Data Visualization with R (2024)

FAQs

Is R good for data Visualisation? ›

Regarding data visualization, I find R to be a robust choice, especially for advanced statistical and predictive analysis, using libraries like ggplot2, plotly. However, for basic data visualization, I would use easy tools: PowerBI / Tableau.

If your survey questions offer two binary options (for example, “yes” and “no”), a pie chart is the simplest go-to option. For a fun alternative that's less information-dense, you can split up the bars to make a sort of modified 100% stacked bar chart.

When creating a visualization with ggplot, we first use the function ggplot and define the data that the visualization will use, then, we define the aesthetics which define the layout, i.e. the x- and y-axes. In a next step, we add the geom-layer which defines the type of visualization that we want to display.

Python and R are both free, open-source languages that can run on Windows, macOS, and Linux. Both can handle just about any data analysis task, and both are considered relatively easy languages to learn, especially for beginners.

One of the main disadvantages of R is its steep learning curve. R has a unique and sometimes inconsistent syntax and logic that can be confusing and frustrating for beginners and even experienced users. R also requires a lot of coding and manual work that other software can do more easily and intuitively.

Bar Chart. Bar charts are great because they are so easy to read. The different length of bars will immediately indicate which choices have been chosen more and which less. This chart type is available to multiple choice questions as well as table questions.

Bar Chart. Bar charts are frequently used and we're taught how to read them starting at a young age. The most simple bar charts, those that illustrate one string and one numeric variable are easy for us to visually read because they use alignment and length. Additionally, bar charts are good for showing exact values.

Bar charts are one of the most common data visualizations. You can use them to quickly compare data across categories, highlight differences, show trends and outliers, and reveal historical highs and lows at a glance. Bar charts are especially effective when you have data that can be split into multiple categories.

R is a free, open source statistical programming language. It is useful for data cleaning, analysis, and visualization.

How to visualize large data sets in R? ›

When it comes to visualizing your data, you may want to use the ggplot2 package, which is based on the grammar of graphics and offers a consistent and flexible way to create beautiful and informative plots. Ggplot2 can handle large datasets, but it may become slow or unresponsive if you have too many points or layers.

R is a language that is designed for statistical computing, graphical data analysis, and scientific research. It is usually preferred for data visualization as it offers flexibility and minimum required coding through its packages.

ggplot2: It is the data visualisation library of R programming language, it is a system for creating graphs and charts based on “The Grammar of Graphics”.

R: R is much better than Python in terms of data visualizations. R was designed to display statistical analysis results, with the fundamental graphics module making it simple to build basic charts and plots. ggplot2 may also be used to create more advanced plots, such as complex scatter plots with regression lines.

Advanced visualization capabilities of the programming language make it suitable for analysis projects. Regarding machine learning, Python is the right choice. R has an eco-structure that focuses on data science and a large number of libraries. Where development using Python is easy to release and maintain.

If your goal is to pick up computer programming more broadly, Python is the way to go. If your goal is to focus purely on statistics and data applications, R might have the edge. To decide whether to start learning Python or R first, ask yourself a few questions: What are your career goals?

It is evident that the source code of R can be used repeatedly and with different data sets in ways that Excel formulas cannot. R clearly shows the code (instructions), data and columns used for an analysis in ways that Excel does not.

R vs Python: key differences

Visualizing data: R is better for creating a program for data visualization while Python is developed for creating interfaces, but not based on converting data into charts or other graphical elements.