2023-07-06_sql-vs-rd-pattern-analysis_executed.ipynb

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    "# Preregistration — SQL vs. Visual Diagrams for Matching Relational Query Patterns — June 2023 Study\n",
    "\n",
    "We are preregistering this study based on the [OSF Google Docs template](https://docs.google.com/document/d/1DaNmJEtBy04bq1l5OxS4JAscdZEkUGATURWwnBKLYxk/edit), which is one of [several preregistration templates](https://osf.io/zab38/wiki/home/?view) that [OSF](https://osf.io/) provides. Our experimental setup is inspired by [Leventidis et al. (2020)](https://doi.org/10.1145/3318464.3389767)."
   ]
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    "## Study Information\n",
    "\n",
    "* **Title:**\n",
    "    The effect of SQL vs. Visual Diagrams on time and correctness matching relational query patterns\n",
    "\n",
    "* **Authors:** \n",
    "    *Anonymous for peer review. The online form on [osf.io](https://osf.io/) will list authors upon publication or embargo expiration.*\n",
    "\n",
    "* **Description:** \n",
    "    Pilot testing has indicated that visual diagrams (RD) improve participant speed at correctly identifying relational query patterns, contrasting with formatted SQL.\n",
    "    We will measure participant time and the proportion of correct answers for two conditions (RD and SQL) and 4 relational query patterns across 32 questions.\n",
    "\n",
    "* **Hypotheses:** We are testing for a total of `3` hypotheses: \n",
    "  * ***Time:*** Let $\\theta_X$ denote the median time per question in seconds for a given condition $X$ per participant. We hypothesize that (1) $\\theta_{RD}$ / $\\theta_{SQL} < 1$, thus participants are relatively faster using `RD` compared to `SQL` and (2) that this holds for each half of the study individually.\n",
    "\n",
    "  * ***Correctness:*** Let $\\delta_X$ denote the mean proportion of correct responses for a given condition $X$. We hypothesize that (3) $\\delta_{RD}$ $\\simeq$ $\\delta_{SQL}$, i.e., participants make a comparable number of correct responses using `RD` or `SQL`.\n"
   ]
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    "## Design Plan\n",
    "\n",
    "* **Study type:**\n",
    "    Experiment.\n",
    "\n",
    "* **Blinding:**\n",
    "    No blinding is involved in this study.\n",
    "\n",
    "* **Study design:** \n",
    "    We have a within-subjects design, i.e., each participant sees questions in both of our modes: `RD` and `SQL`.\n",
    "    Each participant will see a total of `32` questions: `2` modes $\\times$ `4` patterns $\\times$ `2` instances in each half $\\times$ `2` halves.\n",
    "    I.e., they will see each pattern-mode combination `2` times per half.\n",
    "    For each question, the participant will be given a SQL query presented using one of the modes. They must choose the most appropriate of `4` natural-language interpretations of the query, each corresponding to one of our `4` patterns.\n",
    "    Their sequence number determines the mode presented to each participant for a given question — described under the randomization bullet.\n",
    "    The stimuli for each mode is:\n",
    "\n",
    "  * `SQL` — A conventional SQL representation with appropriate indentation.\n",
    "      The SQL text is indented and SQL keywords are color-coded appropriately.\n",
    "  * `RD` — A visual diagram we created of the query.\n",
    "\n",
    "    See the included `supplement/tutorial.pdf` file for a sample of the stimuli and how to read them, one page extracted here:\n",
    "\n",
    "![Example of the four patterns and two modes.](supplement/patterns.png)\n",
    "\n",
    "* **Randomization:** \n",
    "    To reduce ordering effects caused by which mode is presented first, we assign participants as they arrive to alternately start with `SQL` (sequence number `0`) or `RD` (sequence number `1`).\n",
    "    We then alternate the modes the presenter sees with each question.\n",
    "    I.e., `[SQL, RD, SQL, RD...]`.\n",
    "    We randomize the order that patterns are presented in each half separately, ensuring that each combination appears the same number of times and that both halves have the same number of each pattern-mode combination."
   ]
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    "## Sampling Plan\n",
    "\n",
    "* **Existing data:**\n",
    "    Registration before creating data: As of the date of submission of this research plan for preregistration, the data have not yet been collected, created, or realized.\n",
    "    Only pilot data has been collected.\n",
    "\n",
    "* **Explanation of existing data:**\n",
    "    N/A.\n",
    "\n",
    "* **Data collection procedures:** \n",
    "  * ***Population:***\n",
    "    Participants will be recruited through [Amazon Mechanical Turk (AMT)](https://www.mturk.com/), a popular crowdsourcing website used for a variety of tasks, including academic studies.\n",
    "  * ***Recruitment efforts:*** A Human Intelligence Task (HIT) will be posted on AMT. We may repeatedly re-post the HIT as recruitment slows over time.\n",
    "  * ***Inclusion criteria:*** Participants could accept the AMT HIT if they are all of the following:\n",
    "    1. Residing in the USA and, thus, we expect English proficiency.\n",
    "    2. Adults (over 18 years old).\n",
    "    3. Experienced SQL users, self-determined with the prompt: \"Workers should be familiar with SQL at the level of an advanced undergraduate database class, in particular with nested SQL queries.\"\n",
    "    4. Have submitted over `500` approved HITs on AMT.\n",
    "    5. Have more than `97%` approved HIT assignments on AMT.\n",
    "  * ***Exclusion criteria:*** None. Pilot study participants were collected from our institution so did not need to be excluded on AMT.\n",
    "  * ***Timeline:*** Data will be collected from when we start until our **Stopping rule** below is met.\n",
    "  * ***Payment:***\n",
    "    * *AMT Rejection criteria:* A HIT will be accepted and the participant paid only if they correctly answered $\\ge$ `16/32` questions within `50` minutes. Otherwise, the HIT will be rejected.\n",
    "    * *Base pay:* `$6.00` USD.\n",
    "    * *Correctness bonus:* For every correctly answered question after the `16th` the participant receive a bonus payment of `$0.20` USD for a total pay of `$9.20` USD.\n",
    "    * *Time bonus:* Based on total test completion time, the participant will receive a percentage bonus on total pay (including the correctness bonus). Completion within `11` minutes awards a `5%` bonus for a maximum pay of `$9.66`. Each minute faster gets you an additional `5%` bonus up to `40%` for completing within `4` minutes, with a maximum pay of `$12.88`.  \n",
    "\n",
    "* **Sample size:** \n",
    "    Our target sample size is `50` participants.\n",
    "\n",
    "* **Sample size rationale:**\n",
    "    As all 13 pilot participants were faster with `RD` than `SQL`, we did not use a power analysis to choose the sample size. Instead, `50` was chosen as a meaningfully large and round number that is still a multiple of 2 to ensure that we have an equal number of participants among the sequences (see **Randomization**, above)\n",
    "\n",
    "* **Stopping rule:**\n",
    "    We will terminate data collection once our number of complete HITs has reached our maximum target sample size.\n",
    "    Given the strict ***Inclusion criteria*** in our study, it is possible that we won't be able to hit our target sample size.\n",
    "    In that case, we shall restrict our analysis to the data we can collect before paper submission.\n",
    "    We will continue collecting data until we reach the maximum target sample size or the camera-ready paper submission deadline."
   ]
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    "## Variables\n",
    "\n",
    "* **Manipulated Variables:**\n",
    "\n",
    "  * *Mode:* `[SQL, RD]`. See [Study Information](#Study-Information).\n",
    "\n",
    "\n",
    "* **Measured Variables:** \n",
    "\n",
    "  * For each participant and each question they answer, we record:\n",
    "    * ***Time (quantitative):*** The time they take to answer the question.\n",
    "    * ***Pattern (categorical/integer):*** The pattern they were provided in one of the modes for that question.\n",
    "    * ***Choice (integer):*** Their selected pattern from the `4`-option multiple choice question.\n",
    "    * ***Correct (boolean/integer):*** Whether their answer for the `4`-option multiple choice question was correct.\n",
    "\n",
    "  * For each participant, we also record:  \n",
    "    * ***Sequence (categorical):*** The sequence the participant was randomly assigned to (see [Design Plan](#Design-Plan)).\n",
    "    * ***Free-text feedback (string):***  The participant's optional answers to a feedback prompt.\n",
    "\n",
    "\n",
    "* **Indices:** From our collected study data, we will calculate:\n",
    "  * ***Indices for time***\n",
    "     \n",
    "    * **`Median time per mode per participant`:**\n",
    "    This is calculated by taking the median of the `16` ***Time*** records for each mode for each participant.\n",
    "    Using that information, we can calculate the median time across all participants.\n",
    "\n",
    "    * **`Ratio of median time of RD relative to SQL per participant:`**\n",
    "    Using the `Median time per mode per participant`, we will calculate `RD/SQL`.\n",
    "\n",
    "    * **`Quartiles and CIs of median time per mode across all participants:`**\n",
    "    Using the `Median time per mode per participant`, we will calculate the 1st, 2nd (median), 3rd quartiles.\n",
    "\n",
    "    * **`Quartiles and CIs of median time of RD relative to SQL across all participants:`**\n",
    "    Using the `Ratio of median time of RD relative to SQL per participant`, we will calculate the 1st, 2nd (median), 3rd quartiles as well as 95% Confidence Intervals using Bias Corrected and Accelerated (BCa) bootstrapping ([Efron (1987)](https://doi.org/10.2307%2F2289144)).\n",
    "\n",
    "    * **`Per-half indices:`**\n",
    "    We will compute both the `Quartiles and CIs of median time...` indices for each half.\n",
    "        \n",
    "  * ***Indices for correctness***\n",
    "\n",
    "    * **`Proportion of correct responses per mode per participant`:**\n",
    "    For each participant, calculate the proportion of correct responses per mode as: `correct responses / total questions per mode`.\n",
    "    \n",
    "    * **`Mean and CIs of proportion of correct responses per mode across all participants`:**\n",
    "    The mean proportion of correct responses across all participants is calculated by taking the arithmetic mean of all the `Proportion of correct responses per mode per participant` values for a given mode across all participants.\n",
    "    We will calculate 95% Confidence Intervals using Bias Corrected and Accelerated (BCa) bootstrapping."
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    "## Analysis Plan\n",
    "\n",
    "* **Statistical Models:**\n",
    "\n",
    "  * ***Distribution testing:***\n",
    "    We will examine the distributions of our data visually, including for each mode, to ensure there are no problematic distributions.\n",
    "  * ***Hypothesis Testing:***\n",
    "    We will visually examine the 95% BCa bootstrapped confidence intervals for each mean and median.\n",
    "\n",
    "* **Transformations:** N/A.\n",
    "\n",
    "* **Inference criteria:**\n",
    "    We will interpret the results using Interval Estimation rather than dichotomous $p$-value cutoffs (e.g., we will not use *p*<.05 to determine statistical significance).\n",
    "    See [Dragicevik (2016)](http://dx.doi.org/10.1007/978-3-319-26633-6_13) and [Besançon & Dragicevic (2019)](http://dx.doi.org/10.1145/3290607.3310432) for a discussion of using estimation for these types of analyses.\n",
    "    \n",
    "* **Data exclusion:**\n",
    "    To perform a concrete analysis of our data, we would like to minimize the set of outlier points as they will negatively affect the quality of our statistical analysis and introduce unwanted/non-existent bias.\n",
    "    After collecting our data, we will examine the time distribution of each worker. In particular, we will examine how long each worker took to answer the question on a per-question basis. We expect two types of outlier points in our experiments. \n",
    "  1. **Speeders:** Workers that answered a question much faster than the vast majority of participants (usually in the order of a few seconds, i.e., workers tried to rush answering each question without thinking).\n",
    "      The range could vary, but a rough indication of a speeder would be if their time per question is 2-3 standard deviations lower than the mean time per question.\n",
    "  2. **Unusually delayed answers:** This refers to workers who took unusually long to answer a question.\n",
    "      This is most likely attributed to some distraction that made the worker not focus on our question while the timer was running (i.e., a phone call, text message, bathroom break, etc.).\n",
    "      As an online test, we can't know exactly what was the cause of it, but usually, we can identify such data points by noticing their much larger value in time.\n",
    "      Since we capture a time distribution, it is expected to be left-skewed, and thus a rough measure of an *unusually delayed answer* would be about $\\geq 3$ times the mean time per question.\n",
    "  3. **Cheaters:** Previous studies have indicated that workers can leak answers to enable other workers to answer all the questions correctly and quickly. We have used technical measures to limit this possibility and give each user different sets of stimuli. However, if we identify cheaters through log analysis, we will exclude them.\n",
    "  4. **Median:** To minimize the effect of outliers for all the above reasons and to provide a statistically valid unbiased estimator for the ratio of times, we use the median instead of the mean aggregation for time analysis.\n",
    "\n",
    "\n",
    "* **Missing data:**\n",
    "    We will unlikely have missing data because for a participant to submit their results, they must answer all the questions.\n",
    "    However, if we have missing data points from an individual, we will remove the individual completely from our analysis."
   ]
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    "## Other\n",
    "\n",
    "* **Discrepancies between preregistration prose and analysis code:**\n",
    "    The intent of our study design is explained in this section.\n",
    "    In case of any discrepancy between the analysis code below and this section, what is written in this section takes precedence for the preregistration.    "
   ]
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    "# Scripts to analyze the study data\n",
    "\n",
    "(Q1) TIMING PER PARTICIPANT (SQL vs RD)\n",
    "1. Per participant, calculate the median time in seconds spent on SQL and RD (32/2=16 per mode and participant, irrespective of correctness)\n",
    "2. Q1a: show violin plot figure with median times per user compared via gray lines\n",
    "3. Calculate their ratio per user (also gives fraction of users who are faster with one or the other)\n",
    "4. Calculate the median of those fractions and the 95% CI\n",
    "5. Q1b: show violin plot figure with fractions, and also 95% CI\n",
    "\n",
    "(Q2) TIMING PER PARTICIPANT (SQL vs RD / 1st vs. 2nd half)\n",
    "1. Per participant, calculate the median time over all questions answered in 1st half in RD (32/2/2=8) and SQL, and in 2nd half.\n",
    "2. Q2: show repeated measure violin plot figure, showing improvements over time, of 2 halfs\n",
    "3. Calculate the relative ratio for timing 2nd/1st for RD, and SQL including 95% CI\n",
    "\n",
    "(Q3) TIMING PATTERNS ACROSS PARTICIPANTS\n",
    "1. calculate the median time per pattern (4) across the two modes (2). Thus 8 values.\n",
    "2. show repeated measure violin plot figure\n",
    "\n",
    "(Q4) CORRECTNESS (SQL vs RD)\n",
    "1. take mean correct over all questions and all users answered in SQL (32/2*13), or in RD (2 values)\n",
    "2. calculate 95% CI for each, and sampled p-value (perhaps with difference?)"
   ]
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   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90163bd4-f66f-4d4d-a525-a8420532f03e",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\",font_scale=2)\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import bootstrap as scipybootstrap\n",
    "from IPython.display import display\n",
    "\n",
    "# Tell matplotlib to export svg text as text not paths\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "plt.rcParams['axes.axisbelow'] = True   # draw axes and grids behind everything else\n",
    "\n",
    "# Set Jupyter and Pandas to show 3 decimal places, does not work for lists of numbers\n",
    "%precision 3\n",
    "pd.options.display.float_format = '{:,.3f}'.format\n",
    "np.set_printoptions(precision=3)\n",
    "# np.set_printoptions(formatter={'float': lambda x: \"{0:0.3f}\".format(x)})            # TODO: does not work for lists\n",
    "def print(*args):\n",
    "    __builtins__.print(*(\"%.3f\" % a if isinstance(a, float) else a\n",
    "                         for a in args))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60109a40-4d08-4bd6-90ed-a6788cfe728a",
   "metadata": {},
   "source": [
    "## Global Variables Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d1d96cd-4678-4d54-a8eb-d84cf3e14247",
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   "source": [
    "# A set of constant global variables used throughout the notebook\n",
    "num_questions = 32\n",
    "modes = ['SQL', 'RD']\n",
    "mode_to_name = {0: 'SQL', 1: 'RD'}\n",
    "\n",
    "BOOTSTRAPCONFIDENCE = 0.95      # confidence level used for bootstrap\n",
    "BOOTSTRAPMETHOD = 'BCa'         # method used for bootstrap, appears to be better than the textbook version for mean (but not for median), also available as 'percentage'\n",
    "BOOTSTRAPSAMPLES = 10000        # number of resamples\n",
    "filename = 'data/anonymized_data.csv'   # file with appropriately transformed data ready for analysis\n",
    "TRANSFORMDATA = False            # used only once to transform the original data"
   ]
  },
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   "id": "220858af",
   "metadata": {
    "collapsed": false,
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     "outputs_hidden": false
    }
   },
   "source": [
    "## Define subfolder where figures are stored\n",
    "\n",
    "By default, figures will not be saved. If you want to save figures, set savefig to `True`.\n",
    "Learned from: https://github.com/jorvlan/open-visualizations/blob/master/Python/tutorial_2/repeated_measures_python_2.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80874ad0",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory already exists\n"
     ]
    }
   ],
   "source": [
    "savefig = True\n",
    "if savefig:\n",
    "    import os\n",
    "    from os.path import isdir\n",
    "    cwd = os.getcwd()   # Get current working directory, but you can specify your own directory of course.\n",
    "    if  os.path.exists(cwd + \"/figs\"):\n",
    "        print(\"Directory already exists\")\n",
    "        fig_dir = cwd + \"/figs\"     # Assign the existing directory to a variable\n",
    "    elif not os.path.exists(cwd + \"/figs\"):\n",
    "        print(\"Directory does not exist and will be created ......\")\n",
    "        os.makedirs(cwd + \"/figs\")\n",
    "        if isdir(cwd + \"/figs\"):\n",
    "            print('Directory was created succesfully')\n",
    "        fig_dir = cwd + \"/figs\"     # Assign the created directory to a variable\n",
    "    else:\n",
    "        print(\"Something went wrong\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8b86e6-d818-4a15-a5f4-9ca691e84fba",
   "metadata": {},
   "source": [
    "## Loading original data, transforming it, and saving it anonymized\n",
    "\n",
    "Loading the original data, anonymizing it, transforming it to make available for later analysis and saving it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "385495ea-f2c9-4273-9cd9-4e2378417641",
   "metadata": {},
   "outputs": [],
   "source": [
    "if TRANSFORMDATA:\n",
    "    ANONYMIZE = False\n",
    "\n",
    "    # --- Load original study data TODO: This should point to our full study data when it is available\n",
    "    studyfilename = 'data/users-table-pilot.csv'\n",
    "    encodingfilename = 'data/worker_ids.csv'\n",
    "    df = pd.read_csv(studyfilename)\n",
    "\n",
    "    # --- Turn string to array\n",
    "    from ast import literal_eval            # to turn string to array\n",
    "    df['pattern_order']= df['pattern_order'].apply(literal_eval)\n",
    "    display(df)\n",
    "\n",
    "    # --- Anonymize the workers with randomized categories (https://pandas.pydata.org/docs/user_guide/categorical.html#working-with-categories)\n",
    "    if ANONYMIZE:\n",
    "        c = df.worker_id.astype('category')\n",
    "        d = dict(enumerate(c.cat.categories))\n",
    "\n",
    "        from random import shuffle\n",
    "        keys = list(d.keys())\n",
    "        shuffle(keys)\n",
    "        d = dict(zip(d.values(), keys))             # randomize the category assignment s.t. categorical numbers are identical with alphabetical order\n",
    "\n",
    "        with open(encodingfilename, 'w') as f:      # save the category encoding\n",
    "            for key in d.keys():\n",
    "                f.write(\"%s,%s\\n\"%(key,d[key]))\n",
    "\n",
    "        df[\"worker_id\"] = df[\"worker_id\"].map(d)    # replace worker_ids with randomized categories\n",
    "\n",
    "    # display(df)\n",
    "    # The \"current page\" is the section of the study the workers are doing to save their state & prevent them cheating\n",
    "\n",
    "    # --- The following code block transforms the data frame to have one question per row. That simplifies the later analysis.\n",
    "    # reshape df (melt, pivot) to bring multiple question times (e.g. 'q7_time') per row into separate rows\n",
    "    # https://towardsdatascience.com/wide-to-long-data-how-and-when-to-use-pandas-melt-stack-and-wide-to-long-7c1e0f462a98\n",
    "    df2 = df.melt(id_vars=['worker_id', 'sequence_num', 'pattern_order',\n",
    "                           'q1', 'q2','q3', 'q4','q5', 'q6','q7', 'q8', 'q9', 'q10',\n",
    "                           'q11', 'q12','q13', 'q14','q15', 'q16','q17', 'q18', 'q19', 'q20',\n",
    "                           'q21', 'q22','q23', 'q24','q25', 'q26','q27', 'q28', 'q29', 'q30',\n",
    "                           'q31', 'q32'], value_vars=['q1_time', 'q2_time', 'q3_time', 'q4_time','q5_time', 'q6_time', 'q7_time', 'q8_time', 'q9_time', 'q10_time',\n",
    "                                                      'q11_time', 'q12_time', 'q13_time', 'q14_time', 'q15_time', 'q16_time', 'q17_time', 'q18_time', 'q19_time', 'q20_time',\n",
    "                                                      'q21_time', 'q22_time', 'q23_time', 'q24_time', 'q25_time', 'q26_time', 'q27_time', 'q28_time', 'q29_time', 'q30_time',\n",
    "                                                      'q31_time', 'q32_time'], var_name='question', value_name='time')\n",
    "\n",
    "    # replace time in msec with sec in column 'time'\n",
    "    df2['time'] = df2['time'] / 1000\n",
    "\n",
    "    # replace question string 'q7_time' with number '7' in column 'question'\n",
    "    from re import search as re_search               # regular expression\n",
    "    new_column = []\n",
    "    for values in df2['question']:\n",
    "        new_column.append(int(re_search(r'\\d+', values).group()))\n",
    "    df2['question'] = new_column\n",
    "\n",
    "    # choose the right pattern from the list 'pattern_order' and add as column 'pattern'\n",
    "    new_column = []\n",
    "    for (pattern_order_list, ind) in zip(df2['pattern_order'], df2['question']):\n",
    "        new_column.append(pattern_order_list[ind-1])\n",
    "    df2['pattern'] = new_column\n",
    "\n",
    "    # determine the 'mode' (SQL or RD) from 'sequence_num' and 'question'\n",
    "    #   sequence_num = 0 means that the first question is shown in SQL, 1 means we start instead with RD. Then alternate between the two.\n",
    "    #   Thus (sequence_num + question_num) % 2 == 1 means SQL\n",
    "    #   Thus (sequence_num + question_num) % 2 == 0 means RD\n",
    "    new_column = []\n",
    "    for (sequence, question) in zip(df2['sequence_num'], df2['question']):\n",
    "        mode = 'SQL' if (sequence + question) % 2 == 1 else 'RD'\n",
    "        new_column.append(mode)\n",
    "    df2['mode'] = new_column\n",
    "\n",
    "    # determine the 'choice' (among the 4 patterns) made by the user for this question. Requires all the 32 question choices (e.g. 'q7') and index of the question at hand ('question')\n",
    "    questionarray = df2[['q1', 'q2','q3', 'q4','q5', 'q6','q7', 'q8', 'q9', 'q10',\n",
    "                         'q11', 'q12','q13', 'q14','q15', 'q16','q17', 'q18', 'q19', 'q20',\n",
    "                         'q21', 'q22','q23', 'q24','q25', 'q26','q27', 'q28', 'q29', 'q30',\n",
    "                         'q31', 'q32']].to_numpy()\n",
    "    questionindex = df2[[\"question\"]].to_numpy()\n",
    "\n",
    "    new_array = np.take_along_axis(questionarray,questionindex-1,1)     # take the 'questionindex'-th entry from each row of the questionarray (notice 1-index vs 0-indexin)\n",
    "    df2['choice'] = new_array\n",
    "\n",
    "    # determine whether the choice was correct by comparing the ground truth ('pattern') against the choice made ('choice'). Saved as 0/1 value in new column 'correct'\n",
    "    new_column = []\n",
    "    for (pattern, choice) in zip(df2['pattern'], df2['choice']):\n",
    "        correct = 1 if pattern == choice else 0\n",
    "        new_column.append(correct)\n",
    "    df2['correct'] = new_column\n",
    "\n",
    "    # sort by worker and question number, and reset the inde\n",
    "    df2.sort_values(by=['worker_id', 'question'], inplace=True)\n",
    "    df2.reset_index(drop=True, inplace=True)\n",
    "    # display(df2)\n",
    "\n",
    "    # select only the relevant subset of columns\n",
    "    df_anonymized_data = df2[['worker_id', 'question', 'time', 'pattern', 'mode', 'choice', 'correct']]\n",
    "    # display(df3)\n",
    "\n",
    "    # pd.write_csv(filename)\n",
    "    df_anonymized_data.to_csv(filename,\n",
    "                              index=False,\n",
    "                              )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaeee9a1",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Loading anonymized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e29e9e0e",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>worker_id</th>\n",
       "      <th>question</th>\n",
       "      <th>time</th>\n",
       "      <th>pattern</th>\n",
       "      <th>mode</th>\n",
       "      <th>choice</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.925</td>\n",
       "      <td>1</td>\n",
       "      <td>SQL</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.418</td>\n",
       "      <td>2</td>\n",
       "      <td>RD</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14.183</td>\n",
       "      <td>4</td>\n",
       "      <td>SQL</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.465</td>\n",
       "      <td>4</td>\n",
       "      <td>RD</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16.753</td>\n",
       "      <td>3</td>\n",
       "      <td>SQL</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>3.840</td>\n",
       "      <td>2</td>\n",
       "      <td>RD</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>4.081</td>\n",
       "      <td>3</td>\n",
       "      <td>SQL</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>4.327</td>\n",
       "      <td>1</td>\n",
       "      <td>RD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>7.464</td>\n",
       "      <td>2</td>\n",
       "      <td>SQL</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>2.450</td>\n",
       "      <td>4</td>\n",
       "      <td>RD</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>416 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     worker_id  question   time  pattern mode  choice  correct\n",
       "0            0         1  9.925        1  SQL       1        1\n",
       "1            0         2  7.418        2   RD       2        1\n",
       "2            0         3 14.183        4  SQL       3        0\n",
       "3            0         4  4.465        4   RD       4        1\n",
       "4            0         5 16.753        3  SQL       3        1\n",
       "..         ...       ...    ...      ...  ...     ...      ...\n",
       "411         12        28  3.840        2   RD       2        1\n",
       "412         12        29  4.081        3  SQL       2        0\n",
       "413         12        30  4.327        1   RD       1        1\n",
       "414         12        31  7.464        2  SQL       2        1\n",
       "415         12        32  2.450        4   RD       4        1\n",
       "\n",
       "[416 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_anonymized_data = pd.read_csv(filename)\n",
    "display(df_anonymized_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4330c84c",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# Total time users took"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56ad87ce",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worker_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sum\n",
       "worker_id       \n",
       "0          4.217\n",
       "1          3.382\n",
       "2         10.153\n",
       "3          8.044\n",
       "4         10.476\n",
       "5         15.287\n",
       "6          5.552\n",
       "7          9.189\n",
       "8          5.398\n",
       "9          7.594\n",
       "10         3.317\n",
       "11        10.953\n",
       "12         4.717"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dftemp = df_anonymized_data.groupby(['worker_id']).time.agg(['sum'])\n",
    "dftemp['sum'] = dftemp['sum'] / 60\n",
    "display(dftemp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524ecbe1",
   "metadata": {},
   "source": [
    "# Question 1. Figure 1a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "73a42469",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dfq1a:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>worker_id</th>\n",
       "      <th>mode</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>RD</td>\n",
       "      <td>4.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>SQL</td>\n",
       "      <td>9.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>RD</td>\n",
       "      <td>3.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>SQL</td>\n",
       "      <td>5.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>RD</td>\n",
       "      <td>10.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>SQL</td>\n",
       "      <td>21.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>RD</td>\n",
       "      <td>6.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>SQL</td>\n",
       "      <td>17.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>RD</td>\n",
       "      <td>8.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>SQL</td>\n",
       "      <td>12.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>RD</td>\n",
       "      <td>8.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>SQL</td>\n",
       "      <td>33.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>RD</td>\n",
       "      <td>4.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "      <td>SQL</td>\n",
       "      <td>6.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>RD</td>\n",
       "      <td>8.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "      <td>SQL</td>\n",
       "      <td>14.653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>RD</td>\n",
       "      <td>6.207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>SQL</td>\n",
       "      <td>10.743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "      <td>RD</td>\n",
       "      <td>7.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>SQL</td>\n",
       "      <td>13.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>RD</td>\n",
       "      <td>3.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10</td>\n",
       "      <td>SQL</td>\n",
       "      <td>6.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>RD</td>\n",
       "      <td>11.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11</td>\n",
       "      <td>SQL</td>\n",
       "      <td>24.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12</td>\n",
       "      <td>RD</td>\n",
       "      <td>5.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12</td>\n",
       "      <td>SQL</td>\n",
       "      <td>8.329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    worker_id mode  median\n",
       "0           0   RD   4.433\n",
       "1           0  SQL   9.242\n",
       "2           1   RD   3.600\n",
       "3           1  SQL   5.482\n",
       "4           2   RD  10.250\n",
       "5           2  SQL  21.483\n",
       "6           3   RD   6.390\n",
       "7           3  SQL  17.845\n",
       "8           4   RD   8.292\n",
       "9           4  SQL  12.992\n",
       "10          5   RD   8.787\n",
       "11          5  SQL  33.992\n",
       "12          6   RD   4.620\n",
       "13          6  SQL   6.008\n",
       "14          7   RD   8.368\n",
       "15          7  SQL  14.653\n",
       "16          8   RD   6.207\n",
       "17          8  SQL  10.743\n",
       "18          9   RD   7.969\n",
       "19          9  SQL  13.957\n",
       "20         10   RD   3.171\n",
       "21         10  SQL   6.681\n",
       "22         11   RD  11.439\n",
       "23         11  SQL  24.057\n",
       "24         12   RD   5.135\n",
       "25         12  SQL   8.329"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dfq1b:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>mode</th>\n",
       "      <th>RD</th>\n",
       "      <th>SQL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worker_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.433</td>\n",
       "      <td>9.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.600</td>\n",
       "      <td>5.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.250</td>\n",
       "      <td>21.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.390</td>\n",
       "      <td>17.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.292</td>\n",
       "      <td>12.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.787</td>\n",
       "      <td>33.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.620</td>\n",
       "      <td>6.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.368</td>\n",
       "      <td>14.653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.207</td>\n",
       "      <td>10.743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.969</td>\n",
       "      <td>13.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.171</td>\n",
       "      <td>6.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.439</td>\n",
       "      <td>24.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.135</td>\n",
       "      <td>8.329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "mode          RD    SQL\n",
       "worker_id              \n",
       "0          4.433  9.242\n",
       "1          3.600  5.482\n",
       "2         10.250 21.483\n",
       "3          6.390 17.845\n",
       "4          8.292 12.992\n",
       "5          8.787 33.992\n",
       "6          4.620  6.008\n",
       "7          8.368 14.653\n",
       "8          6.207 10.743\n",
       "9          7.969 13.957\n",
       "10         3.171  6.681\n",
       "11        11.439 24.057\n",
       "12         5.135  8.329"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median time RD: 6.39, 95% CI [4.62, 8.37]\n",
      "Median time SQL: 12.99, 95% CI [8.33, 17.84]\n"
     ]
    }
   ],
   "source": [
    "# create two columns mode and median, with 2 rows per worker (used for Fig 1a violines)\n",
    "dfq1a = df_anonymized_data.groupby(['worker_id', 'mode']).time.agg(['median'])\n",
    "dfq1a.reset_index(inplace=True)\n",
    "print('dfq1a:')\n",
    "display(dfq1a)\n",
    "\n",
    "# pivot to have one row per worker (used for Fig 1a individual points)\n",
    "dfq1b = pd.pivot_table(dfq1a, values=['median'], index=['worker_id'], columns=['mode'])\n",
    "dfq1b=dfq1b.droplevel(0, axis=1)\n",
    "print('dfq1b:')\n",
    "display(dfq1b)\n",
    "\n",
    "modes = ['RD', 'SQL']\n",
    "median_time = {}\n",
    "ci = {}\n",
    "ci_delta = {}\n",
    "for mode in modes:\n",
    "    median_time[mode] = np.median(dfq1b[mode])\n",
    "    ci[mode] = scipybootstrap((dfq1b[mode],), statistic=np.median, n_resamples=BOOTSTRAPSAMPLES, confidence_level=BOOTSTRAPCONFIDENCE, method='percentile', axis=0).confidence_interval        #convert array to sequence\n",
    "    ci_delta[mode] = [median_time[mode] - ci[mode].low, ci[mode].high - median_time[mode]]\n",
    "\n",
    "    print(f'Median time {mode}: {median_time[mode]:.2f}, 95% CI [{ci[mode].low:.2f}, {ci[mode].high:.2f}]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3c394044",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define pre-settings\n",
    "figwidth = 10\n",
    "figheight = 6\n",
    "xlab_size = 20\n",
    "ylab_size = 20\n",
    "figfont_size = 24\n",
    "\n",
    "# Define consistent color maps\n",
    "my_cmap_sns_light = [(0.9921568627450981, 0.8156862745098039, 0.6352941176470588), (0.7764705882352941, 0.8588235294117647, 0.9372549019607843)]        # light blue, light orange\n",
    "my_cmap_sns_dark = [(0.9019607843137255, 0.3333333333333333, 0.050980392156862744), (0.19215686274509805, 0.5098039215686274, 0.7411764705882353)]      # dark blue, dark orange\n",
    "my_cmap_dark = sns.color_palette(my_cmap_sns_dark, as_cmap=True)\n",
    "my_cmap_light = sns.color_palette(my_cmap_sns_light, as_cmap=True)\n",
    "\n",
    "\n",
    "# Create empty figure and plot the individual datapoints\n",
    "fig, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "\n",
    "\n",
    "# 1. Violinplots\n",
    "axsns = sns.violinplot(x='median', y='mode', data=dfq1a,\n",
    "                       hue=True, hue_order=[False, True], split=True,   # half violinplots https://stackoverflow.com/questions/53872439/half-not-split-violin-plots-in-seaborn\n",
    "                       inner='quartile',\n",
    "                       cut=0,               # 0 means ending sharp at end points\n",
    "                       width=.7,\n",
    "                       orient = 'h',\n",
    "                       zorder=20,)\n",
    "\n",
    "# change the medium default line to full (https://stackoverflow.com/questions/60638344/quartiles-line-properties-in-seaborn-violinplot)\n",
    "for l in axsns.lines[1::3]:\n",
    "    l.set_linestyle('-')\n",
    "    l.set_linewidth(1.2)\n",
    "    l.set_color('black')\n",
    "    l.set_alpha(0.8)\n",
    "# for i in [0, 2, 3, 5]:         # remove the 25% and 75% quartiles\n",
    "#     l = axsns.lines[i]\n",
    "#     l.set_linestyle('-')\n",
    "#     l.set_linewidth(0)\n",
    "#     l.set_color('red')\n",
    "#     l.set_alpha(0.8)\n",
    "\n",
    "# Apply colorscheme to violinplots https://stackoverflow.com/questions/70442958/seaborn-how-to-apply-custom-color-to-each-seaborn-violinplot\n",
    "from matplotlib.collections import PolyCollection\n",
    "for ind, violin in enumerate(axsns.findobj(PolyCollection)):\n",
    "    violin.set_facecolor(my_cmap_light[ind])\n",
    "\n",
    "\n",
    "# 2. Plot individual points\n",
    "y_tilt = -0.25                                      # Set some delta for the points below the violinplot\n",
    "y_base = np.zeros(dfq1b.values.shape[0]) + y_tilt   # base vector to which to broadcast y-tilt values\n",
    "\n",
    "for i, col in enumerate(modes):\n",
    "    ax.plot(dfq1b[col],\n",
    "            y_base + i,\n",
    "            # 'o',          # circles\n",
    "            '^',            # triangles_up\n",
    "            alpha=1,\n",
    "            zorder=20,      # higher means more visible\n",
    "            markersize=11,\n",
    "            markeredgewidth=0,\n",
    "            # markerfacecolor='none',\n",
    "            markerfacecolor=my_cmap_dark[i],\n",
    "            markeredgecolor=my_cmap_dark[i],)\n",
    "    ax.plot(dfq1b[col],\n",
    "            y_base + i,\n",
    "            # 'o',          # circles\n",
    "            '^',            # triangles_up\n",
    "            markersize=11,\n",
    "            markerfacecolor='white',\n",
    "            markeredgewidth=1,\n",
    "            color ='white',\n",
    "            linewidth = None,\n",
    "            zorder=1,)\n",
    "\n",
    "\n",
    "# 3. Plot gray lines connecting modes\n",
    "for i, idx in enumerate(dfq1b.index):\n",
    "    ax.plot(dfq1b.loc[idx, modes],\n",
    "            [y_tilt, y_tilt+1],\n",
    "            color ='gray', linewidth = 2, linestyle ='-', alpha = .2,\n",
    "            zorder=0)\n",
    "\n",
    "\n",
    "# 4. Plot red line connecting medians\n",
    "ax.plot(np.median(dfq1b, axis=0), [0, 1], color ='red', linewidth = 2, linestyle ='-', alpha = .4)\n",
    "\n",
    "\n",
    "# 5. CI Errorbars\n",
    "for i, mode in enumerate(modes):\n",
    "    plt.errorbar(median_time[mode], i, xerr=np.array([[ci_delta[mode][0], ci_delta[mode][1]]]).T,\n",
    "                 fmt='o', markersize=10,\n",
    "                 # lw = 3,          # if end line for CI\n",
    "                 lw = 5,            # if no ned line for CI\n",
    "                 alpha=1,\n",
    "                 zorder=100,        # higher means more visible\n",
    "                 capsize = 10,\n",
    "                 # capthick = 4,    # end line for CI\n",
    "                 capthick = 0,      # no end line for CI\n",
    "                 # color = 'black',\n",
    "                 color = my_cmap_dark[i],\n",
    "                 )   # my_cmap[1])\n",
    "    ax.text(median_time[mode],\n",
    "            # i+0.36,\n",
    "            i-0.16,\n",
    "            f'{median_time[mode]:.1f}', horizontalalignment='center',\n",
    "            # color='black',\n",
    "            color= my_cmap_dark[i],\n",
    "            fontsize=figfont_size)\n",
    "    # ax.text(ci[mode].low, i+0.1, f'{ci[mode].low:.1f}', horizontalalignment='center', color='black', fontsize=20)\n",
    "    # ax.text(ci[mode].high, i+0.1, f'{ci[mode].high:.1f}', horizontalalignment='center', color='black', fontsize=20)\n",
    "\n",
    "\n",
    "#Additional settings\n",
    "ax.set_yticks(range(len(dfq1b.columns)))\n",
    "ax.set_yticklabels(modes, size= ylab_size)\n",
    "ax.set_ylim(-0.5, 1.5)\n",
    "ax.set_xlabel('Median time per worker (sec)', size = xlab_size)\n",
    "ax.set_ylabel(None)\n",
    "ax.set_yticklabels(['FigX', 'SQL', ])\n",
    "# ax.set_title('Median times per worker', size = title_size)\n",
    "sns.despine()\n",
    "ax.legend_.remove()\n",
    "\n",
    "plt.grid(axis = 'x', linewidth = 0.5, color = 'lightgray')\n",
    "\n",
    "if savefig:\n",
    "    plt.savefig(fig_dir + \"/q1_figure1.pdf\", bbox_inches='tight')\n",
    "    plt.savefig(fig_dir + \"/q1_figure1.svg\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbc86c76",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# Question 1. Figure 1b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb8b617c",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median ratio: 0.571, 95% CI [0.475, 0.617]\n"
     ]
    }
   ],
   "source": [
    "dfq1c = df_anonymized_data.groupby(['worker_id', 'mode']).time.agg(['median'])                               # for each worker, calculate median for both modes\n",
    "dfq1c = pd.pivot_table(dfq1c, values=['median'], index=['worker_id'], columns=['mode'])       # pivot to have one row per worker\n",
    "dfq1c['ratio median'] = dfq1c['median','RD'] / dfq1c['median','SQL']                           # add the ratio between medians of the two modes\n",
    "\n",
    "sample = np.array(dfq1c['ratio median'])                                                     # extract the sample and then create the boostrapped medians\n",
    "data_ratio = dfq1c['ratio median']\n",
    "\n",
    "median_ratio = np.median(data_ratio)\n",
    "ci_ratio = scipybootstrap((data_ratio,), statistic=np.median, n_resamples=BOOTSTRAPSAMPLES, confidence_level=BOOTSTRAPCONFIDENCE, method=BOOTSTRAPMETHOD, axis=0).confidence_interval        #convert array to sequence\n",
    "ci_ratio_delta = [median_ratio - ci_ratio.low, ci_ratio.high - median_ratio]\n",
    "\n",
    "print(f'Median ratio: {median_ratio:.3f}, 95% CI [{ci_ratio.low:.3f}, {ci_ratio.high:.3f}]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b3c5ea47",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define figure settings\n",
    "figwidth = 10\n",
    "figheight = 3\n",
    "xlab_size = 20\n",
    "ylab_size = 20\n",
    "figfont_size = 24\n",
    "\n",
    "# Define consistent color maps\n",
    "my_cmap_sns_dark = [(0.8392156862745098, 0.15294117647058825, 0.1568627450980392)]\n",
    "my_cmap_sns_light = [(0.984313725490196, 0.6039215686274509, 0.6)]\n",
    "my_cmap_dark = sns.color_palette(my_cmap_sns_dark, as_cmap=True)\n",
    "my_cmap_light = sns.color_palette(my_cmap_sns_light, as_cmap=True)\n",
    "\n",
    "# Create data frame for split violinplot\n",
    "dfvp = pd.DataFrame()\n",
    "dfvp[\"values\"] = sample\n",
    "dfvp[\"all\"] = \"\"                                        # attribute that is shared by all entries\n",
    "# print(dfvp)\n",
    "\n",
    "# Create empty figure and plot the individual datapoints\n",
    "fig, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "\n",
    "\n",
    "# 1. Violinplot\n",
    "axsns = sns.violinplot(x='values', y='all',    # y='all' just need to group both types together\n",
    "                       data=dfvp,\n",
    "                       hue = True, hue_order = [False, True],\n",
    "                       split = True, inner = 'quartile',\n",
    "                       cut=0,                  # 0 means ending sharp at end points\n",
    "                       width=.6, scale = 'width',\n",
    "                       # dodge = False,        # When using ``hue`` nesting, setting this to ``True`` will separate the strips for different hue levels along the categorical axis.\n",
    "                       orient = 'h',\n",
    "                       color=my_cmap_light[0])\n",
    "\n",
    "# change the medium default linke to full\n",
    "for l in axsns.lines[1::3]:\n",
    "    l.set_linestyle('-')\n",
    "    l.set_linewidth(1.5)\n",
    "    l.set_color('black')\n",
    "    l.set_alpha(0.8)\n",
    "# for i in [0, 2]:         # remove the 25% and 75% quartiles\n",
    "#     l = axsns.lines[i]\n",
    "#     l.set_linestyle('-')\n",
    "#     l.set_linewidth(0)\n",
    "#     l.set_color('red')\n",
    "#     l.set_alpha(0.8)\n",
    "\n",
    "\n",
    "# 2. Plot individual points\n",
    "y_tilt = -0.13                                   # Set some delta for the points below the violinplot\n",
    "y_base = np.zeros(len(data_ratio)) + y_tilt     # base vector to which to broadcast y-tilt values\n",
    "\n",
    "ax.plot(data_ratio, y_base,\n",
    "        # 'o',\n",
    "        '^',\n",
    "        alpha=1,\n",
    "        zorder=20,      # higher means more visible\n",
    "        markersize=11,\n",
    "        markeredgewidth=0,\n",
    "        # markerfacecolor='none',\n",
    "        markerfacecolor=my_cmap_dark[0],\n",
    "        markeredgecolor=my_cmap_dark[0],\n",
    "        )\n",
    "\n",
    "\n",
    "# 3. CI Errorbars & show numbers\n",
    "axeb = plt.errorbar(median_ratio, 0, xerr=np.array([[ci_ratio_delta[0], ci_ratio_delta[1]]]).T,\n",
    "                    fmt='o',\n",
    "                    markersize=10, alpha=1,\n",
    "                    # lw = 3,\n",
    "                    lw = 5,\n",
    "                    zorder=100,      # higher means more visible\n",
    "                    capsize = 10,\n",
    "                    # capthick = 4,\n",
    "                    capthick = 0,\n",
    "                    # color = 'black',\n",
    "                    color = my_cmap_dark[0],\n",
    "                    )\n",
    "\n",
    "med = np.median(sample)\n",
    "# ax.text(med, 0.32, f'{100*med:.1f}%', horizontalalignment='center', color='black', fontsize=20)\n",
    "# ax.text(med, 0.32, f'{med:.2f}', horizontalalignment='center', color='black', fontsize=20)\n",
    "ax.text(med, -0.1, f'{med:.2f}', horizontalalignment='center',\n",
    "        # color='black',\n",
    "        color = my_cmap_dark[0],\n",
    "        fontsize=figfont_size)\n",
    "# ax.text(ci_ratio.low, 0.04, f'{100*ci_ratio.low:.1f}%', horizontalalignment='center', color='black', fontsize=20)\n",
    "# ax.text(ci_ratio.high, 0.04, f'{100*ci_ratio.high:.1f}%', horizontalalignment='center', color='black', fontsize=20)\n",
    "\n",
    "\n",
    "# Additional settings\n",
    "# ax.set_ylim(-0.2, 0.4)\n",
    "ax.set_ylim(-0.25, 0.35)\n",
    "ax.set_ylabel(None)         # remove the 'all'\n",
    "ax.set_xlim(0.2, 0.8)\n",
    "# ax.set_xlabel('Ratio of median time per worker (RD / SQL)', size = xlab_size)\n",
    "ax.set_xlabel('Ratio of median time per worker (FigX / SQL)', size = xlab_size)\n",
    "sns.despine()               # remove bounding box\n",
    "plt.grid(axis = 'x', linewidth = 0.5, color = 'lightgray')\n",
    "ax.legend_.remove()\n",
    "\n",
    "import matplotlib.ticker as mtick\n",
    "# ax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0))           # show in percentage\n",
    "\n",
    "if savefig:\n",
    "    plt.savefig(fig_dir + \"/q1_figure2.pdf\", bbox_inches='tight')\n",
    "    plt.savefig(fig_dir + \"/q1_figure2.svg\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17c56d9",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d81ad1bc",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XXXXX\\AppData\\Local\\Temp\\ipykernel_30608\\3872081231.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['H1'] = np.where(df['question'].between(1, 16, inclusive='both'), 'H1', '')          # TODO: creates warning but works for now\n",
      "C:\\Users\\XXXXX\\AppData\\Local\\Temp\\ipykernel_30608\\3872081231.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['H2'] = np.where(df['question'].between(17, 32, inclusive='both'), 'H2', '')         # TODO: creates warning but works for now\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <th colspan=\"2\" halign=\"left\">RD</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SQL</th>\n",
       "      <th>RD</th>\n",
       "      <th>SQL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>section</th>\n",
       "      <th>H1</th>\n",
       "      <th>H2</th>\n",
       "      <th>H1</th>\n",
       "      <th>H2</th>\n",
       "      <th>ratio</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worker_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.574</td>\n",
       "      <td>4.231</td>\n",
       "      <td>12.054</td>\n",
       "      <td>8.439</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.003</td>\n",
       "      <td>3.241</td>\n",
       "      <td>6.827</td>\n",
       "      <td>4.050</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.905</td>\n",
       "      <td>9.146</td>\n",
       "      <td>25.600</td>\n",
       "      <td>17.076</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.966</td>\n",
       "      <td>4.619</td>\n",
       "      <td>25.892</td>\n",
       "      <td>11.245</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.489</td>\n",
       "      <td>6.985</td>\n",
       "      <td>28.895</td>\n",
       "      <td>11.956</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.813</td>\n",
       "      <td>5.413</td>\n",
       "      <td>73.099</td>\n",
       "      <td>10.333</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.675</td>\n",
       "      <td>4.580</td>\n",
       "      <td>8.597</td>\n",
       "      <td>5.418</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.829</td>\n",
       "      <td>7.042</td>\n",
       "      <td>21.371</td>\n",
       "      <td>11.062</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.793</td>\n",
       "      <td>3.695</td>\n",
       "      <td>12.188</td>\n",
       "      <td>6.908</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.650</td>\n",
       "      <td>4.139</td>\n",
       "      <td>11.913</td>\n",
       "      <td>13.957</td>\n",
       "      <td>0.235</td>\n",
       "      <td>1.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.557</td>\n",
       "      <td>2.864</td>\n",
       "      <td>6.681</td>\n",
       "      <td>6.902</td>\n",
       "      <td>0.805</td>\n",
       "      <td>1.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.439</td>\n",
       "      <td>9.291</td>\n",
       "      <td>25.381</td>\n",
       "      <td>21.604</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.022</td>\n",
       "      <td>3.512</td>\n",
       "      <td>14.547</td>\n",
       "      <td>6.168</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "mode          RD          SQL           RD   SQL\n",
       "section       H1    H2     H1     H2 ratio ratio\n",
       "worker_id                                       \n",
       "0          4.574 4.231 12.054  8.439 0.925 0.700\n",
       "1          5.003 3.241  6.827  4.050 0.648 0.593\n",
       "2         11.905 9.146 25.600 17.076 0.768 0.667\n",
       "3          8.966 4.619 25.892 11.245 0.515 0.434\n",
       "4         16.489 6.985 28.895 11.956 0.424 0.414\n",
       "5         12.813 5.413 73.099 10.333 0.423 0.141\n",
       "6         12.675 4.580  8.597  5.418 0.361 0.630\n",
       "7          8.829 7.042 21.371 11.062 0.798 0.518\n",
       "8          6.793 3.695 12.188  6.908 0.544 0.567\n",
       "9         17.650 4.139 11.913 13.957 0.235 1.172\n",
       "10         3.557 2.864  6.681  6.902 0.805 1.033\n",
       "11        11.439 9.291 25.381 21.604 0.812 0.851\n",
       "12         7.022 3.512 14.547  6.168 0.500 0.424"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RD, H1: 8.966, 95% CI [6.793, 12.675]\n",
      "RD, H2: 4.580, 95% CI [3.695, 6.985]\n",
      "RD, ratio: 0.544, 95% CI [0.424, 0.798]\n",
      "SQL, H1: 14.547, 95% CI [11.913, 25.600]\n",
      "SQL, H2: 10.333, 95% CI [6.902, 11.956]\n",
      "SQL, ratio: 0.593, 95% CI [0.434, 0.700]\n"
     ]
    }
   ],
   "source": [
    "# Create df6, df7\n",
    "\n",
    "df = df_anonymized_data[['worker_id', 'question', 'time', 'mode']]\n",
    "\n",
    "df['H1'] = np.where(df['question'].between(1, 16, inclusive='both'), 'H1', '')          # TODO: creates warning but works for now\n",
    "df['H2'] = np.where(df['question'].between(17, 32, inclusive='both'), 'H2', '')         # TODO: creates warning but works for now\n",
    "\n",
    "df1 = df[['worker_id','question','time','mode','H1']].rename(columns={'H1': 'section'}) # Two sections: 1st half (H1) and 2nd half (H2)\n",
    "df2 = df[['worker_id','question','time','mode','H2']].rename(columns={'H2': 'section'})\n",
    "df4 = pd.concat([df1, df2])\n",
    "df4 = df4.loc[df4['section'] != '']\n",
    "df4 = df4.reset_index(inplace=False, drop=True)\n",
    "# display(df4)\n",
    "\n",
    "df5 = df4.groupby(['worker_id', 'mode', 'section']).time.agg(['median'])                # for each worker, calculate median for both modes and section\n",
    "df5.reset_index(inplace=True)\n",
    "# display(df5)\n",
    "\n",
    "# pivot to have one row per worker\n",
    "df6 = pd.pivot_table(df5, values=['median'], index=['worker_id'], columns=['mode', 'section'])\n",
    "df6=df6.droplevel(0, axis=1)\n",
    "\n",
    "# relative improvements per user\n",
    "df6['RD', 'ratio'] = df6['RD', 'H2'] / df6['RD', 'H1']\n",
    "df6['SQL', 'ratio'] = df6['SQL', 'H2'] / df6['SQL', 'H1']\n",
    "display(df6)\n",
    "\n",
    "# Median of median task time for each mode and section\n",
    "modes = ['RD', 'SQL']\n",
    "sections = ['H1', 'H2', 'ratio']\n",
    "median_time = {}\n",
    "ci = {}\n",
    "ci_delta = {}\n",
    "for mode in modes:\n",
    "    for section in sections:\n",
    "        column = (mode, section)\n",
    "        median_time[column] = np.median(df6[column])\n",
    "        ci[column] = scipybootstrap((df6[column],), statistic=np.median,\n",
    "                                    n_resamples=BOOTSTRAPSAMPLES,\n",
    "                                    confidence_level=BOOTSTRAPCONFIDENCE,\n",
    "                                    method='percentile',\n",
    "                                    axis=0).confidence_interval        #convert array to sequence\n",
    "        ci_delta[column] = [median_time[column] - ci[column].low, ci[column].high - median_time[column]]\n",
    "\n",
    "        print(f'{mode}, {section}: {median_time[column]:.3f}, 95% CI [{ci[column].low:.3f}, {ci[column].high:.3f}]')\n",
    "\n",
    "# uses df5 to make df7 (used for later plot)\n",
    "modes = ['SQL', 'RD']\n",
    "sections = ['H1', 'H2']\n",
    "df7 = df5.loc[df5['section'].isin(sections)]\n",
    "# display(df7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc21c930",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot (uses df6, df7)\n",
    "\n",
    "# Define pre-settings\n",
    "figwidth = 10\n",
    "figheight = 7\n",
    "xlab_size = 20\n",
    "ylab_size = 20\n",
    "figfont_size = 24\n",
    "\n",
    "# Define consistent color maps\n",
    "my_cmap_sns_light = [(0.7764705882352941, 0.8588235294117647, 0.9372549019607843), (0.9921568627450981, 0.8156862745098039, 0.6352941176470588)]        # light orange, light blue\n",
    "my_cmap_sns_dark = [(0.19215686274509805, 0.5098039215686274, 0.7411764705882353), (0.9019607843137255, 0.3333333333333333, 0.050980392156862744)]      # dark orange, dark blue\n",
    "my_cmap_dark = sns.color_palette(my_cmap_sns_dark, as_cmap=True)\n",
    "my_cmap_light = sns.color_palette(my_cmap_sns_light, as_cmap=True)\n",
    "\n",
    "\n",
    "# Create empty figure and plot the individual datapoints\n",
    "fig, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "\n",
    "\n",
    "# 1. Violinplots\n",
    "axsns = sns.violinplot(x='median', y='section', data=df7,\n",
    "                       hue='mode',\n",
    "                       hue_order=['SQL', 'RD'],\n",
    "                       split=True,   # half violinplots https://stackoverflow.com/questions/53872439/half-not-split-violin-plots-in-seaborn\n",
    "                       inner='quartile',\n",
    "                       cut=0,               # 0 means ending sharp at end points\n",
    "                       width=.4,\n",
    "                       orient = 'h',\n",
    "                       zorder=20,\n",
    "                       palette = my_cmap_light,)\n",
    "\n",
    "# change the medium default line to full (https://stackoverflow.com/questions/60638344/quartiles-line-properties-in-seaborn-violinplot)\n",
    "for l in axsns.lines[1::3]:\n",
    "    l.set_linestyle('-')\n",
    "    l.set_linewidth(1.2)\n",
    "    l.set_color('black')\n",
    "    # l.set_alpha(0.8)\n",
    "\n",
    "# for i in [0, 2, 3, 5]:         # remove the 25% and 75% quartiles\n",
    "#     l = axsns.lines[i]\n",
    "#     l.set_linestyle('-')\n",
    "#     l.set_linewidth(0)\n",
    "#     l.set_color('red')\n",
    "#     l.set_alpha(0.8)\n",
    "\n",
    "\n",
    "# 2. Plot individual points\n",
    "y_base = np.zeros(df6.values.shape[0])  # base vector to which to broadcast y-tilt values\n",
    "y_tilt_mode = [0.3, 0.38]\n",
    "y_tilt_section = [0, 1]\n",
    "for i, mode in enumerate(modes):\n",
    "    for j, section in enumerate(sections):\n",
    "        column = (mode, section)\n",
    "        ax.plot(df6[column],\n",
    "                y_base + y_tilt_mode[i] + y_tilt_section[j],\n",
    "                # 'o',\n",
    "                # '|',\n",
    "                '^',\n",
    "                alpha=1,\n",
    "                zorder=20,      # higher means more visible\n",
    "                markersize=11,\n",
    "                markeredgewidth=0,\n",
    "                # markerfacecolor='none',\n",
    "                markerfacecolor=my_cmap_sns_dark[i],\n",
    "                markeredgecolor=my_cmap_sns_dark[i],)\n",
    "        ax.plot(df6[column],          # white background\n",
    "                y_base + y_tilt_mode[i] + y_tilt_section[j],\n",
    "                # 'o',\n",
    "                # '|',\n",
    "                '^',\n",
    "                markersize=11,\n",
    "                markeredgewidth=1,\n",
    "                markerfacecolor='white',\n",
    "                color ='white',\n",
    "                linewidth = None,\n",
    "                zorder=1,)\n",
    "\n",
    "\n",
    "# # 3. Plot lines connecting points\n",
    "# for idx in df6.index:\n",
    "#     for i, mode in enumerate(modes):\n",
    "#         for j in range(len(sections)-1):\n",
    "#             start = (mode, sections[j])\n",
    "#             end = (mode, sections[j+1])\n",
    "#             ax.plot(df6.loc[idx, [start, end]],\n",
    "#                     [y_tilt_mode[i] + y_tilt_section[j], y_tilt_mode[i] + y_tilt_section[j+1]],\n",
    "#                     color=my_cmap_sns_dark[i], linewidth=2, linestyle='-', alpha=.2, zorder=0)\n",
    "\n",
    "\n",
    "# 4. CI Errorbars & numbers\n",
    "y_tilt_mode = [0.5, 0.55]\n",
    "# y_tilt_section_bar = [0.23, 0.8]\n",
    "# y_tilt_section_number = [0.19, 0.89]\n",
    "for i, mode in enumerate(modes):\n",
    "    for j, section in enumerate(sections):\n",
    "        column = (mode, section)\n",
    "        plt.errorbar(median_time[column], y_tilt_mode[i]+y_tilt_section[j],\n",
    "                     xerr=np.array([[ci_delta[column][0] ,ci_delta[column][1]]]).T,\n",
    "                     fmt='o', markersize=10,\n",
    "                     lw = 3, alpha=1,\n",
    "                     zorder=100,        # higher means more visible\n",
    "                     #             capsize = 10, capthick = 4,\n",
    "                     color = my_cmap_sns_dark[i]    # 'black'\n",
    "                     )\n",
    "        ax.text(median_time[column], y_tilt_mode[i]+y_tilt_section[j] + 0.18, f'{median_time[column]:.1f}',\n",
    "                horizontalalignment='center', color = my_cmap_sns_dark[i],\n",
    "                fontsize=figfont_size)\n",
    "\n",
    "\n",
    "# 5. Plot red line connecting medians\n",
    "for i, mode in enumerate(modes):\n",
    "    ax.plot([median_time[(mode, 'H1')], median_time[(mode, 'H2')]],\n",
    "            [y_tilt_mode[i]+y_tilt_section[0], y_tilt_mode[i]+y_tilt_section[1]],\n",
    "            color=my_cmap_sns_dark[i], linewidth = 3, linestyle ='-',\n",
    "            alpha=.3,\n",
    "            zorder=0)\n",
    "\n",
    "\n",
    "# #Additional settings\n",
    "ax.set_xlabel('Median time per worker and quartiles (sec)', size = xlab_size)\n",
    "ax.set_ylabel(None)\n",
    "ax.set_xlim(0, 35)\n",
    "ax.set_ylim(1.9, -0.25)\n",
    "leg = plt. legend(loc='lower right',\n",
    "                  borderaxespad= 0.2,\n",
    "                  frameon = True,)\n",
    "leg.get_frame().set_alpha(1)\n",
    "leg.get_frame().set_linewidth(0.0)\n",
    "for text, text2 in zip(leg.get_texts(), ['SQL', 'FigX']):\n",
    "    text.set_text(text2)\n",
    "\n",
    "plt.grid(axis = 'x', linewidth = 0.5, color = 'lightgray')\n",
    "sns.despine()               # remove bounding box\n",
    "\n",
    "if savefig:\n",
    "    plt.savefig(fig_dir + \"/q2_figure.pdf\", bbox_inches='tight')\n",
    "    plt.savefig(fig_dir + \"/q2_figure.svg\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd2ab3b",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# Question 3: four patterns\n",
    "\n",
    "(1) calculate the median time per pattern (4) across the two modes (2). Thus 8 values.\n",
    "(2) show repeated measure violin plot figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da5ecbe0",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>worker_id</th>\n",
       "      <th>mode</th>\n",
       "      <th>pattern</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>RD</td>\n",
       "      <td>1</td>\n",
       "      <td>3.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>RD</td>\n",
       "      <td>2</td>\n",
       "      <td>4.542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>RD</td>\n",
       "      <td>3</td>\n",
       "      <td>6.337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>RD</td>\n",
       "      <td>4</td>\n",
       "      <td>4.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>SQL</td>\n",
       "      <td>1</td>\n",
       "      <td>9.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>12</td>\n",
       "      <td>RD</td>\n",
       "      <td>4</td>\n",
       "      <td>3.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>12</td>\n",
       "      <td>SQL</td>\n",
       "      <td>1</td>\n",
       "      <td>7.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>12</td>\n",
       "      <td>SQL</td>\n",
       "      <td>2</td>\n",
       "      <td>9.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>12</td>\n",
       "      <td>SQL</td>\n",
       "      <td>3</td>\n",
       "      <td>10.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>12</td>\n",
       "      <td>SQL</td>\n",
       "      <td>4</td>\n",
       "      <td>6.075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     worker_id mode  pattern  median\n",
       "0            0   RD        1   3.235\n",
       "1            0   RD        2   4.542\n",
       "2            0   RD        3   6.337\n",
       "3            0   RD        4   4.062\n",
       "4            0  SQL        1   9.212\n",
       "..         ...  ...      ...     ...\n",
       "99          12   RD        4   3.728\n",
       "100         12  SQL        1   7.357\n",
       "101         12  SQL        2   9.568\n",
       "102         12  SQL        3  10.256\n",
       "103         12  SQL        4   6.075\n",
       "\n",
       "[104 rows x 4 columns]"
      ]
     },
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    },
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     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <th colspan=\"4\" halign=\"left\">RD</th>\n",
       "      <th colspan=\"4\" halign=\"left\">SQL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pattern</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worker_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.235</td>\n",
       "      <td>4.542</td>\n",
       "      <td>6.337</td>\n",
       "      <td>4.062</td>\n",
       "      <td>9.212</td>\n",
       "      <td>9.437</td>\n",
       "      <td>14.511</td>\n",
       "      <td>7.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.590</td>\n",
       "      <td>3.881</td>\n",
       "      <td>5.722</td>\n",
       "      <td>3.229</td>\n",
       "      <td>3.820</td>\n",
       "      <td>6.677</td>\n",
       "      <td>6.269</td>\n",
       "      <td>10.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.568</td>\n",
       "      <td>13.332</td>\n",
       "      <td>12.968</td>\n",
       "      <td>16.290</td>\n",
       "      <td>8.244</td>\n",
       "      <td>20.711</td>\n",
       "      <td>31.367</td>\n",
       "      <td>22.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.699</td>\n",
       "      <td>9.593</td>\n",
       "      <td>8.220</td>\n",
       "      <td>9.334</td>\n",
       "      <td>7.720</td>\n",
       "      <td>18.619</td>\n",
       "      <td>23.386</td>\n",
       "      <td>17.523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.487</td>\n",
       "      <td>13.705</td>\n",
       "      <td>24.541</td>\n",
       "      <td>11.132</td>\n",
       "      <td>10.759</td>\n",
       "      <td>13.135</td>\n",
       "      <td>21.520</td>\n",
       "      <td>10.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.702</td>\n",
       "      <td>8.167</td>\n",
       "      <td>32.527</td>\n",
       "      <td>5.445</td>\n",
       "      <td>25.289</td>\n",
       "      <td>87.439</td>\n",
       "      <td>11.405</td>\n",
       "      <td>36.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.391</td>\n",
       "      <td>4.580</td>\n",
       "      <td>19.679</td>\n",
       "      <td>3.914</td>\n",
       "      <td>5.270</td>\n",
       "      <td>9.737</td>\n",
       "      <td>23.405</td>\n",
       "      <td>3.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.366</td>\n",
       "      <td>11.875</td>\n",
       "      <td>8.439</td>\n",
       "      <td>12.018</td>\n",
       "      <td>18.546</td>\n",
       "      <td>17.215</td>\n",
       "      <td>13.441</td>\n",
       "      <td>8.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.659</td>\n",
       "      <td>13.281</td>\n",
       "      <td>8.259</td>\n",
       "      <td>3.921</td>\n",
       "      <td>8.605</td>\n",
       "      <td>10.188</td>\n",
       "      <td>13.319</td>\n",
       "      <td>11.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.447</td>\n",
       "      <td>14.052</td>\n",
       "      <td>14.971</td>\n",
       "      <td>3.616</td>\n",
       "      <td>6.475</td>\n",
       "      <td>22.712</td>\n",
       "      <td>14.451</td>\n",
       "      <td>6.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.444</td>\n",
       "      <td>6.462</td>\n",
       "      <td>3.482</td>\n",
       "      <td>2.925</td>\n",
       "      <td>3.433</td>\n",
       "      <td>7.648</td>\n",
       "      <td>10.075</td>\n",
       "      <td>6.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.579</td>\n",
       "      <td>15.940</td>\n",
       "      <td>47.182</td>\n",
       "      <td>12.917</td>\n",
       "      <td>18.217</td>\n",
       "      <td>26.620</td>\n",
       "      <td>33.282</td>\n",
       "      <td>20.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.659</td>\n",
       "      <td>5.232</td>\n",
       "      <td>11.367</td>\n",
       "      <td>3.728</td>\n",
       "      <td>7.357</td>\n",
       "      <td>9.568</td>\n",
       "      <td>10.256</td>\n",
       "      <td>6.075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "mode         RD                         SQL                     \n",
       "pattern       1      2      3      4      1      2      3      4\n",
       "worker_id                                                       \n",
       "0         3.235  4.542  6.337  4.062  9.212  9.437 14.511  7.667\n",
       "1         3.590  3.881  5.722  3.229  3.820  6.677  6.269 10.065\n",
       "2         5.568 13.332 12.968 16.290  8.244 20.711 31.367 22.358\n",
       "3         5.699  9.593  8.220  9.334  7.720 18.619 23.386 17.523\n",
       "4         5.487 13.705 24.541 11.132 10.759 13.135 21.520 10.039\n",
       "5         3.702  8.167 32.527  5.445 25.289 87.439 11.405 36.320\n",
       "6         3.391  4.580 19.679  3.914  5.270  9.737 23.405  3.905\n",
       "7         5.366 11.875  8.439 12.018 18.546 17.215 13.441  8.468\n",
       "8         4.659 13.281  8.259  3.921  8.605 10.188 13.319 11.075\n",
       "9         3.447 14.052 14.971  3.616  6.475 22.712 14.451  6.975\n",
       "10        2.444  6.462  3.482  2.925  3.433  7.648 10.075  6.526\n",
       "11        5.579 15.940 47.182 12.917 18.217 26.620 33.282 20.020\n",
       "12        3.659  5.232 11.367  3.728  7.357  9.568 10.256  6.075"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create df8\n",
    "\n",
    "df = df_anonymized_data[['worker_id', 'pattern', 'time', 'mode']]\n",
    "# print(df)\n",
    "\n",
    "df8 = df.groupby(['worker_id', 'mode', 'pattern']).time.agg(['median'])         # for each worker, calculate median for both modes\n",
    "df8.reset_index(inplace=True)\n",
    "display(df8)\n",
    "\n",
    "# Pivot to have one row per worker\n",
    "df9 = pd.pivot_table(df8, values=['median'], index=['worker_id'], columns=['mode', 'pattern'])\n",
    "df9=df9.droplevel(0, axis=1)\n",
    "display(df9)\n",
    "\n",
    "# Median of median task time for each mode and section (for error plots)\n",
    "modes = ['RD', 'SQL']\n",
    "patterns = [1, 2, 3, 4]\n",
    "median_time = {}\n",
    "ci = {}\n",
    "ci_delta = {}\n",
    "for mode in modes:\n",
    "    for pattern in patterns:\n",
    "        column = (mode, pattern)\n",
    "        median_time[column] = np.median(df9[column])\n",
    "        ci[column] = scipybootstrap((df9[column],), statistic=np.median,\n",
    "                                    n_resamples=BOOTSTRAPSAMPLES,\n",
    "                                    confidence_level=BOOTSTRAPCONFIDENCE,\n",
    "                                    method='percentile',\n",
    "                                    axis=0).confidence_interval        #convert array to sequence\n",
    "        ci_delta[column] = [median_time[column] - ci[column].low, ci[column].high - median_time[column]]\n",
    "# print(median_time)\n",
    "# print(ci)\n",
    "# print(ci_delta)"
   ]
  },
  {
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   "id": "29006886",
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# needs df8 for violin, df9 for points, dictionaries (median_time, ci, ci_delta) for error plots, df6 for individual points\n",
    "\n",
    "modes = ['SQL', 'RD']\n",
    "patterns = [1, 2, 3, 4]\n",
    "y_tilt_section = [0, 1, 2, 3]\n",
    "\n",
    "# Define pre-settings\n",
    "figwidth = 10\n",
    "figheight = 9\n",
    "xlab_size = 20\n",
    "ylab_size = 20\n",
    "figfont_size = 20\n",
    "\n",
    "# Define consistent color maps\n",
    "my_cmap_sns_light = [(0.7764705882352941, 0.8588235294117647, 0.9372549019607843), (0.9921568627450981, 0.8156862745098039, 0.6352941176470588)]        # light orange, light blue\n",
    "my_cmap_sns_dark = [(0.19215686274509805, 0.5098039215686274, 0.7411764705882353), (0.9019607843137255, 0.3333333333333333, 0.050980392156862744)]      # dark orange, dark blue\n",
    "my_cmap_dark = sns.color_palette(my_cmap_sns_dark, as_cmap=True)\n",
    "my_cmap_light = sns.color_palette(my_cmap_sns_light, as_cmap=True)\n",
    "\n",
    "# Create empty figure and plot the individual datapoints\n",
    "fig, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "\n",
    "\n",
    "# 1. Violinplots\n",
    "axsns = sns.violinplot(x='median', y='pattern', data=df8,\n",
    "                       hue='mode',\n",
    "                       hue_order=['SQL', 'RD'],\n",
    "                       split=True,   # half violinplots https://stackoverflow.com/questions/53872439/half-not-split-violin-plots-in-seaborn\n",
    "                       inner='quartile',\n",
    "                       cut=0,               # 0 means ending sharp at end points\n",
    "                       width=.4,\n",
    "                       orient = 'h',\n",
    "                       zorder=20,\n",
    "                       palette = my_cmap_light,)\n",
    "\n",
    "# change the medium default line to full (https://stackoverflow.com/questions/60638344/quartiles-line-properties-in-seaborn-violinplot)\n",
    "for l in axsns.lines[1::3]:\n",
    "    l.set_linestyle('-')\n",
    "    l.set_linewidth(1.2)\n",
    "    l.set_color('black')\n",
    "    l.set_alpha(0.8)\n",
    "# for i in [0, 2, 3, 5]:         # remove the 25% and 75% quartiles\n",
    "#     l = axsns.lines[i]\n",
    "#     l.set_linestyle('-')\n",
    "#     l.set_linewidth(0)\n",
    "#     l.set_color('red')\n",
    "#     l.set_alpha(0.8)\n",
    "\n",
    "\n",
    "# 2. Plot individual points\n",
    "y_base = np.zeros(df6.values.shape[0])  # base vector to which to broadcast y-tilt values\n",
    "y_tilt_mode = [0.3, 0.39]\n",
    "for i, mode in enumerate(modes):\n",
    "    for j, pattern in enumerate(patterns):\n",
    "        column = (mode, pattern)\n",
    "        ax.plot(df9[column],\n",
    "                y_base + y_tilt_mode[i] + y_tilt_section[j],\n",
    "                '^',\n",
    "                alpha=1,\n",
    "                zorder=20,      # higher means more visible\n",
    "                markersize=10,\n",
    "                markeredgewidth=0,\n",
    "                # markerfacecolor='none',\n",
    "                markerfacecolor=my_cmap_sns_dark[i],\n",
    "                markeredgecolor=my_cmap_sns_dark[i],)\n",
    "        ax.plot(df9[column],    # white background behind the markers, but in front of the connecting lines\n",
    "                y_base + y_tilt_mode[i] + y_tilt_section[j],\n",
    "                '^',\n",
    "                markersize=10,\n",
    "                markeredgewidth=1,\n",
    "                markerfacecolor='white',\n",
    "                color ='white',\n",
    "                linewidth = None,\n",
    "                zorder=1,)\n",
    "\n",
    "\n",
    "# # 3. Plot lines connecting individual points\n",
    "# for idx in df9.index:\n",
    "#     for i, mode in enumerate(modes):\n",
    "#         for j in range(len(patterns)-1):\n",
    "#             start = (mode, patterns[j])\n",
    "#             end = (mode, patterns[j+1])\n",
    "#             ax.plot(df9.loc[idx, [start, end]],\n",
    "#                     [y_tilt_mode[i] + y_tilt_section[j], y_tilt_mode[i] + y_tilt_section[j+1]],\n",
    "#                     color =my_cmap_sns_dark[i], linewidth = 2, linestyle ='-', alpha = .2, zorder=0)\n",
    "\n",
    "\n",
    "# 4. CI Errorbars & numbers\n",
    "y_tilt_mode = [0.5, 0.57]\n",
    "for i, mode in enumerate(modes):\n",
    "    for j, section in enumerate(patterns):\n",
    "        column = (mode, section)\n",
    "        plt.errorbar(median_time[column],\n",
    "                     y_tilt_mode[i]+y_tilt_section[j],\n",
    "                     xerr=np.array([[ci_delta[column][0] ,ci_delta[column][1]]]).T,\n",
    "                     fmt='o', markersize=10,\n",
    "                     lw = 3, alpha=1,\n",
    "                     zorder=100,        # higher means more visible\n",
    "                     color = my_cmap_sns_dark[i])    # 'black'\n",
    "        ax.text(median_time[column], y_tilt_mode[i]+y_tilt_section[j] + 0.22, f'{median_time[column]:.1f}',\n",
    "                horizontalalignment='center', color = my_cmap_sns_dark[i],\n",
    "                fontsize=figfont_size)\n",
    "\n",
    "\n",
    "# 5. Plot red line connecting medians\n",
    "for i, mode in enumerate(modes):\n",
    "    for j in range(len(patterns)-1):\n",
    "        start = (mode, patterns[j])\n",
    "        end = (mode, patterns[j+1])\n",
    "        ax.plot([median_time[start], median_time[end], ],\n",
    "                [y_tilt_mode[i] + y_tilt_section[j], y_tilt_mode[i] + y_tilt_section[j+1]],\n",
    "                color =my_cmap_sns_dark[i], linewidth = 3, linestyle ='-', alpha = .3, zorder=0)\n",
    "\n",
    "\n",
    "\n",
    "# # #Additional settings\n",
    "ax.set_xlabel('Median time per worker (sec)', size = xlab_size)\n",
    "ax.set_ylabel(None)\n",
    "ax.set_yticklabels(['P1', 'P2', 'P3', 'P4'])\n",
    "ax.set_xlim(0, 50)\n",
    "ax.set_ylim(3.9, -0.25)\n",
    "leg = plt.legend(loc='lower right',\n",
    "                 borderaxespad= 0.2,\n",
    "                 frameon = True,)\n",
    "leg.get_frame().set_alpha(1)\n",
    "leg.get_frame().set_linewidth(0.0)\n",
    "\n",
    "for text, text2 in zip(leg.get_texts(), ['SQL', 'FigX']):\n",
    "    text.set_text(text2)\n",
    "\n",
    "plt.grid(axis = 'x', linewidth = 0.5, color = 'lightgray')\n",
    "sns.despine()               # remove bounding box\n",
    "\n",
    "if savefig:\n",
    "    plt.savefig(fig_dir + \"/q3_figure.pdf\", bbox_inches='tight')\n",
    "    plt.savefig(fig_dir + \"/q3_figure.svg\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526669f1",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# Question 4: Correctness\n",
    "\n",
    "(1) take mean correct over all questions and all users answered in SQL or RD (32/2*13) and the difference in correctness score\n",
    "(2) calculate 95% CI for each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b281fc0",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dfq4b:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>mode</th>\n",
       "      <th>RD</th>\n",
       "      <th>SQL</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worker_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.938</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.938</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.938</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.938</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.938</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.875</td>\n",
       "      <td>0.938</td>\n",
       "      <td>-0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.938</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.875</td>\n",
       "      <td>0.938</td>\n",
       "      <td>-0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.938</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.938</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "mode         RD   SQL   diff\n",
       "worker_id                   \n",
       "0         0.938 0.938  0.000\n",
       "1         1.000 1.000  0.000\n",
       "2         0.938 0.750  0.188\n",
       "3         0.938 0.938  0.000\n",
       "4         0.938 0.938  0.000\n",
       "5         0.938 0.938  0.000\n",
       "6         1.000 1.000  0.000\n",
       "7         1.000 0.938  0.062\n",
       "8         0.875 0.938 -0.062\n",
       "9         0.938 1.000 -0.062\n",
       "10        0.875 0.938 -0.062\n",
       "11        0.938 0.875  0.062\n",
       "12        0.938 0.875  0.062"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/13 (0.231) better with SQL.\n",
      "4/13 (0.308) better with RD.\n",
      "5/13 (0.385) equally good.\n",
      "mean RD correct = 0.942, 95% CI [0.923, 0.962]\n",
      "mean SQL correct = 0.928, 95% CI [0.889, 0.957]\n",
      "mean difference in correctness = 0.014, 95% CI [-0.019, 0.053]\n"
     ]
    }
   ],
   "source": [
    "# dfq4a: Create two columns mode and mean, with 2 rows per worker\n",
    "dfq4a = df_anonymized_data.groupby(['worker_id', 'mode']).correct.agg(['mean'])\n",
    "dfq4a.reset_index(inplace=True)\n",
    "# display(dfq4a)\n",
    "\n",
    "# dfq4b: Pivot to have one row per worker\n",
    "dfq4b = pd.pivot_table(dfq4a, values=['mean'], index=['worker_id'], columns=['mode'])\n",
    "dfq4b=dfq4b.droplevel(0, axis=1)\n",
    "dfq4b['diff'] = dfq4b['RD'] - dfq4b['SQL']\n",
    "print(\"dfq4b:\\n\")\n",
    "display(dfq4b)\n",
    "\n",
    "# Calculate fraction of those better in either mode\n",
    "num_SQLbetter = np.where(dfq4b['SQL'] > dfq4b['RD'], 1, 0).sum()\n",
    "num_RDbetter = np.where(dfq4b['SQL'] < dfq4b['RD'], 1, 0).sum()\n",
    "num_workers =len(dfq4b)\n",
    "print(f'{num_SQLbetter}/{num_workers} ({num_SQLbetter/num_workers:.3f}) better with SQL.')\n",
    "print(f'{num_RDbetter}/{num_workers} ({num_RDbetter/num_workers:.3f}) better with RD.')\n",
    "print(f'{num_workers-num_RDbetter-num_RDbetter}/{num_workers} ({(num_workers-num_RDbetter-num_RDbetter)/num_workers:.3f}) equally good.')\n",
    "\n",
    "# Mean of mean correctness for each mode Plus 95% CI\n",
    "modes_diff = ['RD', 'SQL', 'diff']\n",
    "mean_correct = {}\n",
    "ci = {}\n",
    "ci_delta = {}\n",
    "for mode in modes_diff:\n",
    "    mean_correct[mode] = np.mean(dfq4b[mode])\n",
    "    ci[mode] = scipybootstrap((dfq4b[mode],), statistic=np.mean, n_resamples=BOOTSTRAPSAMPLES, confidence_level=BOOTSTRAPCONFIDENCE, method='percentile', axis=0).confidence_interval        #convert array to sequence\n",
    "    ci_delta[mode] = [mean_correct[mode] - ci[mode].low, ci[mode].high - mean_correct[mode]]\n",
    "\n",
    "print(f\"mean RD correct = {mean_correct['RD']:.3f}, 95% CI [{ci['RD'].low:.3f}, {ci['RD'].high:.3f}]\")\n",
    "print(f\"mean SQL correct = {mean_correct['SQL']:.3f}, 95% CI [{ci['SQL'].low:.3f}, {ci['SQL'].high:.3f}]\")\n",
    "print(f\"mean difference in correctness = {mean_correct['diff']:.3f}, 95% CI [{ci['diff'].low:.3f}, {ci['diff'].high:.3f}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2506325",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 970x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Removing 'diff' from variables\n",
    "dfq4c=dfq4b.copy()\n",
    "dfq4c.drop('diff', inplace=True, axis=1)\n",
    "modes = ['RD', 'SQL']\n",
    "\n",
    "# Define pre-settings\n",
    "figwidth = 9.7\n",
    "figheight = 6\n",
    "xlab_size = 20\n",
    "ylab_size = 20\n",
    "figfont_size = 24\n",
    "\n",
    "# Define consistent color maps\n",
    "my_cmap_sns_light = [(0.9921568627450981, 0.8156862745098039, 0.6352941176470588), (0.7764705882352941, 0.8588235294117647, 0.9372549019607843)]        # light blue, light orange\n",
    "my_cmap_sns_dark = [(0.9019607843137255, 0.3333333333333333, 0.050980392156862744), (0.19215686274509805, 0.5098039215686274, 0.7411764705882353)]      # dark blue, dark orange\n",
    "my_cmap_dark = sns.color_palette(my_cmap_sns_dark, as_cmap=True)\n",
    "my_cmap_light = sns.color_palette(my_cmap_sns_light, as_cmap=True)\n",
    "\n",
    "# Create empty figure and plot the individual datapoints\n",
    "fig, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "\n",
    "# 1. Violinplots\n",
    "axsns = sns.violinplot(x='mean', y='mode', data=dfq4a,\n",
    "                       hue=True, hue_order=[False, True], split=True,   # half violinplots https://stackoverflow.com/questions/53872439/half-not-split-violin-plots-in-seaborn\n",
    "                       inner='quartile',\n",
    "                       cut=0,               # 0 means ending sharp at end points\n",
    "                       width=.6,\n",
    "                       orient = 'h',\n",
    "                       zorder=20,)\n",
    "\n",
    "# change the medium default line to full (https://stackoverflow.com/questions/60638344/quartiles-line-properties-in-seaborn-violinplot)\n",
    "for l in axsns.lines[1::3]:\n",
    "    l.set_linestyle('-')\n",
    "    l.set_linewidth(1.2)\n",
    "    l.set_color('black')\n",
    "    l.set_alpha(0.8)\n",
    "for i in [0, 2, 3, 5]:         # remove the 25% and 75% quartiles\n",
    "    l = axsns.lines[i]\n",
    "    l.set_linestyle('-')\n",
    "    l.set_linewidth(0)\n",
    "    l.set_color('red')\n",
    "    l.set_alpha(0.8)\n",
    "\n",
    "# Apply colorscheme to violinplots https://stackoverflow.com/questions/70442958/seaborn-how-to-apply-custom-color-to-each-seaborn-violinplot\n",
    "from matplotlib.collections import PolyCollection\n",
    "for ind, violin in enumerate(axsns.findobj(PolyCollection)):\n",
    "    violin.set_facecolor(my_cmap_light[ind])\n",
    "# plt.setp(ax.collections, alpha=.999)  # semi-transparent (https://stackoverflow.com/questions/62597959/seaborn-violinplot-transparency)\n",
    "\n",
    "\n",
    "# 2. Plot individual points [new dot plot]\n",
    "# Set the amount of jitter and create a dataframe containing the jittered x-axis values [kept for legacy reasons, could be simplified with new dot plot\n",
    "jitter = 0.0     # 0.1\n",
    "y_tilt = -0.5\n",
    "\n",
    "# np.random.seed(3)\n",
    "df_jitter = pd.DataFrame(\n",
    "    np.random.uniform(low=y_tilt, high=y_tilt + jitter, size=dfq4c.values.shape),\n",
    "    columns=dfq4c.columns)\n",
    "df_jitter += np.arange(len(dfq4c.columns)) #Add to dataframe a number based on the length on the columns. Otherwise all datapoints would be at the same x-axis location.\n",
    "\n",
    "# for i, col in enumerate(dfq4c):\n",
    "#     ax.plot(dfq4c[col], df_jitter[col],\n",
    "#             '^',\n",
    "#             alpha=1,\n",
    "#             zorder=20,      # higher means more visible\n",
    "#             markersize=10,\n",
    "#             markeredgewidth=1.5,\n",
    "#             # markerfacecolor='none',\n",
    "#             markerfacecolor=my_cmap_dark[i],\n",
    "#             markeredgecolor=my_cmap_dark[i],)\n",
    "\n",
    "\n",
    "# sns.swarmplot(x = \"species\", y = \"petal_length\", data = df)\n",
    "def dotplot(input_x, y0, delta, **args):\n",
    "    unique_values, counts = np.unique(input_x, return_counts=True)  # Count how many times does each value occur\n",
    "\n",
    "    # Convert 1D input into 2D array\n",
    "    scatter_x = [] # x values\n",
    "    scatter_y = [] # corresponding y values\n",
    "    for idx, value in enumerate(unique_values):\n",
    "        for counter in range(0, counts[idx]):\n",
    "            scatter_x.append(value)\n",
    "            scatter_y.append(y0+counter*delta)\n",
    "    plt.scatter(scatter_x, scatter_y, **args)\n",
    "\n",
    "for i, col in enumerate(dfq4c):\n",
    "    dotplot(input_x=dfq4c[col], y0=df_jitter[col][0], delta=0.03,\n",
    "            marker='^',\n",
    "            alpha=1,\n",
    "            zorder=20,      # higher means more visible\n",
    "            color=my_cmap_dark[i],\n",
    "            s=200,\n",
    "            linewidth=0,)\n",
    "\n",
    "\n",
    "# 4. Plot red line connecting means\n",
    "ax.plot(np.mean(dfq4c, axis=0), [0, 1], color ='red', linewidth = 2, linestyle ='-',\n",
    "        alpha = .3,\n",
    "        zorder=4,\n",
    "        )\n",
    "\n",
    "# 5. CI Errorbars\n",
    "for i, mode in enumerate(modes):\n",
    "    plt.errorbar(mean_correct[mode], i, xerr=np.array([[ci_delta[mode][0], ci_delta[mode][1]]]).T,\n",
    "                 fmt='o', markersize=10,\n",
    "                 lw = 5, alpha=1,\n",
    "                 zorder=100,        # higher means more visible\n",
    "                 capsize = 10,\n",
    "                 # capthick = 4,\n",
    "                 capthick = 0,\n",
    "                 # color = 'black',\n",
    "                 color=my_cmap_dark[i],\n",
    "                 )   # my_cmap[1])\n",
    "\n",
    "    # ax.text(median_time[column], y_tilt_mode[i]+y_tilt_section[j] + 0.18, f'{median_time[column]:.1f}',\n",
    "    #         horizontalalignment='center', color = my_cmap_sns_dark[i], fontsize=20)\n",
    "    ax.text(mean_correct[mode],\n",
    "            i-0.2,\n",
    "            f'{mean_correct[mode]:.2f}', horizontalalignment='center',\n",
    "            # color='black',\n",
    "            color=my_cmap_dark[i],\n",
    "            fontsize=figfont_size)\n",
    "    # ax.text(ci[mode].low, i+0.1, f'{ci[mode].low:.2f}', horizontalalignment='center', color='black', fontsize=20)\n",
    "    # ax.text(ci[mode].high, i+0.1, f'{ci[mode].high:.2f}', horizontalalignment='center', color='black', fontsize=20)\n",
    "\n",
    "\n",
    "#Additional settings\n",
    "# ax.set_yticklabels(modes, size= ylab_size)\n",
    "# ax.set_xlim(0.7499, 1.0003)\n",
    "ax.set_xlim(0.74, 1.01)\n",
    "ax.set_ylim(-0.6, 1.5)\n",
    "ax.set_xlabel('Mean correctness per worker', size = xlab_size)\n",
    "ax.set_ylabel(None)\n",
    "ax.set_yticklabels(['FigX', 'SQL'])\n",
    "ax.legend_.remove()\n",
    "plt.grid(axis = 'x', linewidth = 0.5, color = 'lightgray')\n",
    "sns.despine()               # remove bounding box\n",
    "\n",
    "if savefig:\n",
    "    plt.savefig(fig_dir + \"/q4_figure.pdf\", bbox_inches='tight')\n",
    "    plt.savefig(fig_dir + \"/q4_figure.svg\", bbox_inches='tight')"
   ]
  }
 ],
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