ggplot2 Experts in Germany
for precise data visualisation, matched in minutes with vetted freelance specialistsHire experts who create publication-ready charts, reusable visualisation systems and clear analytical reports with ggplot2, R and the tidyverse. FRATCH connects you quickly with precise matches from vetted, available freelance professionals.
Meet FRATCH Experts in Germany, who have recently used ggplot2
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Nikolai G.
Last position:
Clinical Data Manager at Dr. Falk Pharma
- Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.
Sebastian D.
Last position:
Data Scientist at CLADE GmbH
- Designed and implemented a robust Python-based data processing framework that supported the transition from R to Python and significantly improved data science productivity by providing maintainable, standardized modules for frequently used workflows, following coding best practices and DevOps principles
- Evaluated, trained, and deployed machine learning models on cloud platforms and edge devices, enabling fully automated mid-infrared (MIR) data evaluation pipelines that eliminated manual analysis steps and significantly shortened the time from measurement to prediction for customers and internal stakeholders
- Analyzed and interpreted multivariate MIR spectral data from the company’s proprietary analyzer using R and Python, supporting reliable identification and quantitation of chemical compounds in solution
Ebenezer N.
Last position:
Applied Data Science & AI Bootcamp
- Prototyped LLM/RAG document assistant; trained transcriptomics and proteomic data; used Git/Docker for reproducibility.
- Strengthened ML fundamentals applicable to omics (feature engineering, validation, leakage control).
Stefan L.
Last position:
Dashboard for Interactive Data Analysis at LennardtundBirner GmbH
Development & deployment of an interactive dashboard that allows users to select datasets for visual analysis.
The application supports filters, AI-based interpretations, and a chatbot for user interactions.
shiny, openai, mirai, plotly, mapgl, duckdb, AWS, ShinyProxy, Docker Swarm
Abhijith Sai T.
Last position:
AI and AWS Developer at FannieMae
- Architected end-to-end credit risk pipelines by orchestrating Airflow ETLs and training LSTMs/Transformers to predict default and prepayment speeds on MBS portfolios.
- Developed Deep Learning NLP solutions using BERT and LayoutLM for document processing, leveraging Transfer Learning and custom PyTorch loss functions to automate underwriting.
- Optimized R&D lifecycles through Bayesian tuning, Batch Normalization, and MLflow tracking to ensure robust model performance throughout volatile mortgage market cycles.
- Productionized scalable MLOps infrastructure via Docker and INT8 Quantization, deploying low-latency FastAPI microservices on AWS SageMaker with automated CI/CD pipelines.
- Ensured regulatory compliance by integrating SHAP/LIME for explainability and establishing real-time Data Drift monitoring to meet strict FHFA and Fair Lending standards.
Eyasu H.
Last position:
Data Scientist at Deutsche Bundesbank
- Developed web scraping scripts to extract and parse over 5000 product data from the Zalando website.
- Performed ETL processes using Apache Spark in CDSW, loaded the data into the Hadoop ecosystem (HDFS), and managed data using Hive and Impala.
- Implemented machine learning algorithms, achieving 85–90% accuracy on multi-class product classification.
- Integrated Zalando's product and price data into the dashboard with Otto and Takko for interactive visuals.
Zakaria B.
Last position:
Machine Learning / Software Developer at X-FAB Semiconductor Foundries GmbH
- Training Machine Learning models for different purposes.
- Programming with languages such as R and Python, depending on the project requirements.
- Develop, test, and maintain R shiny applications for semiconductor wafer fav production.
- Documentation of the code, project requirements, design decisions, and development processes.
- Perform integration testing, and debug to ensure software functions correctly.
- Coding, debugging, and ensuring quality standards for smooth operations.
- Deployment of Git and GitHub to manage changes and collaborate efficiently with fellow developers.
- Deployment of Docker and Jenkins for applications containerization.
- Staying updated on the latest tools, technologies, and best practices in software development.
- Providing back-up in MES (Manufacturing execution system).
- Getting involved in project management like task priority and project time lines.
- Maintaining and optimizing deployed Apps.
Eugene T.
Last position:
Freelancer at 3d-statistical-learning
- Data preparation and analysis of user behavior for targeted marketing strategies
- Development and implementation of machine learning models, B2C customer segmentation and cluster analysis with Python
- Use of openpyxl and pandas for efficient automation, cleaning and standardization of datasets
- Close collaboration with interdisciplinary teams to integrate data-driven insights; result: +15% increase in marketing efficiency through improved customer retention
- Technologies & Tools: Python (Pandas, NumPy, scikit-learn), SQL (SQLAlchemy), Jupyter Notebook, creation of analysis and result reports (PDF, Word, Excel)
Discover over 15,000 top freelancers
Statistics of experts using ggplot2
Aggregated from the professional profiles of matched freelancers.
Experience
9 years
Position duration
1.8 years
Positions per freelancer
6
Top business areas
Business Intelligence, Information Technology, Research and Development
Top industries
Information Technology, Education, Banking and Finance
Certification focus areas
Business Intelligence, Research and Development, Information Technology
Bachelor's degree or higher
100%
Master's degree or higher
89%
Doctorate
33%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using ggplot2
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
ggplot2 experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (56%)
- Education (44%)
- Banking and Finance (44%)
- Biotechnology (33%)
- Healthcare (33%)
- Professional Services (33%)
- Chemical (22%)
- Manufacturing (22%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What ggplot2 does
ggplot2 is an R package for building data visualisations through a consistent grammar of graphics. It maps variables to visual properties such as position, colour, size and shape, then combines layers, scales and themes. This makes complex analytical findings easier to inspect, explain and reuse.
Where it is used
Companies use ggplot2 for exploratory analysis, management reporting, scientific communication and customer-facing dashboards. Typical deliverables include:
- Time-series, bar, line and scatter plots
- Distribution, correlation and faceted views
- Publication-ready figures and report graphics
- Reusable chart functions for recurring reports
Ecosystem and tooling
Strong work with ggplot2 usually sits within the tidyverse, especially dplyr, tidyr, readr and purrr. Specialists often combine it with R Markdown or Quarto for reproducible reports, Shiny for interactive applications, and patchwork or cowplot for multi-panel layouts. Knowledge of colour scales, coordinate systems, annotations and themes is central to consistent output.
When companies need help
Freelance expertise is useful when an internal team has sound data but unclear visual communication. A specialist can turn raw tables into a coherent visual language, migrate ad hoc charts into maintainable code, or prepare figures for research, finance, marketing and operational decisions. In Germany, remote collaboration is common, while on-site workshops can help align reporting needs and stakeholders.
Signs of strong practice
Look for professionals who separate data preparation from plotting, use tidy data structures and make scales and labels explicit. Quality work considers accessibility, print and screen output, missing values, uncertainty and misleading visual encodings. Useful signs include:
- Clear, modular R code that another team can maintain
- Consistent themes and documented design choices
- Charts tested against the underlying data
- Outputs suited to reports, presentations or applications
Choosing the right specialist
Ask for examples close to the intended audience and decision context, not just attractive charts. A strong expert can explain why a chart type, scale or transformation fits the question and can work with existing R workflows. They should also understand version control, review practices and the difference between static ggplot2 output and interactive tools such as plotly or Shiny.
Frequently asked questions
Not sure where to start with ggplot2? These answers cover the essentials.
ggplot2 is used to create structured data visualisations in R, including exploratory charts, analytical reports and publication figures. It is well suited to repeatable workflows because chart logic can be expressed as code and applied to changing data.
ggplot2 provides a layered grammar that makes mappings, scales and themes easier to organise across a visualisation system. Base R graphics can be direct and lightweight, while ggplot2 is often preferable when a project needs consistent styling, reusable components or many related charts.
A strong ggplot2 specialist often works with dplyr and tidyr for data preparation, R Markdown or Quarto for reporting, and Shiny for interactive applications. Experience with Git, testing, accessibility and data storytelling also supports reliable delivery.
The right level depends on the work. A small set of well-defined charts may need solid R and ggplot2 skills, while a reporting system or research workflow also calls for data modelling, reproducibility and review practices. Assess the specialist against the project’s data complexity and audience.
Yes. ggplot2 projects are usually well suited to remote collaboration because code, data extracts and rendered outputs can be reviewed asynchronously. On-site sessions may still help when teams need to agree on reporting definitions, visual standards or stakeholder requirements.
ggplot2 primarily produces static visualisations, but selected charts can be made interactive with tools such as plotly or embedded in Shiny applications. A specialist should clarify whether the project needs exploration, interaction, animation or simply high-quality static output.
Review whether the chart answers a defined question, represents the data honestly and remains readable at its intended size. For ggplot2, also inspect the code structure, scale choices, labels, treatment of missing values and ease of applying the same design to new data.
Freelancers working with ggplot2 may receive anything from prepared tidy data to incomplete scripts and unclear reporting goals. Clarifying the audience, delivery format, data refresh process and review criteria early helps define the work and prevents visual polish from hiding analytical problems.
The average hourly rate of freelancers in Germany who have used ggplot2 in their recent projects is 91 €, which corresponds to a daily rate of about 728 € based on an 8-hour working day.
Of the freelancers in Germany who have used ggplot2 in their recent projects, 100% hold at least a Bachelor's degree, 89% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Germany who have used ggplot2 in their recent projects have 9 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Germany who have used ggplot2 in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Germany who have used ggplot2 in their recent projects are Information Technology (56%), Education (44%), and Banking and Finance (44%).
The most common business areas among freelancers in Germany who have used ggplot2 in their recent projects are Business Intelligence (89%), Information Technology (78%), and Research and Development (67%).
Main locations of FRATCH Experts, who have recently used ggplot2
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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