submit Assgn#3

Assignment 3.Rmd

@@ -34,7 +34,7 @@ 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")
 
 ```
 
@@ -105,6 +105,18 @@ In Part II your task is to [look up](http://igraph.org/r/) in the igraph documen
 * Ensure that sizing allows for an unobstructed view of the network features (For example, the arrow size is smaller)
 * The vertices are colored according to major
 * The vertices are sized according to the number of comments they have recieved
+```{r}
+
+plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$major, edge.width=EDGE$count)
+
+commentnumb <- count(D1, comment.to)
+names(commentnumb) <- c("id","count")
+commentnumb <- left_join(VERTEX,commentnumb,by=c("id"))
+commentnumb$count[is.na(commentnumb$count)] <- 0
+
+plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$major, edge.width=EDGE$count, edge.arrow.size= 0.5, vertex.size=15+commentnumb$count)
+
+```
 
 ## Part III
 
@@ -121,3 +133,58 @@ Once you have done this, also [look up](http://igraph.org/r/) how to generate th
 ### 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.
+
+```{r}
+library(tidyr)
+library(dplyr)
+library(stringr)
+library(igraph)
+C1 <- read.csv("hudk4050-classes.csv", stringsAsFactors = FALSE, header = TRUE)
+C2 <- C1
+```
+# Data Tidying
+```{r}
+colnames(C2) <- C2[1,]
+C2 <- slice(C2,3:49)
+C2 <- select(C2,1:8)
+C2 <- unite(C2, "name", `First Name`, `Last Name`, sep = " ")
+C2$name <- str_replace(C2$name,"`","")
+C2$name <- str_to_title(C2$name)
+C2 <- C2 %>% mutate_at(2:7, list(toupper))
+C2 <- C2 %>% mutate_at(2:7, str_replace_all, " ", "")
+
+```
+#Data Restructring
+```{r}
+C3 <- C2 %>% gather(label,class, 2:7, na.rm = TRUE, convert = FALSE) %>% select(name, class)
+C3$count <- 1
+C3 <- filter(C3, class != "")
+C3 <- unique(C3)
+C3 <- spread(C3, class, count)
+rownames(C3) <- C3$name
+C3 <- select(C3, -name, -HUDK4050)
+C3[is.na(C3)] <- 0
+```
+# Matrix Operations
+```{r}
+C4 <- as.matrix(C3)
+C4 <- C4 %*% t(C4)
+```
+# Graphing
+```{r}
+g <- graph.adjacency(C4, mode="undirected", diag = FALSE)
+plot(g,layout=layout.fruchterman.reingold, vertex.size = 4, 
+     #degree(g)*0.7, 
+     vertex.label.cex=0.8,
+     vertex.label.color="black",
+     vertex.color="blue")
+```
+
+#Centrality
+```{r}
+sort(degree(g), decreasing = TRUE)
+sort(betweenness(g), decreasing = TRUE)
+```
+
+Based on the data we can find that Yifei Zhang is the most central person in the network, and he/she connects two centralized groups. Therefore, she is the most appropriate person to help students contacting each other.
+