{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis from my journal\n",
    "\n",
    "I've tracked my sleep for a few days, about its quality and length, to have statistics and analyze this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hideCode": true
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hideCode": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: \n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "# Setup\n",
    "library(dplyr)\n",
    "library(ggplot2)\n",
    "data = read.csv('data.csv') # Read data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can begin by checking how often I miss my alarm clock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "data %>% group_by(AwakeningReason) %>% summarize(Count=n()) %>% ungroup() %>% arrange(AwakeningReason) -> data_summary\n",
    "\n",
    "data_summary %>% as.data.frame() -> data_df\n",
    "\n",
    "lbls = paste(data_df$AwakeningReason,\"(\", data_df$Count, \")\")\n",
    "\n",
    "pie(data_df$Count, labels=lbls, col=c('darkcyan','brown3','white'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since I take naps, let's split this pie chart in two parts to see if missing my alarm clock is related to my naps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "data %>% group_by(BeginningTime, AwakeningReason) -> data_awakening\n",
    "\n",
    "data_awakening %>% filter(BeginningTime > (6*60*60)) %>% group_by(AwakeningReason) %>% summarize(Count=n()) %>% ungroup() %>% arrange(AwakeningReason)-> data_nap\n",
    "data_awakening %>% filter(BeginningTime <= (6*60*60)) %>% group_by(AwakeningReason) %>% summarize(Count=n()) %>% ungroup() %>% arrange(AwakeningReason) -> data_night\n",
    "\n",
    "# par(mfrow = c(1, 2))\n",
    "# par(mar=c(0,5,0,5)+.5)\n",
    "\n",
    "draw_pie <- function(name, data, cols) {\n",
    "    pie(data$Count, main=name, labels=paste(data$AwakeningReason,\"(\", data$Count, \")\"), col=cols, width=150)\n",
    "}\n",
    "\n",
    "\n",
    "draw_pie(\"Night\", data_night, c('darkcyan'))\n",
    "draw_pie(\"Nap\", data_nap, c('brown3','white'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check when do I go to sleep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "hist(data$BeginningTime, xlab='Time', main='Histogram of when I go to bed', xlim=c(0, 60*60*24), col='skyblue1', breaks=24, xaxt='n')\n",
    "axis(side=1, at=seq(0, 60*60*24, 3600), labels=seq(0, 24, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see I'm usually going to bed between midnight and 2 AM, but I also take naps in the afternoon (I took 4 naps out of the week of tracking my sleep).\n",
    "\n",
    "Now we can look at how much I sleep each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1oBA4ulPqAyP5PhguAosjsBd20R4UArPJ8kDgWCCwF3bRHhQCCyCwOAJ7YRftQSEwmywPBI4FAnthF+1BIbAAAosjsBd20R4UArPJ8kDgWCCwF3bRHhQCCyCwOAJ7YRftQSEwmywPBI4FAnthF+1BIbAAAosjsBd20R4UArPJ8kDgWPjKBt4zcXz9ihowZ0bU34m3r2zgrwoCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4yYOx4JI8GAsuyYOx4JI8GAsuyYOx4JI8GAsu3iaJa4LA6yM/GAuuJgi8JfKj7eAKJvDO3TWv8RIdC74Dr51hXxuY12rE5uwBAseC78CDF9mxvy8vnzc6e4DAseA7cJsy26vC2ooO2QMEjgXfgcfcUjHzn9beOTx7gMCx4Dvwu9/uMbbg2F79+UVHPAWwi37r4UV/e+VgjcsEjgV+kyWOwOIILI7A4ggsjsDiCCyOwOIILI7A4ggsjsDiCCyOwOIILI7A4ggsjsDiCCyOwOIILI7A4ggsjsDiCCyOwOIILI7A4ggsjsDimi7w9T0bcGwYmtqR7zVVYOQCAosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7A4AosjsDgCiyOwOAKLI7C4/wMWThz980TGwQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "\n",
    "data %>% group_by(Date) %>% summarize(Count=sum(Length)) %>% as.data.frame() -> sleep_per_day\n",
    "\n",
    "boxplot(sleep_per_day$Count, main=\"Amount of time slept each day\", ylim=c(20000,40000), yaxt='n')\n",
    "axis(side=2, at=seq(0, 60*60*24, 3600), labels=paste(seq(0, 24, 1),'h'))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Hide code",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}