Example of writing a Tea program

tea_example_0.ipynb

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      "name": "Example of using Tea",
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/emjun/f0ebafc97c208b9f329b1e8c14f90a7f/tea_example_0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "id": "JyG45Qk3qQLS"
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      "source": [
        "# Tea\n",
        "Tea requires users to describe their data, variables, study design, assumptions about the data, and hypotheses at a high-level. Tea combined computed properties about the data (e.g., normal distribution) with users' assumptions and hypotheses to infer a set of valid statisitcal analyses that test users' hypotheses. Unlike other statistical analysis tools, Tea focuses on capturing users' *explicit* hypotheses and assumptions about the data. \n"
      ]
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      "cell_type": "markdown",
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        "id": "Ua--kwl8Ge7x",
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      "source": [
        "## Example\n",
        "\n",
        "Let's walk through an example! Make sure to install Tea before.  :)"
      ]
    },
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      "cell_type": "markdown",
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        "id": "VLt-_g2lGtHi",
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      "source": [
        "## 1. Import tea"
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "-gsAYPFgGxhL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tea"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KR921S_OQSHG"
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      "source": [
        "## Data\n",
        "\n",
        "Load data."
      ]
    },
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      "source": [
        "\n",
        "tea.data(\"https://homes.cs.washington.edu/~emjun/tea-lang/datasets/UScrime.csv\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Id6tDF1HQSHD"
      },
      "source": [
        "## Variables\n",
        "\n",
        "Declare and annotate the variables of interest."
      ]
    },
    {
      "cell_type": "code",
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        "id": "9_3Uxu7cGS0D",
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      "source": [
        "variables = [\n",
        "    {\n",
        "        'name' : 'So',\n",
        "        'data type' : 'nominal',\n",
        "        'categories' : ['0', '1']\n",
        "    },\n",
        "    {\n",
        "        'name' : 'Prob',\n",
        "        'data type' : 'ratio',\n",
        "        'range' : [0,1]\n",
        "    }\n",
        "]\n",
        "\n",
        "tea.define_variables(variables)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwy7tpFCHlkw",
        "colab_type": "text"
      },
      "source": [
        "## Assumptions\n",
        "\n",
        "Declare any assumptions you may have about the data based on prior visualization or domain knowledge."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIrQI3r4NZRI",
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      "source": [
        "assumptions = {\n",
        "    'groups normally distributed': [['So', 'Prob']],\n",
        "    'Type I (False Positive) Error Rate': 0.05,\n",
        "}\n",
        "\n",
        "tea.assume(assumptions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fCGhA2DNbHh",
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      "source": [
        "## Study Design\n",
        "\n",
        "Express how the data were collected."
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "experimental_design = {\n",
        "                        'study type': 'observational study',\n",
        "                        'contributor variables': 'So',\n",
        "                        'outcome variables': 'Prob',\n",
        "                    }\n",
        "tea.define_study_design(experimental_design)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFc-d10JN3_i",
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      "source": [
        "## Hypothesis\n",
        "Explicitly state a hypothesis about the relationship between the variables in the data."
      ]
    },
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      "cell_type": "code",
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        "id": "5mNM6Q8uOE3h",
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      "source": [
        "tea.hypothesize(['So', 'Prob'], ['So:1 > 0'])"
      ]
    }
  ]
}

Should be tea.data("https://homes.cs.washington.edu/~emjun/tea-lang/datasets/UScrime.csv") at the top, correct?

Great find! 👍

Updated the example!

Thanks :)