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c:ma:2019:schedule

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
Network Analysis in R using igraph package – from Datacamp
Marketing analysis in r statistics from Datacamp

# Week01 (Sep 4, 6)

## ideas and concepts

Introduction to R and others

2. Starting R
3. Entering Commands
4. Exiting from R
5. Interrupting R
6. Viewing the Supplied Documentation
7. Getting Help on a Function
8. Searching the Supplied Documentation
9. Getting Help on a Package
10. Searching the Web for Help
11. Finding Relevant Functions and Packages
12. Searching the Mailing Lists
13. Submitting Questions to the Mailing Lists

using theories 연구문제와 가설 and making hypotheses

Installing R

# Week02 (Sep 11, 13)

## Concepts and ideas

Some basics

1. Introduction
2. Printing Something
3. Setting Variables
4. Listing Variables
5. Deleting Variables
6. Creating a Vector
7. Computing Basic Statistics
8. Creating Sequences
9. Comparing Vectors
10. Selecting Vector Elements
11. Performing Vector Arithmetic
12. Getting Operator Precedence Right
13. Defining a Function
14. Typing Less and Accomplishing More
15. Avoiding Some Common Mistakes
• Educated guess (via theories)
• Difference
• Association
• Variables (vs. ideas, concepts, and constructs)
• see this blog written in Korean
• IV 독립변인
• DV 종속변인
• Control variable 제어변인
• Mediating (Intervening) variable 매개변인

# Week03 (Sep 18, 20)

## 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 커뮤니케이션_연구문제_제기와_가설 individually. The materials will be on quizzes.

Navigating software

1. Introduction
2. Getting and Setting the Working Directory
5. Saving the Result of the Previous Command
6. Displaying the Search Path
7. Accessing the Functions in a Package
8. Accessing Built-in Datasets
9. Viewing the List of Installed Packages
10. Installing Packages from CRAN
11. Setting a Default CRAN Mirror
12. Suppressing the Startup Message
13. Running a Script
14. Running a Batch Script
15. Getting and Setting Environment Variables
16. Locating the R Home Directory
17. Customizing R
1. Introduction
2. Entering Data from the Keyboard
3. Printing Fewer Digits (or More Digits)
4. Redirecting Output to a File
5. Listing Files
6. Dealing with “Cannot Open File” in Windows
10. Writing to CSV Files
11. Reading Tabular or CSV Data from the Web
12. Reading Data from HTML Tables
13. Reading Files with a Complex Structure
15. Saving and Transporting Objects

## Assignment

Assignment for all

Group assignment

• Hypothesis 문서의 예_1의 “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.

# Week04 (Sep 25, 27)

## Concepts and ideas

1. Introduction
2. Appending Data to a Vector
3. Inserting Data into a Vector
4. Understanding the Recycling Rule
5. Creating a Factor (Categorical Variable)
6. Combining Multiple Vectors into One Vector and a Factor
7. Creating a List
8. Selecting List Elements by Position
9. Selecting List Elements by Name
10. Building a Name/Value Association List
11. Removing an Element from a List
12. Flatten a List into a Vector
13. Removing NULL Elements from a List
14. Removing List Elements Using a Condition
15. Initializing a Matrix
16. Performing Matrix Operations
17. Giving Descriptive Names to the Rows and Columns of a Matrix
18. Selecting One Row or Column from a Matrix
19. Initializing a Data Frame from Column Data
20. Initializing a Data Frame from Row Data
21. Appending Rows to a Data Frame
22. Preallocating a Data Frame
23. Selecting Data Frame Columns by Position
24. Selecting Data Frame Columns by Name
25. Selecting Rows and Columns More Easily
26. Changing the Names of Data Frame Columns
27. Editing a Data Frame
28. Removing NAs from a Data Frame
29. Excluding Columns by Name
30. Combining Two Data Frames
31. Merging Data Frames by Common Column
32. Accessing Data Frame Contents More Easily
33. Converting One Atomic Value into Another
34. Converting One Structured Data Type into Another

## 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-test를 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.
• 일반적인 se값은 아래와 같이 구합니다.
• $\displaystyle \sigma_{\hat{p}} = \sqrt{\frac{p*q}{n}} , \;\;\; q = (1 - p)$
• $p = .752$ = 75.2%
• 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.

# Week05 (Oct 2, 4)

## ideas and concepts

### t.test: mtcars

> 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
> 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


### anova: mtcars

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)


### cor

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)


# Week06 (Oct 9, 11)

## Assignment

1. Public opinion in online environments 1)
• 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 . . .)
2. Hypotheses
• Multiple regression hypotheses.

# Week08 (Oct 23, 25)

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.
• 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
• Lecture content
• 정확한 t test 공식등은 외울 필요가 없습니다. (제공됩니다).
• 간단한 t test 계산을 요구할 수 있습니다.
• ANOVA도 마찬가지입니다.

# Week13 (Nov 27, 29)

## announcement

Quiz 2 (on Friday Dec. the 6th) covers:

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

# Week14 (Dec 4, 6)

Group Presentation

# Week16 (June 18, 20)

Final-term covers:
correlation
regression
multiple regression
partial and semipartial correlation
using dummy variables
factor analysis
social network analysis
sna tutorial
sna in r
SNA e.g. lab 06

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