Linear Regression Plots in R Explained When plotting your linear regression model, you'll see the following 4 graphs: - Residuals vs Fitted Values - Normal Q-Q (Quantile-Quantile) Plot - Scale-Location / Spread-Location Plot - Residuals vs Leverage Plot We'll cover what each of these graphs mean and how you can use them to interpret the validity of your linear regression model. Timeline: 0:00 Intro 2:09 Residuals vs Fitted Values 5:45 Normal Q-Q (Quantile-Quantile) Plot 8:58 Scale-Location / Spread-Location Plot 10:03 Residuals vs Leverage Plot Dataset: https://www.kaggle.com/aungpyaeap/fish-market/version/2 Part 1 (Regression Summary): https://www.youtube.com/watch?v=7WPfuHLCn_k Additional info: https://data.library.virginia.edu/diagnostic-plots/

Dplyr Advanced Guide: data cleaning, reshaping, and merging with lubridate, stringr, tidyr, ggplot2 Timeline 0:00 Intro 1:30 Cleaning dates 3:15 String cleaning & extraction 7:30 Reshaping data 10:57 Merging data frames 14:05 Graphing Code: https://github.com/abhimotgi/dataslice/blob/master/R/Dplyr%20Advanced%20Topics.Rmd Datasets: Billboard Hot 100: https://www.kaggle.com/dhruvildave/billboard-the-hot-100-songs MTV 10,000 artists: https://gist.github.com/mbejda/9912f7a366c62c1f296c Cheat sheets: Lubridate: https://evoldyn.gitlab.io/evomics-2018/ref-sheets/R_lubridate.pdf Stringr: https://evoldyn.gitlab.io/evomics-2018/ref-sheets/R_strings.pdf Tidyr: https://community.rstudio.com/t/tidyr-cheatsheet-with-pivot-functions/62981 Ggplot2: https://www.maths.usyd.edu.au/u/UG/SM/STAT3022/r/current/Misc/data-visualization-2.1.pdf

Dplyr Essentials (easy data manipulation in R): select, mutate, filter, group_by, summarise, & more Timeline 0:00 Intro 1:01 Piping 2:15 select() 4:36 mutate() 5:54 filter() 7:19 distinct() 8:39 group_by() 8:53 summarise() 9:40 arrange() 10:26 count() Links: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf https://www.kaggle.com/dhruvildave/billboard-the-hot-100-songs https://github.com/abhimotgi/dataslice/blob/master/R/Dplyr%20Essentials.Rmd

Scrape Websites with Regular Expressions Timeline 0:00 Intro 0:16 Data cleaning 4:01 Basic web scraping (Reddit) 6:03 Advanced web scraping (Untappd) Data sources: https://en.wikipedia.org/wiki/List_of_wheel-well_stowaway_flights https://www.reddit.com/r/politics/ https://untappd.com/brewery/top_rated Links: https://regex101.com/ https://www.smashingmagazine.com/2009/05/introduction-to-advanced-regular-expressions/ https://cheatography.com/davechild/cheat-sheets/regular-expressions/

Regex Basics | Match, Extract, and Clean Text Regex is a tool to find different patterns in text, and it can speed up your workflow if you do any kind of data cleaning or data scraping. In this video, I explain a few essential regex concepts which should be enough for most scenarios. Timeline: 0:00 Intro 0:32 Character classes 2:06 Groups 4:06 Quantifiers 6:08 Anchors 7:32 Escape characters 7:45 Further reading Links: https://regex101.com/ https://www.smashingmagazine.com/2009/05/introduction-to-advanced-regular-expressions/ https://cheatography.com/davechild/cheat-sheets/regular-expressions/

Build a Data Science Portfolio (Free & Easy) with Jekyll & GitHub Pages | Part 3: Customization Part 1: https://youtu.be/wCOInE7-E0I Part 2: https://youtu.be/Td_NjdrwPkQ Timeline Intro 0:00 Removing and adding pages 0:35 Changing images on main pages 1:06 Editing content on a page 1:50 Modifying layouts 2:32 Adding a logo to the navbar 3:18 Setting a favicon 6:18 Changing website's fonts 7:36 Configuring syntax highlighting 8:47 Commit & push changes 10:56 Further reading 11:13 Links: Create favicon: https://realfavicongenerator.net/ Find fonts: https://fonts.google.com/ Syntax highlighting CSS: https://github.com/yaitloutou/sass-code-highlight Additional jekyll plugins: https://github.com/planetjekyll/awesome-jekyll-plugins Custom domains: https://www.namecheap.com/ https://domains.google.com/ GitHub Pages Documentation: https://docs.github.com/en/free-pro-team@latest/github/working-with-github-pages/getting-started-with-github-pages

Build a Data Science Portfolio (Free & Easy) with Jekyll & GitHub Pages | Part 2: Adding Posts Watch Part 1: https://www.youtube.com/watch?v=wCOInE7-E0I Timeline 0:00 Intro 0:34 Removing unused files 1:31 Manually written blog post 4:50 Jupyter notebook blog post 6:37 R Notebook blog post 8:48 Embedding interactive visualizations Markdown syntax: https://www.markdownguide.org/basic-syntax/ Additional Resources: Giraffe Academy Jekyll Tutorial Series: https://youtube.com/playlist?list=PLLAZ4kZ9dFpOPV5C5Ay0pHaa0RJFhcmcB HTML & CSS Tutorials: https://www.w3schools.com/html/ https://www.w3schools.com/css/default.asp GitHub Pages Documentation: https://docs.github.com/en/free-pro-team@latest/github/working-with-github-pages/getting-started-with-github-pages

Build a Data Science Portfolio (Free & Easy) with Jekyll & GitHub Pages | Part 1: Site Configuration Watch Part 2: https://youtu.be/Td_NjdrwPkQ Timeline 0:00 Intro 0:58 Finding & forking a theme 3:05 Folder structure explained 4:43 Site configuration 7:09 Additional resources Requirements Github account: https://github.com Jekyll: https://jekyllrb.com/docs/installation/ and dependencies An IDE: Visual Studio Code https://code.visualstudio.com/ Jekyll Themes: https://github.com/topics/jekyll-theme https://jekyllthemes.io/ http://jekyllthemes.org/ https://jamstackthemes.dev/ssg/jekyll/ The theme that I'm using (Clean Blog Jekyll): https://github.com/StartBootstrap/startbootstrap-clean-blog-jekyll Additional Resources: Giraffe Academy Jekyll Tutorial Series: https://youtube.com/playlist?list=PLLAZ4kZ9dFpOPV5C5Ay0pHaa0RJFhcmcB HTML & CSS Tutorials: https://www.w3schools.com/html/ https://www.w3schools.com/css/default.asp GitHub Pages Documentation: https://docs.github.com/en/free-pro-team@latest/github/working-with-github-pages/getting-started-with-github-pages

R Maps: Beautiful Interactive Choropleth & Scatter Maps with Plotly Timeline 0:00 Intro 0:28 Reading in data (Choropleth) 1:44 Building base graph (Choropleth) 3:06 Functional & aesthetic changes (Choropleth) 8:02 Reading in data (Scatter map) 8:02 Building Base graph (Scatter map) 9:30 Functional & aesthetic changes (Scatter map) 11:50 Further reading Learn how to make beautiful interactive maps in R in just a few minutes! Plotly isn't always the best mapping solution for some problems, but overall, it can function really well. Code: https://github.com/abhimotgi/dataslice/blob/master/R/Maps%20in%20R.R Plotly maps documentation: https://plotly.com/r/choropleth-maps/ https://plotly.com/r/scatter-plots-on-maps/ US Minimum Wage by States: https://www.kaggle.com/lislejoem/us-minimum-wage-by-state-from-1968-to-2017 US States & Codes: https://worldpopulationreview.com/states/state-abbreviations UFO Sightings: https://www.kaggle.com/NUFORC/ufo-sightings

Make Interactive Graphs in R: Creating & Embedding Interactive Graphs with plotly 0:00 Building base graph 3:22 Making interactive graph 10:15 Exporting and embedding graphs Dataset: https://www.kaggle.com/agirlcoding/all-space-missions-from-1957 Plotly documentation: https://plotly.com/ggplot2/getting-started/ Code: https://github.com/abhimotgi/dataslice/blob/master/R/Interactive%20Graphs.Rmd

Web Scrape in Google Sheets: IMPORTXML Function (Part 2) Part 1: https://youtu.be/Q5yVoTqD06M In this video, we scrape Yelp, Craigslist, and TechCrunch using the importxml function in Google Sheets. W3 Schools Tutorial: https://www.w3schools.com/xml/xpath_intro.asp ---------------- Datase ---------------- Craigslist: https://philadelphia.craigslist.org/d/cars-trucks/search/cta TechCrunch: https://techcrunch.com/ Yelp: https://www.yelp.com/search?find_desc=burgers&find_loc=New+York%2C+NY&ns=1 ---------------- Timeline ---------------- 0:00 Intro 0:40 Scraping Craigslist 4:42 Scraping TechCrunch 5:28 Scraping Yelp 5:8:26 Summary

Web Scrape in Google Sheets: IMPORTHTML, IMPORTDATA, & IMPORTFEED Functions (Part 1) Part 2: https://youtu.be/4A12xqQPJXU ---------------- Code ---------------- Tables: var i = 1; [].forEach.call(document.querySelectorAll('table'), function(x) { console.log(i++, x); }); Lists: var i = 1; [].forEach.call(document.querySelectorAll('ul,ol'), function(x) { console.log(i++, x); }); ---------------- Datasets ---------------- Apple Species: https://en.wikipedia.org/wiki/List_of_apple_cultivars Earthquakes: https://en.wikipedia.org/wiki/Lists_of_earthquakes Zha Jiang Mian Recipe: https://www.thespruceeats.com/zhajiangmian-chinese-noodle-recipe-4006698 Github CSV: https://github.com/datasets/population/blob/master/data/population.csv NASA RSS Feed: https://www.nasa.gov/content/nasa-rss-feeds/ ---------------- Timeline ---------------- 0:00 Intro 0:36 ImportHTML 4:54 ImportDATA 5:46 ImportFEED 5:59 Summary

Word Clouds in R: Useful & Beautiful Word Clouds with wordcloud2 In this video, I show you how to make word clouds with the tm library and wordcloud2 library in just a few minutes. We first start by cleaning our text, then building our word cloud. We don't just stop there though, because there are lots of potential improvements you can make to your wordcloud. Code: https://github.com/abhimotgi/dataslice/blob/master/R/Word%20Clouds%20in%20R.R Kaggle Medium Posts Dataset: https://www.kaggle.com/dorianlazar/medium-articles-dataset?select=medium_data.csv Word Cloud 2 Documentation: https://cran.r-project.org/web/packages/wordcloud2/vignettes/wordcloud.html Marti Hearst's Medium Article: https://medium.com/multiple-views-visualization-research-explained/improving-word-clouds-9d4a04b0722b 0:00 Intro & Setup 1:44 Create corpus and clean text 3:44 Create term document matrix 5:14 Build word cloud 6:14 Improve your word cloud 12:09 Outro

Animate Graphs in R: Make Gorgeous Animated Plots with gganimate Katherine Goode's slides: https://goodekat.github.io/presentations/2019-isugg-gganimate-spooky/slides.html#1 gganimate Documentation: https://gganimate.com/reference/index.html Code: https://github.com/abhimotgi/dataslice/blob/master/R/gganimate%20code.R Datanovia walkthrough (this video builds off the examples): https://www.datanovia.com/en/blog/gganimate-how-to-create-plots-with-beautiful-animation-in-r/ Video Game Sales Dataset: https://www.kaggle.com/gregorut/videogamesales Google Play Store Apps Dataset: https://www.kaggle.com/lava18/google-play-store-apps 0:00 Intro 0:55 Graph 1: Transition through time 6:08 Graph 2: Data gradually appears 10:25 Graph 3: Transition between states 13:20 Additional documentation

Make Beautiful Graphs in R: 5 Quick Ways to Improve ggplot2 Graphs In this video, I'll show you a few different tips and tricks to style your ggplot2 graphs and make them less basic looking. With the help of a couple libraries, you can produce really clean and professional ggplot2 graphs without much effort! Code: https://github.com/abhimotgi/dataslice/blob/master/R/Make%20Attractive%20Graphs%20in%20R.R ggthemes: https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/ RColorBrewer palettes:https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/ Data: https://www.kaggle.com/neuromusic/avocado-prices 0:00 Intro 0:34 Base graph 1:04 Adding labels 2:06 Setting a theme 3:34 Emphasize data 6:54 Change the colors 8:32 Change the font 9:49 Summary