class: center, middle, inverse, title-slide # Teaching an Introductory Data Science course with tidyverse workshop ## Wrap up ### Dr. Mine Dogucu ### 2021-12-15 --- layout: true <!-- This file by Mine Dogucu is licensed under a Attribution-ShareAlike 2.5 Generic License (CC BY-SA 2.5) More information about the license can be found at https://creativecommons.org/licenses/by-sa/2.5/ --> <div class="my-header"></div> <div class="my-footer"> CC BY-NC-ND 4.0 <a href="https://mdogucu.ics.uci.edu">Mine Dogucu</a></div> --- class: middle ## Schedule for the Day 10:00 - 10:15 Introduction and Setup 10:15 - 11:15 Introduction to Toolkit and Data Basics 11:20 - 12:30 Data Visualization 1:00 - 1:45 Data Wrangling 1:45 - 2:15 Packages and External Datasets __2:15 - 2:30 Wrap Up__ --- class: model ## If we had more time - Inference using `library(infer)`. - Modeling (linear regression and logistic regression) - Git and GitHub (maybe second year of teaching) --- class: middle ## Textbooks and Other Resources - [Introduction to Data Science](https://ubc-dsci.github.io/introduction-to-datascience/) - [Modern Data Science with R](https://mdsr-book.github.io/mdsr2e/) - [R for Data Science (R4DS)](https://r4ds.had.co.nz/) - [learnR4free](https://www.learnr4free.com/en/index.html) - --- ## Where to Find Datasets - R Packages ```r library(fivethirtyeight) ``` ``` ## Some larger datasets need to be installed separately, like senators and ## house_district_forecast. To install these, we recommend you install the ## fivethirtyeightdata package by running: ## install.packages('fivethirtyeightdata', repos = ## 'https://fivethirtyeightdata.github.io/drat/', type = 'source') ``` ```r library(openintro) ``` ``` ## Loading required package: airports ``` ``` ## Loading required package: cherryblossom ``` ``` ## Loading required package: usdata ``` ``` ## ## Attaching package: 'openintro' ``` ``` ## The following object is masked from 'package:fivethirtyeight': ## ## drug_use ``` ```r library(titanic) library(palmerpenguins) library(bayesrules) ``` ``` ## ## Attaching package: 'bayesrules' ``` ``` ## The following object is masked from 'package:fivethirtyeight': ## ## bechdel ``` Search for package vignette --- class: middle ## Where to Find Datasets - External [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php) [Harvard Dataverse](https://dataverse.harvard.edu/) [Data.gov](https://www.data.gov/) [US Government Data](https://sctyner.github.io/static/presentations/Misc/GraphicsteamISU/2018-11-16-us-govt-data.html) [California Open Data](https://data.ca.gov/) [Los Angeles Open Data](https://data.lacity.org/browse) [NYC OpenData](https://opendata.cityofnewyork.us/) --- class: middle ## Where to Find Datasets - External [Data is Plural](https://docs.google.com/spreadsheets/d/1wZhPLMCHKJvwOkP4juclhjFgqIY8fQFMemwKL2c64vk/edit#gid=0) [UN data](http://data.un.org/) [IPUMS survey data from around the world](https://ipums.org/) [CORGIS: The Collection of Really Great, Interesting, Situated Datasets](https://think.cs.vt.edu/corgis/csv/) [PISA data](https://www.oecd.org/pisa/data/) [Google Dataset Search](https://datasetsearch.research.google.com/) --- class: middle Weekly folder - .Rmd file for taking lecture notes - .Rmd file for homework - .Rmd file for quizzes - datasets - One `.Rproj` file per week. --- class: middle ## Making slides with xaringan package 1. install the package 2. File -> New File -> R Markdown -> From Template -> Ninja Presentation; 3. Knit the template --- class: middle ## Schedule for the Day __10:00 - 10:15 Introduction and Setup__ __10:15 - 11:15 Introduction to Toolkit and Data Basics__ __11:20 - 12:30 Data Visualization__ __1:00 - 1:45 Data Wrangling__ __1:45 - 2:15 Packages and External Datasets__ __2:15 - 2:30 Wrap Up__