{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"d2ebf362-50d5-e762-ecb7-76a48400ac75"},"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ffd5e3be-e229-3509-d0e9-9a9e8b75924f"},"outputs":[],"source":"library(data.table)\nmt=as.data.table(mtcars)\nmt\nmt[,fg:=56]\n\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"efebd4b9-9f50-c3af-9da7-e3225bdf1f34"},"outputs":[],"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\nlibrary(ggplot2) # Data visualization\nlibrary(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\nsystem(\"ls ../input\")\n\n# Any results you write to the current directory are saved as output."}],"metadata":{"_change_revision":0,"_is_fork":false,"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":"3.3.3"}},"nbformat":4,"nbformat_minor":0}