New directory structure for module relaunch WS2019/20

R/L07_Clustering_SMD.Rmd

----
-title: "Clustering with smart meter data"
-output: html_notebook
-editor_options: 
-  chunk_output_type: inline
----
-
-```{r load and format data}
-# load and prepare the data ----
-SMD_Readings=read.csv2("../data/SMD_BIA_Data.csv")
-
-# we use the data for a single week
-time_stamps <- as.numeric(as.character(unique(SMD_Readings$Timestamp)))
-single_week <- as.character(time_stamps[time_stamps>25000  & time_stamps<25700])
-length(single_week)
-
-# select the measurements for one single week
-SMD_Readings <- SMD_Readings[SMD_Readings$Timestamp %in% single_week, ]
-
-# the reshape package helps us to transform the data innto a more condensed table form
-#install.packages("reshape2")
-library("reshape2")
-
-Formatted_Readings <- dcast(SMD_Readings, ID ~ Timestamp, value.var = "Consumption")
-head(Formatted_Readings)
-
-# remmove the IDs from the matrix
-IDs <- Formatted_Readings[,1]
-Formatted_Readings[,1] <- NULL
-```
-
-```{r first k-Means clustering}
-# simple k-means clustering ----
-set.seed(1)
-Cluster1 <- kmeans(Formatted_Readings, centers=3)
-```
-
-
-```{r function to plot the results}
-# Creating cluster visualization ----
-plotcl <- function(SMD_Readings, clusters,lim=c(0,2), ...){
-  plot(colMeans(SMD_Readings[,1:48]), type="n", ylim=lim, ...)
-  for(i in unique(clusters)){
-    lines(colMeans(SMD_Readings[clusters==i,1:48]),col=i, lwd=3)
-  }
-}
-
-plotclWeek <- function(SMD_Readings, clusters,lim=c(0,2), ...){
-  plot(colMeans(SMD_Readings[,]),type="n", ylim=lim, ...)
-  for(i in unique(clusters)){
-    lines(colMeans(SMD_Readings[clusters==i,]),col=i, lwd=3)
-  }
-}
-
-plotcl(Formatted_Readings, Cluster1$cluster, ylab="Consumption (kWh)")
-plotclWeek(Formatted_Readings, Cluster1$cluster, ylab="Consumption (kWh)")
-```
-
-```{r second k-Meand clustering}
-# Improve the clustering ----
-#Normalize the values and then run the k-means again
-set.seed(2)
-Data_Norm <- Formatted_Readings/rowMeans(Formatted_Readings)
-Cluster2 <- kmeans(Data_Norm, centers=3)
-plotcl(Formatted_Readings, Cluster2$cluster, ylab="Consumption (kWh)", main="Max-normaized consumption", lim=c(0,1.2))
-
-#Transform the values to a more normally distributed form and run the k-means agaein
-Datas <- sqrt(Formatted_Readings)
-Datasn <- Datas/rowMeans(Datas)
-set.seed(4)
-Cluster3 <- kmeans(Datasn, centers=3)
-plotcl(Formatted_Readings, Cluster3$cluster, ylab="Consumption (kWh)", main="Sqrt and Max-normaized consumption", lim=c(0,1.2))
-
-```
-
-```{r obtain the optimal number of clusters}
-set.seed(2)
-Clusters <- list()
-Clusters[[1]] <- kmeans(Datasn, centers=2)
-Clusters[[2]] <- kmeans(Datasn, centers=3)
-Clusters[[3]] <- kmeans(Datasn, centers=4)
-Clusters[[4]] <- kmeans(Datasn, centers=5)
-Clusters[[5]] <- kmeans(Datasn, centers=6)
-Clusters[[6]] <- kmeans(Datasn, centers=7)
-Clusters[[7]] <- kmeans(Datasn, centers=8)
-Clusters[[8]] <- kmeans(Datasn, centers=9)
-
-#the total sum of squares
-tot.withinss <- sapply(Clusters, function(v){return(v$tot.withinss)})
-plot(2:9, tot.withinss, xlab="Num. of clusters", ylab="Total sum of squares", type="b")
-
-#the min / max sum of squares in the clusters
-min.withinss <- sapply(Clusters, function(v){return(min(v$withinss))})
-max.withinss <- sapply(Clusters, function(v){return(max(v$withinss))})
-plot(2:9, max.withinss, xlab="Num. of clusters", 
-     ylab="Within clusters sum of squares", type="b", ylim=c(min(min.withinss), max(max.withinss)))
-lines(2:9, min.withinss, type="b", col=2)
-legend("topright", c("Max. WSS", "Min. WSS"), col=c(1,2), lty=1)
-
-numOneElemClusters <- sapply(Clusters, function(v){return(sum(v$size==1))})
-barplot(numOneElemClusters, names.arg = 2:9, main="Single-element clusters", 
-        xlab="Total number of k-Means clusters")
-```
-
-
-```{r hierarchical clustering}
-library(cluster)
-
-#create distance matrix
-C <- 1-cor(t(Formatted_Readings))
-
-Dendrogram <- agnes(C,diss=T,method="complete")
-plot(Dendrogram, which.plot=2) #plot the dendrogram
-
-Cluster4 <- cutree(Dendogram, k=3)
-plotcl(Formatted_Readings, Cluster4, main="Hierarchical clustering results", ylab="Consumption (kWh)", lim=c(0,1.3))
-
-```
-

R/README.md

-This folder contains R files.
\ No newline at end of file

README.md

-# 2018-WS-BIA
+# Business Intelligence & Analytics (EESYS-BIA-M)
 
-This repository contains the R code and data for exercises of the course Business Intelligene and Analytics and is maintained by the chair of Information Systems and Energy Efficient Systems.
\ No newline at end of file
+This repository contains the R code and data for exercises of the course Business Intelligene & Analytics and is maintained by the chair of Information Systems and Energy Efficient Systems ([Dr. Konstantin Hopf](mailto:konstantin.hopf@uni-bamberg.de) and [Andreas Weigert](mailto:andreas.weigert@uni-bamberg.de)).
+
+## Course details
+
+For any details regarding the course, please visit the [Virtual Campus course page for the winter term 2019/20 ](https://vc.uni-bamberg.de/course/view.php?id=38311).
+
+## Schedule (tentative)
+
+**Important:** The Schedule and the scripts will change during the semester. Please check the updates in this GIT repository and the Virtual Campus course.
+
+| Lecture | Topic | Script location(s) |
+| ------- | ----- | ------------------ |
+| L01 | Introduction ||
+| L02 | Business data sources for analytics ||
+| L03 | Talking about data ||
+| L04 | Data quality, outlier and publica data for analytics ||
+| L05 | Space and time in analytics | `Lecture_Scripts/BIA_L05_Geographic_Time_Data.Rmd` |
+| L06 | Cluster analysis 1 ||
+| L07 | Cluster analysis 2 | `Lecture_Scripts/BIA_L07_Clustering_SMD` |
+| L08 | Predictive analytics 1 ||
+| L09 | Predictive analytcis 2 ||
+| L10 | Predictive analytics 3 ||
+| L11 | Analytics for decision support ||
+| L12 | Optimization and simulation ||
+| L13 | Dashboards and BI solutions ||
+| L14 | Legal and ethical issues of data analytics ||
+| L15 | Q&A session ||
+
+| Tutorials | Topic | Script location(s) |
+| --------- | ----- | ------------------ |
+| T01 | Introduction to R 1 | `Tutorial_Scripts/RIntro/` |
+| T02 | Introduction to R 2 | `Tutorial_Scripts/RIntro/` |
+| T03 | Introduction to R 3 | `Tutorial_Scripts/RIntro/` |
+| T04 | Business Analytics in Organizations ||
+| T05 | Research perspectives on BI & A in organizations ||
+| T06 | Case study: Newsletter responses 1 | `Tutorial_Scripts/Case_NewsletterResponses/` |
+| T07 | Case study: Newsletter responses 2 | `Tutorial_Scripts/Case_NewsletterResponses/` |
+| T08 | Case study: Electric Vehicle analysis 1 | `Tutorial_Scripts/Case_ElectricVehicles/` |
+| T09 | Case study: Electric Vehicle analysis 2 | `Tutorial_Scripts/Case_ElectricVehicles/` |
+| T10 | Case study: Prediction for Energy Retailing 1 | `Tutorial_Scripts/Case_EnergyRetailAnalytics/` |
+| T11 | Case study: Prediction for Energy Retailing 2 | `Tutorial_Scripts/Case_EnergyRetailAnalytics/` |
+| T12 | Case study: Prediction for Energy Retailing 3 | `Tutorial_Scripts/Case_EnergyRetailAnalytics/` |
+| T13 | Optimization and Decision Support | `Tutorial_Scripts/Optimization_DecisionSupport/`|
+
+## Additional ressources
+
+| `Tutorial_Scripts/Optimization_DecisionSupport/` | Additional exercises regarding data preparation, outlier detection, data understanding |
\ No newline at end of file