Useful commands or R Markdown Cheat Sheet

Ignacio et al. (2022) Banyard, Hamby, and Grych (2017) Cai et al. (2024) Cano et al. (2020) Cromer et al. (2019) Hong et al. (2018) Ferber and Weller (2022) Callaghan et al. (2019) Chan et al. (2023) Weidacker et al. (2022) Hirshfeld-Becker et al. (2019) Gur et al. (2020) Bram, Gottschalk, and Leeds (2018) Wymbs et al. (2020) Bernstein and McNally (2018) Gildawie, Honeycutt, and Brenhouse (2020) Kuhlman et al. (2023) Kayser et al. (2019) Dvorsky et al. (2019) Kirby et al. (2022) Murphy et al. (2017) Carter, Powers, and Bradley (2020) Ramaiya et al. (2018) Sendzik et al. (2017) Wu, Slesnick, and Murnan (2018) Cornwell et al. (2024) Skinner et al. (2020) Gibbons and Bouldin (2019) Rodman et al. (2019) Higheagle Strong et al. (2020) McRae et al. (2017) Martinez Jr et al. (2022) Vannucci et al. (2019) Noroña-Zhou and Tung (2021) Motsan, Yirmiya, and Feldman (2022) Rich et al. (2019) Krause et al. (2018) Sui et al. (2020) Grych et al. (2020) Vega-Torres et al. (2020) Kliewer and Parham (2019) Griffith, Farrell-Rosen, and Hankin (2023) Siciliano et al. (2023) White et al. (2021) Bettis et al. (2019) Rudolph et al. (2024) Linke et al. (2020) Cornwell et al. (2023) Criss et al. (2017) Koban et al. (2017) Kashdan et al. (2020) Priel et al. (2020) Hannan et al. (2017) Malberg (2023) Tang, Tang, and Gross (2019) Caceres et al. (2024) Gupta, Dickey, and Kujawa (2022) Gee (2022) Khahra et al. (2024) Szoko et al. (2023) Barzilay et al. (2020) Jiang, Paley, and Shi (2022) Xiao et al. (2019) Finkelstein-Fox, Park, and Riley (2018) Sevinc et al. (2019) Blair et al. (2018) Buthmann et al. (2024) Schäfer et al. (2017) Smith and Jen’nan (2024) explored research focusing on various populations and areas to find the effects of several factors like living experience, environment, physical and mental health, and their relationship of emotion regulation and resilience development.

1 Steps for second deliverable

This is where the steps go

library(splitstackshape)
library(stringr)
library(igraph)
## 
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum
## The following object is masked from 'package:base':
## 
##     union
library(networkD3)
library(magrittr)
library(htmlwidgets)
## 
## Attaching package: 'htmlwidgets'
## The following object is masked from 'package:networkD3':
## 
##     JS
library(htmltools)
library(plotly)
## Loading required package: ggplot2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:igraph':
## 
##     groups
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(igraph)
library(spdep)
## Loading required package: spData
## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`
## Loading required package: sf
## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
a<-read.csv("/Users/jinzeyang/Desktop/Fall 2024/Social Networks Analysis/A_Delieverables/US emotion regulation.csv")


authors<-as.data.frame(a[,1])#"Author.s..ID"])
colnames(authors)<-"AU"

authors$AU <- str_remove_all(authors$AU, "[^[\\a-zA-Z ]]")

a1<-cSplit(authors, splitCols = "AU", sep = ";", direction = "wide", drop = TRUE) 
class(a1)
## [1] "data.table" "data.frame"
mat <- as.matrix(a1)

dim(mat)
## [1] 70 16
mat <- cbind(a$EID, mat)
edgelist1<-matrix(NA, 1, 2) 

for (i in 1:1) {
  edgelist11 <- cbind(mat[, i], c(mat[, -c(1:i)]))
  edgelist1 <- rbind(edgelist1,edgelist11)
  edgelist1<-edgelist1[!is.na(edgelist1[,2]),]
  edgelist1<-edgelist1[edgelist1[,2]!="",]
}
dim(edgelist1)
## [1] 407   2
head(edgelist1)
##      [,1]                 [,2]          
## [1,] "2-s2.0-85124618928" "Ignacio D.A."
## [2,] "2-s2.0-85010380267" "Banyard V."  
## [3,] "2-s2.0-85195804072" "Cai Y."      
## [4,] "2-s2.0-85078064575" "Cano M.."    
## [5,] "2-s2.0-85066870128" "Cromer K.D." 
## [6,] "2-s2.0-85054193257" "Hong F."
g<- graph.data.frame(edgelist1[, 2:1], directed = FALSE)
V(g)$type <- V(g)$name %in% edgelist1[ , 2]
table(V(g)$type)
## 
## FALSE  TRUE 
##    70   376
i<-table(V(g)$type)[2]
V(g)$label<-V(g)$name


mat_g2_incidence <- t(get.incidence(g))
dim(mat_g2_incidence)
## [1] 376  70
dta <- data.frame(id=rownames(mat_g2_incidence), count=rowSums(mat_g2_incidence))
head(dta)
##                        id count
## Ignacio D.A. Ignacio D.A.     1
## Banyard V.     Banyard V.     2
## Cai Y.             Cai Y.     1
## Cano M..         Cano M..     1
## Cromer K.D.   Cromer K.D.     1
## Hong F.           Hong F.     1
dta <- dta[order(dta$count, decreasing=T), ]
head(dta)
##                      id count
## Compas B.E. Compas B.E.     3
## Banyard V.   Banyard V.     2
## Gur R.E.       Gur R.E.     2
## Cornwell H. Cornwell H.     2
## Grych J.       Grych J.     2
## Bettis A.H. Bettis A.H.     2
mat_g2_incidence_to_1 <- mat_g2_incidence%*%t(mat_g2_incidence)
diag(mat_g2_incidence_to_1)<-0

dim(mat_g2_incidence_to_1)
## [1] 376 376
table(colSums(mat_g2_incidence_to_1)==0)
## 
## FALSE  TRUE 
##   375     1
table(rowSums(mat_g2_incidence_to_1)==0)
## 
## FALSE  TRUE 
##   375     1
mat_g2_incidence_to_1 <- mat_g2_incidence_to_1 /rowSums(mat_g2_incidence_to_1)
summary(rowSums(mat_g2_incidence_to_1))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       1       1       1       1       1       1       1
# remain isolates, make it zero and get list weights
mat_g2_incidence_to_1[is.na(mat_g2_incidence_to_1)]<-0
listw<-mat2listw(mat_g2_incidence_to_1)


#Creating a dataset for tests, matching the information
pd <- data.frame(id=colnames(mat_g2_incidence_to_1))
head(pd)
##             id
## 1 Ignacio D.A.
## 2   Banyard V.
## 3       Cai Y.
## 4     Cano M..
## 5  Cromer K.D.
## 6      Hong F.
head(colnames(mat_g2_incidence_to_1))
## [1] "Ignacio D.A." "Banyard V."   "Cai Y."       "Cano M.."     "Cromer K.D." 
## [6] "Hong F."
pd$pub<- dta$count[match(pd$id, dta$id)]  
head(pd) 
##             id pub
## 1 Ignacio D.A.   1
## 2   Banyard V.   2
## 3       Cai Y.   1
## 4     Cano M..   1
## 5  Cromer K.D.   1
## 6      Hong F.   1
pd$lag.pub <- round(lag.listw(listw, pd$pub, zero.policy=T, na.action=na.omit),3)
head(pd)
##             id pub lag.pub
## 1 Ignacio D.A.   1     1.0
## 2   Banyard V.   2     1.8
## 3       Cai Y.   1     1.0
## 4     Cano M..   1     1.0
## 5  Cromer K.D.   1     1.0
## 6      Hong F.   1     1.2
# Question 2:
moran.test(pd$pub, listw, zero.policy=TRUE)
## 
##  Moran I test under randomisation
## 
## data:  pd$pub  
## weights: listw  
## n reduced by no-neighbour observations  
## 
## Moran I statistic standard deviate = 11.928, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic       Expectation          Variance 
##       0.397340340      -0.002673797       0.001124628
# Question 3: 
mi <- moran.test(pd$pub, listw, zero.policy=TRUE) #PHUDCFILY
mp_math <- moran.plot(pd$pub, listw, labels=as.character(pd$id), zero.policy = TRUE)

mp_math$new_label <- paste("Author name: ", mp_math$labels, "<br>own pub record: ", pd$pub[match(mp_math$labels, pd$id)], 
                           "<br>Peers' record: ", pd$lag.pub[match(mp_math$labels, pd$id)], sep="") #PHUDCFILY

mphu <-ggplot(mp_math, aes(x=jitter(x), y=jitter(wx), text=new_label)) + geom_point(shape=1, alpha=0) + 
  geom_hline(yintercept=mean(mp_math$wx), lty=2) + 
  geom_vline(xintercept=mean(mp_math$x), lty=2) + theme_minimal() + 
  
  geom_point(data=mp_math[(mp_math$wx>=mean(mp_math$wx)&mp_math$x>=mean(mp_math$x))&mp_math$is_inf==FALSE,], aes(x=x, y=wx), shape=1, alpha=.1) + 
  geom_point(data=mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx>=mean(mp_math$wx))&mp_math$is_inf==TRUE,], aes(x=x, y=wx), shape=9, alpha=.4) +
  
  geom_point(data=mp_math[(mp_math$x<mean(mp_math$x)&mp_math$wx>=mean(mp_math$wx))&mp_math$is_inf==FALSE,], aes(x=x, y=wx), shape=1, alpha=.1) +
  geom_point(data=mp_math[(mp_math$x<mean(mp_math$x)&mp_math$wx>=mean(mp_math$wx))&mp_math$is_inf==TRUE,], aes(x=x, y=wx), shape=9, alpha=.4) +
  
  geom_point(data=mp_math[(mp_math$x<mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==FALSE,], aes(x=x, y=wx), shape=1, alpha=.1) +
  geom_point(data=mp_math[(mp_math$x<mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,], aes(x=x, y=wx), shape=9, alpha=.4) +
  
  geom_point(data=mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==FALSE&(mp_math$x-mp_math$wx<min(mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$x-mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$wx)),], aes(x=x, y=wx), shape=1, alpha=.3, position=position_jitter(h=0.1,w=0.1)) +
  geom_point(data=mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==FALSE&(mp_math$x-mp_math$wx>=min(mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$x-mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$wx)),], aes(x=x, y=wx), shape=9, alpha=.8, colour=rgb(223, 255, 0, max=255, 255/1), position=position_jitter(h=0.1,w=0.1)) +
  geom_point(data=mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,], aes(x=x, y=wx), shape=9, alpha=.8, colour=rgb(255, 0, 126, max=255, 255/1), position=position_jitter(h=0.1,w=0.1)) + 
  xlab("Authors' Own Publication count (y_i)") + ylab("Coauthors' Publication count (y_j)") + ggtitle("Peer effects", 
                                                                                                      subtitle = "") + labs(caption = "(PHUDCFILY)") +
  theme(plot.title = element_text(color=rgb(255, 0, 126, max=255, 255/1), size=14, face="bold"),
        plot.subtitle = element_text(color = "blue"))#
mphu

ggplotly(mphu, tooltip = c("new_label")) %>%
  layout(title = list(text = paste0('<b>Peer effects on Publication Performance</b>',
                                    '<br>',
                                    '<sup style="color:black; font-weight:bold">',
                                    paste(dim(mat_g2_incidence_to_1)[1],' authors in ', dim(mat_g2_incidence)[2],' Publications. Conservative: ', dim(mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,])[1], ' HL cases (pink), Liberal: ', dim(mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==FALSE&(mp_math$x-mp_math$wx>=round(min(mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$x-mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$wx),3)),])[1], ' HL cases (green) </sup>', sep=""),
                                    '<span style="font-size:10px; font-weight:bold; color: rgb(255, 0, 126, max=255, 255/1)">',
                                    paste("\nSource: Scopus. Moran's I = ", round(mi$estimate[1], 3)," P. Value ", ifelse(mi$p.value==0, "< 0.001", mi$p.value), ', Liberal threshold = ', round(min(mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$x-mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$wx),3), '</span>', sep=""))))
# get illustrations for pink points and green points
#Conservative
mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]
##     x wx is_inf      labels    dfb.1_      dfb.x      dffit   cov.r     cook.d
## 58  2  1   TRUE    Gee D.G. 0.3482164 -0.3920042 -0.4104217 1.00748 0.08324622
## 87  2  1   TRUE Kofler M.J. 0.3482164 -0.3920042 -0.4104217 1.00748 0.08324622
## 164 2  1   TRUE  Feldman R. 0.3482164 -0.3920042 -0.4104217 1.00748 0.08324622
## 181 2  1   TRUE  Gross J.J. 0.3482164 -0.3920042 -0.4104217 1.00748 0.08324622
## 198 2  1   TRUE      Liu S. 0.3482164 -0.3920042 -0.4104217 1.00748 0.08324622
##            hat
## 58  0.03031362
## 87  0.03031362
## 164 0.03031362
## 181 0.03031362
## 198 0.03031362
##                                                             new_label
## 58     Author name: Gee D.G.<br>own pub record: 2<br>Peers' record: 1
## 87  Author name: Kofler M.J.<br>own pub record: 2<br>Peers' record: 1
## 164  Author name: Feldman R.<br>own pub record: 2<br>Peers' record: 1
## 181  Author name: Gross J.J.<br>own pub record: 2<br>Peers' record: 1
## 198      Author name: Liu S.<br>own pub record: 2<br>Peers' record: 1
# Liberal
mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==FALSE&(mp_math$x-mp_math$wx>=round(min(mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$x-mp_math[(mp_math$x>=mean(mp_math$x)&mp_math$wx<mean(mp_math$wx))&mp_math$is_inf==TRUE,]$wx),3)),]
##  [1] x         wx        is_inf    labels    dfb.1_    dfb.x     dffit    
##  [8] cov.r     cook.d    hat       new_label
## <0 rows> (or 0-length row.names)
# Question4:
# I tried order =3 and above, but the R shows error that 
# "sp.correlogram: too few included observations in higher lags: reduce order", so I chose order =2

sp.correlogram(listw[[2]], pd$pub, order = 2, method = "I", zero.policy=T) # calculate social correlogram for autocorrelations
## Spatial correlogram for pd$pub 
## method: Moran's I
##           estimate expectation   variance standard deviate Pr(I) two sided    
## 1 (375)  0.3036855  -0.0026738  0.0011236           9.1394          <2e-16 ***
## 2 (81)   0.0089194  -0.0125000  0.0041642           0.3319          0.7399    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot.spcor(sp.correlogram(listw[[2]], pd$pub, order = 2, method = "I", zero.policy=T), xlab = "Social lags", main = "Social correlogram: Autocorrelation with CIs")

2 Decide whether you want to focus on co-authors or want to include all authors, including solo authors.

2.1 What are the implications of this decision? That is, tell me how this decision changes whether you start with one-mode or two-mode data. What about isolates?

Answer:

In Deliverable 1, we excluded solo authors with solo publications in the one-mode network. However, in Deliverable 3, we included solo authors with both solo-authored publications and co-authored publications in the two-mode network. As a result, some solo authors excluded in Deliverable 1 were added back in Deliverable 3 because they had at least one co-authored publication. This inclusion of solo authors with co-authorship connections may explain the difference in the count of human actors between Deliverable 1 and Deliverable 3.

In this context, solo authors with at least one co-authored publication should not be removed as isolates. Retaining them is necessary to ensure more accurate publication counts of authors. Therefore, I decided to include all authors and start with two-mode data, keeping isolates.

2.2 Test for outcome dependence using author’s number of publication as a function of her/his coauthors’ number of publications

Answer:

The magnitude of Moran’s I test is 0.397, as using author’s number of publication as a function of her/his coauthors’ number of publications.

2.3 Visualize this influence level, what may be the mechanism to explain these clusters and outliers? For this you need to replicate the interactive visualization we did in class.

Answer:

There are some authors with one publication who have more than 1.1 co-author publications (Low-High). Additionally, some authors with two publications have more than 1.1 co-author publications (High-High).

Potential mechanisms for these patterns might include peer effects on collaboration productivity. Balanced collaborations can influence authors and their coauthors to have similar numbers of publications. Shared contributions and resources within collaborations may also strengthen cluster patterns, as everyone benefits from shared resources and a distributed workload. Consequently, clusters tend to form around the average counts for authors’ and coauthors’ publications.

However, there are also outliers. Some authors have one solo publication but less than 1.1 co-author publications (Low-Low), and others have more than one solo publication but less than 1.1 co-author publications (High-Low).

These outliers may indicate inequitable contributions and asymmetric productivity in collaborations. Highly productive individuals may publish more because they focus on multiple projects simultaneously, whereas their coauthors contribute less. Alternatively, some authors may not be highly productive, but their coauthors might be less experienced or less active, leading to productivity differences (High-Low). Moreover, authors who are less productive than their coauthors may also become isolates (Low-Low).

2.4 Test how many higher order neighbors should we account for in this network?

Answer：

I have decided to continue retaining isolates, even though they may affect modeling strategies. No cases with wx = 0 or record: 0 were found in the Conservative test, and 0 rows in the Liberal test indicated that isolates do not interfere with the analysis.

There is no significant higher order neighbors. There is only a first-order neighbor.

2.5 If needed create a higher order weight matrix and test for autocorrelation.

2.5.1 How did this change affect the magnitude of the Moran’s I that includes higher order neighbors?

Answer:

There is no higher order neighbors so the results did not apply to the question.

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