In jasonmtroos/rook: Introduction to Data Visualization, Web Scraping, and Text Analysis in R

library(knitr)
opts_chunk$set(echo = FALSE, message = FALSE, cache = FALSE)
library(rook)
library(tidyverse)
library(lubridate)

Course abstract

A systematic introduction to issues of collecting, preparing, analyzing, and visualizing online data.

You will learn how to write, debug, and keep track of your own code using R, a popular programming language for data manipulation, analysis, and visualization.

Objectives

• Basic understanding of big data and social media analytics in the context of social science and humanities research
• Write code in R in order to obtain, prepare, analyze, and visualize data
• Automate data collection from web sites and social media
• Basic proficiency with tools for analyzing large quantities of text
• Monitor and manage the various steps of data collection and analysis for both integrity and replication purposes
• Become a more productive (taking less time to analyze your data) and careful (making fewer mistakes) scientist

Course structure (pre-covid)

if(program() == 'egsh') {
  asis_output("
* Four sessions, Tuesdays and Fridays from 13.30--17.00
")
  }

• Sessions include a mix of lectures, demonstrations, and in-class exercises
• Bring your laptop to every session
  • You must have the necessary rights to install software on your laptop
• Ideally, you will work with data sets relevant to your research:
  • Experiments, surveys, time series, panels, whatever...
  • I will supply data sets as well

Course structure (post-covid)

• Same idea in principle
• I won't be able to do as much debugging of your code, but we will try via screen sharing
• Zoom sessions are slower than in-person, so I will probably skip over some material
• This is my first time teaching via Zoom

Session 1: Course overview and first steps

r print_session_info(1)

• You will create, edit, and compile an R-markdown file that contains both a free text discussion of your data analysis, your code, and any output from that code (including plots).

• We will build an R-markdown file that collects data from an online source, performs a few basic manipulations, and plots the results. You will learn how to use version control software to track changes to this markdown file over time.

Session 2: Acquiring, preparing, and visualizing data

r print_session_info(2)

• You will learn how to write code to acquire data from files located on the web or stored on your local computer, load them into R, and “clean” the data in preparation for further analysis (such as data visualization).

• You will then learn about a powerful yet relatively simple "grammar" for visualizing data that has been implemented in the ggplot2 package in R

• We will also discuss the underlying theory that drives this grammar (including the psychological principles behind effective data visualization), and gain an appreciation for how visualization can lead to insights about data more quickly than statistical analysis.

Session 3: Obtaining data from web sites and social media

r print_session_info(3)

• You will learn how to acquire data from various online sources, such as web pages and the Twitter API, and how to automation these procedures.

• You will continue to gain practice preparing, analyzing, and visualizing these data.

Session 4: Text and sentiment analysis

r print_session_info(4)

• You will learn how to process large amounts of unstructured data (e.g. text documents) to extract important features (e.g., the occurrence of special words).

• You will also learn how to conduct automatic sentiment analysis (scoring text based its positivity or negativity).

if(program() != 'erim') {
  asis_output("
Introductions
====================================================
* Who are you? Which degree program are you in?
* What do you hope to get out of this course?
* Is there a specific data set you need to obtain and/or analyze?
* How much prior experience do you have with R?
")
}

jasonmtroos/rook documentation built on May 24, 2020, 3:16 p.m.