Data visualization contributions can’t be discounted. Visualization makes it easier to understand your data, but most importantly, it serves as an excellent exploratory technique as you endeavor to facilitate accurate data analysis. With a glance, data visualization makes it easier to spot some faults, understand the findings, and communicate your results. Simply put, data visualization is the visual representation using tools such as graphs and charts.
The visual representation gives you a chance to view and analyze data from a different angle. For example, with graphical representation, you can easily spot various concerns than looking at the tabulated or unorganized data. While its importance can’t be overlooked, data visualization isn’t always straightforward. However, the good news is that there are specific tools that you can employ to ease the process. Among such devices that have and continue to win over users globally is R.
R is a free programming language utilized in statistical computations, data analysis, and visualization. While being free is among the main reasons it attracts many users, its contributions have made it a go-to for many users, attracting scholars and researchers as they strive to supercharge their data analysis quests. R provides a range of tools that makes the process a lot more manageable. Its several packages can be utilized in various instances. You can use the ggplot2 package if your primary focus is visualizing data and ggedit for plotting. The ggedit package enhances the aesthetic appeal of your visual representation. R offers numerous advantages, but there is a catch; you need to learn to program. If you are considering adopting R, here is why it wins compared to other solutions.
Versatility
R provides a chance to advance your visualization by using additional elements like animating the visual and including maps. The best part is that the basic functionalities aren’t complicated. You can create incredible line plots, histograms, or scatterplots with the basic functionalities using a tiny bit of code. With such simplicity, you can visualize your data before proceeding to the analysis. It only takes a few seconds, allowing you to quickly get insights that could take time to deduce from tabulated data. With countless ways to visualize data, leveraging various packages offers the versatility needed to create appealing and professional representations, a contribution that gives R an edge.
R’s versatility extends to other functionalities, not just the visualization. For instance, if your research and analysis require unique functions, you won’t have to look for additional solutions. R doesn’t lock you to defined options. You can write your unique functions and comfortably handle your data analysis process, a contribution that saves time, resources, and facilitates accuracy.
Easier data wrangling
The process of cleaning complex and messy data isn’t as easy, an area that R addresses. Data wrangling can make or break your quests, noting that its effectiveness holds a significant weight on how it is consumed and analyzed. As you strive to facilitate accuracy, the process can prove to be a demanding and time-consuming endeavor. It is quite convenient with R, as you enjoy a range of packages designed for manipulation and wrangling, making the process a breeze. Among the solutions includes data.table package, designed to speed up manipulating data sets by simplifying data aggregation and reducing compute time. Others have dplyr and readr package, solutions that can speed up your data analysis and visualization process.
Statistics isn’t everyone’s cup of tea. Luckily, with solutions such as R, and professional services like Homework doer, you can conveniently navigate the field. The professionals can take you through various considerations, including how to get the most from R as you endeavor to analyze your data visualization and create incredible visualizations.
About The Author
Dan Taylor
Dan is responsible for content marketing and branding at GuestPostBlogging. He writes about topics related to startup naming, branding and entrepreneurship.
