Data can be extremely numeric and text-heavy. An easy way to understand data is through charts and graphs. How can data scientists work with huge data and get the insights in an easy-to-understand visual format? That is exactly where programming language R comes into the picture. It is used to understand huge amounts of data through charts and graphs, something we know as data visualization.
Using R as a platform to sift through piles of data and come up with meaningful infographics that make decision-making easy is a different experience from using graphics software to come up with a data visualization output such as ordinary pie charts, for example.
Data visualization can be understood as an advanced technique used to generate and deliver many insights from a huge quantity of data. It offers visual outputs such as charts, graphs, and maps. Data visualization can dig up meaningful data and analytics from an impractically huge quantity of data.
However, the visualized depiction of data can be very useful for developing intuitive applications as it aids and spurs intelligent decision-making.
Several tools and platforms are used for data visualization, and R is one of them. Other important contenders for the top slot in this space include Tableau, Kibana, Infogram, Plotly, and Google Charts. However, data visualization in R is one of the most popular options. It can be used in many different use cases as it embodies a variety of capabilities and functionalities.
To perform data visualization is R, you need to have a distinct skill set. Anyone who wishes to make a career in data science or business analytics needs to learn R for data science.
Every data visualization tool or platform has its own unique strengths. Data visualization in R is particularly helpful in dealing with numeric, statistical, and scientific data. One of the most significant advantages of Data visualization with R is that it does not require serious coding efforts. It is relatively easy to learn and flexible in application.
You can have many options for data visualizations in R. Here are a few significant data visualization methods that become better, easier, and more insightful when using the R software.
This is the most familiar kind of chart. We have seen them since our school days. Bar plots or bar charts use rectangular bars to represent different data categories. The length or the height of the bar represents the value in a proportionate manner. You can use R to make multiple bar plots in seconds out of your raw data. Bar plots describe the comparison between categories through horizontally or vertically plotted bars.
Typically, one axis represents values and the other categories. Bar plots are used mainly for continuous and categorical plotting of variables. By properly setting horizontal parameters to the value, you can generate vertical and horizontal bar plots for specific purposes. However, if you do not know how to use R, you can join an R for data science course and learn the many ways you can make bar plots.
When you perform data visualization using R, you can generate tons of analytical bar plots using different data categories. By using R data science, you can analyze variable changes over time. Data insights on a daily, weekly, and monthly basis can be prepared.
A histogram is similar to a bar chart, the only difference being that a histogram groups the values in continuous ranges. Every bar expresses the height of the number of values. The use of R allows you to generate data visualization output that gives you values in its range besides categorical values as in box plots.
A histogram is like a bar plot but with no gaps between the bars. Instead of one categorical value, the values represented by each bar in a histogram display a range of the value. It contains a rectangular area that provides statistical information about a variable and its frequency.
A histogram is a graphical representation of data points displayed in specified ranges. You can perform data visualization with R to create a histogram. If you wish to learn how to make histograms, you may join an online or hybrid course in R programming for data science.
For R data visualization examples, here are a few use cases:
Box plot is also known as box and whisker plot. It is an important descriptive statistics tool that graphically demonstrates the spread and skewness of numerical data groups. You can use R programming for data science to create box graphs or plots. Typically, it represents data in 5 sets -- minimum, first quartile, median, third quartile, and maximum. The box has lines, called whiskers, extending out of it and representing variability. There can be a single box plot as well multiple box plots.
For R data visualization examples, we can consider the hourly temperature for 24 hours and use data visualization with R to create a box plot. It will show the minimum, maximum, median, and the first and third quartiles. But with R data visualization, particularly the range factoring of a histogram, you can also have the range of minimum, maximum, and other sets of data points.
You must have seen a chart or graph that uses dots to represent different numeric variables. Each of the dots represents its values concerning two variables represented by horizontal and vertical axes, such as the weight and height of a person.
You can perform data visualization using R to generate scatter plots for different data sets. Using the histogram feature of R, you can also get the range depiction. For R data visualization examples, we can consider a heat map.
Data Visualization is a technique to display data groups in charts and graphics to better understand the underlying trends and insights. Data visualizations in R are one of the most popular ways to understand statistical and scientific data.
In this post, we discussed some of the common types of data visualization with R. For a better understanding and a career in data visualization, you can join an R data science program.
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