

The most basic graphics function in R is the plot function. This function has multiple arguments to configure the final plot: add a title, change axes labels, customize colors, or change line types, among others.In this tutorial you will learn how to plot in R and how to fully customize the resulting plot.. Plot function in R The R plot function allows you to …



The {reactablefmtr} package simplifies and enhances the styling and formatting of tables built with the {reactable} R package. The {reactablefmtr} package provides many conditional formatters that are highly customizable and easy to use. Among other things, the reactablefmtr package makes it easier to conditionally add colors to …





In R, you can use the aggregate function to compute summary statistics for subsets of the data.This function is very similar to the tapply function, but you can also input a formula or a time series object and in addition, the output is of class data.frame.In this tutorial you will learn how to use the R aggregate function with several examples, to aggregate rows by a …



Start Exploratory Data Analysis (EDA) in R with a Single Line of Code! GWalkR is an interactive Exploratory Data Analysis (EDA) Tool in R. It integrates the htmlwidgets with Graphic Walker.It can simplify your R data analysis and data visualization workflow, by turning your data frame into a Tableau-style User Interface for visual exploration.





To save a plot as a jpeg image we would perform the following steps. Please note that we need to call the function dev.off() after all the plotting, to save the file and return control to the screen. This will save a jpeg image in the current directory. The resolution of the image by default will be 480x480 pixel.









Example: Interpreting Regression Output in R. The following code shows how to fit a multiple linear regression model with the built-in mtcars dataset using hp, drat, and wt as predictor variables and mpg as the response variable: #fit regression model using hp, drat, and wt as predictors. model <- lm(mpg ~ hp + drat + wt, data = mtcars)





The R-project for statistical computing. R-4.1.2 for Windows (32/64 bit) Download R 4.1.2 for Windows (86 megabytes, 32/64 bit) Installation and other instructions. New features in this version. If you want to double-check that the package you have downloaded matches the package distributed by CRAN, you can compare the md5sum …













This is a generic function, with methods supplied for matrices, data frames and vectors (including lists). Packages and users can add further methods. For ordinary vectors, the result is simply x[subset & !is.na(subset)]. For data frames, the subset argument works on the rows. Note that subset will be evaluated in the data frame, so columns can ...



Generate R bootstrap replicates of a statistic applied to data. Both parametric and nonparametric resampling are possible. For the nonparametric bootstrap, possible resampling methods are the ordinary bootstrap, the balanced bootstrap, antithetic resampling, and permutation. For nonparametric multi-sample problems stratified …





r-square of the model, which corresponds to the proportion of variance explained by the model, and it measures the strength of the relationship between the model and the dependent variable Y on a convenient 0 to scale. the p-value and t-statistic for each regression coefficient in the model. These two metrics are considered to be the ...






