{"id":4947,"date":"2017-12-11T09:14:59","date_gmt":"2017-12-11T03:44:59","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=4947"},"modified":"2021-02-17T21:32:56","modified_gmt":"2021-02-17T16:02:56","slug":"data-visualization-in-r","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/data-visualization-in-r\/","title":{"rendered":"Data Visualization in R &#8211; Upgrade your R Skills to become Data Scientist!"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-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 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07:47:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-16 08:05:10&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-19 13:04:23&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-22 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;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-10 09:02:48&quot;,&quot;http_code&quot;:404},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p>Going further in our R\u00a0tutorial DataFlair series, we will learn about data visualization in R. We will study the evolution of data visualization, R graphics concept and data visualization using ggplot2. We will also explore the various concepts to learn in R data visualization and its pros and cons.<\/p>\n<p><em>Before diving into data visualization in R, you should definitely have a basic knowledge about R graphical analysis. So, check out our easy to learn tutorial on\u00a0<strong><a href=\"https:\/\/data-flair.training\/blogs\/graphical-data-analysis-with-r\/\">R Graphical Analysis<\/a>\u00a0<\/strong>before proceeding ahead.<\/em><\/p>\n<h2>What is R Data Visualization?<\/h2>\n<p>Using the diverse functionalities provided by R, one can create visually appealing data visualizations with only a few lines of code. Data visualization is an efficient technique of gaining insights about data through a visual medium.<\/p>\n<ul>\n<li><span style=\"font-weight: 400\">With the help of visualization techniques, humans can easily gain insights about the hidden patterns in data which might otherwise be neglected.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Using data visualization, one can work with large datasets to efficiently obtain key insights about it.\u00a0<\/span><\/li>\n<\/ul>\n<h3>R Visualization Packages<\/h3>\n<p>Following are some of the essential visualization packages in R Programming:<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/Data-Visualization-in-R.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-66263 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/Data-Visualization-in-R.png\" alt=\"R Visualization Packages - Data Visualization in R\" width=\"802\" height=\"420\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/Data-Visualization-in-R.png 802w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/Data-Visualization-in-R-150x79.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/Data-Visualization-in-R-300x157.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/Data-Visualization-in-R-768x402.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/Data-Visualization-in-R-520x272.png 520w\" sizes=\"auto, (max-width: 802px) 100vw, 802px\" \/><\/a><\/p>\n<h3>Use of R Programming<\/h3>\n<p><span style=\"font-weight: 400\">For most of our work in R Programming, we will use the environment RStudio.<\/span><\/p>\n<p><span style=\"font-weight: 400\">RStudio of R has four panels:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><b>Console &#8211;<\/b><span style=\"font-weight: 400\">\u00a0This is the actual R window, you can enter R commands here. And, thus execute them by pressing enter.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Source &#8211;<\/b><span style=\"font-weight: 400\">\u00a0This is where we can edit scripts. It is where you should always be working. Control-enter sends selected codes to console.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Plots\/Help &#8211;<\/b><span style=\"font-weight: 400\">\u00a0Here plots and help pages will be shown.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Workspace &#8211;<\/b><span style=\"font-weight: 400\">\u00a0Shows which objects you currently have.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Anything following a <strong># symbol<\/strong> is treated as a comment.<\/span><\/p>\n<p><em><strong>Hold On! Please confirm that you have completed &#8211; <a href=\"https:\/\/data-flair.training\/blogs\/r-graphical-models-tutorial\/\">R Graphical Models Tutorial<\/a><\/strong><\/em><\/p>\n<p><em><strong>Note &#8211;<\/strong> We need R data visualization because it provides a clear understanding of patterns in data.\u00a0Also, it has the ability to detect hidden structures in data.<\/em><\/p>\n<h3>R Graphics<\/h3>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/6-DV.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-4949 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/6-DV.png\" alt=\"Data Visualization in R - R Graphics\" width=\"683\" height=\"358\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/6-DV.png 683w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/6-DV-150x79.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/6-DV-300x157.png 300w\" sizes=\"auto, (max-width: 683px) 100vw, 683px\" \/><\/a><\/p>\n<h4>1. Standard Graphics<\/h4>\n<p>R standard graphics available through package graphics, include several functions that provide statistical plots, like:<\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Scatterplots<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Boxplots<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Piecharts<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Barplots etc.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">We use these graphs which are typically a single function call.<\/span><\/p>\n<h4>2. Graphics Devices<\/h4>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Its functions produce output that totally depends on the active graphics device.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">A screen is the default and more frequently used device.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">R graphical devices, like the PDF device, the JPEG device, etc.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">The user just needs to open the graphics output device that she\/he wants. Hence, R takes care of producing the type of output required by the device.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">This means, to produce a certain plot on the screen or as a GIF R graphics file, the R code should exactly\u00a0be the same. You only need to open the target output device before!<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Several devices may be open at the same time, but only one is the active device.<\/span><\/li>\n<\/ul>\n<h4>3. The basics of the grammar of graphics<\/h4>\n<p>Key elements of a statistical graphic:<\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Data<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Aesthetic Mappings<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Geometric Objects<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Statistical Transformations<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Scales<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Coordinates system<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Faceting<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Now, let us discuss each of them.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>3.1 Aesthetic Mappings<\/b><\/p>\n<ul>\n<li>It controls the relation between data variables and graphics variables.<\/li>\n<li><span style=\"font-weight: 400\">Also, it helps to map the temperature variable of a data set into the X variable in a scatter plot.<\/span><\/li>\n<li><span style=\"font-weight: 400\">It helps to map the species of a plant into the colour of dots in graphics.<\/span><\/li>\n<\/ul>\n<p><b>3.2 Geometric Objects<\/b><\/p>\n<p>It shows each observation by a point using the aesthetic mappings that map two variables in the data set into the <em>x,y<\/em> <em>variables<\/em> of the plot.<br \/>\n<b><\/b><\/p>\n<p><b>3.3 Statistical Transformations<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It allows us to calculate and also perform a statistical analysis of the data in the plot.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Also, the statistical transformation uses the data and approximates it by a regression line x,y coordinates.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It counts occurrences of certain values.<\/span><\/li>\n<\/ul>\n<p><b>3.4 Scales<\/b><\/p>\n<p>It maps the data values into values in the coordinate system of the graphics device.<br \/>\n<b><\/b><\/p>\n<p><b>3.5 Coordinate system<\/b><\/p>\n<p>We use it to plot the data.<\/p>\n<ul>\n<li><span style=\"font-weight: 400\">Cartesian<\/span><\/li>\n<li><span style=\"font-weight: 400\">Plot<\/span><br \/>\n<b><\/b><\/li>\n<\/ul>\n<p><b>3.6 Faceting<\/b><\/p>\n<p>It splits the data into subgroups and draws sub-graphs for each group.<\/p>\n<p><em><strong>Time to gain expertise in <a href=\"https:\/\/data-flair.training\/blogs\/descriptive-statistics-in-r\/\">Descriptive Statistics in R Programming<\/a><\/strong><\/em><\/p>\n<h3>Data Visualization in R using ggplot2<\/h3>\n<p style=\"text-align: center\"><em><b>\u201cggplot2 is the most widely used data visualization\u00a0package of the R programming language.\u201d<\/b><\/em><\/p>\n<p>What type of data visualization in <a href=\"https:\/\/en.wikipedia.org\/wiki\/R_(programming_language)\">R<\/a> should be used for what sort of problem? I will provide you with tips which will help you to choose the right type of chart for your specific objectives. We will also learn to implement data visualization in R using ggplot2.<\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Introduction to ggplot2<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Customizing the look and feel<\/span><\/li>\n<\/ul>\n<h4>1. Introduction to ggplot2<\/h4>\n<p>It is a plotting system. We use it to build\u00a0professional-looking graphs. Also, use plots quickly with minimal code. It helps to take care of many complicated things that make plotting difficult. Hence,\u00a0ggplot2 is very different from base R plotting but it is also very flexible and powerful.<br \/>\n<b><\/b><\/p>\n<p>We can create a histogram using ggplot2 as follows:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">library(magrittr)\r\nlibrary(dplyr)\r\nlibrary(ggplot2)\r\ndata_histogram &lt;- mtcars %&gt;%\r\n  mutate(cyl = factor(cyl)) %&gt;%\r\n  group_by(cyl) %&gt;%\r\n  summarize(mean_mpg = round(mean(mpg), 2))\r\n\r\nggplot(data_histogram, aes(x = cyl, y = mean_mpg)) +\r\n  geom_bar(fill = \"coral\", stat = \"identity\")<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Histogram.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-65878\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Histogram.png\" alt=\"Histogram - R Data Visualization\" width=\"495\" height=\"351\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Histogram.png 495w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Histogram-150x106.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Histogram-300x213.png 300w\" sizes=\"auto, (max-width: 495px) 100vw, 495px\" \/><\/a><\/p>\n<p><b>It uses data frames as input:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Data must be in long format.\u00a0<\/span><span style=\"font-weight: 400\">This means each row is an observation and each column is a variable.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Use reshape2 to get data in long format.<\/span><\/li>\n<\/ul>\n<h4>2. Important things to remember for ggplot<\/h4>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It was developed by Hadley Wickham as an implementation of the grammar of graphics.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">ggplot is relatively complete and is a powerful graphics package.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It can do many things but cannot build 3D visuals.<\/span><\/li>\n<\/ul>\n<h4>3. How to install ggplot2 package<\/h4>\n<ul>\n<li>ggplot2 can be easily installed by typing:<\/li>\n<\/ul>\n<p style=\"text-align: center\"><em>install.packages(&#8220;ggplot2&#8221;)<\/em><\/p>\n<ul>\n<li>Make sure that you are using the latest version of R to get the most recent version of ggplot2.<\/li>\n<\/ul>\n<h4>4. Applications of ggplot2<\/h4>\n<ul>\n<li style=\"font-weight: 400\"><b>Aesthetics<\/b><span style=\"font-weight: 400\">: It refers to visual attributes that affect how data is displayed in a graphic, e.g.,<em> color, point size, or line type.<\/em><\/span><\/li>\n<li style=\"font-weight: 400\"><b>Geometric objects<\/b><span style=\"font-weight: 400\">: We use it for a visual representation of observations such as <em>points, lines, polygons, etc.\u00a0<\/em><\/span><\/li>\n<li style=\"font-weight: 400\"><b>Faceting<\/b><span style=\"font-weight: 400\">: It is applied to the same type of graph.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Annotation<\/b><span style=\"font-weight: 400\">: We use it to add text and\/or external graphics to a ggplot.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Positional adjustments<\/b><span style=\"font-weight: 400\">: It helps to reduce the overplotting of points.<\/span><\/li>\n<\/ul>\n<h4>5. Why ggplot2?<\/h4>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It is used professionally.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Easy to manipulate.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Has great support online.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It has knowledge transfers to other packages\/languages.<\/span><\/li>\n<\/ul>\n<p><em><strong>Any queries in R Data Visualization till now? Please comment below.<\/strong><\/em><\/p>\n<h3>What to Learn in Data Visualization in R?<\/h3>\n<p>R Programming helps us to learn this art by offering a set of inbuilt functions and also libraries to build visualizations and present data. Before we move forward for the technical implementation of the visualization, let\u2019s see first how to select the right chart type.<\/p>\n<h4>Selecting the Right Chart Type<br \/>\n<b><\/b><\/h4>\n<p>There are four basic presentation types<span style=\"font-weight: 400\">:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Comparison<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Composition<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Distribution<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Relationship<\/span><\/li>\n<\/ul>\n<p>Following are the most used charts in data visualization:<\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Scatter Plot<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Histogram<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Bar &amp; Stack Bar Chart<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Box Plot<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Area Chart<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Heat Map<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Correlogram<\/span><\/li>\n<\/ul>\n<p>Now we will discuss when to use each of them:<br \/>\n<b><\/b><\/p>\n<h4>1. Scatter Plot<b><\/b><\/h4>\n<p><span style=\"font-weight: 400\">To see the relationship between two continuous variables.<\/span><br \/>\n<b><\/b><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Scatter-Plot.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-65829\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Scatter-Plot.jpg\" alt=\"Scatter-Plot - Data visualization in R\" width=\"498\" height=\"415\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Scatter-Plot.jpg 627w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Scatter-Plot-150x125.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Scatter-Plot-300x250.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Scatter-Plot-520x434.jpg 520w\" sizes=\"auto, (max-width: 498px) 100vw, 498px\" \/><\/a><\/p>\n<h4>2. Histogram<b><\/b><\/h4>\n<p><span style=\"font-weight: 400\">A histogram is used to plot a continuous variable. Also, It helps to break the data into bins and shows the frequency distribution of these bins. Thus, we can always change the bin size and see the effect it has on visualization.<\/span><br \/>\n<b><\/b><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Distribution-of-Average-Ratings-per-User.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-65195\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Distribution-of-Average-Ratings-per-User.png\" alt=\"Distribution of Average Ratings per User\" width=\"484\" height=\"346\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Distribution-of-Average-Ratings-per-User.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Distribution-of-Average-Ratings-per-User-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Distribution-of-Average-Ratings-per-User-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Distribution-of-Average-Ratings-per-User-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Distribution-of-Average-Ratings-per-User-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Distribution-of-Average-Ratings-per-User-520x371.png 520w\" sizes=\"auto, (max-width: 484px) 100vw, 484px\" \/><\/a><\/p>\n<h4>3. Bar Chart<b><\/b><\/h4>\n<p><span style=\"font-weight: 400\">We use bar charts to plot a categorical variable.<\/span><br \/>\n<b><\/b><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/bar-chart-vertical.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-65828\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/bar-chart-vertical.png\" alt=\"bar-chart-vertical - Data Visualization in R\" width=\"480\" height=\"480\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/bar-chart-vertical.png 480w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/bar-chart-vertical-150x150.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/bar-chart-vertical-300x300.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/bar-chart-vertical-160x160.png 160w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/bar-chart-vertical-320x320.png 320w\" sizes=\"auto, (max-width: 480px) 100vw, 480px\" \/><\/a><\/p>\n<p><em><strong>Get to know everything about <a href=\"https:\/\/data-flair.training\/blogs\/bar-chart-in-r\/\">Bar Chart and Histogram in R Programming<\/a><\/strong><\/em><\/p>\n<h4>4. Box Plot<b><\/b><\/h4>\n<p><span style=\"font-weight: 400\">Box plots are used to plot an aggregation of categorical and continuous variables. It is also used for visualizing the spread of the data and detect outliers. Moreover, it shows five statistically significant numbers:<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400\">Minimum<\/span><\/li>\n<li><span style=\"font-weight: 400\">25th percentile<\/span><\/li>\n<li><span style=\"font-weight: 400\">Median<\/span><\/li>\n<li><span style=\"font-weight: 400\">75th percentile and<\/span><\/li>\n<li><span style=\"font-weight: 400\">Maximum.<\/span><br \/>\n<b><\/b><\/li>\n<\/ul>\n<h4><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/ML-boxplot-of-descriptive-analysis.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-65754\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/ML-boxplot-of-descriptive-analysis.png\" alt=\"boxplot - Data Visualization in R\" width=\"474\" height=\"339\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/ML-boxplot-of-descriptive-analysis.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/ML-boxplot-of-descriptive-analysis-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/ML-boxplot-of-descriptive-analysis-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/ML-boxplot-of-descriptive-analysis-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/ML-boxplot-of-descriptive-analysis-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/ML-boxplot-of-descriptive-analysis-520x371.png 520w\" sizes=\"auto, (max-width: 474px) 100vw, 474px\" \/><\/a><\/h4>\n<h4>5. Area Chart<b><\/b><\/h4>\n<p><span style=\"font-weight: 400\">We use it to show the continuity across a variable or data set. It is almost same as a line chart. Also, we can use it for time series plots. We can use it alternatively to plot continuous variables and analyze the underlying trends.<\/span><br \/>\n<b><\/b><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Area-Chart.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-65834\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Area-Chart.jpg\" alt=\"Area Chart - Data Visualization in R\" width=\"516\" height=\"483\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Area-Chart.jpg 516w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Area-Chart-150x140.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Area-Chart-300x281.jpg 300w\" sizes=\"auto, (max-width: 516px) 100vw, 516px\" \/><\/a><\/p>\n<h4>6. Heat Map<b><\/b><\/h4>\n<p><span style=\"font-weight: 400\">We use it for the intensity of colours. It is also used to display a relationship between two or three or many variables in a two-dimensional image. Thus, it allows us to explore two dimensions of the axis and the third dimension by an intensity of colour.<\/span><br \/>\n<b><\/b><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-of-Week-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63206\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-of-Week-Output.png\" alt=\"Heat-Map - Data Visualization in R\" width=\"497\" height=\"355\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-of-Week-Output.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-of-Week-Output-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-of-Week-Output-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-of-Week-Output-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-of-Week-Output-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-of-Week-Output-520x371.png 520w\" sizes=\"auto, (max-width: 497px) 100vw, 497px\" \/><\/a><\/p>\n<h4>7. Correlogram<b><\/b><\/h4>\n<p><span style=\"font-weight: 400\">We use it to test the level of correlation and also among the variable available in the dataset. Thus, the cells of the matrix can be shaded or coloured to show the co-relation value.<\/span><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/correlogram.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-65833\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/correlogram.png\" alt=\"correlogram - Data Visualization in R\" width=\"561\" height=\"315\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/correlogram.png 626w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/correlogram-150x84.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/correlogram-300x168.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/correlogram-520x292.png 520w\" sizes=\"auto, (max-width: 561px) 100vw, 561px\" \/><\/a><\/p>\n<h2>Pros and Cons of Data Visualization in R<\/h2>\n<p><span style=\"font-weight: 400\">Let\u2019s have a look at the advantages and disadvantages of data visualization in R programming:<\/span><\/p>\n<h3>Advantages of Data Visualization in R<\/h3>\n<p><b>1. Understanding<\/b><\/p>\n<p><span style=\"font-weight: 400\">It may be more appealing to look into the business. And, it\u2019s easy to understand through graphics and charts when compared to a written document comprising text and numbers. Thus, it can attract a wider audience. Also, it promotes widespread utilization of those business insights to arrive at better decisions.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>2. Efficiency<\/b><\/p>\n<p><span style=\"font-weight: 400\">Its app allows us to display a lot of information in a small space. While the process of decision making in business is inherently complex and multifaceted, displaying evaluation findings in a graph can allow the companies to organize lots of interrelated information in useful ways.<\/span><br \/>\n<b><\/b><\/p>\n<p><em><strong>Do you know about <a href=\"https:\/\/data-flair.training\/blogs\/generalized-linear-models-in-r\/\">Generalized Linear Models in R Programming<\/a><\/strong><\/em><\/p>\n<p><b>3. Location<\/b><\/p>\n<p><span style=\"font-weight: 400\">Its app that uses features like geographical maps and GIS can be especially relevant for extensive businesses when a location is a very relevant factor. We use maps to show <em>business insights from different places, giving an idea of the severity of issues, the reasons behind them and also the workarounds to address them.<\/em><\/span><\/p>\n<h3>Disadvantages\u00a0of Data Visualization in R<\/h3>\n<p><b>1. Cost<\/b><\/p>\n<p><span style=\"font-weight: 400\">Its applications cost a decent sum of money, and it may not be possible for especially small companies to spend that many resources upon purchasing them. In order to generate reports, many companies may hire professionals to produce charts which may increase the costs. Small enterprises are often working in resource-limited settings, and also getting evaluation results in a timely manner that can often be of high importance.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>2. Distraction<\/b><\/p>\n<p><span style=\"font-weight: 400\">Although at times, the data visualization apps create reports and charts laden with highly complicated and fancy graphics, which may be tempting for the users to focus more on form than on function. The overall value of the graphic representation will be minimal if we first add visual appeal. In a resource-setting, it is also important to think carefully about how resources can be best used. And also not get caught up in the graphics trend without a clear purpose.<\/span><\/p>\n<h2>Summary<\/h2>\n<p>In this article, we took a brief look at the complete concept of data visualization in R. And, we have also focused on ggplot2 in R which is mainly used in data visualization. Apart from ggplot2, we have also learned about visualization along with their pros and cons.<\/p>\n<p><em><strong>Next, you must go through our article on <a href=\"https:\/\/data-flair.training\/blogs\/r-lattice-package\/\">R Lattice Package<\/a><\/strong><\/em><\/p>\n<p>Hope you liked the article. Still, if you have any doubt regarding data visualization in R, ask in the comment section.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Going further in our R\u00a0tutorial DataFlair series, we will learn about data visualization in R. We will study the evolution of data visualization, R graphics concept and data visualization using ggplot2. We will also&#46;&#46;&#46;<\/p>\n","protected":false},"author":7,"featured_media":66263,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[51],"tags":[3503,20754,20753,11197],"class_list":["post-4947","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r","tag-data-visualization-in-r","tag-r-data-visualization-pros-and-cons","tag-r-data-visualization-using-ggplot2","tag-r-ggplot2"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data Visualization in R - Upgrade your R Skills to become Data Scientist! - DataFlair<\/title>\n<meta name=\"description\" content=\"Learn about data visualization in R &amp; explore the R visualization packages, terms of RStudio, R graphics concept, data visualization using ggplot2, what topics to learn in data visualization &amp; its pros and cons.\" \/>\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\/data-visualization-in-r\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Visualization in R - Upgrade your R Skills to become Data Scientist! 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