This is a repo for the coming R course for graduate students, School of Psychology, Nanjing Normal University, Nanjing, China.

Instructor: Prof. Hu Chuan-Peng

Teaching assistants: Yuki; Hejia Sun; Zheng Cai; Songshi Bai; Caiyu Tian

此仓库为南京师范大学心理学研究生课程。

教师：胡传鹏

助教：yuki; 孙禾嘉；蔡镇；柏松石；田彩玉

root_dir
|
|----chapter_1.pptx   # slides for chapter 1
|----chapter_2.pptx   # slides for chapter 2
|----chapter_3.Rmd    # Rmarkdown for chapter 3
|----chapter_3.html   # html for chapter 3
|----chapter_4.Rmd    # Rmarkdown for chapter 4
|----chapter_4.html   # html for chapter 4
|----chapter_5.Rmd    # Rmarkdown for chapter 5
|----chapter_5.html   # html for chapter 5
|----chapter_6.Rmd    # Rmarkdown for chapter 6
|----chapter_6.html   # html for chapter 6
|----chapter_7.Rmd    # Rmarkdown for chapter 7
|----chapter_7.html   # html for chapter 7
|
|----css/             # folder for Xaringan
|----data/            # folder for data used in the lecture
|     |----- match    # folder for match data
|     |----- penguin  # folder for penguin data
| 
|----libs/            # folder for Xaringan
|
|----output/          # folder for Xaringan output?
|
|----picture/         # folder for picture in html
|     |----- chp3     # folder for pictures in chapter 3
|     |----- chp4     # folder for pictures in chapter 4
|     |----- ...
|
|....

1.1 R在心理科学及社会科学中的运用

1.2 R语言使用的示例展示

1.3 课程安排

1.4 如何学好这门课

2.1 要解决的数据分析问题简介？

2.1 如何安装？

2.2 如何方便使用？Rstudio的安装与界面介绍

3.1 路径与工作目录

3.2 读取数据

3.3 了解R里的数据 （R语言中的对象）

4.1 R对象的操控

4.2 逻辑运算

4.3 函数

5.1 数据预处理准备

5.2 数据预处理的基本操作

5.3 数据预处理的进阶操作

6.1 描述性统计

6.2 ggplot2的基本使用

6.3 探索性数据分析(DataExplorer)

7.1 语法实现

7.2 分析的流程

8.1 语法实现

8.2 分析的流程

9.1 语法实现

9.2 分析的流程

10.1. 多种分析方法的实现

10.2 代码整合与规范化

11.1 ggplot2的图层与面板控制

11.2 ggplot2与其他工具的结合

12.1 Rmarkdown

12.2 Latex语法基本介绍

12.3 papaja工具包的介绍

13.1 版本控制与git

13.2 多人协作与git

14.1 计算效应量：Meta-analysis

14.2 计划样本量：Power analysis （模拟）

14.3 计划分析方法：假数据与分析代码（模拟）

14.4 并行处理

15.1 软件版本记录

15.2 容器技术与docker的使用

Chapter 1: Why Learn R (3 lessons)

1.1 The use of R in psychological sciences and social sciences

1.2 Sample demonstration of the use of R language

1.3 Course schedule

1.4 How to learn this course well

Chapter 2: How to start using R: (3 lessons)

2.1 Introduction to the data analysis problem to be solved?

2.1 How to install?

2.2 How to facilitate the use of? Introduction to the installation and interface of RStudio

Chapter 3: How to Import Data (3 lessons)

3.1 Path and working directory

3.2 Reading data

3.3 Understanding Data in R (Objects in the R Language)

Chapter 4: How to Clean Up Data I: R Programming Basics (3 lessons)

4.1 Manipulation of R objects

4.2 Logical operations

4.3 Functions

Chapter 5: How to Clean Up Data II: Data Preprocessing (3 lessons)

5.1 Data Preprocessing Preparation

5.2 Basic operations of data preprocessing

5.3 Advanced operations of data preprocessing

Chapter 6: How to Explore Data: Fundamentals of Descriptive Statistics and Data Visualization (3 lessons)

6.1 Descriptive Statistics

6.2 Basic use of ggplot2

6.3 Element control of ggplot2

Chapter 7: How to perform basic data analysis: t-test and anova (3 lessons)

7.1 Syntax implementation

7.2 Flow of analysis

Chapter 8: How to Perform Basic Data Analysis: Correlation and Regression (3 lessons)

8.1 Syntax Implementation

8.2 The flow of analysis

Chapter 9: How to Perform Basic Data Analysis: Mediation Analysis (3 lessons)

9.1 Syntax Implementation

9.2 Flow of analysis

Chapter 10: Are the Results Robust? The effect of using Multiverse comparison method selection on the results (3 lessons)

10.1. Implementation of Multiverse Analysis Methods

10.2 Code integration and normalization

Chapter 11: How to Get Publishable Images: Advanced Data Visualization (3 lessons)

11.1 Layers and Panel Controls for ggplot2

11.2 Combining ggplot2 with other tools

Chapter 12: From Analysis to Manuscript (3 lessons)

12.1 Rmarkdown

12.2 Basic introduction to Latex syntax

12.3 Introduction to the papaja toolkit

Chapter 13: Collaborative Version Control: Git? (3 lessons)

13.1 Version Control and git

13.2 Multi-Person Collaboration and git

Chapter 14: How Can We Help Plan Our Next Study?

14.1 Calculating effect sizes: Meta-analysis

14.2 Planning sample size: Power analysis (simulation)

14.3 Planning analysis methods: Dummy data and analysis codes (simulation)

14.4 Parallel processing

Chapter 15: How do I get my mentor/collaborator to exactly replicate my analysis? (3 lessons)

15.1 Software Version Logging

15.2 Container technology and the use of docker