Advanced quantitative data analysis

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.title[
# Advanced quantitative data analysis
]
.subtitle[
## Introduction
]
.author[
### Mengni Chen
]
.institute[
### Department of Sociology, University of Copenhagen
]
.date[
### 07/09/2022
]

---

#Introduction
About me 
- Assistant professor in Copenhagen University (05/2021-now)
- Research Scientist (10/2020 – 04/2021) in Cologne University, Germany
- Research Officer (10/2018-09/2020) in Catholic University of Louvain, Belgium
- Post-doc (03/2017 – 12/2017) in Vienna University of Economics and Business, Vienna Institute of Demography, IIASA, Austria
- Phd in University of Hong Kong (2013-2017)
- Research interest: family formation and dissolution, fertility, ageing, population health, population policies

- My office hour: Monday and Friday 10:30-11:30 am, Room 16.2.65

---
#Introduction
About you
- Name

- Year and department

---
#Introduction
About what you know

[I would like to know what you have known about R and statistics](https://docs.google.com/forms/d/e/1FAIpQLSfMQZ4_HvpoVYgA5y6-hwx5afJFHCPXijJZ4N2-bjqaUVddCQ/viewform?usp=sf_link)

---
#About the course
- Main content
  - Intro to R
  - OLS & logistic regression 
  - Panel data management
  - Panel data analysis I: fixed effect
  - Panel data analysis II: difference in difference
  
- Learning approach
  - Lecture for aprox. 45 min
  - In-class exercise: work in groups for 45 min
  - Bring lap-top every class
  - Fagcafe after class, Friday 13:00-17:00
---
#Assessment
  - Portfolio of four assignments: select one of the three topics
    - A1: 3 pages for introduction, theories, and simple regression
      - Deadline is Oct.14, 23:59
    - A2: 2 pages for describing what is fixed effect and applying fixed effect
      - Deadline is Nov. 25, 23:59
    - A3: 2 pages for describing what is random effect and applying randome effect
      - Deadline is Dec. 2, 23:59
    - A4: 2 pages for  describing what is DID and applying DID
      - Deadline is Dec. 16, 23:59
    - A final combined product: max 10 pages for all 
    
  - Individual or group work: 10 + 5 pages per extra person
  - Final exam: 5. January 2023, uploaded in Digital Exam before 12:00 (noon, Danish Time) 
  
---
#Three topics to choose
- How does first childbearing impact women’s subjective wellbeing of women?

- How does entry into a partnership impact people’s subjective wellbeing?

- How does divorce impact people’s subjective wellbeing?
---
#Access to the Pairfam data
- [What is pairfam](https://www.pairfam.de/en/)

- [What you need to know about pairfam: see Tutorial 8-12](https://www.pairfam.de/en/documentation/video-tutorials/)

- How to access to pairfam
  - Fill up the form: go to absalon → this course → files → Data → Form to be filled → send to support@pairfam.de
---
#What is R
If you're mainly interested in using regression to analyze a specific survey, it is
1. ...a rather tedious stats programming language.

2. ...basically a calculator:

```r
1+1
```

```
## [1] 2
```

```r
x <- 1
x+1
```

```
## [1] 2
```
---
#What is R

![](https://pbs.twimg.com/media/CiSMsEpXEAATXry?format=jpg&name=small)

---
#What is R
If you enjoy coding and working with several data sources at once, it is

1. ... a great platform for (social) data science of

2. ... networks, text, geo, pictures...

3. ... an all in one solution.→  I coded these slides in R!

4. ... a huge, dynamic and cooperative community.

[why you should use R](https://www.youtube.com/watch?v=9kYUGMg_14s&list=PLbh5g6tUnVSRwojrTf-ZBTXeCs-oYh9DV&index=94&t=3s)

---
#What you will learn in R

(Social) Data Science

![](https://d33wubrfki0l68.cloudfront.net/571b056757d68e6df81a3e3853f54d3c76ad6efc/32d37/diagrams/data-science.png)

.backgrnote[*Source*: Grolemund and Wickham (2017)]

<src="https://d33wubrfki0l68.cloudfront.net/571b056757d68e6df81a3e3853f54d3c76ad6efc/32d37/diagrams/data-science.png" width="70%" style="display: block; margin: auto;" >

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background-image: url(https://mareds.github.io/r_course/img_site/Tidyverse_packages.png)
background-size: contain
background-position: center
class: clear

---
background-image: url(https://upload.wikimedia.org/wikipedia/commons/thumb/1/1b/R_logo.svg/724px-R_logo.svg.png)
background-size: contain
background-position: center
class: clear

---
background-image: url(https://upload.wikimedia.org/wikipedia/commons/thumb/d/d0/RStudio_logo_flat.svg/1280px-RStudio_logo_flat.svg.png)
background-size: contain
background-position: center
class: clear

--
#[download R studio](https://www.rstudio.com/products/rstudio/download/#download)

---
#Interface in R
<img src="https://d33wubrfki0l68.cloudfront.net/8a64bb047429d7ae0e2acae35c40e421e6439bf6/80e5d/diagrams/rstudio-editor.png", width="550px" height="550px"
     style="position:absolute; top:120px;">

---
# Set R Studio preference
Set your preference
- Tools -> Global options
<img src="https://d33wubrfki0l68.cloudfront.net/7fa44a5471d40025344176ede4169c5ad3159482/1577f/screenshots/rstudio-workspace.png", width="500px" height="500px"
     style="position:absolute; right:10px; top:110px;"> 
  - General
  - Pane Layout
  - Appearance

---
# R Studio workflow
1. Use **Projects** to manage all files (scripts, data, figures and tables) belonging to the same project.
2. Use the editor the write **R scripts**, so you can reproduce your results.

<img src="https://d33wubrfki0l68.cloudfront.net/8a64bb047429d7ae0e2acae35c40e421e6439bf6/80e5d/diagrams/rstudio-editor.png", width="450px" height="450px" 
     style="position:absolute; right:200px; top:220px;"> 
---
#Set up your "IntRo" project
- Click on the Project button.
  - "New Directory"
  - "New Project"
  - Finally, a name for the folder and under "Browse" where you want that folder to be located.
  - Done! Now you should find an empty folder under the path your wanted it to be set up.
  - In the future, start RStudio by double clicking on the project icon in your folder.
  
---
#Set up your "IntRo" project

---
#R Scripts
create a R script

---
#R scripts
OK, you are good to go!

Pro tip: Use the R Script to take notes during the lecture!

"# take notes"

```r
#take notes
x <- 1
x+1
```

```
## [1] 2
```
---
#First steps with R
- Creating objects
  - R is an object-oriented programming language. We create objects with the assignment operator
  
- The assignment operator may be used in both directions: <-, or ->. This can help you write more legible code.
    - For Window, alt + '-' for ' <- '
    - For Mac, Option + '-' for ' <- '
    
- Run code
  - For Windows, ctrl + enter
  - For Mac, cmd + enter

```r
# Create a new object "test_object" and assign to it the result of 5 + 2
test_object <- 5 + 2

#printing the result: type test_object in the "Console" and see what you get
```
---
#First steps with R
- Creating objects
  - Objects can contain all kinds of information.

```r
# text can also be an object
text_object <- "Yes, you can also assign text"

#pring the results: type text_object in the "Console" and see what you get
```
---
#First steps with R
compare the two sets of codes

```r
test_object <- 5+2
test_object + 3
test_object
```

```r
test_object <- test_object + 3 
test_object 
```
--
If you don't use the assignment operator (<-), R will simply print the result of an operation without changing that object (i.e., without any further consequences).

If you actually want to create or modify objects, you need to use the assignment operator and explicitly assign the result of your operation to the object you want to change.

---
#First steps with R
Assigning and printing together
The short-hand version for assigning and printing an object right away uses surrounding **()**:

```r
(test_object <- 5 + 2)
```

---
#First steps with R
Use functions:

function_name(argument1 = value1, argument2 = value2, ...)

Example 1: The **seq()** function, for instance, produces a sequence of numbers and has (among others) the arguments from and to.

```r
(yet_another_object <- seq(from = 1, to = 5))
```

```
## [1] 1 2 3 4 5
```

```r
#or a shorter code
(yet_another_object <- seq(1, 5))
```

```
## [1] 1 2 3 4 5
```
---
#First steps with R
Example 2: The class() function tells you what class/type of object your are dealing with:
--

```r
class(yet_another_object) # "yet_another_object" contails whole numbers, so it is:
```

```
## [1] "integer"
```
--

```r
class(text_object) # "text_object" contains a sentence, so it is:
```

```
## [1] "character"
```
--

```r
class(seq)
```

```
## [1] "function"
```

```r
#Try to run
alphabet <- seq(from = "a", to = "z")
```
--
Functions will throw an error message, if you feed them with an object of a class it cannot handle.
---
#First steps with R
Arithmetic operators
--

```r
2 + 3 
```

```
## [1] 5
```
--
--

```r
2 * 3 
```

```
## [1] 6
```
--

```r
2 / 3 
```

```
## [1] 0.6666667
```
--

```r
2^3   
```

```
## [1] 8
```
--

```r
sqrt(yet_another_object) #square root as a function
```

```
## [1] 1.000000 1.414214 1.732051 2.000000 2.236068
```

---
#First steps with R
Logical operator

and = **&**;
or = **|**;
not= **!**;
belong = **%in%;**

<img src="https://github.com/fancycmn/slidesimage2/blob/main/R%20project10.PNG?raw=true", width="600px" height="280px" 
     style="position:absolute; right:200px; top:350px;">
--

1 y & !x ; 2 x&y; 3 x & !y;

4 x; 5 !(x&y); 6 y; 7 x|y;

---
#Logical operator in action

```r
x <- 2
x==3
```

```
## [1] FALSE
```
--

```r
x!=3
```

```
## [1] TRUE
```
--

```r
x > 5 & 6 # greater than 5 and greater than 6
```

```
## [1] FALSE
```
--

```r
x > 5|1 # greater than 5 or greater than 1
```

```
## [1] TRUE
```
--

```r
x > (5 & 6) | (1 & 2) # more complex comparison
```

```
## [1] TRUE
```
---
#Getting help in R
R has a steep learning curve and is, at times, simply frustrating. The documentation of a specific function can be accessed via typing **?<Name of function>**

```r
?seq
?sqrt
```
---
#Take home
1. R is an object-oriented programming language. Objects are basically containers and they can contain anything: data, functions, other objects, results, figures, and more ... Objects can be called by their name, which needs to start with a letter. If you just type the name of an object, R prints the object.

2. Functions do stuff with objects, which we assign as values to a function's arguments. What exactly a function does, depends on the class of an object. Functions themselves are objects too.

3. Important code
- "?": View a function's help file.
- "#": defines a comment in your code. Comment as much as possible!
- "<-": is the assignment operator of R.
- +, -, *, \, ^ : Arithmetic operators
- & "and", | "or", ! "not" : Logical (Boolean) operators
- class(): tells you the class of an object

**Reference**: Grolemund, G. and H. Wickham (2017). R for Data Science. O'Reilly.
---
class: center, middle
#[Exercise](https://merlin-intro-r.netlify.app/exercises/exercise1.html)