blood_pressure_R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dbfe8739-b395-4d18-b53d-09fbfcf704a2",
   "metadata": {},
   "source": [
    "# Blood pressure in R\n",
    "\n",
    "Just some plots from [blood_pressure.ipynb](./blood_pressure.ipynb) replicated in R 😊 To get R in Jupyter, `conda install -c r r-irkernel` was enough, if I remember right 🤔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c107c0c8-6dda-4301-a736-db95304e5e61",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \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[34mlubridate\u001b[39m 1.9.3     \u001b[32m✔\u001b[39m \u001b[34mtibble   \u001b[39m 3.2.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mpurrr    \u001b[39m 1.0.2     \u001b[32m✔\u001b[39m \u001b[34mtidyr    \u001b[39m 1.3.1\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": [
    "library(ggplot2)\n",
    "library(readxl)\n",
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed7efa10-07cd-4320-8d7c-581a7d7b6293",
   "metadata": {},
   "outputs": [],
   "source": [
    "theme_set(theme_classic(base_size = 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0d5f962-bd52-4ba1-a5fc-056a60e20a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "blood_pressure <- read_xlsx(\"data/blood_pressure.xlsx\")\n",
    "\n",
    "blood_pressure$Meter <- factor(\n",
    "    blood_pressure$Meter,\n",
    "    levels = c(\"Omron M3\", \"Sanitas SBC 22\", \"Boso Medicus X\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f6103a2-cc1b-4387-81df-395a4fc96845",
   "metadata": {},
   "outputs": [
    {
     "data": {
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PSNFEJCKAgJEEBIg+OdqiCwjTQwjp0I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\nCCAkQAAhAQIICRDw3ylGqWgvUd+UAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "blood_pressure <- blood_pressure %>%\n",
    "    mutate(Calibration = case_when(\n",
    "        (blood_pressure$Timestamp > \"2024-01-31\" & blood_pressure$Timestamp < \"2024-02-01\") ~ \"Meter\",\n",
    "        (blood_pressure$Timestamp > \"2024-02-01\" & blood_pressure$Timestamp < \"2024-02-02\") ~ \"Position\",\n",
    "        (blood_pressure$Timestamp > \"2024-02-26\" & blood_pressure$Timestamp < \"2024-02-27\") ~ \"Meter II\"\n",
    "    ))\n",
    "\n",
    "calibration_of_meters <- rename(subset(blood_pressure, Calibration == \"Meter\"), variable = Meter)\n",
    "calibration_of_positon <- rename(subset(blood_pressure, Calibration == \"Position\"), variable = Position)\n",
    "calibration_of_meters_2 <- rename(subset(blood_pressure, Calibration == \"Meter II\"), variable = Meter)\n",
    "\n",
    "rbind(\n",
    "    select(calibration_of_meters, Sys, Dia, Calibration, variable),\n",
    "    select(calibration_of_positon, Sys, Dia, Calibration, variable),\n",
    "    select(calibration_of_meters_2, Sys, Dia, Calibration, variable)\n",
    ") %>%\n",
    "    pivot_longer(c(Sys, Dia), names_to = \"Pressure\") %>%\n",
    "    ggplot(mapping = aes(x = variable, y = value, colour = Pressure)) + \n",
    "    geom_jitter(width = 0.125) + \n",
    "    geom_hline(yintercept = 85, linetype = \"dotted\", colour = \"black\") +\n",
    "    geom_hline(yintercept = 135, linetype = \"dotted\", colour = \"black\") +\n",
    "    facet_wrap(\"Calibration\", scales = \"free_x\") + \n",
    "    theme(legend.position = \"none\") + \n",
    "    labs(x = \"Conditions\", y = \"Blood pressure\", title = \"Calibration\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "05cb736b-d84d-4a3a-b141-ce4cfe7245ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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U1mvSJpSFuemEWSK6r2pC0vvcOXKpK5JR2J2kPCGSqE7orQKEkkX2iPNU13oPY5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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "blood_pressure %>%\n",
    "    subset(is.na(Calibration)) %>%\n",
    "    pivot_longer(c(Sys, Dia), names_to = \"Pressure\") %>%\n",
    "    ggplot(mapping = aes(x = Pulse, y = value, colour = Pressure)) + \n",
    "    geom_point() + \n",
    "    geom_smooth() +\n",
    "    geom_hline(yintercept = 85, linetype = \"dotted\", colour = \"black\") +\n",
    "    geom_hline(yintercept = 135, linetype = \"dotted\", colour = \"black\") +\n",
    "    facet_wrap(\"Meter\") + \n",
    "    theme(legend.position = \"none\") + \n",
    "    labs(y = \"Blood pressure\", title = \"Vs. Pulse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aff20272-cff8-4e29-9c89-66fd8c43501d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94180dfc-18a1-4cbb-afa1-e2ed61d908e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "blood_pressure$Weekday <- weekdays(blood_pressure$Timestamp, abbreviate = TRUE)\n",
    "blood_pressure$Weekday <- factor(\n",
    "    blood_pressure$Weekday,\n",
    "    levels = c(\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a8065d4-9d50-4a78-ac90-7d9ad3c64883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hy6isgMCqt3MESHbURYnQdE5033IHTlZnGO1jf/Mhtuvook1pLDsCKskkgKCKCP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\nADP8H8mpEdEsXLTIAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=7)\n",
    "\n",
    "blood_pressure %>%\n",
    "    subset(is.na(Calibration)) %>%\n",
    "    pivot_longer(c(Sys, Dia), names_to = \"Pressure\") %>%\n",
    "    ggplot(mapping = aes(x = Weekday, y = value, colour = Pressure)) + \n",
    "    geom_point() + \n",
    "    geom_jitter(width = 0.2) +\n",
    "    geom_hline(yintercept = 85, linetype = \"dotted\", colour = \"black\") +\n",
    "    geom_hline(yintercept = 135, linetype = \"dotted\", colour = \"black\") +\n",
    "    facet_wrap(\"Meter\") + \n",
    "    theme(legend.position = \"none\") + \n",
    "    labs(y = \"Blood pressure\", title = \"Weekdays\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee90ba77-0cea-48a1-8591-5bb5def67299",
   "metadata": {},
   "outputs": [],
   "source": [
    "blood_pressure$Time <- hour(blood_pressure$Timestamp) + minute(blood_pressure$Timestamp) / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f6ba2130-eb05-40c9-a265-aa68ef261c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aLrPg6BA+Yi1Q6bfqV28sZKsnEjSVW54LfFy01ieLt41nW8SQSOJCYSdiKtWF02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\n1nFamgSOJ1l+9OdnbxuFc1I6slbRon7t94gXM0jt/vHjEae0EC7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66sfrxmFbMRdXV4EDR1/jrfvA4J8xJQe54N9F9n65fJ8tFi/zP9+u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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 1080
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 18, repr.plot.height = 7)\n",
    "\n",
    "blood_pressure %>%\n",
    "    subset(is.na(Calibration)) %>%\n",
    "    pivot_longer(c(Sys, Dia), names_to = \"Pressure\") %>%\n",
    "    ggplot(mapping = aes(x = Time, y = value, colour = Pressure)) + \n",
    "    geom_point() +\n",
    "    geom_hline(yintercept = 85, linetype = \"dotted\", colour = \"black\") +\n",
    "    geom_hline(yintercept = 135, linetype = \"dotted\", colour = \"black\") +\n",
    "    facet_wrap(\"Meter\") + \n",
    "    theme(legend.position = \"none\") + \n",
    "    labs(y = \"Blood pressure\", x = NULL, title = \"Time of day\") +\n",
    "    coord_polar(start = 0) +\n",
    "    scale_x_continuous(limits = c(0, 24), breaks = seq(0, 24, by = 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1502fbe4-fb5a-489e-987e-13a1af9a3355",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in mean.default(replicates[col]):\n",
      "\"argument is not numeric or logical: returning NA\"\n",
      "Warning message in mean.default(replicates[col]):\n",
      "\"argument is not numeric or logical: returning NA\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Evaluation</th><th scope=col>Kind</th><th scope=col>Blood.Pressure</th><th scope=col>Evaluation.1</th><th scope=col>Kind.1</th><th scope=col>Blood.Pressure.1</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;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>Mean</td><td>Sys</td><td>NA</td><td>Mean</td><td>Dia</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " Evaluation & Kind & Blood.Pressure & Evaluation.1 & Kind.1 & Blood.Pressure.1\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Mean & Sys & NA & Mean & Dia & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 6\n",
       "\n",
       "| Evaluation &lt;chr&gt; | Kind &lt;chr&gt; | Blood.Pressure &lt;chr&gt; | Evaluation.1 &lt;chr&gt; | Kind.1 &lt;chr&gt; | Blood.Pressure.1 &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Mean | Sys | NA | Mean | Dia | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  Evaluation Kind Blood.Pressure Evaluation.1 Kind.1 Blood.Pressure.1\n",
       "1 Mean       Sys  NA             Mean         Dia    NA              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyse <- function(replicates) {\n",
    "    rows = list()\n",
    "    for (col in c(\"Sys\", \"Dia\")) {\n",
    "        rows = c(\n",
    "            rows, \n",
    "            c(\n",
    "                \"Evaluation\" = \"Mean\",\n",
    "                \"Kind\" = col,\n",
    "                \"Blood Pressure\" = mean(replicates[col])\n",
    "            )\n",
    "        )\n",
    "    }\n",
    "\n",
    "    # slice(replicates, 1:2)\n",
    "    \n",
    "    data.frame(rows)\n",
    "}\n",
    "\n",
    "\n",
    "measurements <- blood_pressure[\n",
    "    order(blood_pressure$Side, blood_pressure$Meter, blood_pressure$Timestamp),\n",
    "] %>% \n",
    "    group_by((c(0, diff(blood_pressure$Timestamp)) > 30) %>% cumsum) %>%\n",
    "    analyse() %>%\n",
    "    ungroup()\n",
    "\n",
    "measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5d072ec-04a8-4f26-8d28-c36ef1f888b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3be41d0-d766-498d-8a2c-75ad9c654e5a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in to_vec(for (col in c(\"Sys\", \"Dia\")) col): could not find function \"to_vec\"\n",
     "output_type": "error",
     "traceback": [
      "Error in to_vec(for (col in c(\"Sys\", \"Dia\")) col): could not find function \"to_vec\"\nTraceback:\n"
     ]
    }
   ],
   "source": [
    "to_vec(\n",
    "    for( col in c(\"Sys\", \"Dia\")) col\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3de29bf8-5a09-4bd2-9557-7bb649f3d59c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = list()\n",
    "for (col in c(\"Sys\", \"Dia\")) {\n",
    "    rows = c(rows, col)\n",
    "}\n",
    "\n",
    "rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44ae5219-69f9-4842-afa8-d2ba04f16076",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.frame(rows)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}