assignment 3

Assignment 3.Rmd

@@ -96,7 +96,7 @@ plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender)
 
 plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$gender, edge.width=EDGE$count)
 
-````
+```
 
 ## Part II
 
@@ -117,6 +117,30 @@ Once you have done this, also [look up](http://igraph.org/r/) how to generate th
 * 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**
 
 * 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}
+library(tidyr)
+library(dplyr)
+library(stringr)
+library(igraph)
+
+C1 <- read.csv("hudk4050-classes.csv",stringsAsFactors = FALSE, header = TRUE)
+C2 <- C1
+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,str_replace_all," ","")
+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
+```
 
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