+
+
+
+
+
+
+HUDK4050: Class Activity 6
+Ningyao Xu
+10/16/2018
+ +
+
+Data Management
+#Load data
+DF1 <- read.csv("HUDK405019-clustering.csv", header = TRUE)
+library(tidyr)
+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#rownames == First name + Last name
+for (i in c(1,2,15,16))
+{DF1[,i] = as.character(DF1[,i])}
+DF1$name <- paste(DF1$First.Name, DF1$Last.Name)
+rownames(DF1) <- DF1$name
+DF1 <- DF1[,3:16]
+
+
+#Delete those who write latitude and longtitude twice in the survey
+list <- NULL
+for (i in 1:nrow(DF1))
+ { if (DF1[i,13] == DF1[i,14] )
+list <- c(list,i)}
+DF1 <- DF1[-list,]
+
+
+# reverse those who put latitude and longtitude in wrong order
+a <-NULL
+b<- NULL
+reverse <- grep("E",DF1[,13])
+for (i in reverse)
+ { a = DF1[i,13]
+ b = DF1[i,14]
+ DF1[i,13] <- b
+ DF1[i,14] <- a}
+
+#Find the signal and delete all the things after the signal
+#"°"this is how it shows on windows R
+for (j in c(13:14)){
+for (i in 1:nrow(DF1))
+ { if (grepl("°", DF1[i,j]) )
+ { psn <- as.numeric(regexpr("°", DF1[i,j]))
+ DF1[i,j] <- substr(DF1[i,j], 1, psn-1)}}}
+
+#If you are using mac, use the following one
+for (j in c(13:14)){
+for (i in 1:nrow(DF1))
+ { if (grepl("\\D", DF1[i,j]) )
+ { psn <- as.numeric(regexpr("\\D", DF1[i,j]))
+ DF1[i,j] <- substr(DF1[i,j], 1, psn-1)}}}
+
+#Delete all the space, alphabet from the data and turn all the data into numeric
+ for (i in c(1:11,13,14))
+{ DF1[,i]= gsub("[[:alpha:]]", "", DF1[,i])
+ DF1[,i]= gsub(" ", "", DF1[,i])
+ DF1[,i] = as.numeric(DF1[,i])}## Warning: NAs introduced by coercion# Omit all the NAs from the data
+DF1 <- na.omit(DF1)
+
+DF2 <- data.frame(select_if(DF1,is.numeric))
+#Convert the index numbers of the data fram into the student names.
+
+#Wrangle data using dplyr to include only the numerical values.
+
+#Scale the data so that no variable has undue influence
+
+DF2 <- scale(DF2)
+
+Find lattitudes & longitudes for cities
+#Unfortunately Google has restricted access to the Googple Maps API so the code below no longer works. Instead you have the lats and longs in your data.
+
+#install.packages("ggmap")
+#install.packages("rgdal")
+#library(ggmap)
+#library(tmaptools)
+
+#Request lattitude and longitude from Google Maps API
+#DF2 <- geocode(as.character(DF2$Q1_1), output = "latlon", source = "dsk")Now we will run the K-means clustering algorithm we talked about in class. 1) The algorithm starts by randomly choosing some starting values 2) Associates all observations near to those values with them 3) Calculates the mean of those clusters of values 4) Selects the observation closest to the mean of the cluster 5) Re-associates all observations closest to this observation 6) Continues this process until the clusters are no longer changing
+Notice that in this case we have 10 variables and in class we only had 2. It is impossible to vizualise this process with 10 variables.
+Also, we need to choose the number of clusters we think are in the data. We will start with 4.
+fit <- kmeans(DF2, 4)
+
+#We have created an object called "fit" that contains all the details of our clustering including which observations belong to each cluster.
+
+#We can access the list of clusters by typing "fit$cluster", the top row corresponds to the original order the rows were in. Notice we have deleted some rows.
+
+fit$cluster## Timothy Lee jiahao guo Leonardo Restrepo Xinke Song
+## 4 3 1 3
+## Zixuan Ma Yiwei Qi XINYI ZHOU XIAOJUE LIU
+## 3 3 1 3
+## Minruo Wang Anqi Duan Chengxuan Hu CHAOXIONG CHEN
+## 4 4 3 2
+## Ling Ai Joellyn Heng Ruiqi Wang BOZI JIN
+## 4 4 3 2
+## Qiyang Lin Yiwen Ma Ziyuan Guo Shijie Shao
+## 3 3 3 3
+## Eudora Niu Jiancong Shen Yijia Wu XI YANG
+## 3 3 3 3
+## Beibei Cao Chenyu Yan LINGLING MIAO Maho Hayashi
+## 3 3 4 2
+## Suwon Jung Xiaowen Chen Jiali Jin Lintong Li
+## 2 3 3 3
+## Ningyao Xu Zhongyuan Zhang Yaqi Lu Yujun Zhang
+## 4 4 1 3
+## Xudian Zhang Jie Chen Han Wang
+## 3 3 1#We can also attach these clusters to te original dataframe by using the "data.frame" command to create a new data frame called K4.
+
+DF3 <- data.frame(DF2, fit$cluster)
+
+#Have a look at the DF3 dataframe. Lets change the names of the variables to make it more convenient with the names() command.
+
+Visualize your clusters in ggplot
+#Create a scatterplot that plots location of each student and colors the points according to their cluster
+DF4 <- data.frame(DF1[,13],DF1[,14],fit$cluster)
+names(DF4) <- c("latitude", "longtitude","cluster")
+attach(DF4)
+library(ggplot2)
+ggplot(DF4, aes(x = longtitude, y =latitude, pch = factor(cluster))) +
+ geom_point(aes(color = factor(cluster)))
+
+
+
+
+
+Can you group students from the classes data set in Assignment 2 using K-modes?
+DT1 <- read.csv("hudk4050-classes.csv",header = TRUE)
+DT1$Name <- paste(DT1$First.Name, DT1$Last.Name)
+DT2_dirty <- DT1[,3:9]
+DT3 <- DT2_dirty %>% gather(classnum, classcode, `Class.1`, `Class.2`, `Class.3`, `Class.4`, `Class.5`, `Class.6`) %>% select(-c(classnum))## Warning: attributes are not identical across measure variables;
+## they will be droppedDT3$classcode = gsub(" ", "", DT3$classcode)
+DT3 <- DT3 %>% filter(classcode != "HUDK4050") %>% filter(Name != "ZIMO CHEN")
+DT3$Count = 1
+DT3 <- DT3[which(DT3$classcode != ""),]
+DT4 <- DT3 %>% spread(classcode,Count)
+row.names(DT4) = DT4$Name
+DT4$Name <- NULL
+DT4 = ifelse(is.na(DT4), 0, 1)
+DT5 = as.matrix(DT4)
+DT5 <- scale(DT5)
+library(MASS)##
+## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
+##
+## selectset.seed(123)
+pca=princomp(DT5[,1:50],cor=T)
+screeplot(pca,type="line",lwd=2)
set.seed(123)
+fit2 <- kmeans(DT5,6)
+fit2$size## [1] 1 1 30 2 13 3cluster <- data.frame(fit2$cluster)
+colnames(cluster) <- c("cluster")
+cluster## cluster
+## Alysandra Zhang 3
+## Anqi Duan 3
+## Artemas Wang 3
+## Beibei Cao 6
+## Bernell Downer 3
+## chaoxiong chen 3
+## Chengxuan Hu 5
+## Chenyu Yan 3
+## Christine Odenath 3
+## David Pearce 3
+## Di Mao 5
+## Eudora Xinyi Niu 4
+## HAN GE 3
+## Han Wang 3
+## INDIRA BATAYEVA 3
+## jiahao guo 5
+## Jiancong Shen 3
+## Jie Chen 3
+## Jingru Zhang 5
+## Joellyn Heng 2
+## Leonardo Restrepo 3
+## Ling Ai 3
+## LINGLING MIAO 5
+## Lintong Li 3
+## Luyi Dai 5
+## Maho Hayashi 1
+## Minruo Wang 3
+## Ningyao Xu 5
+## Qiyang Lin 3
+## Ruiqi Wang 5
+## Shijie Shao 5
+## Suwon Jung 3
+## Timothy Lee 3
+## Wanruo Zhang 3
+## XI YANG 5
+## Xiaowen Chen 3
+## xinyi zhou 3
+## XUDIAN ZHANG 5
+## YAQI LU 6
+## Yawei Zhu 3
+## Yigao Liu 4
+## Yiwei Qi 3
+## Yiwen Ma 5
+## yixiao li 6
+## Yixuan Zhu 3
+## Yiyi Xie 3
+## Yujun Zhang 3
+## Zhaozhuo Zheng 3
+## Zhongyuan Zhang 3
+## Ziyuan Guo 5# Just to check, I am in the Applied Statistics program and people in the group 5 are exactly those in the Applied statistics program with me!