week07e-example-comparing-wcoa-temperature.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: comparing temperature in three regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import stats\n",
    "import statsmodels.formula.api as smf\n",
    "import pingouin as pg\n",
    "\n",
    "import PyCO2SYS as pyco2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data. Note: you may need to change the path to the data already downloaded last week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename07 = 'data/wcoa_cruise_2007/32WC20070511.exc.csv'\n",
    "df07 = pd.read_csv(filename07,header=29,na_values=-999,parse_dates=[[6,7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the PyCO2SYS package to calculate seawater carbon chemistry parameters.\n",
    "\n",
    "https://pyco2sys.readthedocs.io/en/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "c07 = pyco2.sys(df07['ALKALI'], df07['TCARBN'], 1, 2,\n",
    "               salinity=df07['CTDSAL'], temperature=df07['CTDTMP'], \n",
    "                pressure=df07['CTDPRS'])\n",
    "\n",
    "df07['OmegaA'] = c07['saturation_aragonite']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a subset of good data in the upper 10m (near surface)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "iisurf07 = ((df07['CTDPRS'] <= 10) &\n",
    "      (df07['NITRAT_FLAG_W'] == 2) & (df07['PHSPHT_FLAG_W'] == 2)\n",
    "      & (df07['CTDOXY_FLAG_W'] == 2) & (df07['CTDSAL_FLAG_W'] == 2) \n",
    "        & (df07['ALKALI_FLAG_W'] == 2) & (df07['TCARBN_FLAG_W'] == 2))\n",
    "\n",
    "df07surf = df07[iisurf07]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing three sample means \n",
    "\n",
    "In this example, we are interested in comparing the observed surface temperature in three regions of the California Current System.\n",
    "\n",
    "##### Create a categorical variable for the region\n",
    "\n",
    "We will divide the data into three categories: north if the Columbia River, between the Columbia River and Golden Gate, and south of the Golden Gate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22      None\n",
       "46      None\n",
       "71      None\n",
       "95      None\n",
       "151     None\n",
       "        ... \n",
       "2275    None\n",
       "2287    None\n",
       "2310    None\n",
       "2334    None\n",
       "2335    None\n",
       "Name: region, Length: 138, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new variable called \"region\" with no values\n",
    "df07surf = df07surf.assign(region = [None]*len(df07surf))\n",
    "df07surf['region']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22      north\n",
       "46      north\n",
       "71      north\n",
       "95      north\n",
       "151     north\n",
       "        ...  \n",
       "2275    south\n",
       "2287    south\n",
       "2310    south\n",
       "2334    south\n",
       "2335    south\n",
       "Name: region, Length: 138, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign string values to region based on latitude\n",
    "northern = (df07surf['LATITUDE'] > 46.2)\n",
    "central = (df07surf['LATITUDE'] <= 46.2) & (df07surf['LATITUDE'] >= 37.8)\n",
    "southern = (df07surf['LATITUDE'] < 37.8) \n",
    "\n",
    "df07surf.loc[northern,'region'] = 'north'\n",
    "df07surf.loc[central,'region'] = 'central'\n",
    "df07surf.loc[southern,'region'] = 'south'\n",
    "df07surf['region']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'region')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.boxplot([df07surf['CTDTMP'][df07surf['region']=='north'],\n",
    "             df07surf['CTDTMP'][df07surf['region']=='central'],\n",
    "             df07surf['CTDTMP'][df07surf['region']=='south']],\n",
    "            labels=['north','central','south'],showmeans=True,notch=True);\n",
    "plt.title('CTDTMP - upper 10m 2007')\n",
    "plt.ylabel('[deg C]')\n",
    "plt.xlabel('region')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. What test (or tests) would you use to determine whether there is a statistically significant difference in surface temperature between these regions?\n",
    "2. What possible alternative tests did you rule out?\n",
    "3. What is the null hypothesis being tested?\n",
    "4. What are the underlying assumptions?\n",
    "5. Try to implement the analysis with pingouin. For reference, see the [pingouin documentation](https://pingouin-stats.org/api.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}