<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/data-wrangling-transforming-33/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://www.r-exercises.com/2017/08/02/data-wrangling-transforming-33/?utm_source=rss&utm_medium=rss&utm_campaign=data-wrangling-transforming-33"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><a rel="nofollow" href="https://i1.wp.com/www.r-exercises.com/wp-content/uploads/2017/05/monitor.png" target="_blank"><img alt="" src="https://i1.wp.com/www.r-exercises.com/wp-content/uploads/2017/05/monitor.png?resize=275%2C140" class="wp-image-5943" data-recalc-dims="1"></a><br>
Data wrangling is a task of great importance in data analysis. Data wrangling, is the process of importing, cleaning and transforming raw data into actionable information for analysis. It is a time-consuming process which is estimated to take about 60-80% of analyst’s time. In this series we will go through this process. It will be a brief series with goal to craft the reader’s skills on the data wrangling task. This is the third part of the series and it aims to cover the transforming of data used.This can include filtering, summarizing, and ordering your data by different means. This also includes combining various data sets, creating new variables, and many other manipulation tasks. At this post, we will go through a few more advanced transformation tasks on <code>mtcars</code> data set, in particular table manipulation. </p>
<p>Before proceeding, it might be helpful to look over the help pages for the <code>ineer_join</code>, <code>full__join</code>, <code>left_join</code>, <code>right_join</code>, <code>semi_join</code>, <code>anti_join</code>, <code>intersect</code>, <code>union</code>, <code>setdiff</code>, <code>bind_rows</code>.</p>
<p>Moreover please load the following libraries and run the following <a rel="nofollow" href="https://github.com/VasTsak/r-exercises/blob/master/ID_creation.R" target="_blank">link</a>.<br>
<code>install.packages(&quot;dplyr&quot;)</code><br>
<code>library(dplyr)</code></p>
<p>Answers to the exercises are available <a rel="nofollow" href="http://www.r-exercises.com/2017/08/02/data-wrangling-transforming-33-solutions" target="_blank">here</a>.</p>
<p>If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.</p>
<p>Exercise 1</p>
<p>Create a new object named <em>car_inner</em> containing the observations that have matching values in both tables <em>mtcars</em> and <em>cars_table</em> using as key the variable <code>ID</code>. </p>
<p>Exercise 2</p>
<p>Create a new object named <em>car_left</em> containing all the observations from the left table (<em>mtcars</em>), and the matched records from the right table (<em>cars_table</em>) using as key the variable <code>ID</code>.</p>
<div>
<aside><img src="http://www.r-exercises.com/2017/08/02/data-wrangling-transforming-33/?utm_source=rss&utm_medium=rss&utm_campaign=data-wrangling-transforming-33"></aside>
<div><strong>Learn more</strong> about Data Pre-Processing in the online course <a rel="nofollow" href="http://www.r-exercises.com/product/r-data-pre-processing-data-management-shape-your-data/" target="_blank">R Data Pre-Processing &amp; Data Management – Shape your Data!</a>. In this course you will learn how to:
<ul>
<li>Work with popular libraries such as dplyr</li>
<li>Learn about methods such as pipelines</li>
<li>And much more</li>
</ul>
</div>
</div>
<p>Exercise 3</p>
<p>Create a new object named <em>car_right</em> containing all the observations from the right table (<em>cars_table</em>), and the matched records from the right table (<em>mtcars</em>) using as key the variable <code>ID</code>.</p>
<p>Exercise 4</p>
<p>Create a new object named <em>car_full</em> containing all the observations when there is a match in either left (<em>cars_table</em>) or right (<em>mtcars</em>) table observation using as key the variable <code>ID</code>.</p>
<p>Exercise 5</p>
<p>Create a new object named <em>car_semi</em> containing all the observations from <em>mtcars</em> where there are matching values in <em>cars_table</em> using as key the variable <code>ID</code>.</p>
<p>Exercise 6<br>
Create a new object named <em>car_anti</em> containing all the observations from <em>mtcars</em> where there are not matching values in <em>cars_table</em> using as key the variable <code>ID</code>.</p>
<p>Exercise 7</p>
<p>Create a new object named <em>cars_inter</em> which contains rows that appear in both tables <em>mtcars</em> and <em>cars</em>.</p>
<p>Exercise 8 </p>
<p>Create a new object named <em>cars_union</em> which contains rows appear in either tables <em>mtcars</em> and <em>cars</em>.</p>
<p>Exercise 9 </p>
<p>Create a new object named <em>cars_diff</em> which contains rows appear in table <em>mtcars</em> and not <em>cars</em>.</p>
<p>Exercise 10 </p>
<p>Append <em>mtcars</em> to <em>cars</em> and assign it at the object <em>car_rows</em>.</p>
<div>
<h3>Related exercise sets:</h3>
<ol>
<li><a rel="nofollow" href="http://www.r-exercises.com/2016/03/21/data-table-exercises-keys-and-subsetting/" title="Data table exercises: keys and subsetting" target="_blank">Data table exercises: keys and subsetting </a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/2017/07/19/data-wrangling-transforming-23/" title="Data wrangling : Transforming (2/3)" target="_blank">Data wrangling : Transforming (2/3) </a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/2016/04/14/merging-dataframes-exercises/" title="Merging Dataframes Exercises" target="_blank">Merging Dataframes Exercises </a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/start-here-to-learn-r/" target="_blank">Explore all our (>1000) R exercises</a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/r-courses/" target="_blank">Find an R course using our R Course Finder</a> directory</li>
</ol>
</div>
	<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://www.r-exercises.com/2017/08/02/data-wrangling-transforming-33/?utm_source=rss&utm_medium=rss&utm_campaign=data-wrangling-transforming-33"> R-exercises</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/Vc2_AavjzYU" height="1"></a></div>