{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "881c55fd-3b61-de77-7963-190298f11b66", "_uuid": "3668ab7cd3e8c4788fe6fe26b5848b6089cde62b" }, "source": [ "[![AnalyticsDojo](https://github.com/rpi-techfundamentals/spring2019-materials/blob/master/fig/final-logo.png?raw=1)](http://www.analyticsdojo.com)\n", "

Introduction to R - Titanic Baseline

\n", "

rpi.analyticsdojo.com

" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e4366588-27f9-a4e3-4d02-1b660e1fd226", "_uuid": "2c7461c172401b7654f874b6de16b6c6b4d41a6e" }, "source": [ "## Running Code using Kaggle Notebooks\n", "- Kaggle utilizes Docker to create a fully functional environment for hosting competitions in data science.\n", "- You could download/run kaggle/python docker image from [GitHub](https://github.com/kaggle/docker-python) and run it as an alternative to the standard Jupyter Stack for Data Science we have been using.\n", "- Kaggle has created an incredible resource for learning analytics. You can view a number of *toy* examples that can be used to understand data science and also compete in real problems faced by top companies. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "193191ca-9119-56cb-1133-3c356f2300ad", "_uuid": "3448fcc64b74a564cc35f7d9586e75e853b84642", "collapsed": true }, "outputs": [], "source": [ "train <- read.csv('../../input/train.csv', stringsAsFactors = F)\n", "test <- read.csv('../../input/test.csv', stringsAsFactors = F)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "21a68add-20c7-984a-7cb3-a21c0dd80cb0", "_uuid": "a2ba10a5bb516a208de6e6c7a20fdab86089ab4a" }, "source": [ "## `train` and `test` set on Kaggle\n", "- The `train` file contains a wide variety of information that might be useful in understanding whether they survived or not. It also includes a record as to whether they survived or not.\n", "- The `test` file contains all of the columns of the first file except whether they survived. Our goal is to predict whether the individuals survived." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "d12daf5f-88c7-2f45-e106-4052d23c0a0d", "_uuid": "21b04f0aaa9cfa11948db6c185f2225cba86a718" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.2500 S
2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer)female 38 1 0 PC 17599 71.2833 C85 C
3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.9250 S
4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1000 C123 S
5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.0500 S
6 0 3 Moran, Mr. James male NA 0 0 330877 8.4583 Q
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllll}\n", " PassengerId & Survived & Pclass & Name & Sex & Age & SibSp & Parch & Ticket & Fare & Cabin & Embarked\\\\\n", "\\hline\n", "\t 1 & 0 & 3 & Braund, Mr. Owen Harris & male & 22 & 1 & 0 & A/5 21171 & 7.2500 & & S \\\\\n", "\t 2 & 1 & 1 & Cumings, Mrs. John Bradley (Florence Briggs Thayer) & female & 38 & 1 & 0 & PC 17599 & 71.2833 & C85 & C \\\\\n", "\t 3 & 1 & 3 & Heikkinen, Miss. Laina & female & 26 & 0 & 0 & STON/O2. 3101282 & 7.9250 & & S \\\\\n", "\t 4 & 1 & 1 & Futrelle, Mrs. Jacques Heath (Lily May Peel) & female & 35 & 1 & 0 & 113803 & 53.1000 & C123 & S \\\\\n", "\t 5 & 0 & 3 & Allen, Mr. William Henry & male & 35 & 0 & 0 & 373450 & 8.0500 & & S \\\\\n", "\t 6 & 0 & 3 & Moran, Mr. James & male & NA & 0 & 0 & 330877 & 8.4583 & & Q \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | \n", "|---|---|---|---|---|---|\n", "| 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.2500 | | S | \n", "| 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | \n", "| 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.9250 | | S | \n", "| 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.1000 | C123 | S | \n", "| 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.0500 | | S | \n", "| 6 | 0 | 3 | Moran, Mr. James | male | NA | 0 | 0 | 330877 | 8.4583 | | Q | \n", "\n", "\n" ], "text/plain": [ " PassengerId Survived Pclass\n", "1 1 0 3 \n", "2 2 1 1 \n", "3 3 1 3 \n", "4 4 1 1 \n", "5 5 0 3 \n", "6 6 0 3 \n", " Name Sex Age SibSp Parch\n", "1 Braund, Mr. Owen Harris male 22 1 0 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 \n", "3 Heikkinen, Miss. Laina female 26 0 0 \n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 \n", "5 Allen, Mr. William Henry male 35 0 0 \n", "6 Moran, Mr. James male NA 0 0 \n", " Ticket Fare Cabin Embarked\n", "1 A/5 21171 7.2500 S \n", "2 PC 17599 71.2833 C85 C \n", "3 STON/O2. 3101282 7.9250 S \n", "4 113803 53.1000 C123 S \n", "5 373450 8.0500 S \n", "6 330877 8.4583 Q " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(train)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "17dca1d6-ac00-2342-bfa9-00739d8b2255", "_uuid": "92fbe1534e780db62d5d81ddb356fd5b45ba166a" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 Q
893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 S
894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 Q
895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 S
896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist)female 22.0 1 1 3101298 12.2875 S
897 3 Svensson, Mr. Johan Cervin male 14.0 0 0 7538 9.2250 S
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllll}\n", " PassengerId & Pclass & Name & Sex & Age & SibSp & Parch & Ticket & Fare & Cabin & Embarked\\\\\n", "\\hline\n", "\t 892 & 3 & Kelly, Mr. James & male & 34.5 & 0 & 0 & 330911 & 7.8292 & & Q \\\\\n", "\t 893 & 3 & Wilkes, Mrs. James (Ellen Needs) & female & 47.0 & 1 & 0 & 363272 & 7.0000 & & S \\\\\n", "\t 894 & 2 & Myles, Mr. Thomas Francis & male & 62.0 & 0 & 0 & 240276 & 9.6875 & & Q \\\\\n", "\t 895 & 3 & Wirz, Mr. Albert & male & 27.0 & 0 & 0 & 315154 & 8.6625 & & S \\\\\n", "\t 896 & 3 & Hirvonen, Mrs. Alexander (Helga E Lindqvist) & female & 22.0 & 1 & 1 & 3101298 & 12.2875 & & S \\\\\n", "\t 897 & 3 & Svensson, Mr. Johan Cervin & male & 14.0 & 0 & 0 & 7538 & 9.2250 & & S \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | \n", "|---|---|---|---|---|---|\n", "| 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | | Q | \n", "| 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | | S | \n", "| 894 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | | Q | \n", "| 895 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | | S | \n", "| 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | | S | \n", "| 897 | 3 | Svensson, Mr. Johan Cervin | male | 14.0 | 0 | 0 | 7538 | 9.2250 | | S | \n", "\n", "\n" ], "text/plain": [ " PassengerId Pclass Name Sex Age \n", "1 892 3 Kelly, Mr. James male 34.5\n", "2 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0\n", "3 894 2 Myles, Mr. Thomas Francis male 62.0\n", "4 895 3 Wirz, Mr. Albert male 27.0\n", "5 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0\n", "6 897 3 Svensson, Mr. Johan Cervin male 14.0\n", " SibSp Parch Ticket Fare Cabin Embarked\n", "1 0 0 330911 7.8292 Q \n", "2 1 0 363272 7.0000 S \n", "3 0 0 240276 9.6875 Q \n", "4 0 0 315154 8.6625 S \n", "5 1 1 3101298 12.2875 S \n", "6 0 0 7538 9.2250 S " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(test)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4de42a6c-db16-edd8-cdb5-46f5f47698f7", "_uuid": "b68939ee570989916c489bffde89d2a857b6923a" }, "source": [ "## Baseline Model: No Survivors\n", "- The Titanic problem is one of classification, and often the simplest baseline of all 0/1 is an appropriate baseline.\n", "- Even if you aren't familiar with the history of the tragedy, by checking out the [Wikipedia Page](https://en.wikipedia.org/wiki/RMS_Titanic) we can quickly see that the majority of people (68%) died.\n", "- As a result, our baseline model will be for no survivors." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "bcff150d-cbff-3c84-3eea-564df3c43b7b", "_uuid": "4bb023cb116910bb2121c7f356d4e57de4b3ffa2", "collapsed": true }, "outputs": [], "source": [ "test[\"Survived\"] <- 0" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "6c95627c-c1eb-aa34-f4b7-32b8ea7ae61c", "_uuid": "822074cd1a3eda10ddcb994287f9986cae917933", "collapsed": true }, "outputs": [], "source": [ "submission <- test[,c(\"PassengerId\", \"Survived\")]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "2241735d-8164-32f2-94ec-6ec45dacb145", "_uuid": "9c494660e043f9774f099ceeb405dfa8d37e1e70" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
PassengerIdSurvived
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\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " PassengerId & Survived\\\\\n", "\\hline\n", "\t 892 & 0 \\\\\n", "\t 893 & 0 \\\\\n", "\t 894 & 0 \\\\\n", "\t 895 & 0 \\\\\n", "\t 896 & 0 \\\\\n", "\t 897 & 0 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PassengerId | Survived | \n", "|---|---|---|---|---|---|\n", "| 892 | 0 | \n", "| 893 | 0 | \n", "| 894 | 0 | \n", "| 895 | 0 | \n", "| 896 | 0 | \n", "| 897 | 0 | \n", "\n", "\n" ], "text/plain": [ " PassengerId Survived\n", "1 892 0 \n", "2 893 0 \n", "3 894 0 \n", "4 895 0 \n", "5 896 0 \n", "6 897 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(submission)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "1329809f-7aed-c67e-383c-8d71e5c7c4ac", "_uuid": "26f53c7376bea4297903b499776401d3b240cd74", "collapsed": true }, "outputs": [], "source": [ "# Write the solution to file\n", "write.csv(submission, file = 'nosurvivors.csv', row.names = F)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "325683d2-2aa7-96a9-45ec-edf46c69f2da", "_uuid": "09f9eb8927ddb8bbee7b55103b707953e925c7f1" }, "source": [ "## The First Rule of Shipwrecks\n", "- You may have seen it in a movie or read it in a novel, but [women and children first](https://en.wikipedia.org/wiki/Women_and_children_first) has at it's roots something that could provide our first model.\n", "- Now let's recode the `Survived` column based on whether was a man or a woman. \n", "- We are using conditionals to *select* rows of interest (for example, where test['Sex'] == 'male') and recoding appropriate columns." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "710483a5-7a49-a018-610a-c0968c44479c", "_uuid": "8b279e9be63feaf0bb5b946e3d32cd752b195e7f", "collapsed": true }, "outputs": [], "source": [ "#Here we can code it as Survived, but if we do so we will overwrite our other prediction. \n", "#Instead, let's code it as PredGender\n", "\n", "test[test$Sex == \"male\", \"PredGender\"] <- 0\n", "test[test$Sex == \"female\", \"PredGender\"] <- 1" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "078e3827-8f89-59ae-ca2d-66fa36bc79eb", "_uuid": "c0a65b6956267bf39b847741501dde9a30f22272" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
PassengerIdSurvived
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\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " PassengerId & Survived\\\\\n", "\\hline\n", "\t 892 & 0 \\\\\n", "\t 893 & 1 \\\\\n", "\t 894 & 0 \\\\\n", "\t 895 & 0 \\\\\n", "\t 896 & 1 \\\\\n", "\t 897 & 0 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PassengerId | Survived | \n", "|---|---|---|---|---|---|\n", "| 892 | 0 | \n", "| 893 | 1 | \n", "| 894 | 0 | \n", "| 895 | 0 | \n", "| 896 | 1 | \n", "| 897 | 0 | \n", "\n", "\n" ], "text/plain": [ " PassengerId Survived\n", "1 892 0 \n", "2 893 1 \n", "3 894 0 \n", "4 895 0 \n", "5 896 1 \n", "6 897 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "submission = test[,c(\"PassengerId\", \"PredGender\")]\n", "#This will Rename the survived column\n", "names(submission)[2] <- \"Survived\"\n", "head(submission)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
PassengerIdnew
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\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " PassengerId & new\\\\\n", "\\hline\n", "\t 892 & 0 \\\\\n", "\t 893 & 1 \\\\\n", "\t 894 & 0 \\\\\n", "\t 895 & 0 \\\\\n", "\t 896 & 1 \\\\\n", "\t 897 & 0 \\\\\n", "\t 898 & 1 \\\\\n", "\t 899 & 0 \\\\\n", "\t 900 & 1 \\\\\n", "\t 901 & 0 \\\\\n", "\t 902 & 0 \\\\\n", "\t 903 & 0 \\\\\n", "\t 904 & 1 \\\\\n", "\t 905 & 0 \\\\\n", "\t 906 & 1 \\\\\n", "\t 907 & 1 \\\\\n", "\t 908 & 0 \\\\\n", "\t 909 & 0 \\\\\n", "\t 910 & 1 \\\\\n", "\t 911 & 1 \\\\\n", "\t 912 & 0 \\\\\n", "\t 913 & 0 \\\\\n", "\t 914 & 1 \\\\\n", "\t 915 & 0 \\\\\n", "\t 916 & 1 \\\\\n", "\t 917 & 0 \\\\\n", "\t 918 & 1 \\\\\n", "\t 919 & 0 \\\\\n", "\t 920 & 0 \\\\\n", "\t 921 & 0 \\\\\n", "\t ⋮ & ⋮\\\\\n", "\t 1280 & 0 \\\\\n", "\t 1281 & 0 \\\\\n", "\t 1282 & 0 \\\\\n", "\t 1283 & 1 \\\\\n", "\t 1284 & 0 \\\\\n", "\t 1285 & 0 \\\\\n", "\t 1286 & 0 \\\\\n", "\t 1287 & 1 \\\\\n", "\t 1288 & 0 \\\\\n", "\t 1289 & 1 \\\\\n", "\t 1290 & 0 \\\\\n", "\t 1291 & 0 \\\\\n", "\t 1292 & 1 \\\\\n", "\t 1293 & 0 \\\\\n", "\t 1294 & 1 \\\\\n", "\t 1295 & 0 \\\\\n", "\t 1296 & 0 \\\\\n", "\t 1297 & 0 \\\\\n", "\t 1298 & 0 \\\\\n", "\t 1299 & 0 \\\\\n", "\t 1300 & 1 \\\\\n", "\t 1301 & 1 \\\\\n", "\t 1302 & 1 \\\\\n", "\t 1303 & 1 \\\\\n", "\t 1304 & 1 \\\\\n", "\t 1305 & 0 \\\\\n", "\t 1306 & 1 \\\\\n", "\t 1307 & 0 \\\\\n", "\t 1308 & 0 \\\\\n", "\t 1309 & 0 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PassengerId | new | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 892 | 0 | \n", "| 893 | 1 | \n", "| 894 | 0 | \n", "| 895 | 0 | \n", "| 896 | 1 | \n", "| 897 | 0 | \n", "| 898 | 1 | \n", "| 899 | 0 | \n", "| 900 | 1 | \n", "| 901 | 0 | \n", "| 902 | 0 | \n", "| 903 | 0 | \n", "| 904 | 1 | \n", "| 905 | 0 | \n", "| 906 | 1 | \n", "| 907 | 1 | \n", "| 908 | 0 | \n", "| 909 | 0 | \n", "| 910 | 1 | \n", "| 911 | 1 | \n", "| 912 | 0 | \n", "| 913 | 0 | \n", "| 914 | 1 | \n", "| 915 | 0 | \n", "| 916 | 1 | \n", "| 917 | 0 | \n", "| 918 | 1 | \n", "| 919 | 0 | \n", "| 920 | 0 | \n", "| 921 | 0 | \n", "| ⋮ | ⋮ | \n", "| 1280 | 0 | \n", "| 1281 | 0 | \n", "| 1282 | 0 | \n", "| 1283 | 1 | \n", "| 1284 | 0 | \n", "| 1285 | 0 | \n", "| 1286 | 0 | \n", "| 1287 | 1 | \n", "| 1288 | 0 | \n", "| 1289 | 1 | \n", "| 1290 | 0 | \n", "| 1291 | 0 | \n", "| 1292 | 1 | \n", "| 1293 | 0 | \n", "| 1294 | 1 | \n", "| 1295 | 0 | \n", "| 1296 | 0 | \n", "| 1297 | 0 | \n", "| 1298 | 0 | \n", "| 1299 | 0 | \n", "| 1300 | 1 | \n", "| 1301 | 1 | \n", "| 1302 | 1 | \n", "| 1303 | 1 | \n", "| 1304 | 1 | \n", "| 1305 | 0 | \n", "| 1306 | 1 | \n", "| 1307 | 0 | \n", "| 1308 | 0 | \n", "| 1309 | 0 | \n", "\n", "\n" ], "text/plain": [ " PassengerId new\n", "1 892 0 \n", "2 893 1 \n", "3 894 0 \n", "4 895 0 \n", "5 896 1 \n", "6 897 0 \n", "7 898 1 \n", "8 899 0 \n", "9 900 1 \n", "10 901 0 \n", "11 902 0 \n", "12 903 0 \n", "13 904 1 \n", "14 905 0 \n", "15 906 1 \n", "16 907 1 \n", "17 908 0 \n", "18 909 0 \n", "19 910 1 \n", "20 911 1 \n", "21 912 0 \n", "22 913 0 \n", "23 914 1 \n", "24 915 0 \n", "25 916 1 \n", "26 917 0 \n", "27 918 1 \n", "28 919 0 \n", "29 920 0 \n", "30 921 0 \n", "⋮ ⋮ ⋮ \n", "389 1280 0 \n", "390 1281 0 \n", "391 1282 0 \n", "392 1283 1 \n", "393 1284 0 \n", "394 1285 0 \n", "395 1286 0 \n", "396 1287 1 \n", "397 1288 0 \n", "398 1289 1 \n", "399 1290 0 \n", "400 1291 0 \n", "401 1292 1 \n", "402 1293 0 \n", "403 1294 1 \n", "404 1295 0 \n", "405 1296 0 \n", "406 1297 0 \n", "407 1298 0 \n", "408 1299 0 \n", "409 1300 1 \n", "410 1301 1 \n", "411 1302 1 \n", "412 1303 1 \n", "413 1304 1 \n", "414 1305 0 \n", "415 1306 1 \n", "416 1307 0 \n", "417 1308 0 \n", "418 1309 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(submission)[2]<-\"new\"\n", "submission" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "9f63501b-df2a-0d41-d4dd-3d0e28fea743", "_uuid": "b0d72f03f864bde6cd405e1bcda795c74764ccf4", "collapsed": true }, "outputs": [], "source": [ "write.csv(submission, file = 'womensurvive.csv', row.names = F)" ] } ], "metadata": { "_change_revision": 0, "_is_fork": false, "anaconda-cloud": {}, "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 2 }