{"id":109907,"date":"2021-11-04T10:15:28","date_gmt":"2021-11-04T14:15:28","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=109907"},"modified":"2022-11-21T09:49:01","modified_gmt":"2022-11-21T14:49:01","slug":"faster-data-exploration-with-dataexplorer","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/faster-data-exploration-with-dataexplorer\/","title":{"rendered":"Faster Data Exploration with DataExplorer"},"content":{"rendered":"\n<p>Data exploration is an important part of the modeling process. It can also take up a fair amount of time. The awesome&nbsp;<a href=\"https:\/\/cran.r-project.org\/web\/packages\/DataExplorer\/DataExplorer.pdf\">DataExplorer<\/a>&nbsp;package in R aims to make this process easier. To get started with&nbsp;<strong>DataExplorer<\/strong>, you\u2019ll need to install it like below:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>install.packages(\"DataExplorer\")<\/code><\/pre>\n\n\n\n<p>Let\u2019s use&nbsp;<strong>DataExplorer<\/strong>&nbsp;to explore a dataset on diabetes.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># load DataExplorer\nlibrary(DataExplorer)\n \n# read in dataset\ndiabetes_data &lt;- read.csv(\"https:\/\/raw.githubusercontent.com\/jbrownlee\/Datasets\/master\/pima-indians-diabetes.csv\", header = FALSE)\n \n# fix column names\nnames(diabetes_data) &lt;- c(\"number_of_times_pregnant\", \"plasma_glucose_conc\", \"diastolic_bp\", \"triceps_skinfold_thickness\", \"two_hr_serum_insulin\", \"bmi\", \"diabetes_pedigree_function\", \"age\", \"label\")\n \n# create report\ncreate_report(diabetes_data)<\/code><\/pre>\n\n\n\n<p>Running the&nbsp;<em>create_report<\/em>&nbsp;line of code above will generate an HTML report file containing a collection of useful information about the data. This includes:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Basic statistics, such as number of rows and columns, number of columns with missing data, count of continuous variables vs. discrete, and the total memory allocation<\/li><li>Data type for each field<\/li><li>Missing data percentages for each column<\/li><li>Univariate distribution for each column<\/li><li>QQ plots<\/li><li>Correlation analysis<\/li><li>PCA<\/li><\/ul>\n\n\n\n<p><br><br>That\u2019s right \u2013 a single line of code can generate all of the above for a given dataset! It\u2019s also possible to get each of these pieces individually. For example, in a single line of code, we can generate histograms for all the numeric variables in the dataset.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>plot_histogram(diabetes_data)<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"640\" height=\"512\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2021\/11\/histogram-theautomatic-net-dataExplorer.png\" alt=\"Faster Data Exploration with DataExplorer\" class=\"wp-image-109925 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2021\/11\/histogram-theautomatic-net-dataExplorer.png 640w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2021\/11\/histogram-theautomatic-net-dataExplorer-300x240.png 300w\" data-sizes=\"(max-width: 640px) 100vw, 640px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 640px; aspect-ratio: 640\/512;\" \/><\/figure>\n\n\n\n<p>Similarly, we can get bar plots for all categorical variables in the dataset<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>plot_bar(diabetes_data)<\/code><\/pre>\n\n\n\n<p>Here\u2019s an example getting the correlation plot:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>plot_correlation(diabetes_data)<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"640\" height=\"582\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2021\/11\/correlation_plot-the-automatic-net.png\" alt=\"\" class=\"wp-image-109965 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2021\/11\/correlation_plot-the-automatic-net.png 640w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2021\/11\/correlation_plot-the-automatic-net-300x273.png 300w\" data-sizes=\"(max-width: 640px) 100vw, 640px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 640px; aspect-ratio: 640\/582;\" \/><\/figure>\n\n\n\n<p><strong>Configuring the report<\/strong><\/p>\n\n\n\n<p>It\u2019s also possible to make adjustments to the output generated by&nbsp;<em>create_report<\/em>. For example, if you don\u2019t want the QQ plots, you could set&nbsp;<em>add_plot_qq = FALSE<\/em><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>config &lt;- configure_report(add_plot_qq = FALSE)\n \ncreate_report(config = config)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-one-hot-encoding\"><strong>One hot encoding<\/strong><\/h3>\n\n\n\n<p><strong>DataExplorer<\/strong>&nbsp;also comes with a function to perform one hot encoding. You can one hot encode all the categorical variables in the dataset by passing the data frame name to the&nbsp;<em>dummify<\/em>&nbsp;function. In this case, we don\u2019t have any categorical variables to encode, so the function will generate a warning.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>dummify(diabetes_data)<\/code><\/pre>\n\n\n\n<p><em>Visit <a href=\"https:\/\/theautomatic.net\/2021\/03\/03\/faster-data-exploration-with-dataexplorer\/\">TheAutomatic.net<\/a> blog for additional insight on this topic and to find DataExplorer scripts and documentation.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data exploration is an important part of the modeling process. It can also take up a fair amount of time.<\/p>\n","protected":false},"author":388,"featured_media":67374,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,338,342],"tags":[10594,4581,487,6591,508],"contributors-categories":[13695],"class_list":{"0":"post-109907","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-ibkr-quant-news","10":"category-r-development","11":"tag-dataexplorer","12":"tag-heatmap","13":"tag-r","14":"tag-rstats","15":"tag-rstudio","16":"contributors-categories-theautomatic-net"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Faster Data Exploration with DataExplorer | IBKR Quant<\/title>\n<meta name=\"description\" content=\"Data exploration is an important part of the modeling process. 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