changed file paths for data load and write functions according to the new file structure

Tutorial_Scripts/Case_AnnualPowerConsumption/BIA_T05_data_preparation.Rmd

@@ -11,7 +11,7 @@ This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M)
 # Read the APC dataset
 
 ```{r Read data}
-APC <- read.csv2("../data/APC-dataset-anonym.csv")
+APC <- read.csv2("../../data/APC-dataset-anonym.csv")
 ```
 
 
@@ -175,7 +175,7 @@ The observations outside the whiskers are drawn as outliers in the boxplot
 ## Identification of outliers with IQR and sigma function
 ```{r}
 # Task 11: Identify outliers with IQR and sigma functions
-source("../R/functions/outlier_identification.R")
+source("../functions/outlier_identification.R")
 
 
 # apply the functions

Tutorial_Scripts/Case_AnnualPowerConsumption/BIA_T05_data_preparation_exercise.Rmd

@@ -11,7 +11,7 @@ This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M)
 # Read the APC dataset
 
 ```{r Read data}
-APC <- read.csv2("../data/APC-dataset-anonym.csv")
+APC <- read.csv2("../../data/APC-dataset-anonym.csv")
 ```
 
 
@@ -115,7 +115,7 @@ APC <- read.csv2("../data/APC-dataset-anonym.csv")
 ## Identification of outliers with IQR and sigma function
 ```{r}
 # Task 11: Identify outliers with IQR and sigma functions
-source("../R/functions/outlier_identification.R")
+source("../functions/outlier_identification.R")
 
 
 # apply the functions

Tutorial_Scripts/Case_ElectricVehicles/BIA_T11-12_Case_Emobility.Rmd

@@ -11,7 +11,7 @@ This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M)
 ##Exercise 1
 ```{r}
 # 1. Load the Data.
-trips <- read.csv2(file="BIA_GPS_TIME_SERIES_1.csv", header=TRUE)
+trips <- read.csv2(file="../../data/emobility/BIA_GPS_TIME_SERIES_1.csv", header=TRUE)
 ```
 
 

Tutorial_Scripts/Case_EnergyRetailAnalytics/BIA_T08_Clustering.Rmd

@@ -18,11 +18,11 @@ library(readr)
 
 ```{r Read and prepare data}
 # read data
-consumption <- read_csv(file="../data/clustering/bam_energy_report_consumption.csv", na = "NULL")
-customer <- read_csv(file="../data/clustering/bam_energy_report_customers.csv", na = "NULL")
-logins <- read_csv(file="../data/clustering/bam_energy_report_logins.csv", na = "NULL")
-survey <- read_csv(file="../data/clustering/bam_energy_report_survey.csv", na = "NULL")
-portal_points <- read_csv(file="../data/clustering/bam_energy_report_portal_points.csv", na = "NULL")
+consumption <- read_csv(file="../../data/clustering/bam_energy_report_consumption.csv", na = "NULL")
+customer <- read_csv(file="../../data/clustering/bam_energy_report_customers.csv", na = "NULL")
+logins <- read_csv(file="../../data/clustering/bam_energy_report_logins.csv", na = "NULL")
+survey <- read_csv(file="../../data/clustering/bam_energy_report_survey.csv", na = "NULL")
+portal_points <- read_csv(file="../../data/clustering/bam_energy_report_portal_points.csv", na = "NULL")
 
 # convert data
 consumption$CustomerID <- as.character(consumption$CustomerID)

Tutorial_Scripts/Case_EnergyRetailAnalytics/BIA_T08_Clustering_exercise.Rmd

@@ -18,11 +18,11 @@ library(readr)
 
 ```{r Read and prepare data}
 # read data
-consumption <- read_csv(file="../data/clustering/bam_energy_report_consumption.csv", na = "NULL")
-customer <- read_csv(file="../data/clustering/bam_energy_report_customers.csv", na = "NULL")
-logins <- read_csv(file="../data/clustering/bam_energy_report_logins.csv", na = "NULL")
-survey <- read_csv(file="../data/clustering/bam_energy_report_survey.csv", na = "NULL")
-portal_points <- read_csv(file="../data/clustering/bam_energy_report_portal_points.csv", na = "NULL")
+consumption <- read_csv(file="../../data/clustering/bam_energy_report_consumption.csv", na = "NULL")
+customer <- read_csv(file="../../data/clustering/bam_energy_report_customers.csv", na = "NULL")
+logins <- read_csv(file="../../data/clustering/bam_energy_report_logins.csv", na = "NULL")
+survey <- read_csv(file="../../data/clustering/bam_energy_report_survey.csv", na = "NULL")
+portal_points <- read_csv(file="../../data/clustering/bam_energy_report_portal_points.csv", na = "NULL")
 
 # convert data
 consumption$CustomerID <- as.character(consumption$CustomerID)

Tutorial_Scripts/Case_EnergyRetailAnalytics/BIA_T09_Classification.Rmd

@@ -20,7 +20,7 @@ library(randomForest) #further random forest
 
 ```{r Load and prepare data}
 # Load data
-load("../data/classification.RData")
+load("../../data/classification.RData")
 
 # Derive and investigate the dependent variable "number of residents"
 adults <- as.integer(ifelse(customers$residents.numAdult=="5 oder mehr",

Tutorial_Scripts/Case_EnergyRetailAnalytics/BIA_T10_Classification2.Rmd

@@ -19,7 +19,7 @@ library(dplyr) #for data wrangling
 
 ```{r Load and prepare data}
 # Load data
-load("../data/classification2.RData")
+load("../../data/classification2.RData")
 
 # Derive and investigate the dependent variable "number of residents"
 adults <- as.integer(ifelse(customers$residents.numAdult=="5 oder mehr",

Tutorial_Scripts/Case_EnergyRetailAnalytics/BIA_T10_Classification2_exercise.Rmd

@@ -19,7 +19,7 @@ library(dplyr) #for data wrangling
 
 ```{r Load and prepare data}
 # Load data
-load("../data/classification2.RData")
+load("../../data/classification2.RData")
 
 # Derive and investigate the dependent variable "number of residents"
 adults <- as.integer(ifelse(customers$residents.numAdult=="5 oder mehr",

Tutorial_Scripts/Case_NewsletterResponses/BIA_T06_CS_newsletter_responses.Rmd

@@ -16,16 +16,16 @@ library(lubridate)
 
 
 ```{r read the files with basic R functions}
-nl_mailsSend <- read.csv2("../data/newsletter/newsletterData_mailsSend.csv",
+nl_mailsSend <- read.csv2("../../data/newsletter/newsletterData_mailsSend.csv",
            encoding = "UTF-8", # this is usually not needed
            stringsAsFactors = F # R formats columns with text as factor, 
                                 # this is not meaningful in our case
            ) 
-nl_clicks <- read.csv2("../data/newsletter/newsletterData_clicks.csv",
+nl_clicks <- read.csv2("../../data/newsletter/newsletterData_clicks.csv",
            encoding = "UTF-8", stringsAsFactors = F)
-nl_links <- read.csv2("../data/newsletter/newsletterData_links.csv",
+nl_links <- read.csv2("../../data/newsletter/newsletterData_links.csv",
            encoding = "UTF-8", stringsAsFactors = F)
-nl_opens <- read.csv2("../data/newsletter/newsletterData_opens.csv",
+nl_opens <- read.csv2("../../data/newsletter/newsletterData_opens.csv",
            encoding = "UTF-8", stringsAsFactors = F)
 ```
 

Tutorial_Scripts/RIntro/BIA_T02_RIntro2.Rmd

@@ -12,7 +12,7 @@ This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M)
 
 # Task 2
 # Read data. Remember the relative path
-Shower <- read.csv2("../data/Shower_data.csv")
+Shower <- read.csv2("../../data/Shower_data.csv")
 
 ?read.csv #help pages for format options of the data file
 
@@ -84,10 +84,10 @@ quantile(Shower_clean$ShowerTime)
 
 ```{r Write and filter data}
 # Task 12
-write.csv2(x = Shower[Shower$Hh_ID == 8899,], file="../output/problematic_shower_data.csv")
+write.csv2(x = Shower[Shower$Hh_ID == 8899,], file="../../output/problematic_shower_data.csv")
 
 # Task 13
-write.csv2(x = Shower[Shower$Hh_ID != 8899,], file="../output/cleaned_shower_data.csv")
+write.csv2(x = Shower[Shower$Hh_ID != 8899,], file="../../output/cleaned_shower_data.csv")
 ```
 
 After cleaning data we have stored the data to the folder "output".

Tutorial_Scripts/RIntro/BIA_T03_RIntro3.Rmd

@@ -7,7 +7,7 @@ editor_options:
 
 This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M), Information Systems and Energy Efficient Systems, University of Bamberg.
 ```{r Solution for Cooldown exercise}
-Shower <- read.csv2("../data/Shower_data.csv")
+Shower <- read.csv2("../../data/Shower_data.csv")
 summary(Shower)
 Shower$group <- as.factor(Shower$group)
 
@@ -20,7 +20,7 @@ tapply(Shower_more_than_ten$Volume, Shower_more_than_ten$group, mean, na.rm=T)
 
 
 ```{r Functions}
-source(file = "../R/functions/pot.R")
+source(file = "../functions/pot.R")
 
 pot(2,3)
 ```
@@ -83,7 +83,7 @@ fruits_weight <- mutate(fruits_weight, fruits_per_pound = round(fruits_per_kg *
 select(arrange(fruits_weight, desc(fruits_per_pound)), fruit, fruits_per_pound)
 
 # Task 7
-source(file = "../R/functions/pot.R")
+source(file = "../functions/pot.R")
 mutate(fruits_weight, fruits_per_kg_pot = pot(fruits_per_kg))
 ```
 
@@ -131,7 +131,7 @@ left_join(fruits, recipe)
 
 ```{r Illustrating analytical results by plotting techniques}
 # Load and prepare data
-Shower <- read.csv2("../data/Shower_data.csv")
+Shower <- read.csv2("../../data/Shower_data.csv")
 Shower$group <- as.factor(Shower$group)
 levels(Shower$group) <- c("First group", "Second group", "Fourth group", 
                           "Third group", "Fifth group", "Sixth group")