{ "cells": [ { "cell_type": "markdown", "id": "6f6aa857", "metadata": {}, "source": [ "# Exercise: Compute summary statistics" ] }, { "cell_type": "code", "execution_count": 1, "id": "8f9bc8b1", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd" ] }, { "cell_type": "markdown", "id": "1be11d54", "metadata": {}, "source": [ "# Load the patient data" ] }, { "cell_type": "code", "execution_count": 2, "id": "d2dfebd3", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('processed_data_predimed.csv')" ] }, { "cell_type": "code", "execution_count": 3, "id": "09554c84", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6245, 18)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 4, "id": "df95a10b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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patient-idlocation-idsexagesmokebmiwaistwthhtndiabhypercholfamhisthormop14toeventeventgroupCity
011Female77Never25.92940.657343YesNoYesYesNo95.538672NoMedDiet + VOOMadrid
121Female68Never34.851500.949367YesNoYesYesNaN103.063655NoMedDiet + NutsMadrid
231Female66Never37.501200.750000YesYesNoNoNo65.590691NoMedDiet + NutsMadrid
341Female77Never29.26930.628378YesYesNoNoNo65.456537NoMedDiet + VOOMadrid
451Female60Never30.021040.662420YesNoYesNoNo92.746064NoControlMadrid
\n", "
" ], "text/plain": [ " patient-id location-id sex age smoke bmi waist wth htn \\\n", "0 1 1 Female 77 Never 25.92 94 0.657343 Yes \n", "1 2 1 Female 68 Never 34.85 150 0.949367 Yes \n", "2 3 1 Female 66 Never 37.50 120 0.750000 Yes \n", "3 4 1 Female 77 Never 29.26 93 0.628378 Yes \n", "4 5 1 Female 60 Never 30.02 104 0.662420 Yes \n", "\n", " diab hyperchol famhist hormo p14 toevent event group City \n", "0 No Yes Yes No 9 5.538672 No MedDiet + VOO Madrid \n", "1 No Yes Yes NaN 10 3.063655 No MedDiet + Nuts Madrid \n", "2 Yes No No No 6 5.590691 No MedDiet + Nuts Madrid \n", "3 Yes No No No 6 5.456537 No MedDiet + VOO Madrid \n", "4 No Yes No No 9 2.746064 No Control Madrid " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "id": "0b4f6091", "metadata": {}, "source": [ "# 1. Did the mediterranean diet help prevent cardiovascular events?\n", "\n", "To answer this question, we need to compute how many cardiovascular \"events\" occured in each group of participants, separated by the diet they followed.\n", "In the data the column `event` contains `Yes` or `No`, indicating if that patient had an cardiovascular event. The column `group` contains which diet they followed.\n", "\n", "We first convert the column ``event'' to a binary value." ] }, { "cell_type": "code", "execution_count": 5, "id": "99b21627-1b48-44ee-bda2-312b0718bd59", "metadata": {}, "outputs": [], "source": [ "df['event'] = df['event'].map({'Yes': 1, 'No': 0})" ] }, { "cell_type": "markdown", "id": "a63e59c5-fe50-433f-a529-f601c795db67", "metadata": {}, "source": [ "* Now compute the total number of events by diet group. Compare the numbers and see if you can answer the question." ] }, { "cell_type": "code", "execution_count": 6, "id": "00bb9eb1", "metadata": {}, "outputs": [], "source": [ "# your code here:\n" ] }, { "cell_type": "markdown", "id": "b052a40a-ae68-4376-8557-541eafb3face", "metadata": {}, "source": [ "* Check how many patients had each group" ] }, { "cell_type": "code", "execution_count": 7, "id": "db946d0f-8204-43a3-853c-41981a9811f4", "metadata": {}, "outputs": [], "source": [ "# your code here:\n" ] }, { "cell_type": "markdown", "id": "64339449-d766-4a2a-85d3-8aafac5533b7", "metadata": {}, "source": [ "There were no equal number in each group, so to be precise we need to put the numbers into perspective of the total. For that:\n", "* Calculate how many events occured relative to the amount of patients in each group (in percentage). \n", "Do this sepearated by diet group." ] }, { "cell_type": "code", "execution_count": 8, "id": "13ad4130-2094-4e7a-a416-f0fd6e810413", "metadata": {}, "outputs": [], "source": [ "# your code here:\n" ] }, { "cell_type": "markdown", "id": "9655c0ac-18e9-4297-9f6e-557bfe95ed5e", "metadata": {}, "source": [ "It seems that the control group had a higher percentage of events than the other two" ] }, { "cell_type": "markdown", "id": "1940d3fe", "metadata": {}, "source": [ "# 2. Smoking\n", "\n", "Did smoking make a difference in the outcome of the study?\n", "Calculate how many events occured by diet group *and* smoking. The idea is that you arrive to a table like this:\n", "\n", "| group | Current | Former | Never |\n", "|:---------------|----------:|---------:|--------:|\n", "| Control | ... | ... | ... |\n", "| MedDiet + Nuts | ... | ... | ... |\n", "| MedDiet + VOO | ... | ... | ... |\n", "\n", "where each entry in the table has the percentage of events for each group. \n", "\n", "Hint: use `pivot_table`" ] }, { "cell_type": "code", "execution_count": 9, "id": "5ab4e70e-6261-4a26-8ad9-14eae15be09c", "metadata": {}, "outputs": [], "source": [ "# your code here\n" ] }, { "cell_type": "markdown", "id": "511c640e-8f0f-449f-af33-85061d89cfd3", "metadata": {}, "source": [ "# 3. Age differences?" ] }, { "cell_type": "markdown", "id": "1a04ea5c-8a27-4e67-aafa-ba34580a8d7f", "metadata": {}, "source": [ "Finally, check that there were no big differences in the age between the groups.\n", "* Calculate the mean and standard deviation of the patient's age, separated by diet group.\n", "\n", "You should be getting a table where diet group are in the rows and gender in columns, like this\n", "\n", "| group | Female | Male |\n", "|:---------------|---------:|-------:|\n", "| Control | 68 | 66.4 |\n", "| MedDiet + Nuts | 67.4 | 65.8 |\n", "| MedDiet + VOO | 67.7 | 66.1 |\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "196fd111-72bc-4b87-b8fb-293547a8c83d", "metadata": {}, "outputs": [], "source": [ "# your code here:\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }