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Fiverr Pro
English
$
USD
I will perform professional data cleaning, wrangling, and statistical analysis i
About this Gig
Stop struggling with messy data. Lets make it analysis-ready.
Data cleaning is 80% of the work, but it's the most critical step for any scientific or business insight. Whether you have inconsistent CSVs, messy Excel files, or complex Biological datasets (RNA-seq/Clinical), I will transform your 'garbage' into high-quality, structured data.
Why choose this gig?
- Reproducible Workflow: I provide clean, commented R scripts.
- Scientific Accuracy: I understand data distribution, outliers, and normalization.
- Efficiency: From simple joins to complex nested data transformations.
What I offer:
- Wrangling: Tidying, Merging (Joins), Pivoting (Long/Wide format).
- Cleaning: Handling missing values (Imputation), Outlier detection, Standardizing units.
- Stats & Modeling: Descriptive statistics, ANOVA/T-tests, or Predictive Modeling.
- Bio-Specialty: Batch effect removal, log-transformations, and metadata mapping.
Platform:
Other
Development technology:
RStudio
Expertise:
Formatting
•
Pivot tables
•
Functions
•
Dashboard
•
Cleaning
FAQ
What file formats do you work with?
I handle almost all standard data formats including CSV, Excel (.xlsx) and TSV. For my scientific clients, I also work with FASTA, FASTQ, and GFF/GTF files if they need metadata extraction or reformatting.
Do you provide the code (R script)?
The Premium tier includes the full, commented script (R or Python) as a standard deliverable. For Basic and Standard tiers, I can provide the script as a Gig Extra if you’d like to see the exact steps I took.
My dataset has a lot of "Missing Values" (NAs). How do you handle those?
It depends on your goal! I can perform Listwise Deletion (removing rows), Mean/Median Imputation, or more advanced K-Nearest Neighbors (KNN) imputation to keep your sample size high while maintaining statistical integrity.
What is "Data Wrangling" exactly?
It’s the process of taking "untidy" data, where variables are headers, multiple observations are in one cell, or datasets are fragmented, and pivoting or merging them into a clean, analyzed-ready format (often called "Tidy Data").
