Zartasha

Standardization, normalization and mean centering of variable are common data processing techniques in Statistics and data analysis. It is important to standardize variables in statistics to compare and analyze different variables on the same scale. If you have two variables, one in inches and the other in centimeters, it’s not possible to compare these variables […]

Descriptive statistics, is like a snapshot of data, that provides the quick look of data before we delve deeper into data handling and manipulation. It helps us understand the features of data like mean, median, mode, and frequency of the data. From these summary statistics, one can visualize data by creating different kinds of interesting

Data visualization is a powerful tool for gaining insights into the patterns and trends hidden within numerical variables. Just like one or more categorical variables can be visualized using ggplot2 package in R, hidden trends of numerical variables can also be traced and discovered using the same package. To get started with the package, as

The data visualization is the important and powerful tool of R software. Using ggplot2 package, one can create multiple kind of bar charts, line and scatter plots etc. to display the world of data in certain figures. Previously, we have discussed basics of ggplot and creating a scatter plot in R using ggplot2, however this

Data visualization is one of the important and interesting aspect of any software. In the landscape of data analysis and interpretation, effective visualization is key to unlocking insights and communicating findings. Like other softwares, R can also visualize data in form of bar charts, Line Plots and other graphs etc. However, data visualization in R