{"metadata": {"kernelspec": {"display_name": "R", "name": "ir", "language": "R"}, "language_info": {"file_extension": ".r", "name": "R", "codemirror_mode": "r", "version": "3.4.1", "mimetype": "text/x-r-source", "pygments_lexer": "r"}}, "nbformat": 4, "nbformat_minor": 1, "cells": [{"execution_count": null, "metadata": {"_uuid": "3dd89563e753202fc88740225557cd266ea50d0d", "_cell_guid": "90c7040e-417e-4dc5-b298-463ac404cb20"}, "source": ["# This R environment comes with all of CRAN preinstalled, as well as many other helpful packages\n", "# The environment is defined by the kaggle/rstats docker image: https://github.com/kaggle/docker-rstats\n", "# For example, here's several helpful packages to load in \n", "\n", "library(ggplot2) # Data visualization\n", "library(readr) # CSV file I/O, e.g. the read_csv function\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "system(\"ls ../input\")\n", "\n", "# Any results you write to the current directory are saved as output."], "outputs": [], "cell_type": "code"}, {"execution_count": null, "metadata": {}, "source": ["data<- read.csv(\"../input/train.csv\")"], "outputs": [], "cell_type": "code"}, {"execution_count": null, "metadata": {}, "source": ["data"], "outputs": [], "cell_type": "code"}, {"execution_count": null, "metadata": {}, "source": ["cur_dir <- \"C:\\\\Users\\\\Admin\\\\Downloads\""], "outputs": [], "cell_type": "code"}, {"execution_count": null, "metadata": {}, "source": ["getwd() "], "outputs": [], "cell_type": "code"}, {"execution_count": null, "metadata": {}, "source": ["write.csv(data, file=\"test.csv\")"], "outputs": [], "cell_type": "code"}, {"execution_count": null, "metadata": {}, "source": [], "outputs": [], "cell_type": "code"}]}