<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-visualization-website-with-shiny/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://r-video-tutorial.blogspot.com/2018/03/data-visualization-website-with-shiny.html"> R tutorial for Spatial Statistics</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>My second Shiny app is dedicated to data visualization.<br>Here users can simply upload any csv or txt file and create several plots:</p>
<li>Histograms (with option for faceting)</li>
<li>Barchart (with error bars, and option for color with dodging and faceting)</li>
<li>BoxPlots (with option for faceting)</li>
<li>Scatterplots (with options for color, size and faceting)</li>
<li>TimeSeries</li>
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<div>Error bars in barcharts are computed with the mean_se function in ggplot2, which computes error bars as mean ± standard error. When the color option is set, barcharts are plotted one next to the other for each color (option dodging).</div>
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<div>For scatterplots, if the option for faceting is provided each plot will include a linear regression lines.</div>
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<div>Some examples are below:</div>
<div><a rel="nofollow" href="https://i1.wp.com/2.bp.blogspot.com/-fKRDuww9dbo/WrdWlR0EuAI/AAAAAAAAbZ4/5mkdoqci254kNMpUe_dwcCN6FIOkIBumgCLcBGAs/s1600/Untitled-1.jpg?ssl=1" target="_blank"><img border="0" src="https://i2.wp.com/2.bp.blogspot.com/-fKRDuww9dbo/WrdWlR0EuAI/AAAAAAAAbZ4/5mkdoqci254kNMpUe_dwcCN6FIOkIBumgCLcBGAs/s320/Untitled-1.jpg?resize=450%2C603&ssl=1" data-original-width="450" width="450" data-original-height="603" data-recalc-dims="1"></a></div>
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<div><a rel="nofollow" href="https://i0.wp.com/4.bp.blogspot.com/-yO7tQFgojE0/WrdWnNz8xEI/AAAAAAAAbZ8/CcL-BU-GJnEi6vf7t60L91VUTnxL2suLgCLcBGAs/s1600/Untitled-2.jpg?ssl=1" target="_blank"><img border="0" src="https://i1.wp.com/4.bp.blogspot.com/-yO7tQFgojE0/WrdWnNz8xEI/AAAAAAAAbZ8/CcL-BU-GJnEi6vf7t60L91VUTnxL2suLgCLcBGAs/s320/Untitled-2.jpg?resize=450%2C606&ssl=1" data-original-width="450" width="450" data-original-height="606" data-recalc-dims="1"></a></div>
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<div>For the time being there is no option for saving plots, apart from saving the image from the screen. However, I would like to implement an option to have plots in tiff at 300dpi, but all the code I tried so far did not work. I will keep trying.</div>
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<div>The app can be accessed here: <a rel="nofollow" href="https://fveronesi.shinyapps.io/DataViz/" target="_blank">https://fveronesi.shinyapps.io/DataViz/</a></div>
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<div>The R code is available here: <a rel="nofollow" href="https://github.com/fveronesi/Shiny_DataViz" target="_blank">https://github.com/fveronesi/Shiny_DataViz</a></div>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://r-video-tutorial.blogspot.com/2018/03/data-visualization-website-with-shiny.html"> R tutorial for Spatial Statistics</a></strong>.</div>
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