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(This article was first published on <strong><a href="http://r-exercises.com/2017/01/28/data-science-for-doctors-part-1-data-display/?utm_source=rss&utm_medium=rss&utm_campaign=data-science-for-doctors-part-1-data-display"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Data science enhances people’s decision making. Doctors and researchers are making critical decisions every day, so it is obvious that those people must have a substantial knowledge of data science. This series aims to help people that are around medical field to enhance their data science skills. </p>
<p>We will work with a health related database the famous “Pima Indians Diabetes Database”. It was generously donated by Vincent Sigillito from Johns Hopkins University. Please find further information regarding the dataset <a target="_blank" rel="nofollow" href="https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.names">here</a>.</p>
<p>This is the first part of the series, it is going to be about data display. </p>
<p>Before proceeding, it might be helpful to look over the help pages for the <code> table</code>, <code>pie</code>, <code> geom_bar</code> , <code> coord_polar</code>, <code> barplot</code>, <code> stripchart</code>, <code> geom_jitter</code>, <code>density</code>, <code> geom_density</code>, <code> hist</code>, <code> geom_histogram</code>,<code> boxplot</code>, <code> geom_boxplot</code>, <code>qqnorm</code>, <code> qqline</code>, <code> geom_point</code>, <code> plot</code>, <code> qqline</code>, <code> geom_point</code> .</p>
<p><code>install.packages('ggplot2')</code><br>
<code>library(ggplot)</code></p>
<p>I have also changed the values of the <code>DaysOfWeek</code> variable, if you wish to do that as well the code for that is : </p>
<p><code>url &lt;- &quot;https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data&quot;</code><br>
<code>data &lt;- read.table(url, fileEncoding=&quot;UTF-8&quot;, sep=&quot;,&quot;)</code><br>
<code>names &lt;- c('preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class')</code><br>
<code>colnames(data) &lt;- names</code></p>
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<h3>Related exercise sets:</h3>
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<li><a title="Reading delimited data" target="_blank" rel="nofollow" href="http://r-exercises.com/2016/01/07/reading-delimited-data/">Reading delimited data </a></li>
<li><a title="Descriptive Analytics-Part 5: Data Visualisation  (Continuous variables)" target="_blank" rel="nofollow" href="http://r-exercises.com/2016/12/04/descriptive-analytics-part-5-data-visualisation-continuous-variables/">Descriptive Analytics-Part 5: Data Visualisation (Continuous variables) </a></li>
<li><a title="Merging Dataframes Exercises" target="_blank" rel="nofollow" href="http://r-exercises.com/2016/04/14/merging-dataframes-exercises/">Merging Dataframes Exercises </a></li>
<li><a target="_blank" rel="nofollow" href="http://r-exercises.com/start-here-to-learn-r/">Explore all our (>1000) R exercises</a></li>
<li><a target="_blank" rel="nofollow" href="http://r-exercises.com/r-courses/">Find an R course using our R Course Finder</a> directory</li>
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