diff --git a/Assignment 3.Rmd b/Assignment 3.Rmd index 649407e..34daa9b 100644 --- a/Assignment 3.Rmd +++ b/Assignment 3.Rmd @@ -6,6 +6,7 @@ Start by installing the "igraph" package. Once you have installed igraph, load t Now upload the data file "comment-data.csv" as a data frame called "D1". Each row represents a comment from one student to another so the first line shows that student "28" commented on the comment of student "21". It also shows the gender of both students and the students' main elective field of study ("major""). ```{r} + D1 <- read.csv("comment-data.csv", header = TRUE) ``` @@ -26,6 +27,7 @@ First we will isolate the variables that are of interest: comment.from and comme library(dplyr) D2 <- select(D1, comment.to, comment.from) #select() chooses the columns + ``` Since our data represnts every time a student makes a comment there are multiple rows when the same student comments more than once on another student's video. We want to collapse these into a single row, with a variable that shows how many times a student-student pair appears. @@ -34,7 +36,7 @@ Since our data represnts every time a student makes a comment there are multiple EDGE <- count(D2, comment.to, comment.from) -names(EDGE) <- c("from", "to", "count") +names(EDGE) <- c("to", "from", "count") ``` @@ -103,8 +105,59 @@ plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, edge.widt In Part II your task is to [look up](http://igraph.org/r/) in the igraph documentation and modify the graph above so that: * Ensure that sizing allows for an unobstructed view of the network features (For example, the arrow size is smaller) +```{r} + +#this section is a small dumpster fire... i kept trying to experiment with variations of the graphs to see the effects... a sort of trial and error approach to learning... after a few hours, i learned some... and was grateful for that + + +tkplot(g,vertex.color=VERTEX$gender,edge.width=EDGE$count) + +plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, edge.width=EDGE$count,vertex.size=EDGE$count*7, edge.arrow.size=.5) + +#https://igraph.org/r/doc/layout.deprecated.html +plot(g,layout=layout.reingold.tilford, vertex.color=VERTEX$gender, edge.arrow.size=.5) + +#https://igraph.org/r/doc/plot.common.html +#circle testing +plot(g,layout=layout.circle, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5) +plot(g,layout=layout.circle, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5, edge.color="darkorange4") +plot(g,layout=layout.circle, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5, edge.color="darkslategrey") +plot(g,layout=layout.circle, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5, edge.color="bisque4") + +#layout.sphere +plot(g,layout=layout.sphere, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5) + +#layout.random +plot(g,layout=layout.random, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5) + +#layout.fruchterman.reingold +plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5) + +#layout.kawai +plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5) + + + +#layout.lgl +plot(g,layout=layout.lgl, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5) + +# +plot(g,vertex.color=VERTEX$gender, gsize = 10, vertex.size=EDGE$count*7, edge.arrow.size=.5) + +``` + * The vertices are colored according to major +```{r} +plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, edge.width=EDGE$count) + +``` + * The vertices are sized according to the number of comments they have recieved +```{r} + +plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, edge.width=EDGE$count) + +``` ## Part III @@ -112,12 +165,148 @@ Now practice with data from our class. This data is real class data directly exp Please create a **person-network** with the data set hudk4050-classes.csv. To create this network you will need to create a person-class matrix using the tidyr functions and then create a person-person matrix using `t()`. You will then need to plot a matrix rather than a to/from data frame using igraph. +```{r} + +#questions +#how can i count the number of instances an issue exists in the data? e.g. double space? +#is there a way to have r give feedback on the number of changes it makes with each command? +#is there an easy way to undo a command? (https://stackoverflow.com/questions/3076526/undo-command-in-r) +# + + +#without the code workout... there is a strong possibly that this section would have taken me until president biden's second term. i'm grateful for the opportunity for 'guided learning' by watching you do it... i'm pretty sure the only credit i deserve here is that i am reasonably adept at copying your work. + + +library(tidyr) +library(dplyr) +library(stringr) +library(igraph) + +#examine the 'person network' (pn) dataset for certain Centrality Measures + +#spare import of the data +x_pn <- read.csv("hudk4050-classes.csv") + +#import the data, assign headers, and remove the first record, and, alla Dr Lang... leave everything that looks like a string as a string +pn1 <- read.csv("hudk4050-classes.csv", + skip = 1, + header = TRUE, + stringsAsFactors = FALSE) + +#remove the first record +pn1 <- pn1[-c(1), ] + + +#remove the last column +pn1 <- pn1[c(1:8)] + +#merge the name column +pn2 <- unite(pn1, "Full.Name", `First.Name`, `Last.Name`, sep = " ") + +#clean up Full.Name +#remove `` +pn2$Full.Name <- str_replace(pn2$Full.Name, "`","") +#remove double spaces +pn2$Full.Name <- str_replace(pn2$Full.Name, " "," ") +#remove leading and ending spaces +pn2$Full.Name <- trimws(pn2$Full.Name, which = c("both")) +#make Title Case +pn2$Full.Name <- str_to_title(pn2$Full.Name) + +#copy +pn3 <- pn2 + +#clean up classes columns +#remove spaces +#https://stackoverflow.com/questions/48953295/replace-a-specific-strings-from-multiple-columns-in-a-dataframe +pn3[,2:7] <- apply(pn3[,2:7],2,function(x) gsub(" ",'',x)) + +#remove leading and ending spaces +#busted +#pn3[,2:7] <- trimws(pn3[,2:7], which = c("both")) +#make UPPER Case +#busted +#pn3 <- str_to_upper(pn3[,2:7]) + +#make UPPER Case per Dr. Lang +pn3 <- pn3 %>% mutate_at(2:7, list(toupper)) + +``` + +```{r} +# Data Restructuring + +#create a dataframe with two variables, student and class +pn4 <- pn3 %>% gather(label, class, 2:7, na.rm = TRUE, convert = FALSE) %>% select(Full.Name,class) + +#create a new variable containing 1s that will become our counts +pn4$count <- 1 + +#remove blank classes +pn4 <- filter(pn4, class !="") + +#remove duplicates +pn4 <- unique(pn4) + +#spread 1s across classes to createa student x class dataframe +pn4 <- spread(pn4, class, count) + +#make row names student names +rownames(pn4) <- pn4$Full.Name + +#remove names column and HUDK4050 +pn4 <- select(pn4, -Full.Name, -HUDK4050) + +#replace blanks with zeros +pn4[is.na(pn4)] <- 0 + + +``` + +# Matrix operations +```{r} + +#convert to matrix +pn5 <-as.matrix(pn4) + +#create person-person matrix +pn5 <- pn5 %*% t(pn5) + + +``` + Once you have done this, also [look up](http://igraph.org/r/) how to generate the following network metrics: * Betweeness centrality and dregree centrality. **Who is the most central person in the network according to these two metrics? Write a sentence or two that describes your interpretation of these metrics** +```{r} + +g <- graph.adjacency(pn5, mode="undirected", diag = FALSE) + +plot(g,layout=layout.fruchterman.reingold, + vertex.size = 4, + #degree(g)*.7, + vertex.label.cex=0.8, + vertex.label.color="black", + vertex.color="gainsboro") + +``` + * Color the nodes according to interest. Are there any clusters of interest that correspond to clusters in the network? Write a sentence or two describing your interpetation. +```{r} + +#Calculate the degree centrality of the nodes, showing who has the most connections +sort(degree(g), decreasing = TRUE) + + +#Calculate the betweenness centrality, showing how many 'shortest paths' pass through each node +sort(betweenness(g), decreasing = TRUE) + + +``` + + ### To Submit Your Assignment Please submit your assignment by first "knitting" your RMarkdown document into an html file and then comit, push and pull request both the RMarkdown file and the html file. diff --git a/Assignment-3.html b/Assignment-3.html new file mode 100644 index 0000000..81238ab --- /dev/null +++ b/Assignment-3.html @@ -0,0 +1,811 @@ + + + + + + + + + + + + + +Assignment-3.utf8 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Assignment 3 - Social Network Analysis

+
+

Part I

+

Start by installing the “igraph” package. Once you have installed igraph, load the package.

+

Now upload the data file “comment-data.csv” as a data frame called “D1”. Each row represents a comment from one student to another so the first line shows that student “28” commented on the comment of student “21”. It also shows the gender of both students and the students’ main elective field of study (“major”").

+
D1 <- read.csv("comment-data.csv", header = TRUE)
+

Before you proceed, you will need to change the data type of the student id variable. Since it is a number R will automatically think it is an integer and code it as such (look at the list of variables by clicking on the data frame arrow in the Data pane. Here you will see the letters “int”" next to the stid variable, that stands for integer). However, in this case we are treating the variable as a category, there is no numeric meaning in the variable. So we need to change the format to be a category, what R calls a “factor”. We can do this with the following code:

+
D1$comment.to <- as.factor(D1$comment.to)
+D1$comment.from <- as.factor(D1$comment.from)
+

igraph requires data to be in a particular structure. There are several structures that it can use but we will be using a combination of an “edge list” and a “vertex list” in this assignment. As you might imagine the edge list contains a list of all the relationships between students and any characteristics of those edges that we might be interested in. There are two essential variables in the edge list a “from” variable and a “to” variable that descibe the relationships between vertices. While the vertex list contains all the characteristics of those vertices, in our case gender and major.

+

So let’s convert our data into an edge list!

+

First we will isolate the variables that are of interest: comment.from and comment.to

+
library(dplyr)
+
## 
+## Attaching package: 'dplyr'
+
## The following objects are masked from 'package:stats':
+## 
+##     filter, lag
+
## The following objects are masked from 'package:base':
+## 
+##     intersect, setdiff, setequal, union
+
D2 <- select(D1, comment.to, comment.from) #select() chooses the columns
+

Since our data represnts every time a student makes a comment there are multiple rows when the same student comments more than once on another student’s video. We want to collapse these into a single row, with a variable that shows how many times a student-student pair appears.

+
EDGE <- count(D2, comment.to, comment.from)
+
+names(EDGE) <- c("to", "from", "count")
+

EDGE is your edge list. Now we need to make the vertex list, a list of all the students and their characteristics in our network. Because there are some students who only recieve comments and do not give any we will need to combine the comment.from and comment.to variables to produce a complete list.

+
#First we will separate the commenters from our commentees
+V.FROM <- select(D1, comment.from, from.gender, from.major)
+
+#Now we will separate the commentees from our commenters
+V.TO <- select(D1, comment.to, to.gender, to.major)
+
+#Make sure that the from and to data frames have the same variables names
+names(V.FROM) <- c("id", "gender.from", "major.from")
+names(V.TO) <- c("id", "gender.to", "major.to")
+
+#Make sure that the id variable in both dataframes has the same number of levels
+lvls <- sort(union(levels(V.FROM$id), levels(V.TO$id)))
+
+VERTEX <- full_join(mutate(V.FROM, id=factor(id, levels=lvls)),
+    mutate(V.TO, id=factor(id, levels=lvls)), by = "id")
+
+#Fill in missing gender and major values - ifelse() will convert factors to numerical values so convert to character
+VERTEX$gender.from <- ifelse(is.na(VERTEX$gender.from) == TRUE, as.factor(as.character(VERTEX$gender.to)), as.factor(as.character(VERTEX$gender.from)))
+
+VERTEX$major.from <- ifelse(is.na(VERTEX$major.from) == TRUE, as.factor(as.character(VERTEX$major.to)), as.factor(as.character(VERTEX$major.from)))
+
+#Remove redundant gender and major variables
+VERTEX <- select(VERTEX, id, gender.from, major.from)
+
+#rename variables
+names(VERTEX) <- c("id", "gender", "major")
+
+#Remove all the repeats so that we just have a list of each student and their characteristics
+VERTEX <- unique(VERTEX)
+

Now we have both a Vertex and Edge list it is time to plot our graph!

+
#Load the igraph package
+
+library(igraph)
+
## 
+## Attaching package: 'igraph'
+
## The following objects are masked from 'package:dplyr':
+## 
+##     as_data_frame, groups, union
+
## The following objects are masked from 'package:stats':
+## 
+##     decompose, spectrum
+
## The following object is masked from 'package:base':
+## 
+##     union
+
#First we will make an object that contains the graph information using our two dataframes EDGE and VERTEX. Notice that we have made "directed = TRUE" - our graph is directed since comments are being given from one student to another.
+
+g <- graph.data.frame(EDGE, directed=TRUE, vertices=VERTEX)
+
+#Now we can plot our graph using the force directed graphing technique - our old friend Fruchertman-Reingold!
+
+plot(g,layout=layout.fruchterman.reingold)
+

+
#There are many ways to change the attributes of the graph to represent different characteristics of the newtork. For example, we can color the nodes according to gender.
+
+plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender)
+

+
#We can change the thickness of the edge according to the number of times a particular student has sent another student a comment.
+
+plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, edge.width=EDGE$count)
+

+
+
+

Part II

+

In Part II your task is to look up in the igraph documentation and modify the graph above so that:

+
    +
  • Ensure that sizing allows for an unobstructed view of the network features (For example, the arrow size is smaller)
    • +
+
#this section is a small dumpster fire... i kept trying to experiment with variations of the graphs to see the effects... a sort of trial and error approach to learning... after a few hours, i learned some... and was grateful for that   
+
+
+tkplot(g,vertex.color=VERTEX$gender,edge.width=EDGE$count)
+
## [1] 1
+
plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, edge.width=EDGE$count,vertex.size=EDGE$count*7, edge.arrow.size=.5)
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
## Warning in layout[, 2] + label.dist * sin(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
#https://igraph.org/r/doc/layout.deprecated.html
+plot(g,layout=layout.reingold.tilford, vertex.color=VERTEX$gender, edge.arrow.size=.5)
+
## Warning in layout_as_tree(structure(list(29, TRUE, c(11, 2, 6, 6, 6, 6, : At
+## structural_properties.c:3346 :graph contains a cycle, partial result is returned
+

+
#https://igraph.org/r/doc/plot.common.html
+#circle testing
+plot(g,layout=layout.circle, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5)
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
plot(g,layout=layout.circle, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5, edge.color="darkorange4")
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
plot(g,layout=layout.circle, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5, edge.color="darkslategrey")
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
plot(g,layout=layout.circle, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5, edge.color="bisque4")
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
#layout.sphere
+plot(g,layout=layout.sphere, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5)
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
#layout.random
+plot(g,layout=layout.random, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5)
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
#layout.fruchterman.reingold
+plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5)
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
#layout.kawai
+plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5)
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
#layout.lgl
+plot(g,layout=layout.lgl, vertex.color=VERTEX$gender, vertex.size=EDGE$count*7, edge.arrow.size=.5)
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
#
+plot(g,vertex.color=VERTEX$gender, gsize = 10, vertex.size=EDGE$count*7, edge.arrow.size=.5)
+
## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+
+## Warning in layout[, 1] + label.dist * cos(-label.degree) * (vertex.size + :
+## longer object length is not a multiple of shorter object length
+

+
    +
  • The vertices are colored according to major
    • +
+
plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, edge.width=EDGE$count)
+

+
    +
  • The vertices are sized according to the number of comments they have recieved
    • +
+
plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, edge.width=EDGE$count)
+

+
+
+

Part III

+

Now practice with data from our class. This data is real class data directly exported from Qualtrics and you will need to wrangle it into shape before you can work with it. Import it into R as a data frame and look at it carefully to identify problems.

+

Please create a person-network with the data set hudk4050-classes.csv. To create this network you will need to create a person-class matrix using the tidyr functions and then create a person-person matrix using t(). You will then need to plot a matrix rather than a to/from data frame using igraph.

+
#questions
+#how can i count the number of instances an issue exists in the data? e.g. double space?
+#is there a way to have r give feedback on the number of changes it makes with each command?
+#is there an easy way to undo a command? (https://stackoverflow.com/questions/3076526/undo-command-in-r)
+#
+
+
+#without the code workout... there is a strong possibly that this section would have taken me until president biden's second term. i'm grateful for the opportunity for 'guided learning' by watching you do it... i'm pretty sure the only credit i deserve here is that i am reasonably adept at copying your work.
+
+
+library(tidyr)
+
## Warning: package 'tidyr' was built under R version 4.0.3
+
## 
+## Attaching package: 'tidyr'
+
## The following object is masked from 'package:igraph':
+## 
+##     crossing
+
library(dplyr)
+library(stringr)
+library(igraph)
+
+#examine the 'person network' (pn) dataset for certain Centrality Measures 
+
+#spare import of the data
+x_pn <- read.csv("hudk4050-classes.csv")
+
+#import the data, assign headers, and remove the first record, and, alla Dr Lang... leave everything that looks like a string as a string  
+pn1 <- read.csv("hudk4050-classes.csv", 
+                skip = 1, 
+                header = TRUE, 
+                stringsAsFactors = FALSE)
+
+#remove the first record
+pn1 <- pn1[-c(1), ] 
+
+
+#remove the last column
+pn1 <- pn1[c(1:8)] 
+
+#merge the name column
+pn2 <- unite(pn1, "Full.Name", `First.Name`, `Last.Name`, sep = " ")
+
+#clean up Full.Name
+#remove ``
+pn2$Full.Name <- str_replace(pn2$Full.Name, "`","")
+#remove double spaces
+pn2$Full.Name <- str_replace(pn2$Full.Name, "  "," ")
+#remove leading and ending spaces
+pn2$Full.Name <- trimws(pn2$Full.Name, which = c("both"))
+#make Title Case 
+pn2$Full.Name <- str_to_title(pn2$Full.Name)
+
+#copy 
+pn3 <- pn2
+
+#clean up classes columns
+#remove spaces 
+#https://stackoverflow.com/questions/48953295/replace-a-specific-strings-from-multiple-columns-in-a-dataframe
+pn3[,2:7] <- apply(pn3[,2:7],2,function(x) gsub(" ",'',x))
+
+#remove leading and ending spaces
+#busted
+#pn3[,2:7] <- trimws(pn3[,2:7], which = c("both"))
+#make UPPER Case 
+#busted
+#pn3 <- str_to_upper(pn3[,2:7])
+
+#make UPPER Case per Dr. Lang
+pn3 <- pn3 %>% mutate_at(2:7, list(toupper))
+
# Data Restructuring
+
+#create a dataframe with two variables, student and class
+pn4 <- pn3 %>% gather(label, class, 2:7, na.rm = TRUE, convert = FALSE) %>% select(Full.Name,class)
+
+#create a new variable containing 1s that will become our counts
+pn4$count <- 1
+
+#remove blank classes
+pn4 <- filter(pn4, class !="")
+
+#remove duplicates
+pn4 <- unique(pn4)
+
+#spread 1s across classes to createa  student x class dataframe
+pn4 <- spread(pn4, class, count)
+
+#make row names student names
+rownames(pn4) <- pn4$Full.Name
+
+#remove names column and HUDK4050
+pn4 <- select(pn4, -Full.Name, -HUDK4050)
+
+#replace blanks with zeros
+pn4[is.na(pn4)] <- 0
+
+
+
+

Matrix operations

+
#convert to matrix
+pn5 <-as.matrix(pn4)
+
+#create person-person matrix
+pn5 <- pn5 %*% t(pn5)
+

Once you have done this, also look up how to generate the following network metrics:

+ +
g <- graph.adjacency(pn5, mode="undirected", diag = FALSE)
+
+plot(g,layout=layout.fruchterman.reingold,
+     vertex.size = 4,
+     #degree(g)*.7,
+     vertex.label.cex=0.8,
+     vertex.label.color="black",
+     vertex.color="gainsboro")
+

+ +
#Calculate the degree centrality of the nodes, showing who has the most connections
+sort(degree(g), decreasing = TRUE)
+
##          Guoliang Xu          Hangshi Jin             Jiaao Qi 
+##                   31                   31                   31 
+##          Jiacong Zhu          Jiahao Shen            Wenqi Gao 
+##                   31                   31                   31 
+##          Xiyun Zhang          Yingxin Xie          Yifei Zhang 
+##                   31                   31                   24 
+##          Xiaojia Liu            Yuxuan Ge         Zhixin Zheng 
+##                   22                   22                   20 
+## Stanley Si Heng Zhao              Dan Lei          Yuting Zhou 
+##                   19                   16                   16 
+##          Xueshi Wang            Zhouda Wu          Ruoyi Zhang 
+##                   14                   14                   12 
+##         Tianyu Chang           Xijia Wang           Yunzhao Wu 
+##                   12                   12                   12 
+##              Jie Yao        Zach Friedman    Nicole Schlosberg 
+##                   11                   11                   10 
+##           Yixiong Xu           Berj Akian          Kaijie Fang 
+##                   10                    9                    9 
+##            Rong Sang          Yucheng Pan      Amanda Oliveira 
+##                    8                    7                    6 
+##             Fei Wang          Jiasheng Yu         Wenning Xiao 
+##                    6                    6                    4 
+##           Yingxin Ye           Danny Shan           Fangqi Liu 
+##                    2                    1                    1 
+##          Hyungoo Lee        Shuying Xiong   Abdul Malik Muftau 
+##                    1                    1                    0 
+##         Ali Al Jabri            Chris Kim              He Chen 
+##                    0                    0                    0 
+##  Mahshad Davoodifard         Qianhui Yuan         Sara Vasquez 
+##                    0                    0                    0 
+##       Vidya Madhavan           Yurui Wang 
+##                    0                    0
+
#Calculate the betweenness centrality, showing how many 'shortest paths' pass through each node
+sort(betweenness(g), decreasing = TRUE)
+
##          Yifei Zhang Stanley Si Heng Zhao              Dan Lei 
+##          260.6143603           97.2791152           83.4785714 
+##         Zhixin Zheng        Zach Friedman    Nicole Schlosberg 
+##           66.2352941           43.3856397           36.6078619 
+##           Yingxin Ye          Xueshi Wang          Yuting Zhou 
+##           34.0000000           24.1453512           19.7898193 
+##            Zhouda Wu          Guoliang Xu          Hangshi Jin 
+##            8.9230159            7.5944061            7.5944061 
+##             Jiaao Qi          Jiacong Zhu          Jiahao Shen 
+##            7.5944061            7.5944061            7.5944061 
+##            Wenqi Gao          Xiyun Zhang          Yingxin Xie 
+##            7.5944061            7.5944061            7.5944061 
+##           Yixiong Xu              Jie Yao          Xiaojia Liu 
+##            5.0523810            4.4984127            3.2007978 
+##            Yuxuan Ge          Yucheng Pan   Abdul Malik Muftau 
+##            3.2007978            0.8333333            0.0000000 
+##         Ali Al Jabri      Amanda Oliveira           Berj Akian 
+##            0.0000000            0.0000000            0.0000000 
+##            Chris Kim           Danny Shan           Fangqi Liu 
+##            0.0000000            0.0000000            0.0000000 
+##             Fei Wang              He Chen          Hyungoo Lee 
+##            0.0000000            0.0000000            0.0000000 
+##          Jiasheng Yu          Kaijie Fang  Mahshad Davoodifard 
+##            0.0000000            0.0000000            0.0000000 
+##         Qianhui Yuan            Rong Sang          Ruoyi Zhang 
+##            0.0000000            0.0000000            0.0000000 
+##         Sara Vasquez        Shuying Xiong         Tianyu Chang 
+##            0.0000000            0.0000000            0.0000000 
+##       Vidya Madhavan         Wenning Xiao           Xijia Wang 
+##            0.0000000            0.0000000            0.0000000 
+##           Yunzhao Wu           Yurui Wang 
+##            0.0000000            0.0000000
+
+

To Submit Your Assignment

+

Please submit your assignment by first “knitting” your RMarkdown document into an html file and then comit, push and pull request both the RMarkdown file and the html file.

+
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/README.md b/README.md index f618913..d59efc9 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,7 @@ The instructions to Assignment 3 are in the Assignment 3.Rmd file. Assignments a **Please complete as much as you can by midnight EDT, 10/21/20** + Once you have finished, commit, push and pull your assignment back to the main branch. Include the .Rmd file and the .html file generated from your .Rmd file. Good luck! \ No newline at end of file