Data Management and Analysis
for Social Sciences 
2021 Data Management and Analysisfor Social Sciences, Instructors: Shih-chan Dai and David,Yen-Chieh Liao (University of Essex)
Course Description
This course aims to provide students with an overview of the theoretical and practical foundations required for managing and analyzing data in R, especially those in social sciences. It starts with an introduction to foundational programming to functional programming , data types as well as basic concepts of statistics. We further will examine the different kinds of probability theory, probability distribution, and data-generating process.
Once getting a solid understanding of the probability theory, we will move on to study hypothesis testing and model estimation. More importantly, this course will combine the theoretical content with a rich set of applications so that students can polish up their programming skills in R as well as apply what they learn in real-world cases.
Syllabus
The syllabus is available here.
Schedule TBA
Lecture
Lab
Office Hours
Books
Programing Application in R
- R for Data Science : Import, Tidy, Transform, Visualize, and Model Data, Garrett Grolemund and Hadley Wickham, 2016
- Advanced R, Hadley Wickham, 2015, Howard Rosenthal, CRC
- Hands-On Programming with R, 1st Edition, Garrett Grolemund, 2014, O'Reilly Media
Data Analysis and Statistical Learning
- An R Companion to Applied Regression, John Fox and Sanford Weisberg, 2019, SAGE
- R for Everyone: Advanced Analytics and Graphics, 2nd Edition, Jared P. Lander, 2017, Addison-Wesley Data and Analytics
- R in Action Data: Analysis and Graphics with R, Robert I. Kabacoff, 2011, Manning Publication
- The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009, Springer
- Machine Learning with R, the tidyverse, and mlr, Hefin I. Rhys, 2019, Manning Publication
- An Introduction to Statistical Learning with Applications in R, Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, 2013
- ggplot2: Elegant Graphics for Data Analysis, Hadley Wickham, 2016, Springer Publication
Lecture Notes (slides will be added before each lecture)
00 - Introduction and Data Science
Data science is a discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of this course is to help you develope skillset in R that will allow you to performe an analysis.
- Getting R (Rstudio, environment, packages and CRAN)
- Introduction to Version Control with Git and Github
Reading: 1, 2, 3 of Lander 2017 | Preface of Grolemund and Wickham 2016
01 - Programing in R Ⅰ
To start programming in R, this class will help you learn the foundational components of programing and data structrue in R.
- Basic R (math, variables, data types, list, matrices, arrays)
- Data Structures (names, values, vectors)
- Subsetting
Reading: 4 of Lander 2017 | 11.6 of Wickham 2015
02 - Programing in R ⅠⅠ
Loops are used in programming to repeat a specific block of code. In this class, you will learn to how to create functions and loop, or combine them together for analysis in R programming.
- Writing R Functions
- Control Statements (loops and iteration)
- Debugging and Condition Handling
Reading: 8, 9 of Lander 2017 | 9 of Hadley 2015
03 - Functional Programming Ⅰ: Base R
The class starts by showing a motivating example, removing redundancy and duplication in code used to clean and summarise data. We will focus onthree building blocks of functional programming: anonymous functions, closures (functions written by functions), and lists of functions. We will learn how to easily extract, summarize, and manipulate lists and how to export the data to desired object, be it another list, a vector.
- Anonymous Functions
- List / Vector Functions (
apply(),lapply(),sapply(), andtapply()) - Manipulating Matrices and Data Frames
Reading: 11 of Lander 2017 | 10, 11 of Hadley 2015
04 - Visialization Ⅰ
This class provides a comprehensive introduction on how to plot data with R’s base graphics, ggplot2 as well as lattice.
- Basic Graphics
latticepacakgeggplot2and ggplot2's Package Ecosystem
Reading: CH7 of Lander 2017 | 22 of Grolemund and Wickham 2016 | Hadley 2016
05 - Visialization ⅠⅠ
- Interactive Graphics & Maps
- Share:
LaTeXBasics,R MarkdownDocuments andShiny R Server+Tableau
Reading: CH7 of Lander 2017 | 22 of Grolemund and Wickham 2016 | Hadley 2016
06 - Manipulation and Data Management
We will covere a variety of methods for importing and exporting data in R environment.
- Importing & Exporting Data
- Tidy and Relational Data
- Data Manipulation
Reading: 6 of Lander 2017 | 10, 11 of Wickham and Grolemund 2016 | 4, 5 of Kabacoff 2011
07 - Functional Programming ⅠⅠ: Tidyverse Fundamentals
This class covers fundamentals of the Tidyverse, tidy data and how to use popular functional packages such as tibble, dplyr, ggplot2, tidyr, and purrr packages for data analysis.
- Tidyverse & Functional Programming
- Pipe, Functions, Iteration and Beyond!
- Functional Programming Tools
- Additional: from
purrrtofurrrin parallel
Reading: 6 of Lander 2017 | 14-17 of Wickham and Grolemund 2016
08 - Basic Statistics
This class covers descriptive statistics, frequency and contingency tables and essential command math functions commonly used in R.
- Math Functions in R
- Descriptive Statistics
Reading: 14, 15 of Lander 2017 | 7.1, 7.2 of Kabacoff 2011
09 - Probability Distribution
This class cover using R to handles all the basic necessities of statistics, including drawing random numbers and calculating distribution values (the focus of this chapter), means, variances, maxmima and minima, correlation.
- Normal Distribution
- Binomial Distribution
- Poisson Distribution
Reading: 14 of Lander 2017
10 - Hypothesis Testing
- Hypothesis Testing for Means
- Hypothesis Testing for Proportions
Reading: 15 of Lander 2017
11 - Sampling Distribution
Reading: 15 of Lander 2017
12 - Correlation, Testing
- Correlation and Covariance
- T-Tests
- Examining Relationships using Visualization
Reading: 7.2-3 of Kabacoff 2011 | 3.2 of John Fox and Sanford Weisberg 2019 | 15.3 of Lander 2017
13 - Analysis of Variance
This class covers how to use R to model basic experimental designs, fitting and interpreting ANOVA type models, and evaluating model assumptions
- One-way ANOVA and One-way ANCOVA
- Two-way factorial ANOVA
Reading: 9 of Kabacoff 2011
14 - Regression Ⅰ: Basic Linear Regression
This class will cover fitting and interpreting linear models, evaluating model assumptions, selecting among competing models.
- Introduction and Assumptions
- Diagnostics and Interpretation
- Formatting the Estimates in R:
broom,stargazerandsummarytoolspackages
Reading: 8 of Kabacoff 2011 | 4, 5 of John Fox and Sanford Weisberg 2019
15 - Regression ⅠⅠ: Generalized Linear Models
This class covers formulating a generalized linear model, predicting categorical outcomes, and modeling count data.
- Generalized linear models and the
glm()function - Model Fit and Regression Diagnostics
Reading: 13 of of Kabacoff 2011 | 6 of John Fox and Sanford Weisberg 2019
16 - Additional Topic: Introduction to Machine Learning
This class briefly introduces what machine learning is, supervised vs unsupervised machine learning, how the Tidyverse play a part in training machine learning classification and regression. Later, we plan to cover the application of the Tidyverse on Tidymodels framework, from pre-processing (rsample and recipes packages), model training (parsnip package), model validation (yardstick package).
- Introduction to Machine Learning
- Tidying and Manipulating Data with the Tidyverse
- Machine Learning with Tidymodels framework
Reading: PART Ⅰ of of Rhys 2019