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Weekly Readings Palmer

Case Study 1

Being Readings
  • o John Rauser Talk
  • o Being a better online reader
Doing Readings
  • o Making your first R Markdown File
  • o Chapter 27: R for Data Science - R Markdown
  • o Chapter 30: R for Data Science - R Markdown workflow
  • o Chapter 1: R for Data Scientists
  • o Getting used to R, Rstudio, and R Markdown (R basics)
  • o R and R-Studio Practice
  • o Chapter 4: R for Data Scientists - Workflow Basics

Case Study 2

Being Readings
  • o Computational Thinking
  • o Optional Reading for new programmers
  • o Effectively Communicating Numbers (pg 1-13)
  • o Hans Rosling: The best stats you’ve ever seen
  • o Questions and data science
Doing Readings
  • o Modern Drive: Chapter 1 Getting Started with Data in R
  • o Using the geom_col function
  • o Using the geom_line function
  • o Chapter 3: R for Data Science - Data visualization
  • o Creating Questions for your project (watch the videos that are free)
  • o Chapter 5: R for Data Science - Data transformation
  • o MCS 335, Git, and Github use
  • o Why Git, Git, and GitHub setup (optional)
  • o Getting Github and Git connected to R-Studio (optional)
  • o Chapter 8: R for Data Science - Projects (optional)
  • o R-Studio and the Git GUI (optional)

Case Study 3

Being Readings
  • o Hans Rosling: The River of Myths
  • o Data Scientist Florence Nightengale
  • o How to Become a Data Scientist, The Self-Starter Way
  • o What’s The Best Path To Becoming A Data Scientist?
Doing Readings
  • o Example of GitHub Issue conversation
  • o Posting Issues on GitHub
  • o reprex R Package
  • o Chapter 28: R for Data Science - Graphics for communication
  • o Chapter 6: R for Data Science - Scripts
  • o Chapter 11: R for Data Science - Data Import
  • o Chapter 28: R for Data Science - Graphics for communication

Case Study 4

Being Readings
  • o What do people do with new data
  • o Data Visualization (Chapter 1 - Look at data)
  • o Being a good critiquer
Doing Readings
  • o Finding data to answer your question
  • o Find a post from the functional art
  • o Chapter 20: R for Data Science - Vectors
  • o Chapter 18: R for Data Science - Pipes
  • o Chapter 7: R for Data Science - Exploratory Data Analysis
  • o devtoools R Package

Case Study 5

Being Readings
  • o Hadley on Tidy Data (skim read)
  • o The art of structured thinking and analyzing
  • o Tools for improving structured thinking (for analysts)
  • o Quartz Reference for How to deal with data issues (optional)
Doing Readings
  • o foreign R Package and read.dbf()
  • o Chapter 11: R for Data Science - Data Import
  • o haven R Package
  • o readxl R Package
  • o downloader R Package
  • o Chapter 12: R for Data Science - Tidy Data
  • o tidy R Package functions
  • o openxlsx R package

Case Study 6

Being Readings
  • o Effectively Communicating Numbers (pg 13-20)
Doing Readings
  • o Regular Expressions in R
  • o Chapter 14: R for Data Science - Strings
  • o RVerbalExpressions package
  • o regexr.com (optional)
  • o Regular Expression examples (optional)
  • o Regular Expression support applet (optional)
  • o Regular Expression for R (optional)
  • o Chapter 13: R for Data Science - Relational Data

Case Study 7

Being Readings
  • o Statistical Concepts in Presenting Data (pgs 72 - 85)
Doing Readings
  • o The Book of Mormon
  • o Populating missing values
  • o Chapter 15: R for Data Science - Factors
  • o forcats R package
  • o Chapter 21: Iteration (21.1-21.5 is all you will need)
  • o Hadley Wickham - Cupcakes to teach for loops
  • o stringi R package and the stri_stats_latex() function

Case Study 8

Being Readings
  • o What charts say
  • o Chapter 4: The Truthful Art: Data, Charts, and Maps for Communication
  • o Plotly: Time Series Blog Post
Doing Readings
  • o lubridate R package
  • o Chapter 16: R for Data Science - Dates and Times
  • o Time Series Visualization Gallery
  • o lubridate Vignette
  • o Tips for timed tests
  • o Completing timed work

Case Study 9

Being Readings
  • o Five principles of effective data visualizations
  • o What charts do
  • o 10 Myths about Data Science
Doing Readings
  • o See task 16
  • o tidyquants R package
  • o dygraphs for R
  • o DT: An R interface to the Data Tables library
  • o timetk R package

Case Study 10

Being Readings
  • o Josh Wills on big data (tech data scientist)
  • o Issues with Spatial Aggregation
Doing Readings
  • o Tidy Spatial Data (Blog Post)
  • o rnaturalearth R Package
  • o geofacet for ggplot2 in R
  • o Using SF package with tidyverse
  • o SF R package
  • o USAboundaries R Package
  • o Video on spatial datums
  • o Video 2 on spatial datums
  • o Using library(sf) to read in spatial data

Case Study 11

Being Readings
  • o Divide and Recombine (plus a history of data science)
  • o Background reading for D&R article (optional)
Doing Readings
  • o Leaflet for R: Introduction
  • o Leaflet for R: Introduction
  • o Leaflet for R: The Map Widget
  • o Leaflet for R: Basemaps
  • o Leaflet for R: Markers
  • o Leaflet for R: Popups and Labels
  • o Leaflet for R: Lines and Shapes
  • o Leaflet for R: Colors
  • o What is Spark?
  • o What is Hadoop?

Case Study 12

Being Readings
  • o Tips for timed tests
  • o Completing timed work
  • o Ethics of a Data Scientist
Doing Readings
  • o Semester Project
  • o Chapter 29: R for Data Science - R Markdown format
  • o Chapter 19: R for Data Science - Functions