![]() ![]() The lubridate package enables easy manipulation of date and time data. This R package makes it easier to work with dates and times. This grammar of graphics means that the user has to tell ‘ggplot2’ about the way variables have to be mapped to aesthetics, so this essentially means that specifying what graphical aspects to using, and ggplot2 will work accordingly based on the details. The R description for the function is “a system for declaratively creating graphics which is based on the Grammar of Graphics”. The numerous functionalities provided by the package enables the analyst to derive insights from data in the most interactive fashion. It is one of the very famous packages in R that provides extensive visual capabilities and presents the results even of complex statistical and mathematical techniques. If multiple data needs to be plotted in the same region, but with separate axes, then this is possible using over plot function in the package. lmplot2, residplot functions that enable the user to drive detailed regression diagnosis through diagnostic plots. They also deal with complex elements involved in statistics-based visualization, e.g. ![]() ![]() These functions enable working with settings related to color, text, and other intricate graphical aspects of the visualization. The graphical capabilities of the package are demonstrated by various functions such as band plot, boxplot2, col2hex, ci2d, hist2d, text plot, sink plot, balloon pilot, plotCI, plot means, etc. The functions in the package work on the concept of calculation and plotting. This package provides visualizations functionalities through multifarious programming tools. The function fit.contrast computes and tests arbitrary contrasts for regression objects. The estimable function computes and tests contrasts and other estimable linear functions of model coefficients for lm, glm, etc. The coefFrame function fits a model to each subgroup defined by, then returns a data frame with one row for each fit and one column for each parameter. The matrix returned by ntrasts can be used as the argument to the contrasts argument of model functions. contrasts convert human-readable contrasts into the form that R requires for computation. It contains various functions such as glh.test which is used to test, print, or summarize a general linear hypothesis for a regression model. The gmodels package provides various tools in R for plotting data. It also provides common data processing methods for treat and format data. Further, the package provides functionalities for visualization of these variables using typical graphical techniques. Each variable in the analysis is scanned and analyzed by the package. This is crucial as it enables the user to understand data and extract insights. It provides an automated data exploration process meant for analytic tasks and predictive modeling. This package deals with automated data exploration and treatment. The intensity of color and the size of the circle are proportional to the correlation coefficients. Positive correlations are displayed in blue and negative correlations in red. The corrplot function incorporating various options gives a visually appealing representation of correlation amongst different variables, which, otherwise, in normal circumstances, like numbers, are difficult to interpret. Various visualization methods or parameter methods in corrplot package are “circle”, “square”, “ellipse”, “number”, “shade”, “color”, and “pie”. Numerous options include choosing requisite colors, text labels, color labels, layout, etc. ![]() The package also provides algorithms to perform matrix reordering. The package provides a graphical display of a correlation matrix and a confidence interval. The extensive capabilities of the package can be gauged from the number of functions it provides. Some of the functions in the package include Anova, avPlots, Boxplot, carPalette, density plots, infIndexPlot, linear hypothesis, logit, outlier test, qqPlot, residual plots, scatterplot, scatterplot matrix, etc. Importing this package into the R environment imports other related packages such as MASS, stats, graphics, etc. It is a big package that provides various functionalities for statistical analysis. This package is Companion to Applied Regression. In the following section, we will look at some of the important packages in R: 1. Different packages have different purposes some are related to statistical techniques, some pertain to visualizations, etc. Though there are certain packages that are widely used due to the functionalities they provide, it isn’t the case that other packages are less important. There are many packages in R, and the selection of a package depends on its application. ![]()
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