{"id":5629,"date":"2018-01-25T09:19:49","date_gmt":"2018-01-25T09:19:49","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=5629"},"modified":"2024-05-11T11:47:15","modified_gmt":"2024-05-11T06:17:15","slug":"exploratory-data-analysis-in-r","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/exploratory-data-analysis-in-r\/","title":{"rendered":"Exploratory Data analysis In R &#8211; Use And Terminologies"},"content":{"rendered":"<div>\n<div class=\"\">\n<p>In this blog, we will learn about the exploratory data analysis in R. Also, we will discuss the basic statistical properties. Moreover, we will look at the Exploratory graph and its use. At last, we will discuss some important Terminologies of EDA.<\/p>\n<p>So, let&#8217;s start Exploratory Data Analysis in R.<\/p>\n<\/div>\n<\/div>\n<div>\n<div class=\"\">\n<h2 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Introduction to Exploratory Data Analysis in R<\/h2>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">To summarize the main characteristics of <a href=\"https:\/\/data-flair.training\/blogs\/data-analytics-comprehensive-guide\/\">data analysis in R<\/a>, EDA is the only approach with the help of descriptive statistics and visual methods. It is not a formal process that contains a strict set of rules. More than anything, EDA is a state of mind. During the initial phases of EDA, you should feel free to investigate every idea that occurs to you. So, some of these ideas will pan out, and some will be dead ends.<\/div>\n<\/div>\n<div class=\"\">\n<h2 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Why do We Use Exploratory Graphs in Data Analysis?<\/h2>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">To understand data properties<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">For finding patterns in data<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">To suggest modeling strategies<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">To \u201cDebug\u201d analyses<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h2 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Terminologies in EDA<\/h2>\n<p>So, following are some important Terminologies in Exploratory Data Analysis in R, let&#8217;s discuss them in detail<\/p>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">i. Variable<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">It is a quantity, quality, or property that you can measure.<\/div>\n<div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Types of variables<\/h4>\n<\/div>\n<div class=\"\"><strong>a. Qualitative Variables<\/strong><\/p>\n<div><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Variables take on values that are names or labels.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Ex.<\/strong>\u00a0The color of a ball (e.g., red, green, blue) or the breed of a dog (e.g., collie, shepherd, terrier.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Types of Qualitative Variables<\/strong><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>1. Nominal: <\/strong><span class=\"adverb\">Basically<\/span>, it displays graphical data \u2014 all orderings are <span class=\"adverb\">equally<\/span> meaningful.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><b>Ex.<\/b> a student\u2019s religion (Atheist, Christian, Muslim, Hindu, \u2026) is nominal.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>2. Ordinal:<\/strong> A categorical variable whose categories can be <span class=\"adverb\">meaningfully<\/span> ordered <span class=\"passivevoice\">is called<\/span> ordinal.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Ex.<\/strong>\u00a0a student\u2019s grade in an exam (A, B, C or Fail) is ordinal.<\/div>\n<\/div>\n<div><\/div>\n<div class=\"\"><strong>b. Quantitative Variables\u00a0<\/strong><\/p>\n<div><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Variables that can <span class=\"passivevoice\">measure<\/span> on a numeric or quantitative scale.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Ex.<\/strong> Age, count of anything etc.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Types of Quantitative Variables:<\/strong><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>1. Discrete:<\/strong> A discrete variable is one that cannot take on all values within the limits of the variable.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Ex. <\/strong>The number<span class=\"complexword\"> of<\/span> children is a discrete numerical variable (a count). The variable cannot have the value 1.7<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>2. Continuous:<\/strong> In this, the variable can take on any value between two specified values.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><b>Ex.<\/b> age of a human: 25 years, 10 months, 2 days, 5 hours<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">ii. Value<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">It is the state of a variable when you measure it. The value of a variable may change from measurement to measurement.<\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">iii. Observation<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">It is a set of measurements made under similar conditions. An observation will contain several values, each associated with a different variable. I\u2019ll sometimes refer to an observation as a data point.<\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">iv. Tabular data<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Basically, it is a set of values, each associated with a variable and an observation. Tabular data is tidy if each value <span class=\"passivevoice\">is placed<\/span> in its own \u201ccell\u201d, each variable in its own column and each observation in its own row.<\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">v. Dataset<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Following are the components of a data\/dataset:<\/div>\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><span class=\"adverb\">Basically<\/span>, a data set <span class=\"passivevoice\">is represented<\/span> as a matrix<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">There is a row for each unit<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">There is a column for each variable<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">A unit is an object which we use to measure, such as a person, or a thing<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">A variable is a characteristic of a unit. We use it to <span class=\"passivevoice\">assign<\/span>\u00a0a number or a category<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>a. Dimensionality of Data Sets<\/strong><\/h4>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Univariate:<\/strong> Measurement made on one variable per subject<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Bivariate:<\/strong> Measurement made on two variables per subject<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Multivariate:<\/strong> Measurement made on many variables per subject<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h2>Visualizations with R<\/h2>\n<p>Apart from descriptive statistics, EDA in R is also heavily dependent on data visualization techniques in order to gain insights and other important information from the data. Data Visualizations like box plots, violin plots, pie charts, bar graphs etc can be used to provide a visual representation of the relationship between the variables. These plots and graphs help in uncovering hidden patterns, trends and insights from the data which might further assist in performing EDA.<\/p>\n<h2>EDA for Data Preprocessing and Feature Engineering<\/h2>\n<p>Exploratory Data Analysis also plays a huge role in Data preprocessing and Feature Engineering. By making use of concepts like missing value imputation, outlier analysis and feature transformation, the data is converted into a format which will be better understood by Machine Learning algorithms. This step eventually contributes to the betterment of the accuracy of Machine Learning algorithms.<\/p>\n<h2 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Numerical Summaries of Data<\/h2>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Numerical measures are very useful in situations that <span class=\"complexword\">require<\/span> decision making and inferences.<\/div>\n<div><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">So, this was all in Exploratory Data Analysis in R. Hope you like our explanation.<\/div>\n<\/div>\n<div class=\"\">\n<h2 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Conclusion<\/h2>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Hence, in this Exploratory Data Analysis in R, we have studied the complete concept of\u00a0<b>Exploratory Data Analysis (EDA). <\/b>Also, we learned about basic statistics in R. Moreover, we discussed some important terminologies in Exploratory Data Analysis in R. Furthermore, if you feel any query, feel free to ask in the\u00a0comment section.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\"><a href=\"https:\/\/en.wikipedia.org\/wiki\/R_(programming_language)\"><strong>Reference for R\u00a0<\/strong><\/a><\/div>\n<\/div>\n<p><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:1467,&quot;href&quot;:&quot;https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/R_(programming_language)&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251001042859\\\/https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/R_(programming_language)&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 08:17:04&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-12 12:22:33&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-15 12:29:21&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-18 15:20:53&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-21 18:00:25&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-25 04:08:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-28 06:54:42&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-31 09:47:17&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-03 17:14:22&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-06 19:17:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-09 21:09:32&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-13 04:31:41&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-16 15:06:53&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-19 19:03:58&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-23 05:30:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-26 10:18:28&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-29 11:45:43&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-01 12:00:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-04 12:09:55&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-07 15:09:43&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-10 18:01:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-13 23:45:59&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-17 05:29:44&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-20 07:23:59&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-23 10:05:24&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-26 14:54:33&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-03-01 16:00:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-04 19:56:49&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-08 03:19:57&quot;,&quot;http_code&quot;:429},{&quot;date&quot;:&quot;2026-03-11 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13:52:44&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-25 13:58:03&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-29 01:16:25&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-02 04:13:02&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-05 06:33:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-08 17:48:43&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-12 03:38:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-15 04:53:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-18 09:15:30&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-21 12:35:48&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-25 03:51:04&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-28 07:13:15&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-01 04:45:49&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-04 06:40:14&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-07 06:45:43&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-10 09:02:48&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-13 16:18:42&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-16 16:46:46&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-19 17:47:20&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-23 10:18:08&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-26 13:31:48&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-29 19:56:22&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-03 11:25:24&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-07-03 11:25:24&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this blog, we will learn about the exploratory data analysis in R. Also, we will discuss the basic statistical properties. Moreover, we will look at the Exploratory graph and its use. At last,&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":36343,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[51],"tags":[16721,4474,4475,16722,11186],"class_list":["post-5629","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r","tag-eda-terminologies","tag-exploratory-data-analysis-in-r","tag-exploratory-data-analysis-with-r","tag-exploratory-graphs-in-data-analysis","tag-r-exploratory-data-analysis"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Exploratory Data analysis In R - Use And Terminologies - DataFlair<\/title>\n<meta name=\"description\" content=\"Exploratory Data Analysis in R tutorial,What is Exploratory Data Analysis with R,exploratory graphs in data analysis,Terminologies in EDA\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/exploratory-data-analysis-in-r\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Exploratory Data analysis In R - Use And Terminologies - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Exploratory Data Analysis in R tutorial,What is Exploratory Data Analysis with R,exploratory graphs in data analysis,Terminologies in EDA\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/exploratory-data-analysis-in-r\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-01-25T09:19:49+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-05-11T06:17:15+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Exploratory-Data-analysis-In-R-01.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Exploratory Data analysis In R - Use And Terminologies - DataFlair","description":"Exploratory Data Analysis in R tutorial,What is Exploratory Data Analysis with R,exploratory graphs in data analysis,Terminologies in EDA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/exploratory-data-analysis-in-r\/","og_locale":"en_US","og_type":"article","og_title":"Exploratory Data analysis In R - Use And Terminologies - DataFlair","og_description":"Exploratory Data Analysis in R tutorial,What is Exploratory Data Analysis with R,exploratory graphs in data analysis,Terminologies in EDA","og_url":"https:\/\/data-flair.training\/blogs\/exploratory-data-analysis-in-r\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2018-01-25T09:19:49+00:00","article_modified_time":"2024-05-11T06:17:15+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Exploratory-Data-analysis-In-R-01.jpg","type":"image\/jpeg"}],"author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/exploratory-data-analysis-in-r\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/exploratory-data-analysis-in-r\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/2c58ecb4f73a39f0ef993f1ddfcd7b89"},"headline":"Exploratory Data analysis In R &#8211; 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