Assignment #3_XI GU

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
 
@@ -105,7 +105,19 @@ 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}
+##Q1
+plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$major,vertex.size=15,edge.arrow.size=0.5)
+##Q2
+plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$major,edge.width=EDGE$count,vertex.size=15,edge.arrow.size=0.5)
+##Q3
+num1 <- count(D2, comment.to)
+names(num1) <- c("id","count")
+num1 <- left_join(VERTEX,num1,by=c("id"))
+num1$count[is.na(num1$count)] <- 0
+plot(g,layout=layout.fruchterman.reingold, vertex.color=VERTEX$major, edge.width=EDGE$count, vertex.size=15+num1$count, edge.arrow.size=0.5)
 
+```
 ## 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.
@@ -121,3 +133,88 @@ 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)
+
+#Input data
+C1 <- read.csv("hudk4050-classes.csv", stringsAsFactors = FALSE, header= TRUE)
+
+#Copy to play with data
+C2 <- C1
+
+```
+#Daa Tidying
+```{r}
+#Make header first row
+colnames(C2) <- C2[1,]
+#Remove unwated rows
+C2 <- slice(C2,3:49)
+#Remove last column
+C2 <- select(C2,1:8)
+#Merge name columns
+C2 <- unite(C2,"name",`First Name`, `Last Name`, sep="")
+#Remove unpredictable characters from names
+C2$name <- str_replace(C2$name, "`", "")
+#Make all name captalized first letters only
+C2$name <- str_to_title(C2$name)
+#Make all class letters capitals
+C2 <- C2 %>% mutate_at(2:7, list(toupper))
+#Remove whitespace between letters and numbers in class
+C2 <- C2 %>% mutate_at(2:7, str_replace_all, " ", "")
+
+```
+
+# Data restructuring
+```{r}
+# Create a dataframe with two variables, student and class
+C3 <- C2 %>%gather(labe, class, 2:7, na.rm = TRUE, convert = FALSE) %>% select(name, class)
+#Create a new variable containing 1s that will become our counts
+C3$count <- 1
+#Remove  blank classes
+C3 <-filter(C3, class != "")
+#Remove duplicates (Danny!)
+C3 <- unique(C3)
+#Spread 1s across classes to create a student x class dataframe
+C3 <- spread(C3, class, count)
+#Make row names student names
+rownames(C3) <- C3$name
+#Remove names column AND HUDK4050
+C3 <- select(C3, -name, -HUDK4050)
+#Shortest
+C3[is.na(C3)] <- 0
+#Cheat way
+C3 <-ifelse(is.na(C3),0,1)
+
+```
+
+#Matrix operations
+```{r}
+#Convert to matrix
+C4 <- as.matrix(C3)
+#Create person-person matrix
+C4 <- C4 %*% t(C4)
+
+```
+
+#Graphing
+```{r}
+g <- graph.adjacency(C4, mode="undirected", diag = FALSE)
+
+plot(g,layout=layout.fruchterman.reingold,
+     vertex.size = 4,
+     vertex.label.cex =0.8,
+     vertex.label.color="black",
+     vertex.color="yellow")
+
+```
+
+#Centrality
+```{r}
+sort(degree(g), decreasing = TRUE)
+
+sort(betweenness(g), decreasing = TRUE)
+```