Time Series Graphs
Time series graphs are used to visualize the changes in a variable over time. They can reveal trends, seasonality, and other patterns in the data.
R
# Create a dataset x <- ts(rnorm(100), start = c(2010, 1), frequency = 12) # Create a time series graph plot(x, type = "l", main = "Time Series Graph of x", xlab = "Year/Month", ylab = "Values", col = "blue") |
Output:
TimeSeries Graph for Graphical Data Analysis
In this example, we use the plot() function to create a time series plot of dataset x. The main, xlab, and ylab arguments are used to add a title and axis labels to the plot.
Graphical Data Analysis in R
Graphical Data Analysis (GDA) is a powerful tool that helps us to visualize and explore complex data sets. R is a popular programming language for GDA as it has a wide range of built-in functions for producing high-quality visualizations. In this article, we will explore some of the most commonly used GDA techniques in the R Programming Language.
For the data visualization, we will be using the mtcars dataset which is a built-in dataset in R that contains measurements on 11 different attributes for 32 different cars.