Savvy marketers are making use of R programming.
The value of R to business intelligence stems from evaluating advanced strategies based on data, discovering new insights and relationships that can be leveraged.
Its influence has reflected in the salaries commanded for understanding it. Human resources site Dice reported R as the highest paid programming skill after SQL.
Options for the Rest of Us
However, everyone is not a programmer or data scientist. Yet the capabilities from R are in demand.
To that end online support and solutions have sprung up to make R programming more accessible and less intimidating from its technical appearance.
For those who are learning R programming for the first time, here are a few resources that makes managing R-based results easier with a capital “E”.
Graphic User Interfaces
One tool that should be considered for marketers seeking a light introduction to R programming is a Graphic User Interface (GUI).
The GUIs are designed to provide a user-friendly environment that eases how code is displayed during development.
Otherwise, users would have to use the R console, a terminal window in which code is created via line commands. While it is possible to get used to it, the line command format in R console can initially seem unintuitive for those struggling to understand programming conventions.
GUIs render visible relationships between user choices and the R commands generated.
Most GUIs are installed as a browser-styled software for laptops and tablets that run either Mac OS X or Windows operation systems.
3 GUIs to Consider
Here are three GUIs, any of which should prove for working with R programming.
Deducer: Touted as an SPSS alternative, this open source interface is designed for professionals who do not have a full background in data science. The GUI is free to download.
R-Studio: Another free open source GUI available, R-Studio is widely used among starting practitioners. It’s popular for providing a thorough tips and hints website. Moreover, the R-Studio company, based in Boston with a distributed team across the US, is influential in the R programming space, having joined Microsoft, Alteryx, Oracle, and other to support the R Consortium, a newly formed support community.
In addition to a Windows version and a Mac OS X version, there’s also a Linux version of R-Studio to provides a web browser based interface to the version of R running on the server.
R-Commander: Yet another GUI, R-Commander is geared for basic statistics to extend usability and ease support. The functionality has grown to include support for linear and generalized-linear models. The R-Commander displays simple menus and dialog boxes to increase user familiarity.
A version of the tool is available as a R programming package called Rcmdr. Packages are plugin files, installed prior to running command lines in the R console.
For starters, there is a key support site called R Journal, which provides hints for programming in R and new package releases. R Journal is an offshoot of the R Foundation, a not for profit group behind the version development of R.
Marketers can also benefit from online communities that provide specific details on events or webinars. These sites are growing. Many are still in the amateur blogging stages, with fits of random posts over time. But few have become robust sources for R programming news.
One of the most reliable and popular is R-bloggers. It covers a number of R programming concerns and related topics. The best aspect about the site is the size of the community that supports it – Over 450 professionals contribute varying aspects of the R programming language. There’s even a job posting section for positions that are seeking experience with the programming language.
Revolutions is a tech blog dedicated to R. It provides a number of roundup posts that can direct readers to the right community.
Data Science Central covers topics that are less R-programming specific. It is focused on big data insights as well as other current database and tools such as Hadoop and data visualization. The site content contains a comprehensive summary of initial data analysis techniques including those that rely on R programming.
There is also a site called Statisticsblog, which provides case studies in a similar manner to Statsblog. Both sites present imaginative ideas as to how R programming can best be leveraged.
Light Courses for Education
Professionals with a budding curiosity can cover the basics in courses. In fact there are quite a few tailored to specific uses and niches.
One example is a free course from Harvard University for professionals who have life science careers. Students learn how to conduct exploratory analysis that relates easily to their research or professional work. In another example, Johns Hopkins teamed with Coursera to create a 4 week course on R programming through its Bloomberg School of Public Health.
Learning sites like Udacityand Datacamp offer more general courses that can help explain how R relates to other languages and data science applications. Many cover associated general topics alongside the code details, such as an overview of what a data scientist is.
Regardless of the niche or source, marketers can gain some sensibility of basic R programming concepts.
Use Hashtags To Find Other Resources
Finally, finding seminars specific to a field topic can help professionals relate R functions to real world examples that generate data and help users imagine how data correlation can potential be studied. For marketers using social media, hashtags can highlight gatherings and news on new conferences. #R and #Rstat are great Twitter hashtags to monitor alongside other straightforward tech tags such as #datascience and #IoT.
The hashtags can also reveal other regular users of a tag. I discovered other Twitter followers, along with a few that share videos regularly in YouTube. This broadened my online learning resources and introduced new people who have a strong interest in R programming.
All of these resources, in varying ways, can ease a team’s interest in better knowing R programming. With each resource, users can decide what is best for building their skills — a review of basics or a stronger effort on advanced techniques.
These resources can help make better business decisions and a thorough understanding of associated graphs, interactive maps, and other calculations that R programming can provide.