The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations.
As an example, R’s ggplot2 package provides the R programmer with dozens of print-quality visualizations – where any visualization can be heavily customized with a minimal amount of code.
In this webinar Dave Langer will provide an introduction to data visualization with the ggplot2 package. The focus of the webinar will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business.
Attendees will learn how to:
• Craft ggplot visualizations, including customization of rendered output.
• Choose optimal visualizations for the type of data and the nature of the analysis at hand.
• Leverage ggplot2’s powerful segmentation capabilities to achieve “visual drill-in of data”.
• Export ggplot2 visualizations from RStudio for use in documents and presentations.
Github:
https://github.com/datasciencedojo/IntroDataVisualizationWithRAndGgplot2
—
Learn more about Data Science Dojo here:
http://bit.ly/2x8ZUuN
See what our past attendees are saying here:
http://bit.ly/2vHHhQI
—
Like Us: https://www.facebook.com/datasciencedojo/
Follow Us: https://twitter.com/DataScienceDojo
Connect with Us: https://www.linkedin.com/company/data-science-dojo
Also find us on:
Google +: https://plus.google.com/+Datasciencedojo
Instagram: https://www.instagram.com/data_science_dojo/
Vimeo: https://vimeo.com/datasciencedojo
AWS.ECommerceService.NoExactMatches: We did not find any matches for your request.
This is a great tur]torial, good job
dumb question, but how do you successfully indent a line of code with the + sign, so that you don’t have something that looks like:
ggplot(data = filename) + geom_point(mapping = aes(x = xvar, y = yvar))
and instead have:
ggplot(data = filename) +
geom_point(mapping = aes(x = xvar, y = yvar))
Nice video to get you hook with ggplot2
Wonderful, this is was so useful and one hour full of knowledge and hand on practice.
Thanks alot guys !
Belo
thanks a lot !!!
Please how do I display equation of the line and r^2 on my plots in R? In excel it is very easy to do this. I am buying into R because of R markdown. Please help out as I need my equation displayed just the way I use to in excel
in 42:30min, you have a color on you bars. but with same code, my bars are having the same color. Why? Please give me a solution. Thanks in advance
Very helpful and appreciated, thanks for uploading
What would be fantastic is if you could please create 10-15min or less summary videos of your lessons just to provide a snap shot of the different codes. That way it would make it extremely easy to revise your information without needing to sit through the repetition of the more indepth explanations we have already heard.
Yeah, proximity, I’m sure that accounts for the variance. Ugh. But thanks for the video. Or webinar. Or hookup or hangout or get together or whatever it’s called. Cheers.
What is the use of factorise here? I thought factorising some variable was going to be used later in exercise.
great introductory guide – thanks for uploading this
worth watching 1 hr..Really helpful. Thanks a lot
I think in last two graphs both density and histogram are wrongly labeled
Thank you very much. You are far too kind
Great intro to ggplot2. Made the basics very clear.
you should do geom_boxplot(notch=T) so folks understand the concept of visually comparing medians; also read_csv preferred over read.csv
I would suggest everyone beginning with ggplot2 to go through this 1hr vedio, it will save you a lot of time understanding the basics.
Infinite SNR – Thanks!
very nice explanation with the dataset. Thank You.
really helpfull
The seventh question, I believe the labs should be as:
labs(x = “Age”, y = “Density”), and
labs(x = “Age”, y = “Survived Count”)
ggplot2 best package in data science for visulaization
excellent video! Thank you very much Dave
Very helpful. I think at the end, the density plot vs histograms issue is, the layered density plots show two different distributions of age and the histograms show one distribution of age and bi-color that distribution by survival. Two different things.
Show really starts after 25min. You should have discussed passengerid and name when you discussed the variables. Is ggplot smart to use factors for visualization? In a few days i will be desperate to remember that i need to factor to get certain visu. It should be the programmer to have contol not the program. No? Is it really sooo complicated to put % in the plot? Not good publicity for such a great package as ggplot.
very useful video… thank you
46:08 May be instead of using copy and paste, we could use, for example:
ggplt = ggplot2(titanic,aes…) and add layers to that
ggplt +
theme_bw().+
labs()….