[[./|Class page]]
multivariate statistics in R
network analysis in R
  * A User’s Guide to Network Analysis in R (Use R!)
  * Statistical Analysis of Network Data with R (Use R!) 2014th Edition
 
[[https://lagunita.stanford.edu]]
[[https://campus.datacamp.com/courses/network-analysis-in-r|Network Analysis in R]] using igraph package -- from Datacamp
[[https://campus.datacamp.com/courses/marketing-analytics-in-r-statistical-modeling/|Marketing analysis in r statistics]] from Datacamp

====== Week01 (Sep 4, 6) ======
<WRAP half column>
===== ideas and concepts  =====
Introduction to R and others
  - Downloading and Installing R
    - [[:the_r_project_for_statistical_computing]]
    - [[:r]], [[:r:getting started]]
  - Starting R
  - Entering Commands
  - Exiting from R
  - Interrupting R
  - Viewing the Supplied Documentation
  - Getting Help on a Function
  - Searching the Supplied Documentation
  - Getting Help on a Package
  - Searching the Web for Help
  - Finding Relevant Functions and Packages
  - Searching the Mailing Lists
  - Submitting Questions to the Mailing Lists
using <del>[[:theories]]</del> [[http://commres.net/wiki/research_methods_lecture_note#%EC%BB%A4%EB%AE%A4%EB%8B%88%EC%BC%80%EC%9D%B4%EC%85%98_%EC%97%B0%EA%B5%AC%EB%AC%B8%EC%A0%9C_%EC%A0%9C%EA%B8%B0%EC%99%80_%EA%B0%80%EC%84%A4|연구문제와 가설]] and making [[:hypothesis|hypotheses]]

</WRAP>
<WRAP half column>
Installing R

===== Assignment =====
</WRAP>
====== Week02 (Sep 11, 13) ======
<WRAP half column>
===== Concepts and ideas =====
Some [[:R:basics|basics]]
  - Introduction
  - Printing Something
  - Setting Variables
  - Listing Variables
  - Deleting Variables
  - Creating a Vector
  - Computing Basic Statistics
  - Creating Sequences
  - Comparing Vectors
  - Selecting Vector Elements
  - Performing Vector Arithmetic
  - Getting Operator Precedence Right
  - Defining a Function
  - Typing Less and Accomplishing More
  - Avoiding Some Common Mistakes

</WRAP>

<WRAP half column>

  * [[:Research Question]]
  * [[:Hypothesis]]
    * Educated guess (via theories)
    * Difference 
    * Association
    * Variables (vs. ideas, concepts, and constructs)
      * [[:Operationalization]]
      * [[:Variables]], [[:Types of Variables]] 
        * see [[http://chohongjoong.com/gnu4/bbs/board.php?bo_table=board02&wr_id=311&sfl=&stx=&sst=wr_datetime&sod=desc&sop=and&page=1|this blog]] written in Korean
        * [[:Independent Variable|IV]] 독립변인
        * [[:Dependent Variable|DV]] 종속변인
        * Control variable 제어변인
        * Mediating (Intervening) variable 매개변인
===== Assignment =====

</WRAP>
====== Week03 (Sep 18, 20) ======
<WRAP half column>
===== Activities =====
  * Grouping. See [[./Group]] page
  * Group discussion on group works
===== Concepts and ideas =====
You __should be knoweldgeable__ about [[:research question]] and [[:hypothesis]] building. However, we will be deal with the issue in the class. Please read the two and [[:research_methods_lecture_note#커뮤니케이션_연구문제_제기와_가설]] individually. <fc #ff0000>The materials will be on quizzes</fc>.

[[:r:navigating|Navigating]] software
  - Introduction
  - Getting and Setting the Working Directory
  - Saving Your Workspace
  - Viewing Your Command History
  - Saving the Result of the Previous Command
  - Displaying the Search Path
  - Accessing the Functions in a Package
  - Accessing Built-in Datasets
  - Viewing the List of Installed Packages
  - Installing Packages from CRAN
  - Setting a Default CRAN Mirror
  - Suppressing the Startup Message
  - Running a Script
  - Running a Batch Script
  - Getting and Setting Environment Variables
  - Locating the R Home Directory
  - Customizing R

[[:r:input_output|Input and output]]
  - Introduction
  - Entering Data from the Keyboard
  - Printing Fewer Digits (or More Digits)
  - Redirecting Output to a File
  - Listing Files
  - Dealing with “Cannot Open File” in Windows
  - Reading Fixed-Width Records
  - Reading Tabular Data Files
  - Reading from CSV Files
  - Writing to CSV Files
  - Reading Tabular or CSV Data from the Web
  - Reading Data from HTML Tables
  - Reading Files with a Complex Structure
  - Reading from MySQL Databases
  - Saving and Transporting Objects

</WRAP>
<WRAP half column>
===== Assignment =====
Assignment for all
  * Read [[:research_methods_lecture_note#커뮤니케이션_연구문제_제기와_가설]]
  * Read [[:research question]]
  * Read [[:hypothesis]]

Group assignment
  * Hypothesis 문서의 [[:hypothesis#예_1]]의 "제3자 효과이론과 침묵의 나선이론 연계성" 논문을 읽고 가설을 기술하시오.
  * 각 가설의 독립변인(Independent variables), 종속변인 (dependent variabless) 등을 나열하시오. 
  * 이 논문에 사용된 이론은 무엇인지 기술하고 설명하시오. 

</WRAP>
====== Week04 (Sep 25, 27) ======
<WRAP half column>
===== Class Activity =====
  * 가설 만들어 보기
    * No need to read [[:theories]]
      * the third person effect
      * [[:Spiral of Silence]]
      * [[:cognitive dissonance]]  
    * Read [[:hypothesis]]
    * [[http://behavioralsciencewriting.blogspot.kr/2011/09/how-to-write-hypothesis.html|how to write hypothesis]] at behavioral science writing. 
    * One sample hypothesis [[http://www.socialresearchmethods.net/kb/hypothes.php|Hypothesis]] at www.socialresearchmethods.net
===== Concepts and ideas =====
[[:r:Data Structures]]
  - Introduction
  - Appending Data to a Vector
  - Inserting Data into a Vector
  - Understanding the Recycling Rule
  - Creating a Factor (Categorical Variable)
  - Combining Multiple Vectors into One Vector and a Factor
  - Creating a List
  - Selecting List Elements by Position
  - Selecting List Elements by Name
  - Building a Name/Value Association List
  - Removing an Element from a List
  - Flatten a List into a Vector
  - Removing NULL Elements from a List
  - Removing List Elements Using a Condition
  - Initializing a Matrix
  - Performing Matrix Operations
  - Giving Descriptive Names to the Rows and Columns of a Matrix
  - Selecting One Row or Column from a Matrix
  - Initializing a Data Frame from Column Data
  - Initializing a Data Frame from Row Data
  - Appending Rows to a Data Frame
  - Preallocating a Data Frame
  - Selecting Data Frame Columns by Position
  - Selecting Data Frame Columns by Name
  - Selecting Rows and Columns More Easily
  - Changing the Names of Data Frame Columns
  - Editing a Data Frame
  - Removing NAs from a Data Frame
  - Excluding Columns by Name
  - Combining Two Data Frames
  - Merging Data Frames by Common Column
  - Accessing Data Frame Contents More Easily
  - Converting One Atomic Value into Another
  - Converting One Structured Data Type into Another

[[:r:data transformations]]

</WRAP>
<WRAP half column>
===== Assignment =====
ga04.making.hypothesis 가설 연습 ajoubb
  * 첫번째, R(rstudio사용)에서 default로 구할 수 있는 mtcars 데이터를 이용하여 t-test와 anova test를 할 수 있는 가설을 만들고, R에서 분석해 보세요. 
    * 가설에 대해서는 [[:hypothesis testing]] 문서를 참조하시기 바랍니다.
    * t-test는 [[:t-test]]를 참조하시기 바랍니다.
      * 4가지 종류의 t-test 중에서 mtcars 데이터의 경우는 몇 번째 것을 사용해야 하는가에 대해서 확인하세요.
    * anova에 대해서는 [[:anova]] 문서를 참조하세요. 
    * R에서의 분석은 각각 t.test와 aov 펑션을 이용해야 합니다. 
  * 두번째, 신문에서의 여론조사 결과에 나오는 error of margin에 대해서 확인해보시기 바랍니다. 
    * 여론조사 결과가 내용인 신문기사 2개를 고릅니다.
      * [[http://www.realmeter.net/%ea%b3%a0%ec%9c%84%ec%a7%81-%ec%9e%90%eb%85%80-%ec%9e%85%ec%8b%9c%eb%b9%84%eb%a6%ac-%ec%a0%84%ec%88%98%ec%a1%b0%ec%82%ac-%ec%b0%ac%ec%84%b175vs%eb%b0%98%eb%8c%8018/|예]]
      * 일반적인 se값은 아래와 같이 구합니다. 
      * $ \displaystyle \sigma_{\hat{p}} = \sqrt{\frac{p*q}{n}} , \;\;\; q = (1 - p) $
      * $ p = .752 $ = 75.2%

  * 파일을 upload한다면 파일이름은
    * ga04.making.hypothesis.그룹이름.ext 과 같이 저장한 후에 올리시기 바랍니다.
    * 위에서 "그룹이름"과 "ext"은 그룹에 따라서 바꾸야 합니다.
      * 3조의 경우는 "그룹이름"대신 03을 사용합니다.
      * ms word파일로 저장을 했다면 파일extension으로 "docx"가 생길겁니다. text파일로 저장을 했다면 "txt"가 생길 것입니다.
    * 따라서 위의 예에 따르면 과제 이름은 
      * ga04.making.hypothesis.03.txt와 같을 겁니다. 



</WRAP>

====== Week05 (Oct 2, 4) ======
<WRAP half column>
===== ideas and concepts  =====
[[:r:probability]]
[[:r:General Statistics]]

==== t.test: mtcars ====
<code>
> mdata <- split(mtcars$mpg, mtcars$am)
> mdata
$`0`
 [1] 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[13] 10.4 14.7 21.5 15.5 15.2 13.3 19.2

$`1`
 [1] 21.0 21.0 22.8 32.4 30.4 33.9 27.3 26.0 30.4 15.8 19.7 15.0
[13] 21.4

> stack(mdata)
   values ind
1    21.4   0
2    18.7   0
3    18.1   0
4    14.3   0
5    24.4   0
6    22.8   0
7    19.2   0
8    17.8   0
9    16.4   0
10   17.3   0
11   15.2   0
12   10.4   0
13   10.4   0
14   14.7   0
15   21.5   0
16   15.5   0
17   15.2   0
18   13.3   0
19   19.2   0
20   21.0   1
21   21.0   1
22   22.8   1
23   32.4   1
24   30.4   1
25   33.9   1
26   27.3   1
27   26.0   1
28   30.4   1
29   15.8   1
30   19.7   1
31   15.0   1
32   21.4   1
> mdata
$`0`
 [1] 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[13] 10.4 14.7 21.5 15.5 15.2 13.3 19.2

$`1`
 [1] 21.0 21.0 22.8 32.4 30.4 33.9 27.3 26.0 30.4 15.8 19.7 15.0
[13] 21.4

> t.test(mpg~am, data=mtcars)

	Welch Two Sample t-test

data:  mpg by am
t = -3.7671, df = 18.332, p-value = 0.001374
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -11.280194  -3.209684
sample estimates:
mean in group 0 mean in group 1 
       17.14737        24.39231 

> t.test(mpg~am, data=mtcars, var.equal=T)

	Two Sample t-test

data:  mpg by am
t = -4.1061, df = 30, p-value = 0.000285
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -10.84837  -3.64151
sample estimates:
mean in group 0 mean in group 1 
       17.14737        24.39231 

> m1 <- mdata[[1]]
> m2 <- mdata[[2]]
> m1
 [1] 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[13] 10.4 14.7 21.5 15.5 15.2 13.3 19.2
> m2
 [1] 21.0 21.0 22.8 32.4 30.4 33.9 27.3 26.0 30.4 15.8 19.7 15.0
[13] 21.4
> m1.var <- var(m1)
> m2.var <- var(m2)
> m1.n <- length(m1)
> m2.n <- length(m2)
> m1.df <- length(m1)-1
> m2.df <- length(m2)-1
> m1.ss <- m1.var*m1.df
> m2.ss <- m2.var*m2.df
> m1.ss
[1] 264.5874
> m2.ss
[1] 456.3092
> m12.ss <- m1.ss+m2.ss
> m12.ss
[1] 720.8966
> m12.df <- m1.df+m2.df
> pv <- m12.ss/m12.df
> pv
[1] 24.02989
> pv/m1.n
[1] 1.264731
> pv/m2.n
[1] 1.848453
> m.se <- sqrt((pv/m1.n)+(pv/m2.n))
> m.se
[1] 1.764422
> m1.m <- mean(m1)
> m2.m <- mean(m2)
> m.tvalue <- (m1.m-m2.m)/m.se
> m.tvalue
[1] -4.106127
</code>

<code>
> t.test(mpg~am, data=mtcars, var.equal=T)

	Two Sample t-test

data:  mpg by am
t = -4.1061, df = 30, p-value = 0.000285
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -10.84837  -3.64151
sample estimates:
mean in group 0 mean in group 1 
       17.14737        24.39231 

</code>
==== anova: mtcars ====
<code>
stats4each = function(x,y) {
   meani <- tapply(x,y,mean)
   vari <- tapply(x,y,var)
   ni <- tapply(x,y,length)
   dfi <- tapply(x,y,length)-1
   ssi <- tapply(x,y,var)*(tapply(x,y,length)-1)
   out <- rbind(meani,vari,ni,dfi,ssi)
   
   return(out)  
}

library(MASS)

tempd <- iris
x <- tempd$Species
y <- tempd$Sepal.Width

tempd <- mtcars
x <- tempd$gear
y <- tempd$mpg

tempd <- mtcars
x <- tempd$am
y <- tempd$mpg


x <- factor(x)
dfbetween <- nlevels(x)-1

stats <- stats4each(y, x)
stats 

sswithin <- sum(stats[5,])
sstotal <- var(y)*(length(y)-1)
ssbetween <- sstotal-sswithin

round(sswithin,2)
round(ssbetween,2)
round(sstotal,2)

dfwithin <- sum(stats[4,])
dftotal <- length(y)-1

dfwithin
dfbetween
dftotal

mswithin <- sswithin / dfwithin
msbetween <- ssbetween / dfbetween
mstotal <- sstotal / dftotal

round(mswithin,2)
round(msbetween,2)
round(mstotal,2)

fval <- round(msbetween/mswithin,2)
fval
siglevel <- pf(q=fval, df1=dfbetween, df2=dfwithin, lower.tail=FALSE)
siglevel

mod <- aov(y~x, data=tempd)
summary(mod)

</code>

==== cor ====
<code>
attach(mtcars)
cor(mpg, hp)

mycor <- cov(mpg,hp)/(sd(mpg)*sd(hp))
mycor

sp <- cov(mpg,hp)*(length(mtcars$hp)-1)
ssx <- var(mpg)*(length(mtcars$mpg)-1)
ssy <- var(hp)*(length(mtcars$hp)-1)

mycor2 <- sp/sqrt(ssx*ssy)
mycor2

mycor2 == mycor
mycor == cor(mpg,hp)
mycor2 == cor(mpg,hp)

</code>



</WRAP>
<WRAP half column>
===== Assignment =====
</WRAP>

====== Week06 (Oct 9, 11) ======
<WRAP half column>
===== ideas and concepts  =====
[[:correlation]]
[[:regression]]
[[:multiple regression]]
  * [[:r:correlation|correlation in r]]
  * [[:r:multiple regression|multiple regression in r]]

[[:Partial and semipartial correlation]]
[[:using dummy variables]]

[[:Statistical Regression Methods]]
[[:Sequential Regression]]


  
</WRAP>
<WRAP half column>
===== Assignment =====
  - Public opinion in online environments ((refer to {{:public.opinion.theories.introduction.pdf}} ))
    * [[:Spiral of Silence]]
    * [[:Pluralistic Ignorance]]
    * [[:The Third Person Effect]]
    * etc. 여론형성과 관련된 사회학적 혹은 사회심리학적 이론을 찾아보고 소개하기, 예로 위의 세가지. 얼마전 사회현상을 어떻게 설명하면 좋을까에 대해서 논의정리하기? 정확한 온라인 환경에서의 여론파악을 위해서 어떤 것이 필요할까?
    * 혹은 다른 문제에 대해서 (. . . 조에 따른 . . .)
  - Hypotheses
    * Multiple regression hypotheses. 
    * Google Survey Questions
 

</WRAP>

====== Week07 (Oct 16, 18) ======
<WRAP half column>
===== ideas and concepts  =====
</WRAP>
<WRAP half column>
===== Assignment =====
</WRAP>

====== Week08 (Oct 23, 25)  ======
<WRAP half column>
__**Mid-term period**__ 

===== Quiz the first one =====
  * Lecture materials + textbook  
  * Textbook: r cookbook: textbook과 관련해서는 예상되는 아웃풋, 아웃풋을 얻기위한 명령어, 명령어(function)에 사용되는 옵션이 의미하는 것 등에 대한 사지선다 혹은 단답식 질문이 나옵니다. 펑션의 옵션사용 등과 같은 정확한 것에 대해서는 질문이 나오지 않습니다. 
    * 예
      * one sample t-test를 하기 위한 명령어를 쓰시오 (x)
      * t.test(sample, mu=100)에서 mu는 무엇을 의미하는가? (o)
      * 다음 중 sapply의 아웃풋 모양으로 적당한 것은? 등등 
    * [[:The r project for statistical computing]]
    * [[:r:Getting started]]
    * [[:r:Basics]]
    * [[:r:Navigating]]
    * [[:r:Input output]]
    * [[:r:Data structures]]
    * [[:r:Data transformations]]
  * Lecture content
    * [[:Hypothesis]], 
    * [[:Research question]], 
    * [[:Research methods lecture note#커뮤니케이션_연구문제_제기와_가설|커뮤니케이션 연구문제 제기와 가설]] 부분만
    * [[:Operationalization]],
    * [[:Variables]], 
    * [[:Types of variables]]
    * [[:Hypothesis testing]]
    * [[:T-test]]
      * 정확한 t test 공식등은 외울 필요가 없습니다. (제공됩니다). 
      * 간단한 t test 계산을 요구할 수 있습니다. 
      * ANOVA도 마찬가지입니다. 
    * [[:ANOVA]]

</WRAP>

====== Week09 (Oct 30, Nov 1) ======
<WRAP half column>
===== ideas and concepts  =====
[[:correlation]]
[[:regression]]
[[:multiple regression]]
  * [[:r:correlation|correlation in r]]
  * [[:r:multiple regression|multiple regression in r]]

[[:Partial and semipartial correlation]]
[[:using dummy variables]]

[[:Statistical Regression Methods]]
[[:Sequential Regression]]

===== Activity =====
[[c/ma/2019/Multiple Regression Exercise]]

</WRAP>
<WRAP half column>
===== Assignment =====
</WRAP>

====== Week10 (Nov 6, 8) ======
<WRAP half column>
===== ideas and concepts  =====
[[:factor analysis]]
</WRAP>
<WRAP half column>
===== Assignment =====
</WRAP>

====== Week11 (Nov 13, 15) ======
<WRAP half column>
===== ideas and concepts  =====
</WRAP>
<WRAP half column>
===== Assignment =====
</WRAP>

====== Week12 (Nov 20, 22) ======
<WRAP half column>
===== ideas and concepts  =====
</WRAP>
<WRAP half column>
===== Assignment =====
[[factor analysis assignment]]

</WRAP>

====== Week13 (Nov 27, 29) ======
<WRAP half column>
===== ideas and concepts  =====
[[:social network analysis]]
[[:r:social network analysis tutorial]]
[[:r:social network analysis|sna in r]] 
[[:sna_eg_stanford|Stanford University egs.]]
</WRAP>
<WRAP half column>
===== announcement  =====
Quiz 2 (on Friday Dec. the 6th) covers:
  * [[:correlation]]
  * [[:regression]]
  * [[:multiple regression]]
    * [[:partial and semipartial correlation]]
    * [[:using dummy variables]]
  * [[:factor analysis]]
Some R outputs will be used to ask the related concepts and ideas (the above). 

===== Assignment =====
</WRAP>

====== Week14 (Dec 4, 6) ======
<WRAP half column>
Group Presentation
</WRAP>

====== Week15 (Dec 11, 13) ======
<WRAP half column>

[[./assignment week15]]
</WRAP>

====== Week16 (June 18, 20) ======
<WRAP half column>
__**Final-term**__ covers:
correlation
regression
multiple regression
partial and semipartial correlation
using dummy variables
factor analysis
[[:social network analysis]]
[[:r:social network analysis tutorial|sna tutorial]]
[[:r:social network analysis|sna in r]] 
[[:sna_eg_stanford:lab06|SNA e.g. lab 06]]

Some R outputs will be used to ask the related concepts and ideas (the above).

</WRAP>