R Programming in Data Science: Setup and Start

Mixkit: Known for its user-friendly interface, Mixkit provides a vast collection of free video assets, including motion graphics and video templates. While they aren't directly from Videohive, they offer a comparable quality that can be integrated into your projects seamlessly.
Videezy: Videezy offers a variety of free and paid video assets, including After Effects templates. Their extensive library is perfect for those looking to enhance their videos without breaking the bank.
Motion Elements: While Motion Elements primarily focuses on premium content, they regularly feature free downloads that include video templates similar to those found on Videohive. Their search interface allows you to filter out the free options easily.
Editor’s Depot: This site provides free editing tools and templates, ensuring you have access to some elements similar to Videohive offerings. Though the selection might not be as vast, it’s a valuable resource for cost-effective production.
Motion Array: With a solid reputation, Motion Array occasionally offers free downloads that replicate some Videohive elements. Subscribing to their newsletter might help you get alerts about any new freebies available.

R is powerful, but not intuitive. There is a strong and diverse R ecosystem, and data scientists are expected to mix and match from the different versions and packages. Before you can even begin programming, you have to choose, install, and set up R to work for you.

In this course, Mark Niemann-Ross provides a direct and efficient introduction to the many flavors of the R programming language, including base R, tidyverse R, R Open from Microsoft, and Bioconductor R. He also provides a peek at programming with R interactively and via the command line, and introduces some helpful packages for working with SQL, 3D graphics, data, and clusters in R. At the end of this short course, you will have installed a version of R along with a few core libraries and an optimized IDE.

Topics include:

Installing R on Windows, Macintosh, and Linux
Choosing a development environment
Installing useful libraries
Using R at the command line

Top Rated News