Data Visualization Website with Shiny

My second Shiny app is dedicated to data visualization.
Here users can simply upload any csv or txt file and create several plots:

Histograms (with option for faceting)

Barchart (with error bars, and option for color with dodging and faceting)

BoxPlots (with option for faceting)

Scatterplots (with options for color, size and faceting)

TimeSeries

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).

For scatterplots, if the option for faceting is provided each plot will include a linear regression lines.

Some examples are below:

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.

The app can be accessed here: https://fveronesi.shinyapps.io/DataViz/

The R code is available here: https://github.com/fveronesi/Shiny_DataViz

Learning R for Data Visualization [Video]

Last year Packt asked me to develop a video course to teach various techniques of data visualization in R. Since I love the idea of video courses and tutorials, and I also enjoy plotting data, I readily agreed.
The result is this course, published last March, which I will briefly present below.

The course is available here:
https://www.packtpub.com/big-data-and-business-intelligence/learning-r-data-visualization-video

I wanted to create a course that was easy to follow, and at the same time could provide a good basis even for the most advanced forms of data visualization available today in R.
Packt was interested in presenting ggplot2, which is definitely the most advanced way of creating static plots. Since I regularly use ggplot2 and I find it a tremendous tool, I was glad to be able to present its functionalities more in details. Three chapters are dedicated to this package. Here I present all the most important types of plots: histograms, box-plots, scatterplots, bar-charts and time-series. Moreover, a whole chapter is dedicated to embellish the default plots by adding elements, such as text labels and much more.

However, I am also very interested in interactive plotting, which I believe is now rapidly becoming commonplace for lots of applications. For this reason two chapters are completely dedicated to interactive plots. In the first I present the package rCharts, which is extremely powerful but also a bit tricky to use at times. In many cases there is little documentation to work with, and for developing the course I found myself often wondering through stackoverflow searching for answers. Luckily for all of us, Prof. Ramnath Vaidyanathan, the creator of rCharts, is always available to answer all the users' questions quickly and clearly. In chapter 5 the viewer will be able to start from zero and quickly create nice interactive versions of all the plots I covered with ggplot2. 

The last chapter is dedicated to Shiny and it is aimed at the creation of a full website for importing and plotting data. Here the reader will first learn the basics of Shiny and then will write the code to create the website and add lots of interesting functionalities.

I hope this video course will help R users become familiar with data visualization.
I would also like to take this opportunity to stress that I am open to support viewers throughout the learning process, meaning that if you have any question about the material in the course you should not hesitate one second in contacting me at info@fabioveronesi.net