R for Windows

R for Windows provides a complete environment for data manipulation, statistical calculation, and visualization generation. Users can perform regression analysis, hypothesis testing, clustering, and time series analysis. The platform includes a package management system that extends capabilities toward bioinformatics, econometrics, and data mining. The interactive console allows command execution line by line, while the script editor facilitates process automation. R for Windows imports data from CSV, Excel, SPSS, SAS, and SQL database formats via ODBC connections. Handling large matrices and vectorized operations optimizes performance on machines with limited resources.

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• Function summary(): descriptive statistics. Calculates and presents a six-value summary for numeric vectors: minimum, first quartile, median, arithmetic mean, third quartile, and maximum. For factors and categorical variables, displays absolute frequencies of each level present in the data. For data.frame objects, applies the summary to each column individually according to its native data type. Results print to console with column-aligned tabular formatting.
• Function lm(): multiple linear regression. Fits ordinary linear regression models using the least squares method. The primary syntax uses formulas of the form y ~ x1 + x2 to specify predictors. Returns an object containing estimated coefficients, fitted values, residuals, and covariance matrix. Applying summary() to this object extracts standard errors, t-statistics, and p-values for each coefficient.
• Function plot(): basic scatter plots. Generates two-dimensional visualizations from numeric vectors. Main parameters include type to define mark style (points, lines, both), col to specify colors, and main for title header. When applied to lm objects, produces four diagnostic plots: residuals vs fitted, normal Q-Q, scale-location, and residuals vs leverage.
• Function t.test(): mean comparison. Performs Student's t-tests for one or two samples, including paired and unequal variance variants. Calculates the confidence interval for the mean difference at the level specified by conf.level. The alternative parameter allows one-sided or two-sided tests. Returns t-statistic, Welch-Satterthwaite degrees of freedom, and exact p-value.
• Function read.csv(): tabular data import. Reads comma-separated value (CSV) files and converts them into a data.frame. Parameters include header indicating whether the first row contains column names, sep defining an alternative delimiter for semicolon-separated files, and na.strings specifying which strings to treat as missing values. Handles encodings via fileEncoding to prevent issues with accented characters.
• Function aggregate(): grouping and summarization. Splits a dataset into subsets based on one or more categorical variables and applies an aggregation function to each group. Basic syntax is aggregate(x, by = list(group), FUN = mean). Supports any function returning a scalar value, such as sum, length, sd, or custom functions. Returns a data.frame with one column per grouping variable and one column per aggregated result.
• Function merge(): combining data frames. Merges two data frames using common key columns, equivalent to JOIN operations in relational databases. Parameters by, by.x, and by.y specify linking columns. Arguments all, all.x, all.y control whether unmatched rows are included (LEFT, RIGHT, or FULL JOIN). Automatically resolves naming conflicts by adding .x and .y suffixes to duplicate columns.
• Function apply(): margin iteration. Applies a function to rows (MARGIN = 1), columns (MARGIN = 2), or both dimensions (MARGIN = c(1,2)) of a matrix or array. The function can be native like sum or mean, or any user-defined operation. Returns a vector, matrix, or list depending on result structure. For data frames, automatically converts to numeric matrix when all columns are compatible.
• Function glm(): generalized linear models. Extends lm() for exponential family distributions such as binomial (logistic regression), Poisson (counts), or Gamma (positive skewed data). The family parameter specifies the distribution and link function. Includes iteratively reweighted least squares (IRLS) estimation. Returns coefficients, residual deviance, log-likelihood, and AIC criteria.
• Function ggplot(): grammar of graphics visualization. Belongs to the ggplot2 package and builds plots layer by layer. The initial ggplot() function defines data and aesthetic mappings using aes(). Geometric layers like geom_point() for points, geom_line() for lines, or geom_bar() for bars are added. Scales, themes, and labels are controlled with the + operator. The system automatically applies legends and coordinated axes.
• Function prcomp(): principal component analysis. Performs dimensionality reduction via singular value decomposition. Centers and optionally scales data before calculation. Returns standard deviations of each component, rotation matrix (loadings), and observation coordinates in the new space. The summary() function shows cumulative explained variance for selecting the optimal number of components.
• Function arima(): time series models. Fits ARIMA (AutoRegressive Integrated Moving Average) models to sequential data. Parameters order = c(p,d,q) specify autoregressive order, differencing degree, and moving average order. Includes seasonal components via seasonal. Estimation uses maximum likelihood with Kalman filtering. Returns coefficients, standard errors, log-likelihood, and AIC/BIC criteria for model selection.

The development history of R for Windows began in 1993 when Robert Gentleman and Ross Ihaka created the R language at the University of Auckland, New Zealand. The GNU Project adopted R in 1995 under the GPL license. The Windows version was developed by Brian Ripley and other contributors starting in 1997, adapting the core codebase written originally in C, Fortran, and R. The Windows graphical environment uses native Win32 API interface libraries. The current core development team includes Peter Dalgaard, Martin Maechler, Duncan Murdoch, and other members of the R Core Team. The first stable version 1.0.0 was released in February 2000. Since then the project has received contributions from statisticians and computer scientists worldwide through the revision system and CRAN (Comprehensive R Archive Network). R for Windows is distributed as an executable installer that includes the interpreter, basic graphical interface, and complete documentation in HTML and PDF formats.

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Price: Free   Size: 12.2 MB   Version: 7.6.2   OS: Windows, Linux, MacOS