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Author: Admin | 2025-04-28
Together one after another, instead of a nested function call.> nrow(subset(mtcars, cyl == 4)) # Nested without the pipe character[1] 11> mtcars |> subset(cyl == 4) |> nrow() # Using the pipe character[1] 11Another alternative to nested functions, in contrast to using the pipe character, is using intermediate objects:> mtcars_subset_rows subset(mtcars, cyl == 4)> num_mtcars_subset nrow(mtcars_subset_rows)> print(num_mtcars_subset)[1] 11While the pipe operator can produce code that is easier to read, it has been advised to pipe together at most 10 to 15 lines and chunk code into sub-tasks which are saved into objects with meaningful names.[40] Here is an example with fewer than 10 lines that some readers may still struggle to grasp without intermediate named steps:(\(x, n = 42, key = c(letters, LETTERS, " ", ":", ")")) strsplit(x, "")[[1]] |> (Vectorize(\(chr) which(chr == key) - 1))() |> (`+`)(n) |> (`%%`)(length(key)) |> (\(i) key[i + 1])() |> paste(collapse = ""))("duvFkvFksnvEyLkHAErnqnoyr")Object-oriented programming[edit]The R language has native support for object-oriented programming. There are two native frameworks, the so-called S3 and S4 systems. The former, being more informal, supports single dispatch on the first argument and objects are assigned to a class by just setting a "class" attribute in each object. The latter is a Common Lisp Object System (CLOS)-like system of formal classes (also derived from S) and generic methods that supports multiple dispatch and multiple inheritance[41]In the example, summary is a generic function that dispatches to different methods depending on whether its argument is a numeric vector or a "factor":> data c("a", "b", "c", "a", NA)> summary(data) Length Class Mode 5 character character > summary(as.factor(data)) a b c NA's 2 1 1 1Modeling and plotting[edit]Diagnostic plots from plotting “model” (q.v. “plot.lm()” function). Notice the mathematical notation allowed in labels (lower left plot).The R language has built-in support for data modeling and graphics. The following example shows how R can generate and plot a linear model with residuals.# Create x and y valuesx 1:6y x^2# Linear regression model y = A + B * xmodel lm(y ~ x)# Display an in-depth summary of the modelsummary(model)# Create a 2 by 2 layout for figurespar(mfrow = c(2, 2))# Output diagnostic plots of the modelplot(model)Output:Residuals: 1 2 3 4 5 6 7 8 9 10 3.3333 -0.6667 -2.6667 -2.6667 -0.6667 3.3333Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -9.3333 2.8441 -3.282 0.030453 * x 7.0000 0.7303 9.585 0.000662 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Residual standard error: 3.055 on 4 degrees of freedomMultiple R-squared: 0.9583, Adjusted R-squared: 0.9478F-statistic: 91.88 on 1 and 4 DF, p-value: 0.000662"Mandelbrot.gif" graphic created in R. (Note: Colors differ from actual output.)This Mandelbrot set example highlights the use of complex numbers. It models the first 20 iterations of the equation z = z2 + c, where c represents different complex constants.Install the package that provides the write.gif() function beforehand:install.packages("caTools")R Source code:library(caTools)jet.colors colorRampPalette( c("green", "pink", "#007FFF", "cyan", "#7FFF7F", "white", "#FF7F00", "red", "#7F0000"))dx 1500 # define widthdy 1400 # define heightC complex( real = rep(seq(-2.2, 1.0, length.out
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