STAT201-Group17-ProjectProposal.ipynb

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   "id": "c5ea7515-4ce0-4c78-8928-2f837c7bb556",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in system(\"timedatectl\", intern = TRUE):\n",
      "“running command 'timedatectl' had status 1”\n"
     ]
    }
   ],
   "source": [
    "# install.packages(\"tidymodels\")\n",
    "# install.packages(\"infer\")\n",
    "suppressPackageStartupMessages(library(tidyverse))\n",
    "suppressPackageStartupMessages(library(tidymodels))\n",
    "suppressPackageStartupMessages(library(repr))\n",
    "suppressPackageStartupMessages(library(infer))\n",
    "suppressPackageStartupMessages(library(GGally))\n",
    "options(repr.matrix.max.rows = 6)\n",
    "options(repr.plot.width = 6, repr.plot.height = 6)\n",
    "set.seed(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f928fb-239b-463b-afb6-1d41b9135c0f",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Climate Change: Study temperature and precipitation in historical vs contemporary weather records   \n",
    "**STAT 201  Project Proposal - Group 17  \n",
    "Date:         March 03, 2022**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17388a0e-8ae8-449d-b820-8ef60422b94f",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Introduction\n",
    "\n",
    "Climate change refers to long-term shifts in temperatures and weather patterns (United Nations, n.d.-b). The Canadian Climate and Weather Database provides weather records from numerous weather stations across Canada. Each weather station contains weather records in hourly, daily, and monthly formats. We intend to study the presence of climate change from some historical years to the present. In order to obtain a meaningful and relatively large time span, the group queried stations with data available between 1950 and 2022. From the 8796 weather stations, the group randomly selected **ABBOTSFORD A** weather station to use for this study (Historical Data, 2021, November 25).\n",
    "\n",
    "The group chooses daily temperature and daily precipitation as the random variable across two categories - historical vs contemporary. In both variables, the group chooses to study the parameter mean. We believe that the difference in mean in daily temperature and precipitation can aid us to study the claimed effect of climate change: hotter temperature and increased drought (United Nations, n.d.-a). Thus, this project aims to answer:\n",
    "  \n",
    "- Is there a significant difference in the weather record of the 1950s and the 2000s in terms of temperature and precipitation? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace6d383-eca7-4499-8c81-23a622dfe12a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Preliminary Results\n",
    "\n",
    "### Data Importing, Cleaning, and Wrangling\n",
    "  \n",
    "We first downloaded and imported daily data from 1951 to 1955 and 2001 to 2005."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b09ee1d-0728-4c70-87f4-570b0a367e25",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "climate_1951 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1951_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1952 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1952_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1953 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1953_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1954 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1954_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1955 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1955_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2001 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2001_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2002 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2002_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2003 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2003_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2004 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2004_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2005 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2005_P1D.csv\", show_col_types = FALSE)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1350c3e5-03cf-489a-a3ea-fbb57271d979",
   "metadata": {},
   "source": [
    "Next, we combined the yearly data and mutated a column to indicate the recorded time. The data in the 1951-1955 are labeled as `historical` and 2001-2005 as `contemporary`. We further combined these two data frames to serve as the raw sample for this study.\n",
    "      \n",
    "The raw sample contains 31 variables of weather measurements in which we decided to use the following as our possible parameters: \n",
    "- `Year`: the year of the weather record \n",
    "- `Mean Temp`: average temperature of the day in degrees Celsius\n",
    "- `Total Precip`: total amount of precipitation measured in millimetres. \n",
    "  \n",
    "The following codes demonstrate the steps of data cleaning and wrangling:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "077dc27b-ce13-40a3-99d3-14628eb3135d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "climate_historical <- \n",
    "    rbind(climate_1951, climate_1952, climate_1953, climate_1954, climate_1955) %>%\n",
    "    mutate(Category = as.factor(\"historical\"))\n",
    "\n",
    "climate_contemporary <- \n",
    "    rbind(climate_2001, climate_2002, climate_2003, climate_2004, climate_2005) %>%\n",
    "    mutate(Category = as.factor(\"contemporary\"))\n",
    "\n",
    "climate_merge <- rbind(climate_historical, climate_contemporary) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbb4edc4-f76e-48a2-b5d9-3d1a7f63a754",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Category</th><th scope=col>Year</th><th scope=col>Mean_Temp</th><th scope=col>Total_Precip</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&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>historical</td><td>1951</td><td> 3.6</td><td>21.3</td></tr>\n",
       "\t<tr><td>historical</td><td>1951</td><td> 3.6</td><td>32.0</td></tr>\n",
       "\t<tr><td>historical</td><td>1951</td><td> 4.2</td><td> 1.3</td></tr>\n",
       "\t<tr><td>historical</td><td>1951</td><td>-0.3</td><td> 0.0</td></tr>\n",
       "\t<tr><td>historical</td><td>1951</td><td> 0.8</td><td> 0.0</td></tr>\n",
       "\t<tr><td>historical</td><td>1951</td><td> 2.3</td><td> 0.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Category & Year & Mean\\_Temp & Total\\_Precip\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t historical & 1951 &  3.6 & 21.3\\\\\n",
       "\t historical & 1951 &  3.6 & 32.0\\\\\n",
       "\t historical & 1951 &  4.2 &  1.3\\\\\n",
       "\t historical & 1951 & -0.3 &  0.0\\\\\n",
       "\t historical & 1951 &  0.8 &  0.0\\\\\n",
       "\t historical & 1951 &  2.3 &  0.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 4\n",
       "\n",
       "| Category &lt;fct&gt; | Year &lt;dbl&gt; | Mean_Temp &lt;dbl&gt; | Total_Precip &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| historical | 1951 |  3.6 | 21.3 |\n",
       "| historical | 1951 |  3.6 | 32.0 |\n",
       "| historical | 1951 |  4.2 |  1.3 |\n",
       "| historical | 1951 | -0.3 |  0.0 |\n",
       "| historical | 1951 |  0.8 |  0.0 |\n",
       "| historical | 1951 |  2.3 |  0.0 |\n",
       "\n"
      ],
      "text/plain": [
       "  Category   Year Mean_Temp Total_Precip\n",
       "1 historical 1951  3.6      21.3        \n",
       "2 historical 1951  3.6      32.0        \n",
       "3 historical 1951  4.2       1.3        \n",
       "4 historical 1951 -0.3       0.0        \n",
       "5 historical 1951  0.8       0.0        \n",
       "6 historical 1951  2.3       0.0        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "climate_sample <- climate_merge %>%\n",
    "    select(Category, Year, \"Mean Temp (°C)\", \"Total Precip (mm)\") %>%\n",
    "    rename(\"Mean_Temp\" = \"Mean Temp (°C)\",\n",
    "           \"Total_Precip\" = \"Total Precip (mm)\")\n",
    "head(climate_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81fbbd9b-4ffb-40d3-b55f-97842704bf40",
   "metadata": {},
   "source": [
    "*Table 1. Table of sample weather record at Abbotsford A weather station.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51220b5a-b81d-4411-b8b9-b275c27e4218",
   "metadata": {},
   "source": [
    "### Preliminary Data Explore - Temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64bd1df-3ce7-4d03-bfed-0a3bbea0d77a",
   "metadata": {},
   "source": [
    "United Nations (n.d.-b) states that the temperature in the recent decade is higher than last century. We visualized the temperature change with a boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "086f7940-0d09-473c-b872-fc1e83abd93a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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BvfzBfLaLmx3ZU9woGsd7Zqs5exGX2p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\nvWkFhRYQYYT+tns5+bzhyIM2dEahXY8YQh+fWkuyuXja53bPgjv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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_temp <- climate_sample %>%\n",
    "    filter(!is.na(Mean_Temp)) %>%\n",
    "    select(Category, Mean_Temp)\n",
    "\n",
    "temp_boxplot <- station_temp %>%\n",
    "    ggplot(aes(x = Category, y = Mean_Temp)) +\n",
    "    geom_boxplot() +\n",
    "    labs(x = \"Temporal Category\", y = \"Daily Mean Temperature (°C)\") +\n",
    "    ggtitle(\"Boxplot of Temperatures\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "temp_boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e63eb7-6300-41fa-84cb-daa2590a5372",
   "metadata": {},
   "source": [
    "*Figure 1. Boxplot of daily mean temperature at Abbotsford A weather station*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b819d3-5826-4d98-9ca5-d4f142d19a40",
   "metadata": {},
   "source": [
    "From this boxplot, we can observe that there is a small difference in the mean of daily `Mean_Temp` between the historical and contemporary weather records. However, is there a ***real*** difference in the mean of the daily temperature? We answer this question by bootstrapping."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61d0281-4c04-4036-b5d1-3f3e17a50f87",
   "metadata": {},
   "source": [
    "### Bootstrapping for Point Estimates - Temperature\n",
    "\n",
    "We first visualized the sample distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f749e453-3d03-422e-9e4f-cda25d8fd711",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 14, repr.plot.height = 6)\n",
    "\n",
    "station_temp_sample_distribution <- station_temp %>%\n",
    "    ggplot(aes(x = Mean_Temp, fill = Category)) +\n",
    "    geom_histogram(binwidth = 1, color = \"lightgrey\") +\n",
    "    facet_grid(. ~ Category) +\n",
    "    labs(x = \"Daily temperature (°C)\", y = \"Frequence\") +\n",
    "    ggtitle(\"Sample distribution of daily mean temperature\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "station_temp_sample_distribution   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432d1932-17b4-4286-8e6c-ecad43c075f9",
   "metadata": {},
   "source": [
    "*Figure 2. Sample distribution of daily mean temperature at Abbotsford A weather station.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fffdff10-70e3-477e-98a3-dc0a4b783a25",
   "metadata": {},
   "source": [
    "We then used a bootstrap distribution to estimate the standard error in the point estimate and generated a 95% confidence interval for each point estimate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63aabc65-3a67-4942-88e7-a1c1c6d8d3a1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "set.seed(100)\n",
    "\n",
    "temp_historical <- station_temp %>%\n",
    "    filter(Category == \"historical\")\n",
    "\n",
    "temp_historical_mean <- temp_historical %>%\n",
    "    summarize(mean = mean(Mean_Temp)) %>%\n",
    "    pull()\n",
    "\n",
    "temp_historical_bootstrap <- temp_historical %>%\n",
    "    specify(response = Mean_Temp) %>%\n",
    "    generate(reps = 1000, type = \"bootstrap\") %>%\n",
    "    calculate(stat = \"mean\")\n",
    "\n",
    "temp_historical_percentile_ci <- temp_historical_bootstrap %>%\n",
    "    get_ci(level = 0.95, type = \"percentile\")\n",
    "\n",
    "visualize(temp_historical_bootstrap, binwidth = 0.05) +\n",
    "    shade_ci(endpoints = temp_historical_percentile_ci) +\n",
    "    labs(title = \"\", x = \"Sample mean of daily temperature (°C)\") +\n",
    "    geom_vline(xintercept = temp_historical_mean, size = 2, linetype = \"dashed\") +\n",
    "    theme(text = element_text(size = 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b091b9f-e88e-4ee9-8959-1952d6465cd2",
   "metadata": {},
   "source": [
    "*Figure 3. Bootstrap distribution of the historical sample means with 95% confidence interval.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1aaca163-d0b6-4369-a00d-1e28bed83ca4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EogBAAAiJRACAAAECmBEAAAIFICIQAA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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(100)\n",
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "\n",
    "temp_contemporary <- station_temp %>%\n",
    "    filter(Category == \"contemporary\")\n",
    "\n",
    "temp_contemporary_mean <- temp_contemporary %>%\n",
    "    summarize(mean = mean(Mean_Temp)) %>%\n",
    "    pull()\n",
    "\n",
    "temp_contemporary_bootstrap <- temp_contemporary %>%\n",
    "    specify(response = Mean_Temp) %>%\n",
    "    generate(reps = 1000, type = \"bootstrap\") %>%\n",
    "    calculate(stat = \"mean\")\n",
    "\n",
    "temp_contemporary_percentile_ci <- temp_contemporary_bootstrap %>%\n",
    "    get_ci(level = 0.95, type = \"percentile\")\n",
    "\n",
    "visualize(temp_contemporary_bootstrap, binwidth = 0.05) +\n",
    "    shade_ci(endpoints = temp_contemporary_percentile_ci) +\n",
    "    labs(title = \"\", x = \"Sample mean of daily temperature (°C)\") +\n",
    "    geom_vline(xintercept = temp_contemporary_mean, size = 2, linetype = \"dashed\") +\n",
    "    theme(text = element_text(size = 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffdc2b40-e4a9-46e3-bb80-cd79d402e91b",
   "metadata": {},
   "source": [
    "*Figure 4. Bootstrap distribution of the contemporary sample means with 95% confidence interval.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa3e1b9-e65f-46f3-8ffc-a2fa39af724c",
   "metadata": {},
   "source": [
    "Table 2 summarizes the point estimate for each category and reports their 95% confidence interval. \n",
    "\n",
    "| Category    | Point Estimate $\\bar{x}$  |  Lower CI  |  Upper CI\n",
    "| :----------: | :----------: | :----------: |:----------:|\n",
    "| historical|9.22|8.95|9.49\n",
    "| contemporary|10.96|10.69|11.23\n",
    "\n",
    "<center><i>Table 2. Table of the estimates for daily mean temperature.</i>    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66f7b43-4a25-4c6c-9302-c1b53eaba161",
   "metadata": {},
   "source": [
    "And we interpret this as:\n",
    "- If we repeated our sampling procedure a large number of times, we expect about 95% of the confidence intervals [8.95, 9.49] to capture the value of the population parameter $\\mu$ - the mean of daily temperature from 1951 to 1955.  \n",
    "- If we repeated our sampling procedure a large number of times, we expect about 95% of the confidence intervals [10.69, 11.23] to capture the value of the population parameter $\\mu$ - the mean of daily temperature from 2001 to 2005. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e87fb1-6efb-443d-85ec-be51ed622324",
   "metadata": {},
   "source": [
    "### Hypothesis Testing - Temperature\n",
    "\n",
    "Our initial hypothesis testing is conducted using simulation based permutation with the `infer` package. We chose a significant value of $\\alpha$ = 0.05 as a standard value in the statistics field. Although this might change as we progress further into the project.  \n",
    "We first calculated the observed test statistic, then generated a simulation from the null distribution. We further obtained the P-value. The following codes demonstrate the steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a81dbc0-9533-451d-9177-2ca9deabd1e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Please be cautious in reporting a p-value of 0. This result is an approximation based on the number of `reps` chosen in the `generate()` step. See `?get_p_value()` for more information.”\n"
     ]
    },
    {
     "data": {
      "image/png": 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3fvtmvavHmzECIiIqJFixZalAYAAOASgRAAPMDPz2/0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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(100)\n",
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "\n",
    "obs_diff_in_temp <- station_temp %>%\n",
    "    specify(formula = Mean_Temp ~ Category) %>%\n",
    "    calculate(stat = \"diff in means\", order = c(\"contemporary\", \"historical\"))\n",
    "\n",
    "null_diff_in_temp <- station_temp %>%\n",
    "    specify(formula = Mean_Temp ~ Category) %>%\n",
    "    hypothesize(null = \"independence\") %>%\n",
    "    generate(reps = 1000, type = \"permute\") %>%\n",
    "    calculate(stat = \"diff in means\", order = c(\"contemporary\", \"historical\"))\n",
    "\n",
    "diff_in_temp_plot <- null_diff_in_temp %>%\n",
    "    visualize(bins = 10) +\n",
    "    shade_p_value(obs_stat = obs_diff_in_temp, direction = \"both\") +\n",
    "    xlab(\"Difference between sample means (°C)\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "diff_in_temp_plot\n",
    "\n",
    "p_value <- null_diff_in_temp %>%\n",
    "    get_p_value(obs_stat = obs_diff_in_temp, direction = \"both\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e1a7752-c5ab-471f-8c8c-868ae1073354",
   "metadata": {},
   "source": [
    "*Figure 5. Figure of simulation distribution and P-value of daily temperature hypothesis test.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ebfdae-70d5-42da-a420-2caac674943c",
   "metadata": {},
   "source": [
    "Notice that simulation returned a p-value of 0; however, we should never report a p-value of 0 because this suggests that making a Type I error is impossible. Since we used 1000 reps to calculate the sample, we conclude:  \n",
    "**The p-value is < 0.001, we reject the null hypothesis $H_0$ in favor of $H_A$ at the 5% significant level.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538a9e92-baa5-4987-af62-575b462d681d",
   "metadata": {},
   "source": [
    "We further computed the confidence interval of this test statistic with a 95% confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79dd45b6-a396-4661-a320-67f05bb9f059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G2vQmTWuNmcKsdaxf+y6PDhw9V/ptVqXbx4sfIUkNjY2EuXLjlM38d9TaV/5nWz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\nTME8Fg8AAAB4yGKxlJa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AAACJEaPsqoEGLAgAG//vrr5s2bp0+f/tFHH8XExOTn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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "set.seed(100)\n",
    "diff_in_temp_bootstrap_dist <- station_temp %>%\n",
    "    specify(formula = Mean_Temp ~ Category) %>%\n",
    "    generate(reps = 1000, type = \"bootstrap\") %>%\n",
    "    calculate(stat = \"diff in means\", order = c(\"contemporary\", \"historical\"))\n",
    "\n",
    "diff_in_temp_ci <- \n",
    "     diff_in_temp_bootstrap_dist %>% \n",
    "     get_confidence_interval(level = 0.95, type = \"percentile\")\n",
    "\n",
    "diff_in_temp_ci_plot <- diff_in_temp_bootstrap_dist %>%\n",
    "    visualize(bins = 10) +\n",
    "    shade_ci(diff_in_temp_ci) +\n",
    "    xlab(\"Difference between sample means (°C)\") +\n",
    "    scale_x_continuous(limits = c(-0.3, 2.5)) +\n",
    "    geom_vline(xintercept = 0, size = 2, linetype = \"dashed\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "diff_in_temp_ci_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ab98c92-b4b3-4c8a-8874-f14917f3f812",
   "metadata": {},
   "source": [
    "*Figure 6. Figure of simulated bootstrap distribution of confidence interval of sample statistic.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d245d2e-9393-4767-a74c-98a0c6476645",
   "metadata": {},
   "source": [
    "Notice that the value 0 is not included in our confidence interval, again suggesting that $\\bar{x}_{contemporary}$ and $\\bar{x}_{historical}$ are truly different.  \n",
    "\n",
    "Table 3 summarizes the value we obtained for this test statistic.  95% confidence interval. \n",
    "\n",
    "| Test Statistic    | Observed Test Statistic |  p-value | Lower CI  |  Upper CI\n",
    "| :----------: | :----------: | :----------: |:----------:| :---------:|\n",
    "| $\\bar{x}_{contemporary}$ - $\\bar{x}_{historical}$|1.74 | <0.001|1.36|2.14|\n",
    "\n",
    "<center><i>Table 3. Table of the estimates for test statistic difference in mean temperature.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79d8f01-fc7b-446c-8bb9-42731e903cd9",
   "metadata": {},
   "source": [
    "## Methods - Plan\n",
    "\n",
    "This study wishes to verify the claim of climate change effect from our weather record sample. Moving forward, the group intends to perform a similar analysis on precipitation data. At the first glance, we noticed that the majority of the values in the precipitation variable are zero, which brings the point estimate of the sample mean to a minimal level (close to zero) for both historical and contemporary categories. How to better utilize this dataset will be the next task the group plans to focus on. The group also plans to investigate the asymptotics method as a comparison to the simulation-based method for all the estimates.  \n",
    "  \n",
    "This study serves as an empirical analysis of climate change for the Abbotsford region and may impact how the municipalities plan to adjust their local agriculture industry facing the rising temperature and drought situation in the future.  \n",
    "  \n",
    "Can our study be generalized to a broader population outside of the Abbotsford region? What other dataset do we need to include for such generalization to be valid and meaningful? These are the questions for future studies to focus on. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd8a98c-8ad4-4801-93e7-1a159946afb4",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ef8c51-fb2a-4be7-9616-5666c8999040",
   "metadata": {},
   "source": [
    "## Reference\n",
    "  \n",
    "Historical Data. (2021, November 25), Government of Canada, weather, climate and hazards. https://climate.weather.gc.ca/historical_data/search_historic_data_e.html  \n",
    "  \n",
    "United Nations. (n.d.-a), Causes and effects of climate change\n",
    "https://www.un.org/en/climatechange/science/causes-and-effects-of-climate-change  \n",
    "\n",
    "United Nations. (n.d.-b), What is climate change? https://www.un.org/en/climatechange/what-is-climate-change  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ed28177-6daf-4292-8c7e-60a12d36f2e6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
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  "language_info": {
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   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.2"
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