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Learn the R programming language in this tutorial course. This is a hands-on overview of the statistical programming language R, one of the most important tools in data science. 💻Course Files: 🤍 💻 Course created by Barton Poulson from datalab.cc. 🔗 Check out the datalab.cc YouTube channel: 🤍 🔗 Watch more free data science courses at 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Welcome ⌨️ (0:02:20) Installing R ⌨️ (0:07:17) RStudio ⌨️ (0:11:52) Packages ⌨️ (0:19:16) plot() ⌨️ (0:27:49) Bar Charts ⌨️ (0:32:10) Histograms ⌨️ (0:39:44) Scatterplots ⌨️ (0:44:39) Overlaying Plots ⌨️ (0:52:30) summary() ⌨️ (0:55:49) describe() ⌨️ (1:00:17) Selecting Cases ⌨️ (1:06:14) Data Formats ⌨️ (1:21:39) Factors ⌨️ (1:28:34) Entering Data ⌨️ (1:34:18) Importing Data ⌨️ (1:42:29) Hierarchical Clustering ⌨️ (1:49:35) Principal Components ⌨️ (1:59:16) Regression ⌨️ (2:08:36) Next Steps Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
R programming for beginners is meant to introduce you to R programming without the stress. Using R for statistical analysis and data science is becoming increasingly popular. At first, using R and R studio might seem a little intimidating. Watch this video and you'll find that it's not that hard at all. This is a very gentle introduction to R programming. This channel is supported by Nested Knowledge - an online platform that supports the entire literature review process. Please do check them out at this link: 🤍?&utm_source=greg-martin&utm_medium=social&utm_content=RPYT
R programming is easy. In this video, I'll walk you though how to clean your data; how to manipulate (or wrangle) your data; how to summarize your data; how to create tables and describe your data; how to visualize your data using ggplot (a powerful data visualization package that I use to create bar charts, boxplots, scatterplots, histograms, line graphs and more) . And of course how to analyze your data. The data analysis includes using hypothesis testing and p-values to make inference about a population using sample data. To do this, I'll teach you how to do a t-test, ANOVA, chi-squared test and linear regression models. So if you're a data scientists or researcher doing statistical analysis or some kind of quantitative analysis of data, this video is for you. I cover all of this in one hour.
R programming tutorial with everything you need to know to start coding in RStats and RStudio. All the basics and fundamentals for non-coders and beginners in R programming! This is the perfect first step in your journey to master Data Science. Awesome R programming textbooks that Max has bought for himself and liked: R Graphics Cookbook: Practical Recipes for Visualizing Data: 🤍 R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics: 🤍 R for Data Science: Import, Tidy, Transform, Visualize, and Model Data: 🤍 We offer tutoring with Max for 100 $/hr ! Contact us at dynamicdatascript🤍gmail.com 00:00 Introduction 01:47 Base R overview 08:16 RStudio overview 10:30 Variable assignment 12:50 Numerics 13:30 Logicals 14:03 Characters 15:50 Factors 16:40 Vectors 20:50 Lists 23:20 Data frames 26:08 Matrices 27:34 Indexing 37:23 Functions 45:05 Packages 48:39 Summary R download: 🤍 R Studio download: 🤍 R Q&A: 🤍 R Style guide: 🤍 R Cheatsheet: 🤍 🏎️ R performance playlist 🤍 🧮 dplyr playlist 🤍 #R #Rprogramming #Rtutorial #RStats #RStudio #DDS #DDSR
R is an open source programming language designed for statistical computing. Here's my efforts at learning the basics (I have an exam in R this April sooo better get going!). Follow me on instagram: 🤍_paige_y 🤍 If you're a dog-lover, follow Buster on instagram: 🤍_buster_y_theborderterrier 🤍 My WFH office and filming equipment (affiliate links): Gaming chair - 🤍 Daylight lamp - 🤍 Laptop stand - 🤍 Tripod 1 - 🤍 Tripod 2 - 🤍 Mini Tripod - 🤍 Lights & Backdrop - 🤍 Gimbal - 🤍 Film on iPhone XS - 🤍 FAQ: How old are you? I'm 22. What's your job? I work as a Risk Analyst in Insurance Consulting, and am studying to become a qualified actuary. What did you do at university? I studied Physical Natural Sciences at the University of Cambridge, and specialised in Astrophysics. If you'd like to see more from me, check out my playlists: Cambridge vlogs - 🤍 Days in the life - 🤍 Actuary grad job - 🤍 Actuarial studies - 🤍 Real time study with me - 🤍 Intro credit: 🤍Manique Here
🔥Professional Certificate Program In Data Science: 🤍 🔥Data Science Bootcamp (US Only): 🤍 In this R Programming Full Course In 7 Hours video, we'll learn about What is R, variables, and data types in R. This R Programming for Beginners is the ideal video for anyone starting with R Programming and Data Analysis. We'll Understand Data Handling, Manipulation, and Visualization in R. So, let's get started with this R Tutorial! Dataset Link - 🤍 This R Programming Full Course Video Covers the following Topics: What is R Programming Variables and Data Types in R Logical Operators Vectors List Matrix Data Frame Flow Control Functions in R Data Manipulation in R- dplyr Data Manipulation in R- tidyr Data Visualization In R Time Series Analysis in R ⏩ Check out the Data Analytics Playlist: link: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 #RprogrammingFullCourse #RProgrammingforBeginners #RProgrammingFullTutorial #RTutorial #RTutorialForBeginners #RProgrammingForBeginners #RLanguageTutorial #LearnRProgramming #DataAnalytics #Simplilearn What Is R Programming? R is an open-source programming language used for statistical computing. It is one of the most popular programming languages today. R was inspired by S+, which is similar to the S programming language. R has various data structures and operators. It can be integrated with other programming languages like C, C, Java, and Python. This Data Analyst Master’s Program in collaboration with IBM will make you an expert in data analytics. In this Data Analytics course, you'll learn analytics tools and techniques, how to work with SQL databases, the languages of R and Python, how to create data visualizations, and how to apply statistics and predictive analytics in a business environment. What are the learning objectives? Simplilearn’s Data Analyst Master's Program developed in collaboration with IBM will provide you with extensive expertise in the booming data analytics field. This data analytics certification training course will teach you how to master descriptive and inferential statistics, hypothesis testing, regression analysis, data blending, data extracts, and forecasting. Through this course, you will also gain expertise in data visualization techniques using Tableau and Power BI, learning how to organize data and design dashboards. In this data analyst certification online course, a special emphasis is placed on those currently employed in the non-technical workforce. Through this Data Analytics course, those with a basic understanding of mathematical concepts will be able to complete the course and become an expert in data analytics. This learning experience melds the knowledge of Data Analytics with hands-on demos and projects via CloudLab. Upon completing this course, you will have all the skills required to become a successful data analyst. Why become Data Analyst? By 2020, the World Economic Forum forecasts that data analysts will be in demand due to increasing data collection and usage. Organizations view data analysis as one of the most crucial future specialties due to the value that can be derived from data. Data is more abundant and accessible than ever in today’s business environment. In fact, 2.5 quintillion bytes of data are created each day. With an ever-increasing skill gap in data analytics, the value of data analysts is continuing to grow, creating new job and career advancement opportunities. The facts are that professionals who enter the Data Science field will have their pick of jobs and enjoy lucrative salaries. Who should take up this course? Aspiring professionals of any educational background with an analytical frame of mind are best suited to pursue the Data Analyst Master’s Program, including: 1. IT professionals 2. Banking and finance professionals 3. Marketing managers 4. Sales professionals 5. Supply chain network managers 6. Beginners in the data analytics domain 7. Students in UG/ PG programs 👉Learn more at:🤍 For more information about Simplilearn courses, visit: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
R programming for beginners - This video is an introduction to R programming. I have another channel dedicated to R teaching: 🤍 In this video I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life. This video was sponsored by the University of Edinburgh. Find out more about their programmes at 🤍 This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.
R programming is typically used to analyze data and do statistical analysis. In this video, I talk about why R is a better option than other statistical packages and software options (including SPSS, STATA, SAS etc.) I also give a short demonstration in which I calculate the mean, median of two variables, plot a histogram and calculate the correlation coefficient.
Learn the fundamentals of the R programming language Bryan Jenks YouTube Channel: 🤍 Code Gist: 🤍 💖 Support The Channel! 🤍 Website & Brad Traversy Udemy Course Links: 🤍 Follow Traversy Media: 🤍 🤍 🤍 Timestamps: 0:00 Intro 1:06 About R 8:48 Installing R 10:26 Installing RStudio 12:03 How to make R files 18:24 RStudiio panes and basic Hotkeys 20:42 Variables 23:56 Data Types 26:06 Vectors 29:09 Lists 30:23 Matrix 31:11 Arrays 33:03 Factors 34:39 Data Frames 38:22 FUN STUFF STARTS 40:41 Dpylr Data Cleaning 53:50 Making Beautiful Charts With GGPLOT2 1:03:21 Exploratory Data Analysis Example 1:13:46 Importing Your Data Into R 1:20:26 Some Very Useful Packages 1:29:31 Outro
This video gives you four tips on how to improve code performance in R: - Avoid running same code multiple times - Avoid appending results to existing objects - Vectorization - Store intermediate results #rstats #DataScience#Rtutorial #Rprogramming #Ronlinetraining #Rforbeginners Our blog series on R performance: Which part of the code is slow? 🤍 How to make code faster 🤍 Parallelization 🤍 INWT: 🤍 Music: Summer Smile - Silent Partner 🤍 R Logo: R Logo (🤍 by The R Foundation (🤍 used under CC-BY-SA 4.0 (🤍 / Rotated from original
CODE R (1977) - OPENING. The adventures on California's Channel Island combined three different elements of life-saving: the fire department, the police department, and the ocean rescue department.
YOU CAN SUPPORT ME HERE: 🤍 One picture is worth a thousand words. That’s why visualizing data and model results is a crutial skill for any data scientist. {sjPlot} package became my favorite tool for visualization. That’s why I want to share with you some simple but very effective commands which will make you more productive today. So, let’s visualize Wage dataset, visualize bunch of models and see what people earn and what factors determine the salary. For more details and R code go to: 🤍 Enjoy! 🥳
The 🤍 uses AI to turn your regular written (or spoken) instructions into R code. In this video I try a range of different instructions - from specific to vague - to test how well the AI can generate code (and the spoiler is that it is pretty darn good, it might put me out of a job eventually). My SkillShare courses: (includes a 1 month free trial) Getting started with R 🤍 How to Write an Academic Journal Article 🤍 How to read an academic paper 🤍 Introduction to Flipped Classroom Teaching 🤍 Connect with me: 🐥 My Twitter - 🤍 💻 My Website - 🤍 Join my Facebook Community - 🤍
🔔 *Subscribe for weekly R videos:* 🤍 Here's how to simplify R code in RStudio and automatically clean up messy R code using a keyboard shortcut in RStudio. This video covers how to use RStudio's default method to clean up R code, then look at how to install the "styler" package, set up keyboard shortcuts, and also how to clean up code in both plain R files and in Rmd markdown files. 🎉 *Enjoyed this video?* Leave a comment below to share what you liked the most! 0:00 Intro 0:40 RStudio built-in code reformatting 2:15 Install styler 3:09 Reformat code with styler 3:40 Rebind keyboard shortcut 5:05 Reformat markdown-contained R code
Table of Contents: 00:44 - Install and Setup R Extensions 04:54 - Setup R Extensions 06:17 - Setting up a radian R console Finding Path to Radian on Windows: Once radian is installed, instead of typing `which radian`, type `Get-Command radian` in PowerShell, or `where radian` if you're using a CMD console.
SUBSCRIBE for more failed, cancelled & beta games ► 🤍 Failed Nintendo Pitches ► 🤍 Rare Nintendo Consoles ► 🤍 Today on Game History Secrets, Liam talks about his journey uncovering the development of Sonic R, a Sonic game made by Traveller's Tales and overseen by the usual developers of Sonic the Hedghog games, Sonic Team, for the Sega Saturn. Liam Robertson is a video game researcher and archiver. In his spare time, Liam creates videos and articles for various outlets, and will also be contributing to the Did You Know Gaming channel. Follow DYKG on: Twitter: 🤍 Instagram: 🤍 Facebook: 🤍 Website: 🤍 Sources/Further Reading: 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 GameInformer Magazine issue #124, p113 SEGA Saturn Magazine (JP) - June 1997 issue, P40 SEGA Saturn Magazine (UK) - August 1997 issue, p19 SEGA Saturn Magazine (UK) - October 1997 issue, p21 Tips & Tricks Magazine - September 1997 issue (031), p47 Electronics Boutique Christmas 1997 Catalog, p85 #sonic #sonicr #sonicthehedgehog #sega
Prise en main du logiciel R - Tutoriel : Vos premiers pas avec R. - Dans la série R pour les nuls - #statistique #biostatistique #R Vidéo d'initiation permettant de démarrer l'utilisation de R, logiciel dédié au traitement statistique des données. Aucun prérequis n'est nécessaire. Laissez vous guider dans vos tous premiers pas avec R pour voler ensuite de vos propres ailes. Si vous aimez cette chaîne et ses vidéos, recommandez-la et likez les vidéos avec un pouce bleu et soutenez mon travail sur tipeee : 🤍
Welcome to Coffee Walk Ep. 205! My Holy Grail Blend Blue Island Coffee is Here: 🤍 As always… GO FAST, HAVE FUN & HAVE A GREAT WEEKEND!! And thank you for spending your Friday with us. #denniscollinscarworld #coffeewalk #ford #mustang COFFEE WALK SHIRTS: USA: 🤍 International: 🤍 COFFEE WALK TEAM SOCIALS: Instagram: 🤍thedenniscollins 🤍thekelseycollins 🤍alexbohling 🤍zachgorgas 🤍patterns.media COLLINS BROS JEEP (Jeep CJ & Wrangler Experts): Web: 🤍 Facebook: 🤍 Instagram: 🤍 Phone: 972-442-6189 Address: 3101 W FM 544 Wylie, TX 75098 Email: Social🤍CBJeep.com BLKMTN (Jeep JK & JL Parts, Accessories, Conversions & Upgrades): Web: 🤍 Facebook: 🤍 Instagram: 🤍 *All Audio and SFX legally downloaded and licensed from Envato.
In this tutorial session, you will learn to set up the R programming environment in Visual Studio code. Please watch the video till the end, I have explained everything in detail in this tutorial. We have an R extension for Visual Studio Code that supports extended syntax highlighting, code completion, linting, formatting, interacting with R terminals, viewing data, plots, workspace variables, help pages, managing packages, and working with R Markdown documents. Let's Begin- Step 1: Downloading R and Installing on windows 10. link: 🤍 Note : For Windows users, it is recommended to check the Save version number in the registry during installation so that the R extension can find the R executable automatically. Step 2: Install the language server in R. install.packages("languageserver") Step 3: Open VS code then install the 'R extension' for Visual Studio Code. Step 4: Let's create an R file and start coding. Example code: # Define 2 vectors cars - c(1, 3, 6, 4, 9) trucks - c(2, 5, 4, 5, 12) # Graph cars using a y-axis that ranges from 0 to 12 plot(cars, type="o", col="blue", ylim=c(0,12)) # Graph trucks with red dashed lines and square points lines(trucks, type="o", pch=22, lty=2, col="red") # Create a title with a red, bold/italic font title(main="Autos", col.main="red", font.main=4) Step 5: Running R code. If you found this video tutorial useful and would like to support us, you can do so by buying me a coffee using the below link! 🤍 #RsetupVisualStudioCode #coolithelp
Do you have R scripts where you repeat the same chunk of code over and over? You need to learn about the DRY principle! DRY stands for Don't Repeat Yourself. Following this principle will make your code easier to manage and help you to avoid propogating errors across your project. Check out this episode to see how Pat uses the DRY principle to minimize duplication and then demonstrates how to expand his code using the DRY principle. In this episode, Pat will use functions from the #mikropml R package and data handling functions from #dplyr in #RStudio. The accompanying blog post can be found at 🤍 If you're interested in taking an upcoming 3 day R workshop, email me at riffomonas🤍gmail.com! R: 🤍 RStudio: 🤍 Raw data: 🤍 Workshops: 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Introduction 8:00 Replacing duplicated R scripts with DRY code 15:35 Creating additional models with DRY code 20:39 Strategies for making code DRY 24:36 Challenge problems
If you have a function that you are repeating multiple times in R using the map or apply functions, you can speed it up by running it in parallel using the furrr package. In this episode of Code Club, Pat runs the run_ml function from mikropml 100 times using the future_map function to get it to run faster as he tries to evaluate different hyperparameter settings for a machine learning model. He describes how to set up parallelization and uses the tictoc package to time the performance of the code run in series vs parallel. The data he uses is from a microbiome study his lab has published looking for biomarkers associated with colorectal cancer. In this episode, Pat will use functions from the #furrr, #tictoc, and #mikropml R packages and data handling functions from dplyr and the rest of the tidyverse in #RStudio. The accompanying blog post can be found at 🤍 If you're interested in taking an upcoming 3 day R workshop, email me at riffomonas🤍gmail.com! R: 🤍 RStudio: 🤍 Raw data: 🤍 Workshops: 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Introduction 2:54 Timing serial execution of code 6:26 Parallelizing code with furrr package 12:56 Synthesizing 100 splits 15:08 Recap
This YCRC workshop is a two-hour intermediate course on writing efficient R code and covers how to perform common operations faster in R and how to profile and benchmark your code to make your R scripts run more efficiently. Recorded April 22, 2020.
The fastest Julia algorithm was 30,249 times faster than the worst R! Check out how the others approaches compare! Calling other languages in your R programming code is easy. In this tutorial, we made use of Julia programming language directly in R to see if it could accelerate our code. We contrast 5 different algorithms and talk about when you should (or should not!) use them. Subscribe to learn more about RStats and how you can make your code faster. 👀 00:00 Introduction and dataset 01:26 Setting up Julia 02:18 1st approach: Growing a vector with for loops & translating R code to Julia code 06:57 2nd approach: Dataframe pre-allocation 08:25 3rd approach: Lists - vectorization 10:04 4th approach: Linear algebra - matrices 11:35 5th approach: Dataframe manipulation 13:29 Global all-approach benchmarking 🐱 Get the code here: 🤍 🏎️ R performance playlist 🤍 🧮 dplyr playlist 🤍 #R #Rprogramming #Juliaprogramming #Rtutorial #RStats #performance #RStudio #datascience #DDS #DDSR #datatable #dplyr
Welcome to Coffee Walk Ep. 187! Sometimes you find the Holy Grail of 1968.5 R-Code Mustangs and you have to gift a real Holy Grail to the seller. This is one of those times! As always… GO FAST, HAVE FUN & HAVE A GREAT WEEKEND!! #denniscollinscarworld #coffeewalk #mustang #rcode COFFEE WALK SHIRTS ARE NOW AVAILABLE!! USA: 🤍 International: 🤍 COFFEE WALK TEAM SOCIALS: Instagram: 🤍thedenniscollins 🤍thekelseycollins 🤍alexbohling 🤍zachgorgas 🤍patterns.media 🤍theseanpettiford For 25% off ALL BLUE ISLAND COFFEE on 🤍 please use... Promo Code: coffee walk *all lower case, space in between coffee and walk* More about Collins Bros Jeep Web: 🤍 Facebook: 🤍 Instagram: 🤍 Phone: 972-442-6189 Address: 3101 W FM 544 Wylie, TX 75098 Email: Social🤍CBJeep.com More about BLKMTN Web: 🤍 Facebook: 🤍 Instagram: 🤍 *All Audio and SFX legally downloaded and licensed from Envato
Welcome to Coffee Walk Ep. 200! Pre-Order your Holy Grail Blend Blue Island Coffee Here: 🤍 As always… GO FAST, HAVE FUN & HAVE A GREAT WEEKEND!! And thank you for spending your Friday with us. #denniscollinscarworld #coffeewalk #mustang #shelby COFFEE WALK SHIRTS: USA: 🤍 International: 🤍 COFFEE WALK TEAM SOCIALS: Instagram: 🤍thedenniscollins 🤍thekelseycollins 🤍alexbohling 🤍zachgorgas 🤍patterns.media COLLINS BROS JEEP (Jeep CJ & Wrangler Experts): Web: 🤍 Facebook: 🤍 Instagram: 🤍 Phone: 972-442-6189 Address: 3101 W FM 544 Wylie, TX 75098 Email: Social🤍CBJeep.com BLKMTN (Jeep JK & JL Parts, Accessories, Conversions & Upgrades): Web: 🤍 Facebook: 🤍 Instagram: 🤍 *All Audio and SFX legally downloaded and licensed from Envato.
The Sweave is an R function which can combine R programming language and latex documents for reproducing results. This video is a demonstration of how to run Sweave to obtain a dvi or pdf file. It is expected that the viewers already familiar with R and Latex commands. For Sweave function, RStudio software is introduced. Useful Links: 1. MikTeX: 🤍 2. R program: 🤍 3. RStudio: 🤍 4. Sumatra PDF: 🤍 If this worked and helped you ► Do not forget to Like, Comment, and Subscribe! = ► To Watch all our videos, click here - 🤍 ► Subscribe to our channel, click here - 🤍
If you do not have a good system or the system configuration is not good enough for processing complex code or algorithm, that should not stop our learning curve there. Thanks to Google Colab and Kaggle kernels, we can still continue with our learning. In this tutorial, I will be taking you through these two amazing links which are widely used by the Data Science community for enhancing their learning. Link for accessing Kaggle Kernel: 🤍 (Click on +New Notebook and then select the Language of your choice : R, Python) Link for accessing Google Colab: 🤍 Ed Adityawarman's article on how to use R in Google Colab: 🤍 How to use R and Python together in Colab 1. Open your favorite browser. 2. Create a new notebook: 🤍 3. Run rmagic by executing this command %load_ext rpy2.ipython. 4. After that, every time you want to use R, add %%R in the beginning of each cell. Other options for running R script online : 🤍 🤍 Note : You need to use the "print" command to print the output Other options for running Python script online: 🤍 🤍 Note : You need to use the "print" command to print the output
YOU CAN SUPPORT ME HERE: 🤍 If you ever wandered how to correctly describe the results of statistical tests and models, this video is for you. In a few minutes you’ll learn how to report the results of correlations, t-tests, Generalised Linear Models, Mixed-Effects models, Bayesian Models and even more 😉 For more details and R code go to: 🤍 Enjoy! 🥳
Creating an R package is easier than you may think! Here we create a simple package that contains the minimum file structure, a custom function, and a help file for that function. Thanks to RStudio, this can be done in 2 minutes! Contact me, e. g. to discuss (online) R workshops / trainings / webinars: LinkedIn: 🤍 Twitter: 🤍 Xing: 🤍 Facebook: 🤍 🤍 R Workshops: 🤍 Blog (German, translate option): 🤍 Playlist: Music chart history 🤍
*Note: I may be compensated, but you will not be charged, if you click on the links below. In this video, Monika Wahi shows you four different ways to run code in R GUI. Files on Github: 🤍 Data from American Hospital Directory: 🤍 Video where we use the code being demonstrated: 🤍 💡 If you are a public health practitioner looking for a targeted program for enhancing your data science skills, please sign up to participate in my 30-minute market research interview so I can get your feedback: 🤍 WANT TO SUPPORT MONIKA ON SOCIAL MEDIA? ❤️Subscribe to Monika's YouTube channel: 🤍 🧡Follow/connect with Monika on LinkedIn: 🤍 💛Follow Monika on Twitter: 🤍 💚Try Monika’s courses on LinkedIn Learning: 🤍 💙Try Monika's boutique research methods and data science courses here: 🤍 Timestamps and links as the come up are below: 00:30 What this video is about 00:58 How you can tell if a window is a code window 01:16 Description/information about the console window 01:55 The lines of code we will practice running 02:21 Demonstration of method #1: Highlight code, right-click, and choose “run line or selection”. 03:41 Demonstration of method #2: Type code directly into the console and hit enter. 04:09 Demonstration of clearing the console using right-click and choosing “clear window” 04:37 Demonstration of method #3: Highlight code, then hold down the Control key and hit “r”. 05:32 Demonstration of method #r: Highlight code, then choose Edit – Run line or selection. How to Run Code in R Four Different Ways - Demonstration
One sick 1966 Fairlane 427 FE, R-Code car! What a killer machine! Caught it just before getting loaded onto the trailer! Listen to that BBF! Oh how I'd love to open it up on a freshly paved country road!
R-Ladies Baltimore (Bmore) hosted a meetup on Wednesday May 4, 2022 featuring an awesome presentation from Isabella Ghement (🤍IsabellaGhement, 🤍 the principal of Ghement Statistical Consulting Company and an expert statistician, R trainer and statistical consultant. She gave a presentation on how to use the R package purrr.
Top posts from r/Extremelyinfuriating on Reddit. Join the community subreddit at 🤍 ! Video credits below. Narrator ► 🤍 Also check out: r/Techsupportgore | melted :( 🤍 r/Foundsatan | how? 🤍 r/Insaneparents | BRUH CHILL 🤍
How to insert R code to be executed in R Markdown using code chunks. To download an html file of the text shown in the video, run download.file("🤍 "02-crash-course-in-rmd.nb.html") in the R console. The file will be downloaded to your current working directory (run getwd() in the R console to determine this). The raw R Markdown code used to generate the html file is available at 🤍 General information about Data Wrangling and Visualization is available at 🤍
Set your R code free; turn it into a command-line tool by Jeroen Janssens Visit 🤍 to learn more. Abstract: If your data analyses involve coding, then you know how liberating it is to use and create functions. They hide complexity, improve testability, and enable reusability. In this talk I explain how you can really set your R code free: by turning it into a command-line tool. The command line can be a very flexible and efficient environment for working with data. It's specialized in combining tools that are written in all sorts of languages (including R and Python), running them in parallel, and applying them to massive amounts of (streaming) data. Although the command line itself has quite a learning curve, turning your existing R code into a tool is, as I demonstrate, a matter of a few steps. I discuss how your new tool can be combined with existing tools in order to obtain, scrub, explore, and model data at the command line. Finally, I share some best practices regarding interface design. Bio: Jeroen Janssens is an independent data science consultant and an RStudio-certified instructor. He enjoys visualizing data, implementing machine learning models, and building solutions using Python, R, JavaScript, and Bash. He’s passionate about helping and teaching others to do such things. Jeroen runs Data Science Workshops, a training and coaching firm that organizes open enrollment workshops, in-company courses, inspiration sessions, hackathons, and meetups. Previously, he was an assistant professor at Jheronimus Academy of Data Science and a data scientist at Elsevier in Amsterdam and various startups in New York City. He is the author of Data Science at the Command Line, published by O’Reilly Media. Jeroen holds a PhD in machine learning from Tilburg University and an MSc in artificial intelligence from Maastricht University. He lives with his wife and two kids in Rotterdam, the Netherlands. Twitter: 🤍 Presented at the 2021 New York R Conference (September 9, 2021)
YOU CAN SUPPORT ME HERE: 🤍 In this video, we'll learn how to use three most important resampling techniques: train-test split, cross-validation and bootstrapping. Random sampling, stratified sampling for numeric and categorical variables. What is not in the video are grouped resampling and time specific resampling, they deserve a separate video each. For more details and R code go to: 🤍 Enjoy! 🥳
The benefit of coding for reproducibilty is reusing your R code to generate a figure that you've used before, but for a different dataset. Pedagogically, it's also a great way to learn to code because you can take code that works, break it with new data and learn how the code works by fixing it. That's just what Pat will do in this episode of Code Club. Pat will use RStudio and functions from the ggplot2 package and other packages from the tidyverse. The accompanying blog post can be found at 🤍 Do you have a figure that you would like to receive a critique or help improving? Let me know and I'd be happy to arrange a guest appearance! If you're interested in taking an upcoming 3 day R workshop, email me at riffomonas🤍gmail.com! R: 🤍 RStudio: 🤍 Raw data: 🤍 Workshops: 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Reproducibility 3:04 Modifying adonis code 14:01 Modifying ordination code 21:33 Conclusion
This video will show you how to execute R and Python codes in a single Jupyter notebook.
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