R Programming Language Jobs

I have written code in R that requires interpretation and Python code that needs modification. The goal is to develop a system that updates lottery predictions automatically by entering new winning combinations. Key Requirements: 1. Deliver results for the Pick3 game first as proof of concept. 2. Create a file that generates new predictions based on updated winning combinations for games like Mega Million, PowerBall, Cash4life, and others (details provided). 3. Demonstrate functionality via screen sharing to ensure accuracy and alignment with requirements. Ideal Skills and Experience: - Proficiency in R and Python programming languages. - Strong understanding of algorithms and statistical modeling. - Experience with predictive modeling or similar projects.

I need an expert in genomic data analysis, specifically with SNP array data and GEBV modeling. Tasks include: - Data preprocessing and quality control - Statistical analysis and modeling - Visualization of results Ideal skills and experience: - Proficiency in bioinformatics tools and software - Strong background in statistics and modeling - Experience with SNP array data and GEBV modeling - Ability to create clear, informative visualizations Looking for someone detail-oriented and experienced in handling complex genomic datasets.

I have just completed a series of in-depth interviews and, alongside some supporting quantitative notes, I now need the stories inside this mixed-method dataset to be surfaced and clearly articulated. The project is centred on narrative analysis: I want to understand how participants construct meaning, where their storylines converge or diverge, and what overarching plotlines emerge. All interview audio is already transcribed verbatim and lightly anonymised. You will begin by familiarising yourself with the transcripts, then move into coding and narrative structuring. Because I have supplementary numeric observations for each respondent (e.g., demographic tags, brief rating scales), I’m keen for these to be woven into the story map rather than treated as a separate appendix. NVivo o...

I’m running a comparative genomic-selection study, benchmarking about ten prediction models that range from rrBLUP and GBLUP to Random Forest, LightGBM, CNN and ElasticNet. The experiments are in motion but the dataset keeps growing, so I’m looking for a research-minded intern who can jump in, work entirely in R, and keep me posted with regular, concise updates. Here’s where I need your help: • Data preprocessing & cleaning – imputation, SNP quality filters, population structure checks. • Model development & tuning – implement or refine the models above, explore hyper-parameter grids and suggest new algorithms when they make sense. • Performance evaluation & analysis – rigorous cross-validation, predictive-ability metric...

I am preparing a full-length manuscript for submission to a Q1 SSCI economics journal on the theme of machine learning–driven economic-growth prediction. The core of the article must showcase concrete applications and real-world case studies rather than abstract algorithmic discussions. I want a genuinely global perspective, so the empirical section should compare or combine economies across different income levels rather than concentrating on a single region. All quantitative work has to rely on publicly available government databases—think World Bank, OECD, IMF, national statistical offices—so that review-ers can easily replicate the results. You are free to merge multiple sources as long as every dataset is openly accessible. Key expectations • 8,000–1...

I have a sizable set of customer data sitting in CSVs and I want it turned into clear, actionable insights. Right now I’m still deciding which angle will be most valuable—anything from simple descriptive trends through to predictive or even prescriptive models is on the table—so I’d like your input on what the data can realistically deliver. Here’s what you’ll be working with • Customer profiles (demographics, acquisition source) • Historical purchase records and spend patterns • Engagement logs from email and in-app activity What I need from you 1. A short plan outlining the analysis path you recommend once you’ve skimmed a sample of the data. Feel free to propose segmentation, churn risk scoring, lifetime value estima...

I have a data set from comparative studies and I need the full analytical cycle carried out in SPSS right away. The work covers three layers: descriptive statistics to profile the sample, inferential tests to detect significant differences between the study groups, and a regression model to pinpoint predictors of the main outcome. Please import the raw file into SPSS, verify coding, run the analyses, and return a concise report that explains each result in plain language together with the .sav file and syntax so everything is fully reproducible. I am ready to start immediately and would like the first set of tables and figures back as soon as you complete them so we can keep momentum.

I'm looking for a skilled programmer to help me with my assignment across Python, Java, and C++. The focus is on learning concepts rather than completing a project or solving specific problems. Ideal Skills and Experience: - Proficiency in Python, Java, and C++ - Strong teaching and communication skills - Experience with academic programming assignments - Patience and ability to explain complex concepts clearly Please provide a brief overview of your teaching approach and relevant experience.

I need a predictive analysis on a mixed data set. The goal is to predict next day's mood based on today's social media usage. Key Requirements: - Analyze survey data on mood and social media hours - Use predictive modeling techniques - Provide clear, actionable insights Ideal Skills and Experience: - Expertise in predictive analysis - Proficiency with mixed data sets - Strong background in data interpretation and reporting I'm looking for a freelancer who can deliver accurate predictions and insights.

I need a clear, insight-driven analysis of the structured data stored in my relational databases. The tables are already cleansed at a basic level; now I want to dig deeper—descriptive statistics, correlations, trend discovery, and any notable anomalies that jump out once the numbers are explored properly. The workflow I imagine is straightforward: connect directly to the database (credentials will be supplied), pull the relevant tables, and run the analysis in Python or R using standard libraries such as pandas/NumPy or dplyr/tidyverse. If you prefer SQL-heavy exploration first, that’s fine too; the goal is to surface findings in a format that’s easy to digest. Deliverables • A concise written report (PDF or Markdown) summarising key insights, supported by clear...