{"id":62951,"date":"2019-07-13T18:24:50","date_gmt":"2019-07-13T12:54:50","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=62951"},"modified":"2024-08-02T15:42:58","modified_gmt":"2024-08-02T10:12:58","slug":"r-data-science-project-uber-data-analysis","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/r-data-science-project-uber-data-analysis\/","title":{"rendered":"Project in R &#8211; Uber Data Analysis Project"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:1475,&quot;href&quot;:&quot;https:\\\/\\\/techvidvan.com\\\/tutorials\\\/machine-learning-tutorial&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20250521210425\\\/https:\\\/\\\/techvidvan.com\\\/tutorials\\\/machine-learning-tutorial\\\/&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 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13:25:03&quot;,&quot;http_code&quot;:429},{&quot;date&quot;:&quot;2026-05-03 23:44:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-07 08:26:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-10 09:13:21&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-13 09:42:32&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-17 03:09:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-21 05:54:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-24 05:59:54&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-27 14:21:18&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-30 14:37:56&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-02 15:17:56&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-05 16:02:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-09 18:21:19&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-13 23:39:45&quot;,&quot;http_code&quot;:404}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-13 23:39:45&quot;,&quot;http_code&quot;:404},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p>Welcome to part 2 of R and Data Science Projects designed by DataFlair. In our series of R projects, we are trying to use all the concepts related to Machine learning, AI and Data Science.<\/p>\n<p>We recommend you to follow all the steps given in the projects so that you will master the technology rapidly. In today&#8217;s R project, we will analyze the <em><strong>Uber Pickups in New York City dataset<\/strong><\/em>. This is more of a data visualization project that will guide you towards using the ggplot2 library for understanding the data and for developing an intuition for understanding the customers who avail the trips. So, before we start, take a quick revision to <em><strong><a href=\"https:\/\/data-flair.training\/blogs\/data-visualization-in-r\/\">data visualization concepts<\/a><\/strong><\/em>.<\/p>\n<h2>R Data Science Project &#8211; Uber Data Analysis<\/h2>\n<p>Talking about our Uber data analysis project, data storytelling is an important component of <a href=\"https:\/\/techvidvan.com\/tutorials\/machine-learning-tutorial\/\"><em><strong>Machine Learning<\/strong><\/em><\/a>\u00a0through which companies are able to understand the background of various operations. With the help of visualization, companies can avail the benefit of understanding the complex data and gain insights that would help them to craft decisions. You will learn how to implement the ggplot2 on the Uber Pickups dataset and at the end, master the art of data visualization in R.<\/p>\n<p>In this project, we will uncover the Uber pickups pattern of New York City at different temporal intervals. This will help not only in analyzing the general trends in the amount of rides, but also in analyzing the shape of the curve, for example, how many rides were given at particular day of the week or particular hour of the day.<\/p>\n<p>With such patterns, it is possible not only to reveal periods with the increased demand for Uber but also to define how the demand changes during the day or month and even which of the bases is the most popular. They are essential in enhancing market strategies towards operations and customer relations.<\/p>\n<p><strong><em>You can download the dataset utilized in this project here &#8211;<\/em> <\/strong><a href=\"https:\/\/drive.google.com\/file\/d\/1emopjfEkTt59jJoBH9L9bSdmlDC4AR87\/view\"><strong>Uber Dataset<\/strong><\/a><\/p>\n<h3>1. Importing the Essential Packages<\/h3>\n<p>In the first step of our R project, we will import the essential packages that we will use in this uber data analysis project. Some of the<em><strong> important libraries of R<\/strong><\/em> that we will use are &#8211;<\/p>\n<ul>\n<li>ggplot2<\/li>\n<\/ul>\n<p>This is the backbone of this project. ggplot2 is the most popular data visualization library that is most widely used for creating aesthetic visualization plots.<\/p>\n<ul>\n<li>ggthemes<\/li>\n<\/ul>\n<p>This is more of an add-on to our main ggplot2 library. With this, we can create better create extra themes and scales with the mainstream ggplot2 package.<\/p>\n<ul>\n<li>lubridate<\/li>\n<\/ul>\n<p>Our dataset involves various time-frames. In order to understand our data in separate time categories, we will make use of the lubridate package.<\/p>\n<ul>\n<li>dplyr<\/li>\n<\/ul>\n<p>This package is the lingua franca of <em><strong><a href=\"https:\/\/data-flair.training\/blogs\/manipulating-and-processing-data-in-r\/\">data manipulation in R<\/a><\/strong><\/em>.<\/p>\n<ul>\n<li>tidyr<\/li>\n<\/ul>\n<p>This package will help you to tidy your data. The basic principle of tidyr is to tidy the columns where each variable is present in a column, each observation is represented by a row and each value depicts a cell.<\/p>\n<ul>\n<li>DT<\/li>\n<\/ul>\n<p>With the help of this package, we will be able to interface with the <em><strong><a href=\"https:\/\/data-flair.training\/blogs\/javascript-tutorials-home\/\">JavaScript\u00a0<\/a><\/strong><\/em>Library called &#8211; Datatables.<\/p>\n<ul>\n<li>scales<\/li>\n<\/ul>\n<p>With the help of graphical scales, we can automatically map the data to the correct scales with well-placed axes and legends.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">library(ggplot2)\r\nlibrary(ggthemes)\r\nlibrary(lubridate)\r\nlibrary(dplyr)\r\nlibrary(tidyr)\r\nlibrary(DT)\r\nlibrary(scales)<\/pre>\n<p><strong>Input Screenshot 1:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.0-Importing-Libraries-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63216\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.0-Importing-Libraries-1.png\" alt=\"Importing Libraries \" width=\"645\" height=\"206\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.0-Importing-Libraries-1.png 645w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.0-Importing-Libraries-1-150x48.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.0-Importing-Libraries-1-300x96.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.0-Importing-Libraries-1-520x166.png 520w\" sizes=\"auto, (max-width: 645px) 100vw, 645px\" \/><\/a><br \/>\n<strong>Input Screenshot 2:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.1-Importing-Libraries-.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63219\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.1-Importing-Libraries-.png\" alt=\"Importing Libraries in R\" width=\"693\" height=\"264\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.1-Importing-Libraries-.png 693w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.1-Importing-Libraries--150x57.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.1-Importing-Libraries--300x114.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.1-Importing-Libraries--520x198.png 520w\" sizes=\"auto, (max-width: 693px) 100vw, 693px\" \/><\/a><br \/>\n<strong>The Input Screenshot 3:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.2-Importing-Libraries.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63220\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.2-Importing-Libraries.png\" alt=\"R import libraries\" width=\"691\" height=\"571\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.2-Importing-Libraries.png 691w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.2-Importing-Libraries-150x124.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.2-Importing-Libraries-300x248.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/1.2-Importing-Libraries-520x430.png 520w\" sizes=\"auto, (max-width: 691px) 100vw, 691px\" \/><\/a><\/p>\n<h3>2. Creating vector of colors to be implemented in our plots<\/h3>\n<p>In this step of data science project, we will create a vector of our colors that will be included in our plotting functions. You can also select your own set of colors.<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">colors = c(\"\"#CC1011\", \"#665555\", \"#05a399\", \"#cfcaca\", \"#f5e840\", \"#0683c9\", \"#e075b0\"\")<\/pre>\n<p><strong>Input Screenshot 4:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/create-vector-of-colors.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63357\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/create-vector-of-colors.jpg\" alt=\"create vector of colors\" width=\"939\" height=\"57\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/create-vector-of-colors.jpg 939w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/create-vector-of-colors-150x9.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/create-vector-of-colors-300x18.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/create-vector-of-colors-768x47.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/create-vector-of-colors-520x32.jpg 520w\" sizes=\"auto, (max-width: 939px) 100vw, 939px\" \/><\/a><\/p>\n<h3>3. Reading the Data into their designated variables<\/h3>\n<p>Now, we will read several csv files that contain the data from April 2014 to September 2014. We will store these in corresponding data frames like apr_data, may_data, etc. After we have read the files, we will combine all of this data into a single dataframe called \u2018data_2014\u2019.<\/p>\n<p><em><strong>To master this R Uber data analysis project, you need to know everything related to <a href=\"https:\/\/data-flair.training\/blogs\/r-data-frame-introduction-operations\/\">data frames in R<\/a><\/strong><\/em><\/p>\n<p>Then, in the next step, we will perform the appropriate formatting of Date.Time column. Then, we will proceed to create factors of time objects like day, month, year etc.<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">apr_data &lt;- read.csv(\"uber-raw-data-apr14.csv\")\r\nmay_data &lt;- read.csv(\"uber-raw-data-may14.csv\")\r\njun_data &lt;- read.csv(\"uber-raw-data-jun14.csv\")\r\njul_data &lt;- read.csv(\"uber-raw-data-jul14.csv\")\r\naug_data &lt;- read.csv(\"uber-raw-data-aug14.csv\")\r\nsep_data &lt;- read.csv(\"uber-raw-data-sep14.csv\")\r\n\r\ndata_2014 &lt;- rbind(apr_data,may_data, jun_data, jul_data, aug_data, sep_data)\r\n\r\ndata_2014$Date.Time &lt;- as.POSIXct(data_2014$Date.Time, format = \"%m\/%d\/%Y %H:%M:%S\")\r\n\r\ndata_2014$Time &lt;- format(as.POSIXct(data_2014$Date.Time, format = \"%m\/%d\/%Y %H:%M:%S\"), format=\"%H:%M:%S\")\r\n\r\ndata_2014$Date.Time &lt;- ymd_hms(data_2014$Date.Time)\r\n\r\ndata_2014$day &lt;- factor(day(data_2014$Date.Time))\r\ndata_2014$month &lt;- factor(month(data_2014$Date.Time, label = TRUE))\r\ndata_2014$year &lt;- factor(year(data_2014$Date.Time))\r\ndata_2014$dayofweek &lt;- factor(wday(data_2014$Date.Time, label = TRUE))\r\n<\/pre>\n<p><strong>Input Screenshot 5:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.1-Reading-FIle.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63221\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.1-Reading-FIle.png\" alt=\"reading the data\" width=\"723\" height=\"530\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.1-Reading-FIle.png 723w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.1-Reading-FIle-150x110.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.1-Reading-FIle-300x220.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.1-Reading-FIle-520x381.png 520w\" sizes=\"auto, (max-width: 723px) 100vw, 723px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">data_2014$hour &lt;- factor(hour(hms(data_2014$Time)))\r\ndata_2014$minute &lt;- factor(minute(hms(data_2014$Time)))\r\ndata_2014$second &lt;- factor(second(hms(data_2014$Time)))<\/pre>\n<p><strong>Input Screenshot 6:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.2-Reading-FIle.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63222\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.2-Reading-FIle.png\" alt=\"reading data\" width=\"720\" height=\"79\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.2-Reading-FIle.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.2-Reading-FIle-150x16.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.2-Reading-FIle-300x33.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/2.2-Reading-FIle-520x57.png 520w\" sizes=\"auto, (max-width: 720px) 100vw, 720px\" \/><\/a><\/p>\n<h3>Plotting the trips by the hours in a day<\/h3>\n<p>In the next step or R project, we will use the ggplot function to plot the number of trips that the passengers had made in a day. We will also use dplyr to aggregate our data. In the resulting visualizations, we can understand how the number of passengers fares throughout the day. We observe that the number of trips are higher in the evening around 5:00 and 6:00 PM.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">hour_data &lt;- data_2014 %&gt;%\r\n           group_by(hour) %&gt;%\r\n               dplyr::summarize(Total = n()) \r\ndatatable(hour_data)<\/pre>\n<p><strong>Input Screenshot 7:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.0-Plotting-the-trips-by-the-hours-in-a-day-Code.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63223\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.0-Plotting-the-trips-by-the-hours-in-a-day-Code.png\" alt=\"Plotting the trips\" width=\"747\" height=\"122\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.0-Plotting-the-trips-by-the-hours-in-a-day-Code.png 747w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.0-Plotting-the-trips-by-the-hours-in-a-day-Code-150x24.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.0-Plotting-the-trips-by-the-hours-in-a-day-Code-300x49.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.0-Plotting-the-trips-by-the-hours-in-a-day-Code-520x85.png 520w\" sizes=\"auto, (max-width: 747px) 100vw, 747px\" \/><\/a><\/p>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.1-Plotting-the-trips-by-the-hours-in-a-day-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63224 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.1-Plotting-the-trips-by-the-hours-in-a-day-Output.png\" alt=\"uber data analysis\" width=\"743\" height=\"541\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.1-Plotting-the-trips-by-the-hours-in-a-day-Output.png 743w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.1-Plotting-the-trips-by-the-hours-in-a-day-Output-150x109.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.1-Plotting-the-trips-by-the-hours-in-a-day-Output-300x218.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.1-Plotting-the-trips-by-the-hours-in-a-day-Output-520x379.png 520w\" sizes=\"auto, (max-width: 743px) 100vw, 743px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot(hour_data, aes(hour, Total)) + \r\n        geom_bar( stat = \"identity\", fill = \"steelblue\", color = \"red\") +\r\n           ggtitle(\"Trips Every Hour\") +\r\n            theme(legend.position = \"none\") +\r\n            scale_y_continuous(labels = comma)\r\n\r\nmonth_hour &lt;- data_2014 %&gt;%\r\n          group_by(month, hour) %&gt;%\r\n             dplyr::summarize(Total = n())\r\n\r\nggplot(month_hour, aes(hour, Total, fill = month)) + \r\n       geom_bar( stat = \"identity\") +\r\n          ggtitle(\"Trips by Hour and Month\") +\r\n           scale_y_continuous(labels = comma)<\/pre>\n<p><strong>Input Screenshot 8:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.2-Trips-every-Hour-Plot-Code.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63227\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.2-Trips-every-Hour-Plot-Code.png\" alt=\"Trips every Hour Plot Code\" width=\"717\" height=\"134\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.2-Trips-every-Hour-Plot-Code.png 717w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.2-Trips-every-Hour-Plot-Code-150x28.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.2-Trips-every-Hour-Plot-Code-300x56.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.2-Trips-every-Hour-Plot-Code-520x97.png 520w\" sizes=\"auto, (max-width: 717px) 100vw, 717px\" \/><\/a><\/p>\n<p><strong>Input Screenshot 9:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.3-month_hour.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63229\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.3-month_hour.png\" alt=\"month_hour\" width=\"724\" height=\"198\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.3-month_hour.png 724w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.3-month_hour-150x41.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.3-month_hour-300x82.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.3-month_hour-720x198.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.3-month_hour-520x142.png 520w\" sizes=\"auto, (max-width: 724px) 100vw, 724px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-and-Months.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63190 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-and-Months.png\" alt=\"uber data analysis\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-and-Months.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-and-Months-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-and-Months-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-and-Months-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-and-Months-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-and-Months-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-in-a-Day-Plot-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63193 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-in-a-Day-Plot-Output.png\" alt=\"uber data analysis\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-in-a-Day-Plot-Output.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-in-a-Day-Plot-Output-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-in-a-Day-Plot-Output-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-in-a-Day-Plot-Output-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-in-a-Day-Plot-Output-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Hours-in-a-Day-Plot-Output-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<h3>Plotting data by trips during every day of the month<\/h3>\n<p>In this section of DataFlair R project, we will learn how to plot our data based on every day of the month. We observe from the resulting visualization that 30th of the month had the highest trips in the year which is mostly contributed by the month of April.<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">day_group &lt;- data_2014 %&gt;%\r\n          group_by(day) %&gt;%\r\n             dplyr::summarize(Total = n()) \r\ndatatable(day_group)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.4-day_group.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63232 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.4-day_group.png\" alt=\"uber data analysis\" width=\"758\" height=\"566\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.4-day_group.png 758w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.4-day_group-150x112.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.4-day_group-300x224.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.4-day_group-520x388.png 520w\" sizes=\"auto, (max-width: 758px) 100vw, 758px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot(day_group, aes(day, Total)) + \r\n        geom_bar( stat = \"identity\", fill = \"steelblue\") +\r\n           ggtitle(\"Trips Every Day\") +\r\n            theme(legend.position = \"none\") +\r\n            scale_y_continuous(labels = comma)<\/pre>\n<p><strong>Input Screenshot 10:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.5-Trips-Everyday-Code-Plot.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63235\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.5-Trips-Everyday-Code-Plot.png\" alt=\"plotting data by trips\" width=\"723\" height=\"131\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.5-Trips-Everyday-Code-Plot.png 723w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.5-Trips-Everyday-Code-Plot-150x27.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.5-Trips-Everyday-Code-Plot-300x54.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.5-Trips-Everyday-Code-Plot-720x131.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.5-Trips-Everyday-Code-Plot-520x94.png 520w\" sizes=\"auto, (max-width: 723px) 100vw, 723px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Every-Day-of-the-Month-Code-Plot-Output1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63329 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Every-Day-of-the-Month-Code-Plot-Output1.png\" alt=\"uber data analysis\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Every-Day-of-the-Month-Code-Plot-Output1.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Every-Day-of-the-Month-Code-Plot-Output1-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Every-Day-of-the-Month-Code-Plot-Output1-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Every-Day-of-the-Month-Code-Plot-Output1-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Every-Day-of-the-Month-Code-Plot-Output1-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Every-Day-of-the-Month-Code-Plot-Output1-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">day_month_group &lt;- data_2014 %&gt;%\r\n         group_by(month, day) %&gt;%\r\n             dplyr::summarize(Total = n())\r\n\r\nggplot(day_month_group, aes(day, Total, fill = month)) + \r\n        geom_bar( stat = \"identity\") +\r\n           ggtitle(\"Trips by Day and Month\") +\r\n            scale_y_continuous(labels = comma) +\r\n            scale_fill_manual(values = colors)<\/pre>\n<p><strong>Input Screenshot 11:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63238\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group.png\" alt=\"plotting data by trips\" width=\"705\" height=\"214\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group.png 705w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group-150x46.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group-300x91.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group-520x158.png 520w\" sizes=\"auto, (max-width: 705px) 100vw, 705px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63331 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months1.png\" alt=\"uber data analysis\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months1.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months1-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months1-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months1-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months1-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months1-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<h3>Number of Trips taking place during months in a year<\/h3>\n<p>In this section, we will visualize the number of trips that are taking place each month of the year. In the output visualization, we observe that most trips were made during the month of September. Furthermore, we also obtain visual reports of the number of trips that were made on every day of the week.<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">month_group &lt;- data_2014 %&gt;%\r\n          group_by(month) %&gt;%\r\n             dplyr::summarize(Total = n()) \r\ndatatable(month_group)<\/pre>\n<p><strong>Output Screenshot:<\/strong><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.7-month_group.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63242 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.7-month_group.png\" alt=\"uber data analysis\" width=\"749\" height=\"479\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.7-month_group.png 749w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.7-month_group-150x96.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.7-month_group-300x192.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.7-month_group-520x333.png 520w\" sizes=\"auto, (max-width: 749px) 100vw, 749px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot( , aes(month, Total, fill = month)) + \r\n        geom_bar( stat = \"identity\") +\r\n           ggtitle(\"Trips by Month\") +\r\n            theme(legend.position = \"none\") +\r\n            scale_y_continuous(labels = comma) +\r\n            scale_fill_manual(values = colors)<\/pre>\n<p><strong>Input Screenshot 12:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.8-Trips-by-Month-Plot.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63245\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.8-Trips-by-Month-Plot.png\" alt=\" Trips by Month Plot\" width=\"719\" height=\"156\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.8-Trips-by-Month-Plot.png 719w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.8-Trips-by-Month-Plot-150x33.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.8-Trips-by-Month-Plot-300x65.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.8-Trips-by-Month-Plot-520x113.png 520w\" sizes=\"auto, (max-width: 719px) 100vw, 719px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-By-Month.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63194 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-By-Month.png\" alt=\"Uber data analysis\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-By-Month.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-By-Month-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-By-Month-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-By-Month-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-By-Month-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-By-Month-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">month_weekday &lt;- data_2014 %&gt;%\r\n         group_by(month, dayofweek) %&gt;%\r\n             dplyr::summarize(Total = n())\r\n\r\nggplot(month_weekday, aes(month, Total, fill = dayofweek)) + \r\n       geom_bar( stat = \"identity\", position = \"dodge\") +\r\n          ggtitle(\"Trips by Day and Month\") +\r\n           scale_y_continuous(labels = comma) +\r\n           scale_fill_manual(values = colors)<\/pre>\n<p><strong>Input Screenshot 13:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63249\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group-1.png\" alt=\"day month group\" width=\"705\" height=\"214\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group-1.png 705w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group-1-150x46.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group-1-300x91.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/3.6-day-month-group-1-520x158.png 520w\" sizes=\"auto, (max-width: 705px) 100vw, 705px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63195 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months.png\" alt=\"uber data analysis\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Days-and-Months-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<h3>Finding out the number of Trips by bases<\/h3>\n<p>In the following visualization, we plot the number of trips that have been taken by the passengers from each of the bases. There are five bases in all out of which, we observe that B02617 had the highest number of trips. Furthermore, this base had the highest number of trips in the month B02617. Thursday observed highest trips in the three bases &#8211; B02598, B02617, B02682.<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot(data_2014, aes(Base)) + \r\n geom_bar(fill = \"darkred\") +\r\n scale_y_continuous(labels = comma) +\r\n ggtitle(\"Trips by Bases\")<\/pre>\n<p><strong>Input Screenshot 14:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.0-Trips-by-Bases.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63252\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.0-Trips-by-Bases.png\" alt=\" Trips by Bases\" width=\"720\" height=\"100\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.0-Trips-by-Bases.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.0-Trips-by-Bases-150x21.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.0-Trips-by-Bases-300x42.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.0-Trips-by-Bases-520x72.png 520w\" sizes=\"auto, (max-width: 720px) 100vw, 720px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Number-of-Trips-by-Bases-Plot.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63333 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Number-of-Trips-by-Bases-Plot.png\" alt=\"uber data analysis\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Number-of-Trips-by-Bases-Plot.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Number-of-Trips-by-Bases-Plot-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Number-of-Trips-by-Bases-Plot-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Number-of-Trips-by-Bases-Plot-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Number-of-Trips-by-Bases-Plot-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Number-of-Trips-by-Bases-Plot-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot(data_2014, aes(Base, fill = month)) + \r\n geom_bar(position = \"dodge\") +\r\n scale_y_continuous(labels = comma) +\r\n ggtitle(\"Trips by Bases and Month\") +\r\n scale_fill_manual(values = colors)<\/pre>\n<p><strong>Input Screenshot 15:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.1-Trips-by-Bases-and-Months.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63254\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.1-Trips-by-Bases-and-Months.png\" alt=\"Trips by Bases and Months\" width=\"718\" height=\"126\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.1-Trips-by-Bases-and-Months.png 718w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.1-Trips-by-Bases-and-Months-150x26.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.1-Trips-by-Bases-and-Months-300x53.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.1-Trips-by-Bases-and-Months-520x91.png 520w\" sizes=\"auto, (max-width: 718px) 100vw, 718px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Months-Plot-Output1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63334 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Months-Plot-Output1.png\" alt=\"uber data analysis\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Months-Plot-Output1.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Months-Plot-Output1-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Months-Plot-Output1-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Months-Plot-Output1-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Months-Plot-Output1-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Months-Plot-Output1-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot(data_2014, aes(Base, fill = dayofweek)) + \r\n\u00a0geom_bar(position = \"dodge\") +\r\n\u00a0scale_y_continuous(labels = comma) +\r\n\u00a0ggtitle(\"Trips by Bases and DayofWeek\") +\r\n\u00a0scale_fill_manual(values = colors)<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Days-of-the-Week-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63197 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Days-of-the-Week-Output.png\" alt=\"uber data analysis\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Days-of-the-Week-Output.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Days-of-the-Week-Output-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Days-of-the-Week-Output-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Days-of-the-Week-Output-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Days-of-the-Week-Output-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Trips-by-Bases-and-Days-of-the-Week-Output-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<h3>Creating a Heatmap visualization of day, hour and month<\/h3>\n<p>In this section, we will learn how to plot heatmaps using ggplot(). We will plot five heatmap plots &#8211;<\/p>\n<ul>\n<li>First, we will plot <a href=\"https:\/\/en.wikipedia.org\/wiki\/Heat_map\">Heatmap<\/a> by Hour and Day.<\/li>\n<li>Second, we will plot Heatmap by Month and Day.<\/li>\n<li>Third, a Heatmap by Month and Day of the Week.<\/li>\n<li>Fourth, a Heatmap that delineates Month and Bases.<\/li>\n<li>Finally, we will plot the heatmap, by bases and day of the week.<\/li>\n<\/ul>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">day_and_hour &lt;- data_2014 %&gt;%\r\n         group_by(day, hour) %&gt;%\r\n            dplyr::summarize(Total = n())\r\n\r\ndatatable(day_and_hour)\r\n<\/pre>\n<p><strong>Input Screenshot 16:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63257\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code.png\" alt=\"day_hour_month code\" width=\"715\" height=\"124\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code.png 715w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code-150x26.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code-300x52.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code-520x90.png 520w\" sizes=\"auto, (max-width: 715px) 100vw, 715px\" \/><\/a><\/p>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63258 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code-Output.png\" alt=\"uber data analysis\" width=\"737\" height=\"537\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code-Output.png 737w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code-Output-150x109.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code-Output-300x219.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/4.2-day_hour_month-code-Output-520x379.png 520w\" sizes=\"auto, (max-width: 737px) 100vw, 737px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot(day_and_hour, aes(day, hour, fill = Total)) +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0geom_tile(color = \"white\") +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ggtitle(\"Heat Map by Hour and Day\")<\/pre>\n<p><strong>Input Screenshot 17:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.0-heatmap-hour_day.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63259\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.0-heatmap-hour_day.png\" alt=\"creating heatmap visualization\" width=\"709\" height=\"81\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.0-heatmap-hour_day.png 709w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.0-heatmap-hour_day-150x17.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.0-heatmap-hour_day-300x34.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.0-heatmap-hour_day-520x59.png 520w\" sizes=\"auto, (max-width: 709px) 100vw, 709px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/HeatMap-Hour-and-Day-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63204\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/HeatMap-Hour-and-Day-Output.png\" alt=\"Heat Map Hour and Day\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/HeatMap-Hour-and-Day-Output.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/HeatMap-Hour-and-Day-Output-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/HeatMap-Hour-and-Day-Output-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/HeatMap-Hour-and-Day-Output-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/HeatMap-Hour-and-Day-Output-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/HeatMap-Hour-and-Day-Output-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot(day_month_group, aes(day, month, fill = Total)) +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0geom_tile(color = \"white\") +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ggtitle(\"Heat Map by Month and Day\")<\/pre>\n<p><strong>Input Screenshot 18:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.1-Heatmap-Month-and-Day.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63260\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.1-Heatmap-Month-and-Day.png\" alt=\"Heatmap Month and Day\" width=\"705\" height=\"79\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.1-Heatmap-Month-and-Day.png 705w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.1-Heatmap-Month-and-Day-150x17.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.1-Heatmap-Month-and-Day-300x34.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.1-Heatmap-Month-and-Day-520x58.png 520w\" sizes=\"auto, (max-width: 705px) 100vw, 705px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63205\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-Output.png\" alt=\"Heat map by Month and Day\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-Output.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-Output-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-Output-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-Output-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-Output-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-Output-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot(month_weekday, aes(dayofweek, month, fill = Total)) +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0geom_tile(color = \"white\") +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ggtitle(\"Heat Map by Month and Day of Week\")<\/pre>\n<p><strong>Input Screenshot 19:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.2-Heatmap-Month-and-Day-of-Week.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63261\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.2-Heatmap-Month-and-Day-of-Week.png\" alt=\"Heatmap Month and Day of Week\" width=\"697\" height=\"79\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.2-Heatmap-Month-and-Day-of-Week.png 697w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.2-Heatmap-Month-and-Day-of-Week-150x17.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.2-Heatmap-Month-and-Day-of-Week-300x34.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.2-Heatmap-Month-and-Day-of-Week-520x59.png 520w\" sizes=\"auto, (max-width: 697px) 100vw, 697px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/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=\"size-full wp-image-63206 aligncenter\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Day-of-Week-Output.png\" alt=\"Month and Day of Week\" width=\"1344\" height=\"960\" 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: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">month_base &lt;-\u00a0 data_2014 %&gt;%\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0group_by(Base, month) %&gt;%\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0dplyr::summarize(Total = n())\u00a0\r\n\r\nday0fweek_bases &lt;-\u00a0 data_2014 %&gt;%\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0group_by(Base, dayofweek) %&gt;%\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0dplyr::summarize(Total = n())\u00a0\r\n\r\nggplot(month_base, aes(Base, month, fill = Total)) +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0geom_tile(color = \"white\") +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ggtitle(\"Heat Map by Month and Bases\")<\/pre>\n<p><strong>Input Screenshot 20:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.3-Heatmap-Month-and-Bases.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63262\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.3-Heatmap-Month-and-Bases.png\" alt=\"Heatmap Month and Bases\" width=\"557\" height=\"255\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.3-Heatmap-Month-and-Bases.png 557w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.3-Heatmap-Month-and-Bases-150x69.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.3-Heatmap-Month-and-Bases-300x137.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.3-Heatmap-Month-and-Bases-520x238.png 520w\" sizes=\"auto, (max-width: 557px) 100vw, 557px\" \/><\/a><br \/>\n<strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Bases-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63336\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Bases-Output.png\" alt=\"Month and Bases Output\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Bases-Output.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Bases-Output-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Bases-Output-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Bases-Output-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Bases-Output-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Month-and-Bases-Output-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">ggplot(day0fweek_bases, aes(Base, dayofweek, fill = Total)) +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0geom_tile(color = \"white\") +\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ggtitle(\"Heat Map by Bases and Day of Week\")<\/pre>\n<p><strong>Input Screenshot 21:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.4-Days-and-Base-of-Week.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63263\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.4-Days-and-Base-of-Week.png\" alt=\"Days and Base of Week\" width=\"615\" height=\"79\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.4-Days-and-Base-of-Week.png 615w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.4-Days-and-Base-of-Week-150x19.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.4-Days-and-Base-of-Week-300x39.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/5.4-Days-and-Base-of-Week-520x67.png 520w\" sizes=\"auto, (max-width: 615px) 100vw, 615px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Bases-and-Days-of-Weeks-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63211\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Bases-and-Days-of-Weeks-Output.png\" alt=\"Bases and Days of Weeks \" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Bases-and-Days-of-Weeks-Output.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Bases-and-Days-of-Weeks-Output-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Bases-and-Days-of-Weeks-Output-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Bases-and-Days-of-Weeks-Output-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Bases-and-Days-of-Weeks-Output-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Bases-and-Days-of-Weeks-Output-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<h3>Creating a map visualization of rides in New York<\/h3>\n<p>In the final section, we will visualize the rides in New York city by creating a geo-plot that will help us to visualize the rides during 2014 (Apr &#8211; Sep) and by the bases in the same period.<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">min_lat &lt;- 40.5774\r\nmax_lat &lt;- 40.9176\r\nmin_long &lt;- -74.15\r\nmax_long &lt;- -73.7004\r\n\r\nggplot(data_2014, aes(x=Lon, y=Lat)) +\r\n  geom_point(size=1, color = \"blue\") +\r\n     scale_x_continuous(limits=c(min_long, max_long)) +\r\n      scale_y_continuous(limits=c(min_lat, max_lat)) +\r\n        theme_map() +\r\n           ggtitle(\"NYC MAP BASED ON UBER RIDES DURING 2014 (APR-SEP)\")\r\n\r\nggplot(data_2014, aes(x=Lon, y=Lat, color = Base)) +\r\n  geom_point(size=1) +\r\n     scale_x_continuous(limits=c(min_long, max_long)) +\r\n      scale_y_continuous(limits=c(min_lat, max_lat)) +\r\n       theme_map() +\r\n          ggtitle(\"NYC MAP BASED ON UBER RIDES DURING 2014 (APR-SEP) by BASE\")<\/pre>\n<p><strong>Input Screenshot 22:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/creating-a-map-visualization.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63264\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/creating-a-map-visualization.png\" alt=\"creating a map visualization\" width=\"512\" height=\"191\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/creating-a-map-visualization.png 512w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/creating-a-map-visualization-150x56.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/creating-a-map-visualization-300x112.png 300w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \/><\/a><br \/>\n<strong>Output:<\/strong><\/p>\n<div id=\"attachment_63213\" style=\"width: 1354px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/NYC-Map-1-Output.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-63213\" class=\"wp-image-63213 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/NYC-Map-1-Output.png\" alt=\"Creating a map visualization\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/NYC-Map-1-Output.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/NYC-Map-1-Output-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/NYC-Map-1-Output-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/NYC-Map-1-Output-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/NYC-Map-1-Output-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/NYC-Map-1-Output-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><p id=\"caption-attachment-63213\" class=\"wp-caption-text\">Uber data analysis using R<\/p><\/div>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Map-2-Output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63214 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Map-2-Output.png\" alt=\"Uber data analyis using R\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Map-2-Output.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Map-2-Output-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Map-2-Output-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Map-2-Output-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Map-2-Output-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Map-2-Output-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<h2>Summary<\/h2>\n<p>At the end of the Uber data analysis R project, we observed how to create data visualizations. We made use of packages like ggplot2 that allowed us to plot various types of visualizations that pertained to several time-frames of the year. With this, we could conclude how time affected customer trips. Finally, we made a geo plot of New York that provided us with the details of how various users made trips from different bases.<\/p>\n<p>Hope you enjoyed the above R Data Science Project. Keep visiting <strong>DataFlair<\/strong> for more interesting projects related to the latest technologies like Big Data, R and Data Science. If you face any issue while practicing the same, comment us below. We will definitely help.<\/p>\n<p><em><strong>Master R technology for Free &#8211; Check <a href=\"https:\/\/data-flair.training\/blogs\/r-tutorials-home\/\">R Tutorials Series<\/a><\/strong><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Welcome to part 2 of R and Data Science Projects designed by DataFlair. In our series of R projects, we are trying to use all the concepts related to Machine learning, AI and Data&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":63424,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[51],"tags":[20584,20541,20585],"class_list":["post-62951","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r","tag-data-science-project","tag-r-project","tag-uber-data-analysis-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Project in R - Uber Data Analysis Project - DataFlair<\/title>\n<meta name=\"description\" content=\"R Uber data analysis project - Analyzing the Uber Pickups in New York City dataset. Create data visualizations and use R packages\" \/>\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\/r-data-science-project-uber-data-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Project in R - Uber Data Analysis Project - DataFlair\" \/>\n<meta property=\"og:description\" content=\"R Uber data analysis project - Analyzing the Uber Pickups in New York City dataset. 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