Coding R

Purpose

This skill allows the AI to execute R programming tasks for data manipulation, visualization, and analysis using packages like tidyverse and ggplot2, focusing on data frames, statistical modeling, RMarkdown reports, Shiny apps, and package development.

When to Use

• When handling tabular data with data frames, such as cleaning and transforming datasets.
• For creating visualizations with ggplot2, like scatter plots or histograms.
• In statistical modeling scenarios, e.g., linear regression on datasets.
• Building interactive apps with Shiny or generating reports via RMarkdown.
• Developing or extending R packages for custom data science workflows.

Key Capabilities

• Manipulate data frames using tidyverse functions (e.g., dplyr for filtering and mutating).
• Generate plots with ggplot2, including layers, themes, and faceting.
• Perform statistical modeling with base R or packages like lm() for regression.
• Create RMarkdown documents for reproducible reports, including code chunks and outputs.
• Develop Shiny apps for interactive dashboards and package development using devtools.
• Integrate with data science pipelines, such as reading from CSV or connecting to databases.

Usage Patterns

Always prefix R code with the skill ID "coding-r" in agent commands, e.g., "Use coding-r to load and plot data". Invoke via code blocks in responses, ensuring scripts are self-contained. For multi-step tasks, break into functions: first load libraries, then process data, and finally output results. Use R scripts (.R files) for complex workflows, calling them with source("script.R"). If environment variables are needed (e.g., for API keys in packages), set them like Sys.setenv(API_KEY = "$MY_API_KEY") before running code.

Common Commands/API

• Load tidyverse: library(tidyverse) followed by df <- read_csv("data.csv") %>% filter(column > 10).
• Create a ggplot: library(ggplot2); ggplot(df, aes(x=var1, y=var2)) + geom_point() + theme_minimal().
• Statistical modeling: model <- lm(y ~ x, data=df); summary(model).
• RMarkdown basics: Start with --- title: "Report" output: html_document --- in a .Rmd file, then add code chunks like ```{r} print(summary(df)) ```.
• Shiny app skeleton: library(shiny); ui <- fluidPage(); server <- function(input, output) {}; shinyApp(ui, server).
• Package development: Use devtools::create("mypackage") to initialize, then add functions in R/ folder.

Integration Notes

Integrate R code into larger workflows by embedding in Python via rpy2 (e.g., import rpy2.robjects as robjects; robjects.r('library(tidyverse)')), or use reticulate for Python-R bridging. For web services, deploy Shiny apps on Shiny Server or shinyapps.io, configuring with environment variables like $SHINY_API_KEY for authentication. Use config files (e.g., YAML) for parameters: create a config.yml with api_key: $MY_API_KEY, then read in R with yaml::yaml.load_file("config.yml"). Ensure R version compatibility (e.g., >=4.0) and install dependencies via install.packages(c("tidyverse", "ggplot2")) before execution.

Error Handling

Use tryCatch() for robust code: tryCatch({ result <- lm(y ~ x, data=df) }, error = function(e) print(paste("Error:", e))). Check for missing packages with if (!require(tidyverse)) install.packages("tidyverse"). Handle data issues like NA values with df <- df %>% drop_na() before operations. For Shiny, debug with shiny::runApp(launch.browser=TRUE) and log errors via options(shiny.error = recover). Always validate inputs, e.g., if (is.null(df)) stop("Data frame is missing"). If API calls fail (e.g., in httr package), retry with httr::RETRY("GET", url, times=3).

Concrete Usage Examples

1. Data Analysis and Plotting: To analyze a CSV file and create a scatter plot, use: library(tidyverse); library(ggplot2); df <- read_csv("data.csv"); ggplot(df, aes(x=age, y=income)) + geom_point() + labs(title="Age vs Income"). This loads data, filters if needed, and outputs the plot.

2. Statistical Modeling in RMarkdown: For a regression report, create an RMarkdown file: --- output: html_document --- # Analysis ``{r} library(tidyverse); model <- lm(sales ~ advertising, data=df); summary(model) ``. Render with rmarkdown::render("report.Rmd")` to generate an HTML output with results.

Graph Relationships

• Related to: ID: coding-python (shares data science cluster for integrated workflows)
• Related to: ID: coding-julia (overlaps in statistical modeling and data analysis)
• Connected via tags: "statistics" with other skills like data-analysis, and "coding" cluster for general programming tools