#R Programming Language

My second most asked question is always how I learnt R     I taught myself mainly by just trial and error (I have to actually physically do something I can’t just watch videos as I don’t take it in) so I started with the very basics.   I think it’s so easy to overdo and feel like you need to know how to do everything or a lot of things at the start. Stick to simple things like understanding the R studio interface and loading packages and other basic commands     (after this most things I learnt were googling very specifically what I needed to do and adding the command to a ‘useful command’ list I have)    Next:  Following a vignette from start to finish (one that would be similar to what I would soon need) I then would go through and click on functions to look at the arguments (this tells you all the parameters for the function) and how I can change them if needed!    Finally try swirl it’s so easy to just load directly in the terminal and you learn as you go!        What’s your top tips?    I also have so many more so make sure you follow!      #phd #student #coding #rprogramming #university #tipsandtricks

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A tip if you’re trying to learn R ⬇️ SWIRL is a package within R Studio that has tutorials so you can “learn R within R.” I did the R Programming course as an assignment a year or so ago and now use it to refresh my memory about basic terms and codes within R. It also looks like there are quite a few “courses” within SWIRL that are not just for beginners if you already know some R and want to advance - although I haven’t tried them yet 😄 Share this with your friends who might find this useful since R is surprisingly necessary for a lot of majors and academic fields 👩🏼‍💻 #rprogramming #collegetips #gradschool #womeninstem #r

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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