R Studio & Data Analysis Help

If you are coming from Python or Excel, R feels alien. Arrays start at index 1, not 0. Assignment uses `<-` instead of `=`. And the error messages are notoriously unhelpful.

Most students fail R assignments not because they don't understand the statistics, but because they get stuck on **Data Wrangling**. Trying to filter a dataset with `dplyr` and getting a "could not find function" error because of a library conflict is a rite of passage we help you skip.

R is just a calculator. The real value is the **Statistical Interpretation**. A script that outputs a number is useless if you don't know if that number allows you to reject the Null Hypothesis.

We provide a full "Methods and Results" write-up with every order. We explain *why* we chose a specific test (e.g., "We used Welch’s t-test because the variances were unequal") and what the results actually mean for your research question.

Modern professors demand reproducibility. They don't want a Word doc with pasted screenshots; they want a **Knitted R Markdown (.Rmd)** file.

We specialize in fixing LaTeX compilation errors in R. We ensure your code chunks are properly set (`echo=FALSE`, `message=FALSE`), your tables are formatted using `kable` or `stargazer`, and your bibliography compiles correctly.

Excel charts look amateur in a thesis. You need **ggplot2**.

We move beyond basic histograms. We create complex, multi-layered visualizations that highlight your findings: