R Data Cleaning & Wrangling with tidyverse

I will write a clean, reproducible tidyverse data wrangling pipeline for your dataset — covering data import (readr, readxl, or DBI), column type coercion, missing value handling, string cleaning with stringr, date parsing with lubridate, duplicate removal, variable derivation with dplyr mutate, reshaping with tidyr pivot, and a clean analysis-ready tibble exported in your required format. Tidyverse pipelines written with consistent style, proper NA handling, and reproducible import paths are the foundation of reliable R analysis; the alternative is a collection of scripts with hardcoded paths, implicit type assumptions, and missing value behaviour that differs across machines.

The pipeline uses a pipe-based workflow with dplyr, tidyr, stringr, and lubridate, includes a data quality summary using skimr or DataExplorer, and is documented with inline comments and a README describing the pipeline's purpose, inputs, and outputs. An RMarkdown version is available on request.

This service suits R analysts, academic researchers, and data science teams with messy raw data needing a clean, reproducible wrangling pipeline before analysis or modelling.