Integration of R in Companies

is a programming language widely used for statistical analysis, data mining, and visualization. Many companies have incorporated R into their products and services to leverage these capabilities. Here are some examples of companies integrating R into their products and how they use it:

Microsoft

Microsoft incorporates R into various products beyond SQL Server. Below are some examples and how R is utilized:

1. Power BI

Purpose: Enable advanced visualizations and analysis using R scripts directly in reports.
Example:

# R Script in Power BI to create a visualization
library(ggplot2)
ggplot(dataset, aes(x = Column1, y = Column2)) + geom_point()

2. Azure Machine Learning

Purpose: Allow data scientists to build and deploy machine learning models using R on the Azure platform.
Example:

# Using Azure Machine Learning with R
library(AzureML)
ws <- workspace(
  id = "workspace_id",
  auth = "authorization_token",
  apiEndpoint = "https://studio.azureml.net"
)
dataset <- download.datasets(ws, "Iris Dataset")
model <- train(dataset, method = "rpart")

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3. Visual Studio

Purpose: Provide an integrated development environment (IDE) for R, improving developer productivity.
Example:

# Development in R with Visual Studio
install.packages("ggplot2")
library(ggplot2)
data(mpg)
ggplot(mpg, aes(x = displ, y = hwy, color = class)) + geom_point()

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4. Microsoft R Open

Purpose: Distribute an optimized version of R with performance and reproducibility improvements.
Example:

# Using Microsoft R Open
library(MRO)
set.seed(123)
data <- rnorm(100)
summary(data)

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5. SQL Server Machine Learning Services

Purpose: Run R scripts directly in the database for advanced analytics.
Example:

EXEC sp_execute_external_script
  @language = N'R',
  @script = N'
    library(dplyr)
    data <- data.frame(x = rnorm(100), y = rnorm(100))
    result <- data %>% summarise(mean_x = mean(x), mean_y = mean(y))
    result',
  @input_data_1 = N'';

The integration of R into these products allows Microsoft to provide powerful tools for data analysis, visualization, and machine learning, meeting the needs of data scientists and analysts across different platforms and environments.

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1. IBM

IBM Watson: The Watson platform uses R for advanced analytics and statistical modeling, providing insights from structured and unstructured data.

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2. Google

Google Cloud Platform: Google supports R in its cloud computing service, enabling users to run R analyses at scale on Google’s infrastructure.

3. Oracle

Oracle R Enterprise: This product integrates R with the Oracle Database, allowing users to perform R analytics directly within the database and leverage Oracle’s processing power.

4. TIBCO Software

TIBCO Enterprise Runtime for R (TERR): A corporate implementation of R designed to be compatible with open-source R but offering additional performance and support.

5. SAS

SAS Viya: A data analytics platform that supports R integration, allowing users to combine SAS functionalities with R scripts.

6. H2O.ai

H2O: A machine learning platform supporting R for building and running machine learning models.

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7. RStudio, PBC

RStudio: Provides an integrated development environment (IDE) for R, widely used by developers and data analysts.

Why Companies Adopt R

Leading technology and data analytics companies have incorporated R into their products to offer advanced data analysis and processing capabilities to their clients. The flexibility and power of R make it a popular choice for various business applications.
Specific reasons for R adoption include:

• Robust data analysis capabilities.
• Flexibility in adapting to various needs.
• Wide adoption in the scientific and business communities.
• Open-source nature, reducing costs.

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Mainframes

R can be installed and used on both IBM z/OS and IBM i (formerly iSeries or AS/400), though it is not provided by default. After installation, R functions like it would in any other environment.

Industries like banking, insurance, telecommunications, and retail heavily rely on mainframes for critical operations due to their reliability, scalability, and processing power. Integrating R into these environments enables:

• Advanced data analytics directly on the mainframe.
• Leveraging the robustness and scalability of mainframes with R’s analytical capabilities.

By combining the strengths of R and mainframes, companies can derive valuable insights from large datasets and enhance decision-making processes.

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