In R, we have a hist() function that does the task for us. When you have to represent a single variable in a way that the probability distribution of that univariate data comes visible, you prefer the histogram as a graphical representation. Now, the graph above looks more compact in comparison with the previous one, as this one has the title, axis labels, colours for bars and borders, etc. The output of the bar chart with additional arguments Let’s see how this changes our graphical layout. Each of these specifies the label for the x-axis, label for the y-axis, main title for the graph, the colour of bars, and the colour of the border respectively. ![]() Here in this code, we have used the xlab, ylab, main, col, and border as additional arguments. Ylab = "Frequencies", main = "Cars with number of Gears", #Creating a barplot visualization with additional argumentsīarplot(table(mtcars$gear), xlab = "Number of Gears", we can customize the boxplot function with these multiple options. ![]() This graph though looks shabby as we don’t have the labels for X-axis, Y-axis, title for the graph, colours, etc. You don’t always need to go through entire data to get meaningful insight. Here, we can easily say that most of the cars we have in our data are with three gear system. See the visualization below:īarplot visualization for the gear variable from the mtcars data We have used the table() function that allows us to achieve the frequency associated with each gear value. Here, the variable used from mtcars represents the number of gears a can could have. This chart creates a bar for distinct grouping values of the variable on the X-axis and then plots their frequencies on the Y-axis. We are using the mtcars data for creating a barplot visualization here. Whenever we have variables that contain categorical values, variables with limited numeric values, we can use the bar charts to present a visual chart based on those. These are one of those few charts, data visualizations that we have studied throughout our high school days. We will discuss these charts (with some advanced features between each of them) one by one in detail through this article. Graphs, which are important parts of data visualization, make this decision-making task easier for them as they can see all the ups and downs, all the patterns and trends, and almost everything of the data in a simple pictorial chart.įour basic plots are used in R Programming: This helps the management to take the decisions precisely without even actually taking efforts to go through the entire table. In this article, we will discuss the basic data visualization techniques used under R Programming with hands-on examples.ĭata visualization is a technique of representing data as a graph, or in a pictorial format. There are dedicated tools for data visualization in recent years that have reduced a lot of hours behind the desktop (tools such as Tableau, PowerBI, etc.), most of the users still prefer programming languages itself due to the cost being involved in those data visualization tools. Though it may take a lot of working hours to develop a visualization behind a computer and with thousands of data rows, it is worth all those efforts. You should present high-level data without too many details to the top-level management, while the various departments should be shown an overall picture with more details of their specific department.Data visualization is a technique being used for almost 250 years (definitely more than that, just an approximation). Here, I talk about four scenarios where you should choose your data viz according to the audience you are presenting for. But with a good story, it is unforgettable” - Daniel Weisberg
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