#Rprogramming

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#Rprogramming Reel by @doc.furshh - 📊 How to create figure panels in R? ✨ I used to design scientific figures separately and combine them using Adobe Illustrator, until I found this ha
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@doc.furshh
📊 How to create figure panels in R? ✨ I used to design scientific figures separately and combine them using Adobe Illustrator, until I found this hack to stitch all figures using R programming. Not many know of this trick, so make sure to save this post! ✨ The patchwork package works like magic. You just need to create individual bioinformatics plots (works best with ggplot2 objects) and then apply simple operations to combine them. 📨 DM me if you need further clarifications 💬 Tell me how do you create your own figure panels ------------------------------------------------------ #programming #coding #programmer #developer #coder #computerscience #codinglife #code #hacks #lifehacks #hack #rstudio #dataanalysis #bigdata #dataanalytics
#Rprogramming Reel by @kertutenso (verified account) - 1️⃣ "R for Data Science" by Wickham et al. is widely recommended across stats forums as one of the best books to learn hands on R programming. The is
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@kertutenso
1️⃣ “R for Data Science” by Wickham et al. is widely recommended across stats forums as one of the best books to learn hands on R programming. The is available online for free at www.r4ds.hadley.nz 2️⃣ HarvardX Data Science R Basics is free to audit assumes no prior knowledge and teaches you foundational programming concepts and operations (it doesn’t get into statistical modeling yet). 3️⃣ “An Introduction to Statistical Learning with Applications in R” by James et al. can be a little bit more technical and advanced (as it actually covers statistical topics), but comes with great real-life R coding examples.The PDF of the book is available online for free at www.statlearning.com 4️⃣ www.rscreencasts.com has a long list of screencast videos by data scientist David Robinson where he shares real-world examples of live data analyses in R, including how to approach analysis, what packages and methods he uses, as well as general R tricks and tips. 5️⃣ If you prefer more interactive learning, you might enjoy swirl (swirlstats.com) that teaches you R programming interactively inside the R console, no reading books or watching courses required. ❓Any other good recs? Drop them in the comments! #rprogramming #rstudio #datascientist #womenintech #womeninstem
#Rprogramming Reel by @jackieinbiotech - I've found this really useful in learning how to use R from bioinformatics and here's why: 1.Comprehensive overview of R in bioinformatics The book
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@jackieinbiotech
I’ve found this really useful in learning how to use R from bioinformatics and here’s why: 1.Comprehensive overview of R in bioinformatics The book offers a thorough introduction to R programming specifically tailored for bioinformatics, making it an essential guide for learning how to use R for analyzing biological data. 2.Clear practical examples It provides clear, hands-on examples and case studies that demonstrate how R can be used for tasks such as analyzing genomic data, visualizing results, and performing statistical analysis, making it accessible even for beginners. 3.Integration of biological concepts with R programming Allows learners to not only master programming skills but also understand their direct application in the world of bioinformatics, making the resource exceptionally useful for both new and seasoned researchers in the field. 🔗 Link to this pdf available in the resources highlight. Share this with your lab bestie 👩🏽‍🔬 #biochemistry #molecularbiology #lifescience #sciencereels #bioinformatics #studytools #studyresources
#Rprogramming Reel by @thephdstudent (verified account) - Top programming book recommendations for learning how to code or up-skilling in Python, JavaScript & R👩🏽‍💻📖🧠 👩🏽‍💻 Python: Python Crash Course
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@thephdstudent
Top programming book recommendations for learning how to code or up-skilling in Python, JavaScript & R👩🏽‍💻📖🧠 👩🏽‍💻 Python: Python Crash Course (3rd edition) by Eric Matthes 💭 Summary: Beginner friendly book for anyone looking to learn or up-skill in Python this book will teach you basic programming concepts and practice writing clean code with exercises for each topic. 👩🏽‍💻 JavaScript: The Nature of Code: Simulating Natural Systems with JavaScript by Daniel Shiffman (the most aesthetic coding book ever 💓) 💭 Summary: This book will teach you how to use Java to model physics, biology and neural networks by blending art and maths and programming. It will also show you how to create cool dynamic visualisations with interactive simulations inspired by nature! 👩🏽‍💻 R: The Book of R: A First Course in Programming and Statistics by Tillman Davies 💭 Summary: This book provides a comprehensive introduction to R programming, covering fundamental programming concepts and statistical analysis techniques. 👩🏽‍💻 Improve Coding Skills: Think Like a Programmer: An Introduction to Creative Problem Solving by V. Anton Spraul 💭 Summary: This book breaks down the ways that programmers solve problems and teaches you: how to Think Like a Programmer. Each chapter tackles a single programming concept, like classes, pointers, and recursion, and open-ended exercises throughout challenge you to apply your knowledge. #phdstudent #phd #tech #womenintech #womeninstem #codinglife #bioinformatics #coding #programming #pythonprogramming #javaprogramming #rprogramming
#Rprogramming Reel by @citizendatascientist (verified account) - Here is a 2 Month Plan to Learn R programming Month 1 📌 Week 1: ➡️ Start with the basics of R programming language by studying its syntax and data ty
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@citizendatascientist
Here is a 2 Month Plan to Learn R programming Month 1 📌 Week 1: ➡️ Start with the basics of R programming language by studying its syntax and data types. ➡️ Learn how to install and set up RStudio, which is an Integrated Development Environment (IDE) for R. ➡️ Take online tutorials and practice exercises to get familiarise with R programming. 📌 Week 2: ➡️ Learn about data structures in R such as vectors, matrices, and data frames. ➡️ Practice data manipulation techniques such as subsetting, merging, and transforming data. 📌 Week 3: ➡️ Learn about functions and control structures in R. ➡️ Practice writing and executing functions to perform various tasks. 📌 Week 4: ➡️ Explore R packages and libraries to expand the functionality of R. ➡️ Learn how to install and use packages such as ggplot2, dplyr, and tidyr for data visualization and data analysis. Month 2 📌 Week 5: ➡️ Learn about statistical analysis and modeling techniques in R. ➡️ Practice performing descriptive statistics, hypothesis testing, and regression analysis. 📌 Week 6: ➡️ Explore machine learning algorithms in R. ➡️ Learn about supervised and unsupervised learning algorithms such as decision trees, k-means clustering, and linear regression. 📌 Week 7: ➡️ Learn about web scraping and text analysis in R. ➡️ Practice extracting data from websites and analyzing text data using R. 📌 Week 8: ➡️ Consolidate your learning by working on a project. ➡️ Choose a real-world problem and use R to analyze data and provide insights. 📌 Daily activities: ➡️ Practice coding every day for at least an hour. ➡️ Watch R programming tutorials on websites such as DataCamp and Udemy. ➡️ Participate in online forums and communities to ask questions and get help from other R programmers. ➡️ Read blogs and articles on R programming to keep up with the latest trends and best practices. Comment ' Yes ' If you want free learning Resources #Rprogramming #learntocode #datadriven #datascience #dataanalysis #datavisualization #statistics #machinelearning #web_scraping #textanalysis #coding #programming #onlinelearning #datamanipulation #datastructures #dataframes #vectors #matrices #ggplot2 #dplyr #tidyr #regressionanal
#Rprogramming Reel by @miss_management - In IIMs, they don't just teach CV making - they teach CV marketing. Big brand names on your profile build instant credibility, differentiation and rec
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@miss_management
In IIMs, they don’t just teach CV making — they teach CV marketing. Big brand names on your profile build instant credibility, differentiation and recall. So here are some high-value certifications from top companies that you should know about: (Comment “Resume” for links but do go through the caption before applying) 🔵 1. Deloitte — IFRS e-Learning (FREE) Eligibility: Anyone can register 100% FREE Certificate: Yes What you’ll learn: IFRS standards, financial reporting concepts, accounting treatment of assets/liabilities, revenue recognition, global accounting compliance. 🔵 2. KPMG India — Certification in Accounting & Auditing Eligibility: Students & graduates from commerce / business backgrounds (others also allowed) Certificate: Yes What you’ll learn: Accounting standards, audit procedures, internal controls, financial statements, fraud risk assessment, compliance frameworks. 🔵 3. Microsoft — Power BI Data Analyst Associate Eligibility: Open to all Learning materials are free Certificate: Yes What you’ll learn: Power BI dashboards, DAX, ETL, data modelling, data storytelling, business analytics fundamentals. 🔵 4. Microsoft — Azure Fundamentals (AZ-900) Eligibility: Anyone Learning path is free Certificate: Yes What you’ll learn: Cloud concepts, Azure architecture, governance, security, cost management — perfect for non-tech students entering tech strategy roles. 🔵 5. Google — Data Analytics Professional Certificate Eligibility: Anyone Certificate: Yes What you’ll learn: SQL, R programming, spreadsheets, Python basics, Tableau, data cleaning, business case analytics, real datasets. 🔵 6. EY — Certificate in Corporate Finance Eligibility: UG/PG students, MBAs, finance aspirants Certificate: Yes What you’ll learn: Capital structure, valuation, M&A basics, fundraising, financial modelling, risk assessment, corporate finance fundamentals. 🔵 7. KPMG — Lean Six Sigma Green Belt Eligibility: Students & working professionals Certificate: Yes What you’ll learn: DMAIC methodology, process improvement, statistical analysis, quality control tools, real project case studies. . . . . . . [Job, placements, Salary, Career] #iim #mba #job #resume #salary
#Rprogramming Reel by @codewithprashantt (verified account) - Unlock the core tools every data scientist should know! From writing powerful code to building predictive models, these essentials form the backbone o
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@codewithprashantt
Unlock the core tools every data scientist should know! From writing powerful code to building predictive models, these essentials form the backbone of modern data science. 💻 Programming: Python and R make it easy to clean data, automate workflows, and build advanced analytics. 📊 Data Analysis: Pandas and NumPy help you manipulate large datasets, while Jupyter provides an interactive space to experiment and visualize results. 📈 Visualization: Matplotlib, Seaborn, and Plotly turn raw numbers into clear, insightful visuals that tell meaningful stories. 📉 Business Intelligence: Power BI and Tableau transform dashboards into decisions — helping teams track performance and uncover trends. 🤖 Machine Learning: Scikit-learn and PyTorch power everything from simple models to deep learning systems that predict, classify, and optimize. If you’re exploring data science or leveling up your skills, mastering these tools will give you a solid foundation to build real-world projects and stand out in the field. 🚀 🔑 Suggested Keywords data science tools, python, r programming, pandas, numpy, jupyter notebook, data visualization, matplotlib, seaborn, plotly, tableau, power bi, machine learning, scikit learn, pytorch, data analytics, beginner data science, learn data science, ai tools 📢 Hashtags #DataScience #MachineLearning #Python #RProgramming #DataAnalytics Pandas NumPy Jupyter Visualization Matplotlib Seaborn Plotly PowerBI Tableau ScikitLearn PyTorch AI TechEducation LearnDataScience DataTools
#Rprogramming Reel by @python_for_bioinformatics - Trying RStudio for the first time with my friend: We didn't do analysis... We did damage. 😅 #bioinformatics #biotechnology #rstudio #rprogramming
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@python_for_bioinformatics
Trying RStudio for the first time with my friend: We didn’t do analysis... We did damage. 😅 #bioinformatics #biotechnology #rstudio #rprogramming
#Rprogramming Reel by @yourphdpal - Save the list of these AI Tools You'll Need for every section of Your Research paper:⬇️ 1. Title & Topic Selection Scite Assistant - Checks topic re
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@yourphdpal
Save the list of these AI Tools You’ll Need for every section of Your Research paper:⬇️ 1. Title & Topic Selection Scite Assistant – Checks topic relevance with real citations Consensus – Finds evidence-based answers from published research 2. Abstract Jenni AI – Generates concise abstracts using your inputs Writefull Abstract Generator – Tailored for academic tone & brevity 3. Introduction Elicit – Helps you identify gaps, motivations, and key themes PaperBrain – Summaries for building strong problem statements 4. Literature Review Research Rabbit – Visualizes literature networks Litmaps – Helps track, map, and expand your review Semantic Scholar – For high-quality, AI-filtered papers 5. Methodology Scispace Copilot – Explains methods from existing papers IBM SPSS Tutorials (AI-Assisted) – Guides for statistical techniques 6. Data Analysis ChatGPT / Claude – Code generation & interpretation Orange Data Mining – Visual, no-code ML workflows JASP – AI-assisted statistical analysis Julius AI- Python or R programming 7. Results & Findings ChatGPT – Converts raw output into clean descriptions GrammarlyGO – Polishes clarity and academic tone Datawrapper – Makes publication-ready charts 8. Discussion Scite AI – Check how other papers interpret similar findings Explainpaper – Helps simplify complex interpretations 9. Conclusion & Implications Jenni AI – Helps create concise, impactful takeaways Writefull – Ensures academic phrasing 10. References Zotero + ZoteroBib – Automatic citations in any style EndNote Click – One-click access to PDFs & references 11. Plagiarism & Language Check Turnitin Draft Coach – Pre-submission plagiarism scan Writefull Revise – Academic grammar correction Grammarly – General clarity & tone Follow for more @yourphdpal #researchlife #phdjourney #phdlife #researcher #phdmotivation #lifeofaresearcher #lifeofaresearcher #phdstudent #phdcommunity #doctorate #phdmemes #aitools #aiforresearch #ethicalai #aiforacademics
#Rprogramming Reel by @codersarts - If you're interested in creating dashboards using R Shiny in R programming or need assistance with implementing one, our team at CodersArts is here to
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@codersarts
If you're interested in creating dashboards using R Shiny in R programming or need assistance with implementing one, our team at CodersArts is here to support you. Contact us at contact@codersarts.com for personalized guidance, consultations, or any inquiries related to R Shiny dashboard development and R programming. We can assist you with designing interactive and informative dashboards, integrating data visualization elements, and optimizing performance for your data analysis projects or business applications. #dataanalysis #datavisualization #datascience #dashboarddesign #rshiny #rprogrammin #codersarts
#Rprogramming Reel by @analytics_essentials - A role of a Data Analyst and a Data Scientist has overlapping responsibilities, so let me give you a clear picture of what these roles are. A Data An
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@analytics_essentials
A role of a Data Analyst and a Data Scientist has overlapping responsibilities, so let me give you a clear picture of what these roles are. A Data Analyst’s major responsibility is to analyze the data, find insights from the data and present the results of your analysis to stakeholders. Skills required for Data Analyst role: ✅ SQL ✅ Excel ✅ Data Preprocessing ✅ Data Analysis and statistics ✅ Power BI/Tableau ✅ Communication ✅Storytelling On the other hand, responsibilities of a Data scientist includes all the responsibilities of Data Analyst plus a few more additional skills. Data scientists are responsible for using advanced statistical and programming skills to help businesses solve a problem. They often work on statistical analysis, building machine learning models including large language models,using optimization techniques. Skills required for a Data scientist role in addition to Data Analyst skills are: ✅ Advanced SQL ✅ Progarmming languages like Python/R Programming ✅ Machine Learning ✅ Predictive Modeling ✅ Data Mining techniques ✅ Optimization techniques #dataanalytics #dataanalyst #datascientist #datascience #dataanalystlife #roleofadataanalyst #roleofadatascientist #dataanalystskills #datascientistskills

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