Expert R Programming Help for Data Analysis

From complex statistical models to beautiful ggplot2 visualizations, get expert assistance with R and RStudio. We deliver clean code and clear interpretations for your academic success.

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Our Comprehensive R & RStudio Services

We support the entire data analysis workflow in R, whether you’re working within the Tidyverse ecosystem or using base R for a complex programming task.

  • Tidyverse & Data Wrangling
    Expert help with dplyr, tidyr, and other Tidyverse packages for efficient and readable data manipulation, cleaning, and preparation.

  • Statistical Modeling
    We help you implement, diagnose, and interpret a wide range of statistical models, from lm() and glm() to advanced machine learning with caret or tidymodels.

  • Data Visualization with ggplot2
    Create publication-quality plots. We assist with everything from basic scatter plots to complex, multi-layered graphics with custom themes and annotations.

  • Reproducible Reporting
    We help you create dynamic and professional reports, assignments, or thesis chapters using R Markdown, integrating your code, output, and narrative seamlessly.

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What We Can Help You With: A Detailed Breakdown

Our R experts can assist with specific packages, functions, and analytical challenges. whether you’re working within the Tidyverse ecosystem or using base R for a complex programming task.

Data Management and Wrangling (The Tidyverse)

  • Data Import: Reading data from various formats (readr, readxl).

  • Data Manipulation with dplyr: Using mutate(), select(), filter(), summarise(), and group_by().

  • Data Tidying with tidyr: Reshaping data with pivot_longer() and pivot_wider().

  • Working with strings and factors: Cleaning and manipulating text data (stringr) and categorical variables (forcats).

Statistical Analysis and Modeling

  • Basic Statistics: T-tests (t.test()), ANOVA (aov()), Chi-squared tests (chisq.test()).

  • Linear & Generalized Linear Models: lm() and glm() (e.g., Logistic, Poisson regression).

  • Mixed-Effects Models: Linear and generalized linear mixed models using lme4 or nlme.

  • Time-Series Analysis: Forecasting and modeling with packages like forecast and zoo.

  • Survival Analysis: Kaplan-Meier plots and Cox proportional hazards models.

  • Machine Learning: Using caret or tidymodels for classification, regression (e.g., Random Forest, XGBoost), and cross-validation.

Data Visualization and Reporting

  • Advanced ggplot2: Building plots layer by layer, customizing aesthetics (aes), scales, themes, and facets.

  • Interactive Graphics: Creating interactive plots with packages like plotly and leaflet.

  • Report Generation: Crafting academic papers and reports in R Markdown (.Rmd) to produce PDF, Word, or HTML documents.

  • Table Creation: Making presentation-ready tables with packages like gtsummary, gt, or stargazer.

R Programming and Environment

  • Debugging: Helping you find and fix errors in your R scripts.

  • Custom Functions: Writing custom functions to make your code more efficient and reusable.

  • Loops & Apply Family: Using loops and functions like lapply() and sapply() for iterative tasks.

  • RStudio Support: Assistance with project management and navigating the RStudio IDE.

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