</td><td>Male  </td><td>White</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " PATIENT\\_NUM & age & sex & race\\\\\n",
       " <chr> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 000fae46-91b6-a373-1134-296eb55705e1 & 69 & Male   & White\\\\\n",
       "\t 001ac6f0-8c11-79fc-8521-b004f687d67f & 55 & Female & White\\\\\n",
       "\t 00444938-6d64-989b-b975-519e92b26171 &  2 & Female & White\\\\\n",
       "\t 0223b8f5-ddf1-be38-64c5-4b69c5f311f4 & 47 & Female & White\\\\\n",
       "\t 0277b5f2-d7ed-0784-bcc6-39fa409f04fc & 23 & Female & White\\\\\n",
       "\t 028184d4-b458-043c-189d-2f5b229db856 & 44 & Male   & White\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 6 × 4\n",
       "\n",
       "| PATIENT_NUM &lt;chr&gt; | age &lt;dbl&gt; | sex &lt;chr&gt; | race &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 000fae46-91b6-a373-1134-296eb55705e1 | 69 | Male   | White |\n",
       "| 001ac6f0-8c11-79fc-8521-b004f687d67f | 55 | Female | White |\n",
       "| 00444938-6d64-989b-b975-519e92b26171 |  2 | Female | White |\n",
       "| 0223b8f5-ddf1-be38-64c5-4b69c5f311f4 | 47 | Female | White |\n",
       "| 0277b5f2-d7ed-0784-bcc6-39fa409f04fc | 23 | Female | White |\n",
       "| 028184d4-b458-043c-189d-2f5b229db856 | 44 | Male   | White |\n",
       "\n"
      ],
      "text/plain": [
       "  PATIENT_NUM                          age sex    race \n",
       "1 000fae46-91b6-a373-1134-296eb55705e1 69  Male   White\n",
       "2 001ac6f0-8c11-79fc-8521-b004f687d67f 55  Female White\n",
       "3 00444938-6d64-989b-b975-519e92b26171  2  Female White\n",
       "4 0223b8f5-ddf1-be38-64c5-4b69c5f311f4 47  Female White\n",
       "5 0277b5f2-d7ed-0784-bcc6-39fa409f04fc 23  Female White\n",
       "6 028184d4-b458-043c-189d-2f5b229db856 44  Male   White"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"3.59 % of patients in our dataset have been diagnosed with an epilepsy ICD10 code.\"\n"
     ]
    }
   ],
   "source": [
    "# merging the patient id list with the demographic data\n",
    "epi_demo<- epilepsy_icd10_patient_ids %>%\n",
    "  left_join(demographics_joined, by= \"PATIENT_NUM\") %>%\n",
    "  arrange(PATIENT_NUM)\n",
    "\n",
    "head(epi_demo)\n",
    "\n",
    "# calculating percentage of patients with epilepsy from original cohort:\n",
    "percentage_epilepsy <-  (nrow(epilepsy_icd10_patient_ids) / n_distinct(data$PATIENT_NUM)) * 100\n",
    "\n",
    "print(paste(round(percentage_epilepsy,2), '% of patients in our dataset have been diagnosed with an epilepsy ICD10 code.'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f678b7",
   "metadata": {},
   "source": [
    "### Investigating the presence of other diagnostic codes in our defined patient cohort\n",
    "Our cohort consists of patients with epilepsy. \n",
    "Let's say we are interested in comparing which patients with epilepsy also have been diagnosed with migranes (ICD GROUP CODE G43).\n",
    "\n",
    "We create a variable called `icd` and set it to `G43`. (you can replace the ICD code \"G43\" with the ICD code of your choice in the next chunk of code)\n",
    "\n",
    "We then search for this ICD code of interest within our cohort. \n",
    "\n",
    "This will be done in a few steps.\n",
    "(you can replace the ICD code \"G43\" with the ICD code of your choice in the next chunk of code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "44c74481",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (ICD GROUP CODE G43)\n",
    "icd <- 'G43'\n",
    "\n",
    "##first, creating a data-frame including only patients diagnosed with migraines \n",
    "\n",
    "migraine_icd10<- diagnosis_icd10 %>%\n",
    "  filter(grepl(icd,TVAL_CHAR)) %>% \n",
    "  select(PATIENT_NUM, CONCEPT_PATH,TVAL_CHAR) %>%\n",
    "  rename(icd_10_code= TVAL_CHAR)%>%\n",
    "  group_by(PATIENT_NUM)\n",
    "\n",
    "#next, extracting only the patient id's and removing any duplicates.\n",
    "migraine_icd10_patient_ids<- migraine_icd10 %>%\n",
    "  select(PATIENT_NUM) %>%\n",
    "  distinct()\n",
    "\n",
    "\n",
    "#finally, merging the epilepsy patient id list with the migraine patient id list to create a list including only patients that have both epilepsy and migraine ICD10 codes \n",
    "epilepsy_migraine <- epilepsy_icd10_patient_ids %>%\n",
    "  inner_join(migraine_icd10_patient_ids, by = \"PATIENT_NUM\")\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb624ff2",
   "metadata": {},
   "source": [
    "Now, let's examine the original epilepsy dataframe and add a column indicating which patients also have migraines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6f6ec45e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PATIENT_NUM</th><th scope=col>migraines</th><th scope=col>age</th><th scope=col>sex</th><th scope=col>race</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&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>000fae46-91b6-a373-1134-296eb55705e1</td><td>0</td><td>69</td><td>Male  </td><td>White</td></tr>\n",
       "\t<tr><td>001ac6f0-8c11-79fc-8521-b004f687d67f</td><td>1</td><td>55</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>00444938-6d64-989b-b975-519e92b26171</td><td>0</td><td> 2</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>0223b8f5-ddf1-be38-64c5-4b69c5f311f4</td><td>0</td><td>47</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>0277b5f2-d7ed-0784-bcc6-39fa409f04fc</td><td>0</td><td>23</td><td>Female</td><td>White</td></tr>\n",
       "\t<tr><td>028184d4-b458-043c-189d-2f5b229db856</td><td>0</td><td>44</td><td>Male  </td><td>White</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 6 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " PATIENT\\_NUM & migraines & age & sex & race\\\\\n",
       " <chr> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 000fae46-91b6-a373-1134-296eb55705e1 & 0 & 69 & Male   & White\\\\\n",
       "\t 001ac6f0-8c11-79fc-8521-b004f687d67f & 1 & 55 & Female & White\\\\\n",
       "\t 00444938-6d64-989b-b975-519e92b26171 & 0 &  2 & Female & White\\\\\n",
       "\t 0223b8f5-ddf1-be38-64c5-4b69c5f311f4 & 0 & 47 & Female & White\\\\\n",
       "\t 0277b5f2-d7ed-0784-bcc6-39fa409f04fc & 0 & 23 & Female & White\\\\\n",
       "\t 028184d4-b458-043c-189d-2f5b229db856 & 0 & 44 & Male   & White\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 6 × 5\n",
       "\n",
       "| PATIENT_NUM &lt;chr&gt; | migraines &lt;dbl&gt; | age &lt;dbl&gt; | sex &lt;chr&gt; | race &lt;chr&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 000fae46-91b6-a373-1134-296eb55705e1 | 0 | 69 | Male   | White |\n",
       "| 001ac6f0-8c11-79fc-8521-b004f687d67f | 1 | 55 | Female | White |\n",
       "| 00444938-6d64-989b-b975-519e92b26171 | 0 |  2 | Female | White |\n",
       "| 0223b8f5-ddf1-be38-64c5-4b69c5f311f4 | 0 | 47 | Female | White |\n",
       "| 0277b5f2-d7ed-0784-bcc6-39fa409f04fc | 0 | 23 | Female | White |\n",
       "| 028184d4-b458-043c-189d-2f5b229db856 | 0 | 44 | Male   | White |\n",
       "\n"
      ],
      "text/plain": [
       "  PATIENT_NUM                          migraines age sex    race \n",
       "1 000fae46-91b6-a373-1134-296eb55705e1 0         69  Male   White\n",
       "2 001ac6f0-8c11-79fc-8521-b004f687d67f 1         55  Female White\n",
       "3 00444938-6d64-989b-b975-519e92b26171 0          2  Female White\n",
       "4 0223b8f5-ddf1-be38-64c5-4b69c5f311f4 0         47  Female White\n",
       "5 0277b5f2-d7ed-0784-bcc6-39fa409f04fc 0         23  Female White\n",
       "6 028184d4-b458-043c-189d-2f5b229db856 0         44  Male   White"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# merging the joint list to the original epilepsy patient list, and creating a migraine column indicating if the patient with epilepsy also has a migraine diagnosis (yes=1/ no=0):\n",
    "\n",
    "epilepsy_merged <- epilepsy_icd10_patient_ids %>%\n",
    "  left_join(epilepsy_migraine %>% mutate(migraines = 1), by = \"PATIENT_NUM\") %>%\n",
    "  replace_na(list(migraines = 0))  %>%\n",
    "  left_join(epi_demo, by=\"PATIENT_NUM\") %>%\n",
    "  arrange(PATIENT_NUM)\n",
    "\n",
    "head(epilepsy_merged)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03227d0f",
   "metadata": {},
   "source": [
    "The following table shows the percentage of patients with migraines in the epilepsy patient cohort.\n",
    "\n",
    "We can see that only a small percentage of our patients with epilepsy also experience migraines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "75582fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>migraines</th><th scope=col>count</th><th scope=col>percentage</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>398</td><td>95.22</td></tr>\n",
       "\t<tr><td>1</td><td> 20</td><td> 4.78</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 3\n",
       "\\begin{tabular}{lll}\n",
       " migraines & count & percentage\\\\\n",
       " <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0 & 398 & 95.22\\\\\n",
       "\t 1 &  20 &  4.78\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 3\n",
       "\n",
       "| migraines &lt;dbl&gt; | count &lt;int&gt; | percentage &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 0 | 398 | 95.22 |\n",
       "| 1 |  20 |  4.78 |\n",
       "\n"
      ],
      "text/plain": [
       "  migraines count percentage\n",
       "1 0         398   95.22     \n",
       "2 1          20    4.78     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculating the number and percentage of patient with migraines in the epilepsy patient cohort\n",
    "epilepsy_migraine_count <- epilepsy_merged %>%\n",
    "  group_by(migraines) %>%\n",
    "  summarise(count=n()) %>%\n",
    "  mutate(percentage = round((count / sum(count)) * 100,2)) \n",
    " \n",
    "epilepsy_migraine_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3ec672",
   "metadata": {},
   "source": [
    "### Investigating what medications have been administered to our cohort of interest\n",
    "\n",
    "Let's now look into the medications data. We can isolate the medications data by searching the CONCEPT_PATH variable for the term `ACT Medications`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3b6046e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>CONCEPT_PATH</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>\\ACT Medications\\A [Preparations]\\Abemaciclib\\Abemaciclib Oral Tablet\\Abemaciclib 100 Mg Oral Tablet [Verzenio]                                                                                                                                              </td></tr>\n",
       "\t<tr><td>\\ACT Medications\\A [Preparations]\\Acetaminophen / Codeine\\Acetaminophen / Codeine Oral Tablet\\Acetaminophen 300 Mg / Codeine Phosphate 15 Mg Oral Tablet                                                                                                     </td></tr>\n",
       "\t<tr><td>\\ACT Medications\\A [Preparations]\\Acetaminophen / Dextromethorphan / Doxylamine\\Acetaminophen / Dextromethorphan / Doxylamine Oral Solution\\Acetaminophen 21.7 Mg/Ml / Dextromethorphan Hydrobromide 1 Mg/Ml / Doxylamine Succinate 0.417 Mg/Ml Oral Solution</td></tr>\n",
       "\t<tr><td>\\ACT Medications\\A [Preparations]\\Acetaminophen / Hydrocodone\\Acetaminophen / Hydrocodone Oral Tablet\\Acetaminophen 300 Mg / Hydrocodone Bitartrate 10 Mg Oral Tablet                                                                                        </td></tr>\n",
       "\t<tr><td>\\ACT Medications\\A [Preparations]\\Acetaminophen / Hydrocodone\\Acetaminophen / Hydrocodone Oral Tablet\\Acetaminophen 300 Mg / Hydrocodone Bitartrate 5 Mg Oral Tablet                                                                                         </td></tr>\n",
       "\t<tr><td>\\ACT Medications\\A [Preparations]\\Acetaminophen / Hydrocodone\\Acetaminophen / Hydrocodone Oral Tablet\\Acetaminophen 325 Mg / Hydrocodone Bitartrate 7.5 Mg Oral Tablet                                                                                       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 1\n",
       "\\begin{tabular}{l}\n",
       " CONCEPT\\_PATH\\\\\n",
       " <chr>\\\\\n",
       "\\hline\n",
       "\t \\textbackslash{}ACT Medications\\textbackslash{}A {[}Preparations{]}\\textbackslash{}Abemaciclib\\textbackslash{}Abemaciclib Oral Tablet\\textbackslash{}Abemaciclib 100 Mg Oral Tablet {[}Verzenio{]}                                                                                                                                              \\\\\n",
       "\t \\textbackslash{}ACT Medications\\textbackslash{}A {[}Preparations{]}\\textbackslash{}Acetaminophen / Codeine\\textbackslash{}Acetaminophen / Codeine Oral Tablet\\textbackslash{}Acetaminophen 300 Mg / Codeine Phosphate 15 Mg Oral Tablet                                                                                                     \\\\\n",
       "\t \\textbackslash{}ACT Medications\\textbackslash{}A {[}Preparations{]}\\textbackslash{}Acetaminophen / Dextromethorphan / Doxylamine\\textbackslash{}Acetaminophen / Dextromethorphan / Doxylamine Oral Solution\\textbackslash{}Acetaminophen 21.7 Mg/Ml / Dextromethorphan Hydrobromide 1 Mg/Ml / Doxylamine Succinate 0.417 Mg/Ml Oral Solution\\\\\n",
       "\t \\textbackslash{}ACT Medications\\textbackslash{}A {[}Preparations{]}\\textbackslash{}Acetaminophen / Hydrocodone\\textbackslash{}Acetaminophen / Hydrocodone Oral Tablet\\textbackslash{}Acetaminophen 300 Mg / Hydrocodone Bitartrate 10 Mg Oral Tablet                                                                                        \\\\\n",
       "\t \\textbackslash{}ACT Medications\\textbackslash{}A {[}Preparations{]}\\textbackslash{}Acetaminophen / Hydrocodone\\textbackslash{}Acetaminophen / Hydrocodone Oral Tablet\\textbackslash{}Acetaminophen 300 Mg / Hydrocodone Bitartrate 5 Mg Oral Tablet                                                                                         \\\\\n",
       "\t \\textbackslash{}ACT Medications\\textbackslash{}A {[}Preparations{]}\\textbackslash{}Acetaminophen / Hydrocodone\\textbackslash{}Acetaminophen / Hydrocodone Oral Tablet\\textbackslash{}Acetaminophen 325 Mg / Hydrocodone Bitartrate 7.5 Mg Oral Tablet                                                                                       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 1\n",
       "\n",
       "| CONCEPT_PATH &lt;chr&gt; |\n",
       "|---|\n",
       "| \\ACT Medications\\A [Preparations]\\Abemaciclib\\Abemaciclib Oral Tablet\\Abemaciclib 100 Mg Oral Tablet [Verzenio]                                                                                                                                               |\n",
       "| \\ACT Medications\\A [Preparations]\\Acetaminophen / Codeine\\Acetaminophen / Codeine Oral Tablet\\Acetaminophen 300 Mg / Codeine Phosphate 15 Mg Oral Tablet                                                                                                      |\n",
       "| \\ACT Medications\\A [Preparations]\\Acetaminophen / Dextromethorphan / Doxylamine\\Acetaminophen / Dextromethorphan / Doxylamine Oral Solution\\Acetaminophen 21.7 Mg/Ml / Dextromethorphan Hydrobromide 1 Mg/Ml / Doxylamine Succinate 0.417 Mg/Ml Oral Solution |\n",
       "| \\ACT Medications\\A [Preparations]\\Acetaminophen / Hydrocodone\\Acetaminophen / Hydrocodone Oral Tablet\\Acetaminophen 300 Mg / Hydrocodone Bitartrate 10 Mg Oral Tablet                                                                                         |\n",
       "| \\ACT Medications\\A [Preparations]\\Acetaminophen / Hydrocodone\\Acetaminophen / Hydrocodone Oral Tablet\\Acetaminophen 300 Mg / Hydrocodone Bitartrate 5 Mg Oral Tablet                                                                                          |\n",
       "| \\ACT Medications\\A [Preparations]\\Acetaminophen / Hydrocodone\\Acetaminophen / Hydrocodone Oral Tablet\\Acetaminophen 325 Mg / Hydrocodone Bitartrate 7.5 Mg Oral Tablet                                                                                        |\n",
       "\n"
      ],
      "text/plain": [
       "  CONCEPT_PATH                                                                                                                                                                                                                                                      \n",
       "1 \\\\ACT Medications\\\\A [Preparations]\\\\Abemaciclib\\\\Abemaciclib Oral Tablet\\\\Abemaciclib 100 Mg Oral Tablet [Verzenio]                                                                                                                                              \n",
       "2 \\\\ACT Medications\\\\A [Preparations]\\\\Acetaminophen / Codeine\\\\Acetaminophen / Codeine Oral Tablet\\\\Acetaminophen 300 Mg / Codeine Phosphate 15 Mg Oral Tablet                                                                                                     \n",
       "3 \\\\ACT Medications\\\\A [Preparations]\\\\Acetaminophen / Dextromethorphan / Doxylamine\\\\Acetaminophen / Dextromethorphan / Doxylamine Oral Solution\\\\Acetaminophen 21.7 Mg/Ml / Dextromethorphan Hydrobromide 1 Mg/Ml / Doxylamine Succinate 0.417 Mg/Ml Oral Solution\n",
       "4 \\\\ACT Medications\\\\A [Preparations]\\\\Acetaminophen / Hydrocodone\\\\Acetaminophen / Hydrocodone Oral Tablet\\\\Acetaminophen 300 Mg / Hydrocodone Bitartrate 10 Mg Oral Tablet                                                                                        \n",
       "5 \\\\ACT Medications\\\\A [Preparations]\\\\Acetaminophen / Hydrocodone\\\\Acetaminophen / Hydrocodone Oral Tablet\\\\Acetaminophen 300 Mg / Hydrocodone Bitartrate 5 Mg Oral Tablet                                                                                         \n",
       "6 \\\\ACT Medications\\\\A [Preparations]\\\\Acetaminophen / Hydrocodone\\\\Acetaminophen / Hydrocodone Oral Tablet\\\\Acetaminophen 325 Mg / Hydrocodone Bitartrate 7.5 Mg Oral Tablet                                                                                       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating a new dataframe containing only medication data:\n",
    "\n",
    "medications<- data %>%\n",
    "  filter(grepl(\"\\\\ACT Medications\", CONCEPT_PATH)) \n",
    "\n",
    "#showing list with unique values of medications included in the dataset:\n",
    "\n",
    "medication_list<- medications %>%  \n",
    "  select(CONCEPT_PATH)%>%\n",
    "  distinct() %>%\n",
    "  arrange(CONCEPT_PATH)\n",
    "\n",
    "head(medication_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d056a22",
   "metadata": {},
   "source": [
    "For our epilepsy case study, we are interested in knowing whether a patient has been administered Carbamazepine or Pregabalin. \n",
    "- Carbamazepine is used to manage and treat epilepsy, trigeminal neuralgia, and acute manic and mixed episodes in bipolar I disorder.\n",
    "- Pregabalin treats nerve and muscle pain, including fibromyalgia. It can also treat seizures.\n",
    "\n",
    "\n",
    "We will search our dataset for these medications and isolate which patients are associated to those observations.\n",
    "\n",
    "We then isolate our results to our cohort of interest. \n",
    "\n",
    "The resulting table contains information on whether or not a patient who was diagnosed with epilepsy was ever administered carbamazepine or pregabalin. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4a6a1ba9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`mutate_all()` ignored the following grouping variables:\n",
      "\u001b[36m•\u001b[39m Column `PATIENT_NUM`\n",
      "\u001b[36mℹ\u001b[39m Use `mutate_at(df, vars(-group_cols()), myoperation)` to silence the message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PATIENT_NUM</th><th scope=col>carbamazepine</th><th scope=col>pregabalin</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>00444938-6d64-989b-b975-519e92b26171</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>07fde594-2d72-bb7f-9e7e-b646a7224623</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>081cc2a9-8380-e3c9-ea11-615b69ef8783</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>097a1d0a-db9a-9ae8-7639-9300e3199484</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>0a728cec-84cd-2774-2530-df8408505738</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>0ae17096-efd8-e4ce-79b2-c9d7a70992d8</td><td>1</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " PATIENT\\_NUM & carbamazepine & pregabalin\\\\\n",
       " <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 00444938-6d64-989b-b975-519e92b26171 & 1 & 0\\\\\n",
       "\t 07fde594-2d72-bb7f-9e7e-b646a7224623 & 1 & 0\\\\\n",
       "\t 081cc2a9-8380-e3c9-ea11-615b69ef8783 & 1 & 0\\\\\n",
       "\t 097a1d0a-db9a-9ae8-7639-9300e3199484 & 1 & 0\\\\\n",
       "\t 0a728cec-84cd-2774-2530-df8408505738 & 1 & 0\\\\\n",
       "\t 0ae17096-efd8-e4ce-79b2-c9d7a70992d8 & 1 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| PATIENT_NUM &lt;chr&gt; | carbamazepine &lt;dbl&gt; | pregabalin &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 00444938-6d64-989b-b975-519e92b26171 | 1 | 0 |\n",
       "| 07fde594-2d72-bb7f-9e7e-b646a7224623 | 1 | 0 |\n",
       "| 081cc2a9-8380-e3c9-ea11-615b69ef8783 | 1 | 0 |\n",
       "| 097a1d0a-db9a-9ae8-7639-9300e3199484 | 1 | 0 |\n",
       "| 0a728cec-84cd-2774-2530-df8408505738 | 1 | 0 |\n",
       "| 0ae17096-efd8-e4ce-79b2-c9d7a70992d8 | 1 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "  PATIENT_NUM                          carbamazepine pregabalin\n",
       "1 00444938-6d64-989b-b975-519e92b26171 1             0         \n",
       "2 07fde594-2d72-bb7f-9e7e-b646a7224623 1             0         \n",
       "3 081cc2a9-8380-e3c9-ea11-615b69ef8783 1             0         \n",
       "4 097a1d0a-db9a-9ae8-7639-9300e3199484 1             0         \n",
       "5 0a728cec-84cd-2774-2530-df8408505738 1             0         \n",
       "6 0ae17096-efd8-e4ce-79b2-c9d7a70992d8 1             0         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"122 patient(s) with epilepsy were taking carbamazepine\"\n",
      "[1] \"1 patient(s) with epilepsy were taking pregabalin\"\n"
     ]
    }
   ],
   "source": [
    "#filtering original dataset to patients receiving Carbamazepine or/and Pregabalin\n",
    "\n",
    "carba_preg<- medications %>%\n",
    "  filter(grepl(\"Carbamazepine\",CONCEPT_PATH) |\n",
    "         grepl(\"Pregabalin\", CONCEPT_PATH)) %>%\n",
    "  select(PATIENT_NUM,CONCEPT_PATH)\n",
    "\n",
    "#creating a new column indicating if a patient has been prescribed Carbamazepine or Pregabalin (yes=1,no=0) for easier visualization and data manipulation\n",
    "cleaned_medication <- carba_preg %>%\n",
    "  mutate(carbamazepine= if_else(grepl(\"Carbamazepine\", CONCEPT_PATH) , 1,0),\n",
    "         pregabalin=if_else(grepl(\"Pregabalin\", CONCEPT_PATH) , 1,0) ) %>%\n",
    "  group_by(PATIENT_NUM) %>%\n",
    "  summarize(\n",
    "    carbamazepine = max(carbamazepine),\n",
    "    pregabalin = max(pregabalin)\n",
    "  ) %>%\n",
    "  arrange(PATIENT_NUM)\n",
    "\n",
    "patient_icd10_med <- epilepsy_icd10_patient_ids %>% \n",
    "  left_join(cleaned_medication, by=\"PATIENT_NUM\") %>%\n",
    "  mutate_all(~replace(., is.na(.), 0)) %>%\n",
    "  arrange(PATIENT_NUM)\n",
    "\n",
    "head(cleaned_medication)\n",
    "\n",
    "print(paste(sum(patient_icd10_med$carbamazepine), \"patient(s) with epilepsy were taking carbamazepine\"))\n",
    "print(paste(sum(patient_icd10_med$pregabalin), \"patient(s) with epilepsy were taking pregabalin\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3ef527",
   "metadata": {},
   "source": [
    "### Investigating lab results over time for our cohort of interest\n",
    "\n",
    "We can isolate the lab related information by searching for 'ACT Lab Test Results' in the CONCEPT_PATH column.\n",
    "\n",
    "Below is a preview of the labs available in the dataset. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f952032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>CONCEPT_PATH</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>\\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\WBC Count\\WBC by Automated count        </td></tr>\n",
       "\t<tr><td>\\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\RBC Count\\RBC by Automated count        </td></tr>\n",
       "\t<tr><td>\\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\Hemoglobin\\Hemoglobin                   </td></tr>\n",
       "\t<tr><td>\\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\Hematocrit\\Hematocrit by Automated count</td></tr>\n",
       "\t<tr><td>\\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\MCV\\MCV by Automated count              </td></tr>\n",
       "\t<tr><td>\\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\MCH\\MCH by Automated count              </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 1\n",
       "\\begin{tabular}{l}\n",
       " CONCEPT\\_PATH\\\\\n",
       " <chr>\\\\\n",
       "\\hline\n",
       "\t \\textbackslash{}ACT Lab Test Results\\textbackslash{}Hematology/Coagulation\\textbackslash{}Hematology\\textbackslash{}WBC Count\\textbackslash{}WBC by Automated count        \\\\\n",
       "\t \\textbackslash{}ACT Lab Test Results\\textbackslash{}Hematology/Coagulation\\textbackslash{}Hematology\\textbackslash{}RBC Count\\textbackslash{}RBC by Automated count        \\\\\n",
       "\t \\textbackslash{}ACT Lab Test Results\\textbackslash{}Hematology/Coagulation\\textbackslash{}Hematology\\textbackslash{}Hemoglobin\\textbackslash{}Hemoglobin                   \\\\\n",
       "\t \\textbackslash{}ACT Lab Test Results\\textbackslash{}Hematology/Coagulation\\textbackslash{}Hematology\\textbackslash{}Hematocrit\\textbackslash{}Hematocrit by Automated count\\\\\n",
       "\t \\textbackslash{}ACT Lab Test Results\\textbackslash{}Hematology/Coagulation\\textbackslash{}Hematology\\textbackslash{}MCV\\textbackslash{}MCV by Automated count              \\\\\n",
       "\t \\textbackslash{}ACT Lab Test Results\\textbackslash{}Hematology/Coagulation\\textbackslash{}Hematology\\textbackslash{}MCH\\textbackslash{}MCH by Automated count              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 1\n",
       "\n",
       "| CONCEPT_PATH &lt;chr&gt; |\n",
       "|---|\n",
       "| \\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\WBC Count\\WBC by Automated count         |\n",
       "| \\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\RBC Count\\RBC by Automated count         |\n",
       "| \\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\Hemoglobin\\Hemoglobin                    |\n",
       "| \\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\Hematocrit\\Hematocrit by Automated count |\n",
       "| \\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\MCV\\MCV by Automated count               |\n",
       "| \\ACT Lab Test Results\\Hematology/Coagulation\\Hematology\\MCH\\MCH by Automated count               |\n",
       "\n"
      ],
      "text/plain": [
       "  CONCEPT_PATH                                                                                         \n",
       "1 \\\\ACT Lab Test Results\\\\Hematology/Coagulation\\\\Hematology\\\\WBC Count\\\\WBC by Automated count        \n",
       "2 \\\\ACT Lab Test Results\\\\Hematology/Coagulation\\\\Hematology\\\\RBC Count\\\\RBC by Automated count        \n",
       "3 \\\\ACT Lab Test Results\\\\Hematology/Coagulation\\\\Hematology\\\\Hemoglobin\\\\Hemoglobin                   \n",
       "4 \\\\ACT Lab Test Results\\\\Hematology/Coagulation\\\\Hematology\\\\Hematocrit\\\\Hematocrit by Automated count\n",
       "5 \\\\ACT Lab Test Results\\\\Hematology/Coagulation\\\\Hematology\\\\MCV\\\\MCV by Automated count              \n",
       "6 \\\\ACT Lab Test Results\\\\Hematology/Coagulation\\\\Hematology\\\\MCH\\\\MCH by Automated count              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a new dataframe containing only the lab test results for all patients in the original dataset:\n",
    "lab_test_results<- data %>%\n",
    "   filter(grepl(\"\\\\ACT Lab Test Results\",CONCEPT_PATH)) \n",
    "\n",
    "# creating a list with unique types of lab tests included in the dataset:\n",
    "lab_list<- data %>%\n",
    "   filter(grepl(\"\\\\ACT Lab Test Results\",CONCEPT_PATH)) %>%\n",
    "  select(CONCEPT_PATH)%>%\n",
    "  distinct()\n",
    "\n",
    "head(lab_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac333942",
   "metadata": {},
   "source": [
    "In the following example, we will focus on AST (aspartate aminotransferase) and ALT (alanine transaminase) lab test results. \n",
    "We will also only focus on lab results from 2023. \n",
    "- AST is an enzyme found in the liver, muscles, and other organs. When cells that contain AST are damaged, they release AST into the blood\n",
    "- An ALT blood test is used to diagnose liver disorders. ALT is a liver enzyme that is released in the blood when liver cells are damaged.\n",
    "\n",
    "(you can replace \"ALT\" or \"AST\" with your choice of lab test, to add additional lab tests just add a \"|\" before the next lab test name)\n",
    "\n",
    "\n",
    "*Note that the number of lab result entries can vary widely per patient, and even multiple entries per day are possible. In the graphic representation and statistical measurement tables, each individual lab result is treated as an individual data point, without the use of weighted averages or other methodological statistical adjustments.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bddd73de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PATIENT_NUM</th><th scope=col>lab_test</th><th scope=col>value</th><th scope=col>TIMESTAMP</th></tr>\n",
       "\t<tr><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;dttm&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0015fb36-0cad-3a48-87d9-a0f61c12c80d</td><td>alt</td><td>25.2</td><td>2023-12-23 02:34:46</td></tr>\n",
       "\t<tr><td>0015fb36-0cad-3a48-87d9-a0f61c12c80d</td><td>ast</td><td> 9.1</td><td>2023-12-23 02:34:46</td></tr>\n",
       "\t<tr><td>00bcc24e-4602-a868-1ce2-947f1ec1c00f</td><td>alt</td><td>33.8</td><td>2023-10-04 22:16:14</td></tr>\n",
       "\t<tr><td>00bcc24e-4602-a868-1ce2-947f1ec1c00f</td><td>ast</td><td>18.2</td><td>2023-10-04 22:16:14</td></tr>\n",
       "\t<tr><td>00d75a34-3344-8c62-401b-eb1fc27a4ba5</td><td>alt</td><td>30.1</td><td>2023-12-08 05:32:11</td></tr>\n",
       "\t<tr><td>00d75a34-3344-8c62-401b-eb1fc27a4ba5</td><td>ast</td><td>24.9</td><td>2023-12-08 05:32:11</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " PATIENT\\_NUM & lab\\_test & value & TIMESTAMP\\\\\n",
       " <chr> & <chr> & <dbl> & <dttm>\\\\\n",
       "\\hline\n",
       "\t 0015fb36-0cad-3a48-87d9-a0f61c12c80d & alt & 25.2 & 2023-12-23 02:34:46\\\\\n",
       "\t 0015fb36-0cad-3a48-87d9-a0f61c12c80d & ast &  9.1 & 2023-12-23 02:34:46\\\\\n",
       "\t 00bcc24e-4602-a868-1ce2-947f1ec1c00f & alt & 33.8 & 2023-10-04 22:16:14\\\\\n",
       "\t 00bcc24e-4602-a868-1ce2-947f1ec1c00f & ast & 18.2 & 2023-10-04 22:16:14\\\\\n",
       "\t 00d75a34-3344-8c62-401b-eb1fc27a4ba5 & alt & 30.1 & 2023-12-08 05:32:11\\\\\n",
       "\t 00d75a34-3344-8c62-401b-eb1fc27a4ba5 & ast & 24.9 & 2023-12-08 05:32:11\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 4\n",
       "\n",
       "| PATIENT_NUM &lt;chr&gt; | lab_test &lt;chr&gt; | value &lt;dbl&gt; | TIMESTAMP &lt;dttm&gt; |\n",
       "|---|---|---|---|\n",
       "| 0015fb36-0cad-3a48-87d9-a0f61c12c80d | alt | 25.2 | 2023-12-23 02:34:46 |\n",
       "| 0015fb36-0cad-3a48-87d9-a0f61c12c80d | ast |  9.1 | 2023-12-23 02:34:46 |\n",
       "| 00bcc24e-4602-a868-1ce2-947f1ec1c00f | alt | 33.8 | 2023-10-04 22:16:14 |\n",
       "| 00bcc24e-4602-a868-1ce2-947f1ec1c00f | ast | 18.2 | 2023-10-04 22:16:14 |\n",
       "| 00d75a34-3344-8c62-401b-eb1fc27a4ba5 | alt | 30.1 | 2023-12-08 05:32:11 |\n",
       "| 00d75a34-3344-8c62-401b-eb1fc27a4ba5 | ast | 24.9 | 2023-12-08 05:32:11 |\n",
       "\n"
      ],
      "text/plain": [
       "  PATIENT_NUM                          lab_test value TIMESTAMP          \n",
       "1 0015fb36-0cad-3a48-87d9-a0f61c12c80d alt      25.2  2023-12-23 02:34:46\n",
       "2 0015fb36-0cad-3a48-87d9-a0f61c12c80d ast       9.1  2023-12-23 02:34:46\n",
       "3 00bcc24e-4602-a868-1ce2-947f1ec1c00f alt      33.8  2023-10-04 22:16:14\n",
       "4 00bcc24e-4602-a868-1ce2-947f1ec1c00f ast      18.2  2023-10-04 22:16:14\n",
       "5 00d75a34-3344-8c62-401b-eb1fc27a4ba5 alt      30.1  2023-12-08 05:32:11\n",
       "6 00d75a34-3344-8c62-401b-eb1fc27a4ba5 ast      24.9  2023-12-08 05:32:11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>lab_test</th><th scope=col>mean</th><th scope=col>sd</th><th scope=col>n</th><th scope=col>se</th><th scope=col>lower_ci</th><th scope=col>upper_ci</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>alt</td><td>36.77405</td><td>13.93331</td><td>2883</td><td>0.2594968</td><td>36.26544</td><td>37.28267</td></tr>\n",
       "\t<tr><td>ast</td><td>22.96264</td><td>11.43122</td><td>2883</td><td>0.2128973</td><td>22.54536</td><td>23.37992</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " lab\\_test & mean & sd & n & se & lower\\_ci & upper\\_ci\\\\\n",
       " <chr> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t alt & 36.77405 & 13.93331 & 2883 & 0.2594968 & 36.26544 & 37.28267\\\\\n",
       "\t ast & 22.96264 & 11.43122 & 2883 & 0.2128973 & 22.54536 & 23.37992\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 7\n",
       "\n",
       "| lab_test &lt;chr&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; | n &lt;int&gt; | se &lt;dbl&gt; | lower_ci &lt;dbl&gt; | upper_ci &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| alt | 36.77405 | 13.93331 | 2883 | 0.2594968 | 36.26544 | 37.28267 |\n",
       "| ast | 22.96264 | 11.43122 | 2883 | 0.2128973 | 22.54536 | 23.37992 |\n",
       "\n"
      ],
      "text/plain": [
       "  lab_test mean     sd       n    se        lower_ci upper_ci\n",
       "1 alt      36.77405 13.93331 2883 0.2594968 36.26544 37.28267\n",
       "2 ast      22.96264 11.43122 2883 0.2128973 22.54536 23.37992"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filtering the original dataset to include only AST and ALT test results from the year 2023, cleaning the data, and organizing it by patient id:\n",
    "\n",
    "alt_ast<- lab_test_results %>%\n",
    "  filter(grepl(\"ALT|AST\",CONCEPT_PATH),\n",
    "         TIMESTAMP >= as.Date(\"2023-01-01\") & TIMESTAMP <= as.Date(\"2023-12-31\")) %>%\n",
    "  mutate(lab_test =case_when(grepl(\"AST\", CONCEPT_PATH) ~ \"ast\",\n",
    "                              grepl(\"ALT\", CONCEPT_PATH) ~ \"alt\")) %>%\n",
    "  rename(value=NVAL_NUM) %>%\n",
    "  select(PATIENT_NUM, lab_test, value, TIMESTAMP) %>%\n",
    " arrange(PATIENT_NUM, TIMESTAMP) %>%\n",
    "  distinct()\n",
    "  \n",
    "head(alt_ast)\n",
    "\n",
    "#creating a table with statistic summary values \n",
    "alt_ast_sum<- alt_ast %>%\n",
    "  group_by(lab_test) %>%\n",
    "  summarise(mean=mean(value),\n",
    "            sd= sd(value),\n",
    "            n=n()) %>%\n",
    "  mutate( se = sd / sqrt(n),\n",
    "          lower_ci= mean - 1.96*se ,\n",
    "          upper_ci= mean +1.96*se)\n",
    "\n",
    "alt_ast_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541b85c3",
   "metadata": {},
   "source": [
    "Let's now graph a distribution of ALT and AST values in 2023.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bd4bced2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkzZzZp0uS9994rOdXT03PdunUl2w8cOLBt27a33nqre/fuiYmJc+bMWb58+dtv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\nIiHsAAAi8R9TG5l4FYsztAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating a box plot graph of the results. \n",
    "\n",
    "ggplot(data=alt_ast)+\n",
    "  geom_boxplot(aes(x=lab_test, y=value, fill= lab_test))+\n",
    "  theme_bw()+\n",
    "  labs(x= \"Lab Test\", y= \"Value (u/l)\", title= paste(\"Liver Transaminase Levels in 2023, n=\", nrow(alt_ast)), fill= \" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b5d3d8",
   "metadata": {},
   "source": [
    "Let's say we are interested in a specific patient with epilepsy. \n",
    "Below, we plot the AST and ALT lab trends over the year 2023 for patient id # 1f9e58a1-0b6f-7f53-1495-8b05b69ad4ed.\n",
    "\n",
    "You can modify this code for any patient of interest by changing the `patient_of_interest` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "95bfbc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SwtXVSjzA4AAGCmkNjBNA249B1bKT5eu2htoBbJ7E5yuWQzyuwAAABmBIkdTI++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UtXt6GJArnNd5YRPWrVjvPjnh5QUDAYSb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\nQbADAABQif8fO4vHRN6hTR4AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "patient_of_interest <- \"1f9e58a1-0b6f-7f53-1495-8b05b69ad4ed\"\n",
    "\n",
    "#filtering for the specific patient's AST and ALT levels over time using the 2023 alt/ast dataframe we previously created: \n",
    "\n",
    "patient_group<- alt_ast %>% \n",
    "  filter(grepl(patient_of_interest, PATIENT_NUM))\n",
    "\n",
    "### creating a plot showing the results over time: \n",
    "\n",
    "ggplot(data = patient_group) +\n",
    "  geom_line( aes(x = TIMESTAMP, y = value, color = lab_test)) +\n",
    "  geom_point( aes(x = TIMESTAMP, y = value, color = lab_test)) +\n",
    "  theme_bw() +\n",
    "  theme(\n",
    "    legend.position = \"bottom\",  \n",
    "    axis.text.x = element_text(angle = 45, hjust = 1) \n",
    "  )+\n",
    "    labs(x = \"Date\", y = \"U/L\", title = \" Liver transaminase levels over time for patient '#', since event 'X' at 1.1.2023\", color =\" \")+\n",
    "  guides(color = guide_legend(nrow = 1, title.position = \"top\", title.hjust = 0.5))\n",
    "  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a447c1b6",
   "metadata": {},
   "source": [
    "## Investigating procedural data over time. Case study: Appendectomy.\n",
    "\n",
    "Finally, let's take a look at the procedures data. \n",
    "\n",
    "The procedures are defined by CPT codes. \n",
    "We will explore the procedures in this data set and look at specific procedures of interest in a given time frame.\n",
    "\n",
    "We can isolate the procedure related information by searching for 'ACT Procedures' in the CONCEPT_PATH column.\n",
    "\n",
    "Below is an example of the procedures documented in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8cbb9462",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a list with unique procedures in the dataset : \n",
    "\n",
    "procedures <- data %>%\n",
    "  filter(grepl(\"\\\\\\\\ACT Procedures\", CONCEPT_PATH)) %>%\n",
    "  distinct(CONCEPT_PATH, TVAL_CHAR) %>%\n",
    "  rename(procedure=TVAL_CHAR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850b0037",
   "metadata": {},
   "source": [
    "Below, we filter the procedural data to identify a cohort of patients who have undergone an appendectomy since the beginning of 2020.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "814f1c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PATIENT_NUM</th><th scope=col>procedure</th><th scope=col>TIMESTAMP</th><th scope=col>VARIABLE_TYPE</th><th scope=col>DATE</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>034cf0cf-3901-3e09-e7ce-f135e68563f2</td><td>Appendectomy</td><td>2021-11-18 16:45:31</td><td>ACT Procedures CPT</td><td>2021-11-18</td></tr>\n",
       "\t<tr><td>04d657b5-4d32-41f0-dd69-ea2ce63171a4</td><td>Appendectomy</td><td>2021-08-12 12:53:31</td><td>ACT Procedures CPT</td><td>2021-08-12</td></tr>\n",
       "\t<tr><td>0dd3a55e-a7ac-c500-d9f7-e8404f895550</td><td>Appendectomy</td><td>2022-01-02 11:29:09</td><td>ACT Procedures CPT</td><td>2022-01-02</td></tr>\n",
       "\t<tr><td>10069500-025a-aedf-0faa-cff1f353b47f</td><td>Appendectomy</td><td>2022-05-27 03:19:19</td><td>ACT Procedures CPT</td><td>2022-05-27</td></tr>\n",
       "\t<tr><td>1ad25b98-ef3b-8384-4fb6-b1afd723b64a</td><td>Appendectomy</td><td>2023-08-15 00:39:56</td><td>ACT Procedures CPT</td><td>2023-08-15</td></tr>\n",
       "\t<tr><td>23a98b50-2224-a2db-786a-87f8161fc4b1</td><td>Appendectomy</td><td>2020-08-17 11:31:32</td><td>ACT Procedures CPT</td><td>2020-08-17</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " PATIENT\\_NUM & procedure & TIMESTAMP & VARIABLE\\_TYPE & DATE\\\\\n",
       " <chr> & <chr> & <dttm> & <chr> & <date>\\\\\n",
       "\\hline\n",
       "\t 034cf0cf-3901-3e09-e7ce-f135e68563f2 & Appendectomy & 2021-11-18 16:45:31 & ACT Procedures CPT & 2021-11-18\\\\\n",
       "\t 04d657b5-4d32-41f0-dd69-ea2ce63171a4 & Appendectomy & 2021-08-12 12:53:31 & ACT Procedures CPT & 2021-08-12\\\\\n",
       "\t 0dd3a55e-a7ac-c500-d9f7-e8404f895550 & Appendectomy & 2022-01-02 11:29:09 & ACT Procedures CPT & 2022-01-02\\\\\n",
       "\t 10069500-025a-aedf-0faa-cff1f353b47f & Appendectomy & 2022-05-27 03:19:19 & ACT Procedures CPT & 2022-05-27\\\\\n",
       "\t 1ad25b98-ef3b-8384-4fb6-b1afd723b64a & Appendectomy & 2023-08-15 00:39:56 & ACT Procedures CPT & 2023-08-15\\\\\n",
       "\t 23a98b50-2224-a2db-786a-87f8161fc4b1 & Appendectomy & 2020-08-17 11:31:32 & ACT Procedures CPT & 2020-08-17\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 5\n",
       "\n",
       "| PATIENT_NUM &lt;chr&gt; | procedure &lt;chr&gt; | TIMESTAMP &lt;dttm&gt; | VARIABLE_TYPE &lt;chr&gt; | DATE &lt;date&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 034cf0cf-3901-3e09-e7ce-f135e68563f2 | Appendectomy | 2021-11-18 16:45:31 | ACT Procedures CPT | 2021-11-18 |\n",
       "| 04d657b5-4d32-41f0-dd69-ea2ce63171a4 | Appendectomy | 2021-08-12 12:53:31 | ACT Procedures CPT | 2021-08-12 |\n",
       "| 0dd3a55e-a7ac-c500-d9f7-e8404f895550 | Appendectomy | 2022-01-02 11:29:09 | ACT Procedures CPT | 2022-01-02 |\n",
       "| 10069500-025a-aedf-0faa-cff1f353b47f | Appendectomy | 2022-05-27 03:19:19 | ACT Procedures CPT | 2022-05-27 |\n",
       "| 1ad25b98-ef3b-8384-4fb6-b1afd723b64a | Appendectomy | 2023-08-15 00:39:56 | ACT Procedures CPT | 2023-08-15 |\n",
       "| 23a98b50-2224-a2db-786a-87f8161fc4b1 | Appendectomy | 2020-08-17 11:31:32 | ACT Procedures CPT | 2020-08-17 |\n",
       "\n"
      ],
      "text/plain": [
       "  PATIENT_NUM                          procedure    TIMESTAMP          \n",
       "1 034cf0cf-3901-3e09-e7ce-f135e68563f2 Appendectomy 2021-11-18 16:45:31\n",
       "2 04d657b5-4d32-41f0-dd69-ea2ce63171a4 Appendectomy 2021-08-12 12:53:31\n",
       "3 0dd3a55e-a7ac-c500-d9f7-e8404f895550 Appendectomy 2022-01-02 11:29:09\n",
       "4 10069500-025a-aedf-0faa-cff1f353b47f Appendectomy 2022-05-27 03:19:19\n",
       "5 1ad25b98-ef3b-8384-4fb6-b1afd723b64a Appendectomy 2023-08-15 00:39:56\n",
       "6 23a98b50-2224-a2db-786a-87f8161fc4b1 Appendectomy 2020-08-17 11:31:32\n",
       "  VARIABLE_TYPE      DATE      \n",
       "1 ACT Procedures CPT 2021-11-18\n",
       "2 ACT Procedures CPT 2021-08-12\n",
       "3 ACT Procedures CPT 2022-01-02\n",
       "4 ACT Procedures CPT 2022-05-27\n",
       "5 ACT Procedures CPT 2023-08-15\n",
       "6 ACT Procedures CPT 2020-08-17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filtering to create a new dataframe including patients with a history of an appendectomy procedure\n",
    "\n",
    "appendectomy<- data %>%\n",
    "  filter(grepl(\"Appendectomy\", TVAL_CHAR)) %>%\n",
    "  rename(procedure=TVAL_CHAR) %>%\n",
    "  select(-CONCEPT_PATH, -NVAL_NUM) %>%\n",
    "  distinct()\n",
    "\n",
    "# filtering to only include patients who underwent an appendectomy since the beginning of the year 2020\n",
    "\n",
    "appendectomy_2020<- appendectomy %>%\n",
    "  filter(TIMESTAMP >= as.Date(\"2020-01-01\")) %>%\n",
    "  arrange(PATIENT_NUM)\n",
    "\n",
    "head(appendectomy_2020)"
   ]
  }
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