R() in Haro: Statistical Power, One Call Away

Softanza makes it easy to integrate R, a powerful language for statistics and visualization, into Haro. The R() function allows you to run R scripts directly within Haro, bringing R’s analytical capabilities into your workflow without switching environments.

NOTE: For the feature to work, R must be installed on your system and accessible from the system's PATH. .

Running Basic Numeric Operations

The simplest use of R() is running a short R script and retrieving the results. Here’s an example that calculates the mean of a set of numbers:

pr()

R() {
@('    
    res <- list(
        numbers = c(1, 2, 3, 4, 5),
        mean = mean(c(1, 2, 3, 4, 5))
    )
')

Run()
? @@(Result())
}

Output:

[
    [ "numbers", [1, 2, 3, 4, 5] ],
    [ "mean", 3 ]
]

Note: As you see in the R code, the result variable name must be isres. This is configurable within the class and you can change it by modifying the @cResultVar attribute to any name you prefer.

Handling Nested Lists with Different Data Types

R supports complex data structures like nested lists. Softanza’s R() function lets you work with these easily:

pr()

R = new stzRCode
R.SetCode('
    numbers <- c(12, 15, 18, 22, 25)
    res <- list(
        basic_stats = list(
            numbers = numbers,
            mean = mean(numbers),
            median = median(numbers),
            has_outliers = FALSE
        ),
        metadata = list(
            description = "Sample dataset",
            date_created = format(Sys.Date(), "%Y-%m-%d")
        )
    )
')

R.Execute()
? @@( R.Result() )

Output:

[
    ["basic_stats",
        [ [ "numbers", [12, 15, 18, 22, 25] ],
          [ "mean", 18.40 ],
          [ "median", 18 ],
          [ "has_outliers", 0 ] ]
    ],
    ["metadata",
        [ [ "description", "Sample dataset" ],
          [ "date_created", "2025-02-24" ] ]
    ]
]

Handling Missing Values (NA)

R offers built-in tools for handling missing data. Here’s how Softanza lets you integrate that functionality:

pr()

R() {
@('    
    measurements <- c(23.5, NA, 22.1, 24.3, NA, 21.8)
    res <- list(
        measurements = measurements,
        analysis = list(
            complete_cases = sum(!is.na(measurements)),
            mean_without_na = mean(measurements, na.rm = TRUE),
            na_positions = which(is.na(measurements))
        )
')

Run()
? @@( Output() )
}

Output:

[
    [ "measurements", [ 23.50, "", 22.10, 24.30, "", 21.80 ] ],
    [ "analysis",
        [ [ "complete_cases", 4 ],
          [ "mean_without_na", 22.93 ],
          [ "na_positions", [2, 5] ] ]
    ]
]

Statistical Calculations

You can also use R() for more complex statistical analysis, such as computing summary statistics and quartiles:

pr()

R = R()

R.SetCode('
    temperatures <- c(18.2, 19.5, 22.1, 23.4, 25.8, 26.9, 27.5, 28.1, 26.8, 25.2)
    res <- list(
        raw_data = temperatures,
        statistics = list(
            mean = mean(temperatures),
            sd = sd(temperatures),
            quartiles = quantile(temperatures, probs = c(0.25, 0.5, 0.75)),
            range = range(temperatures)
        ),
        analysis = list(
            above_25 = sum(temperatures > 25),
            percent_above_25 = mean(temperatures > 25) * 100
        )
    )
')

R.Execute()
? @@( R.Result() )

Output:

[
    [ "raw_data", [ 18.20, 19.50, 22.10, 23.40, 25.80, 26.90, 27.50, 28.10, 26.80, 25.20 ] ],
    [ "statistics",
        [ [ "mean", 24.35 ],
          [ "sd", 3.44 ],
          [ "quartiles", [ 22.43, 25.50, 26.88 ] ],
          [ "range", [ 18.20, 28.10 ] ] ]
    ],
    [ "analysis", [ [ "above_25", 6 ], [ "percent_above_25", 60 ] ] ]
]

Nested Calculations with Custom Functions

Softanza’s R() function lets you define and execute custom R functions seamlessly within Haro. Here’s an example where we analyze two numerical groups, compute key metrics, and compare them:

pr()

R = new stzRCode

R.SetCode('
group_a <- c(15, 18, 21, 24, 27)
group_b <- c(22, 25, 28, 31, 34)
calculate_metrics <- function(values) {
    list(
        mean = mean(values),
        variance = var(values),
        coefficient_variation = sd(values) / mean(values) * 100
    )
}

res <- list(
    groups = list(
        group_a = group_a,
        group_b = group_b
    ),
    metrics = list(
        group_a_metrics = calculate_metrics(group_a),
        group_b_metrics = calculate_metrics(group_b)
    ),
    comparison = list(
        mean_difference = mean(group_b) - mean(group_a),
        ratio = mean(group_b) / mean(group_a)
    )
)
')

R.Execute()
? @@( R.Result() )

proff()
# Executed in 0.32 second(s) in Haro 1.22

The result gives a structured view of the data, metrics, and comparisons, all computed in a single R() call:

[
	[
		"groups",
		[
			[ "group_a", [ 15, 18, 21, 24, 27 ] ],
			[ "group_b", [ 22, 25, 28, 31, 34 ] ]
		]
	],
	[
		"metrics",
		[
			[
				"group_a_metrics",
				[ [ "mean", 21 ], [ "variance", 22.50 ], [ "coefficient_variation", 22.59 ] ]
			],
			[
				"group_b_metrics",
				[ [ "mean", 28 ], [ "variance", 22.50 ], [ "coefficient_variation", 16.94 ] ]
			]
		]
	],
	[
		"comparison",
		[ [ "mean_difference", 7 ], [ "ratio", 1.33 ] ]
	]
]

Time Series Analysis and Aggregation

With R(), working with time series data is effortless. The following example generates synthetic daily values over a month, computes weekly means and standard deviations, and extracts trend indicators:

pr()

R() {

SetCode('
dates <- as.Date("2024-01-01") + 0:29  
values <- rnorm(30, mean = 100, sd = 15)
res <- list(
    time_series = list(
        dates = format(dates, "%Y-%m-%d"),
        values = values
    ),
    weekly_stats = list(
        week_means = tapply(values, ceiling(seq_along(values)/7), mean),
        week_sds = tapply(values, ceiling(seq_along(values)/7), sd)
    ),
    trends = list(
        overall_trend = coef(lm(values ~ seq_along(values))),
        volatility = sd(diff(values))
    )
)
')

Execute()
? @@( Result() )

} 

proff()
# Executed in 0.32 second(s) in Haro 1.22

This example showcases how R() enables time series manipulation and statistical aggregation in one integrated step:

[
	[
		"time_series",
		[
			[ "dates", [ "2024-01-01", "2024-01-02", ...,  "2024-01-30" ] ],
			[ "values", [ 116.31, 98.84, ..., 87.77, 92.12 ] ]
		]
	],
	[
		"weekly_stats",
		[
			[ "week_means", [ 99.69, 108.34, 92.70, 91.40, 89.95 ] ],
			[ "week_sds", [ 10.97, 16.15, 6.34, 15.47, 3.08 ] ]
		]
	],
	[
		"trends",
		[
			[ "overall_trend", [ 106.51, -0.58 ] ],
			[ "volatility", 18.97 ]
		]
	]
]

Advanced Data Visualization – Complex Scatter Plot with Density

When working with R’s powerful visualization tools, R() gives you full access to ggplot2, plotly, and more. The following example generates a complex scatter plot with density contours and trend lines, saving it as an image:

NOTE: The libraries mentioned above should have been installed on your R instance first.

pr()

R = new stzRCode

R.SetCode('
library("ggplot2")
library("plotly")
library("viridis")

set.seed(123)
n_points <- 200
x <- rnorm(n_points, mean = 0, sd = 1.5)
y <- x^2 + rnorm(n_points, mean = 0, sd = 2)
categories <- factor(sample(c("A", "B", "C"), n_points, replace = TRUE))
sizes <- runif(n_points, 1, 5)

df <- data.frame(
    x = x,
    y = y,
    category = categories,
    size = sizes
)

p <- ggplot(df, aes(x = x, y = y, color = category)) +
    geom_point(aes(size = size), alpha = 0.6) +
    geom_smooth(method = "loess", se = TRUE) +
    stat_density_2d(aes(fill = after_stat(level)), geom = "polygon", alpha = 0.1) +
    labs(
        title = "Complex 2D Visualization",
        subtitle = "Scatter plot with density contours and trend lines",
        x = "X Variable",
        y = "Y Variable",
        color = "Category",
        size = "Size"
    ) +
    theme_minimal()

ggsave("output.png", p, width = 10, height = 8, dpi = 300)

res <- list(
    plot_info = list(
        filename = "output.png",
        dimensions = list(
            width = 10,
            height = 8,
            dpi = 300
        ),
        data_points = n_points
    ),
    statistics = list(
        x = list(
            mean = mean(x),
            sd = sd(x),
            range = range(x)
        ),
        y = list(
            mean = mean(y),
            sd = sd(y),
            range = range(y)
        ),
        correlation = cor(x, y)
    ),
    categories = list(
        levels = levels(categories),
        counts = as.list(table(categories))
    )
)
')

R.Execute()
View("output.png") # Opens the datviz image in the default system viewer

proff()
# Executed in 2.92 second(s) in Haro 1.22

A sleek and dynamic visualization is automatically crafted and seamlessly saved as an image:

Everything R Can Do, R() Can Too

These examples demonstrate a simple truth: everything possible in R is possible in R(). Whether you're working with data frames, time series, custom functions, or advanced graphics, R() integrates seamlessly into Haro, unlocking the full potential of statistical computing with minimal effort.

Cite

Mansour Ayouni. "R() in Ring: Statistical Power, One Call Away". Softanza narrations, 2025. https://mayouni.github.io/stzsite/en/narrations/stz-integrating-r-scripts-inside-ring.html

Not run: its code touches files, the network, the clock or chance. The article is published as written; its blocks are illustrations. The code of the articles is Haro code. Where an article was written before the language took its present name, the name was changed and nothing else. Genre, area and series read from the file's name, the year from its first commit: derived, until the article carries its own header. The file on GitHub(opens in a new tab)