Commit 89ed967c authored by Weigert, Andreas's avatar Weigert, Andreas
Browse files

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

parent 8ba69660
......@@ -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
......
......@@ -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
......
......@@ -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)
```
......
......@@ -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)
......
......@@ -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)
......
......@@ -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$residents.numAdult=="5 oder mehr",
......
......@@ -19,7 +19,7 @@ library(dplyr) #for data wrangling
```{r Load and prepare data}
# Load data
load("../data/classification2.RData")
load("../../data/classification2.RData")
# Derive and investigate the dependent variable "number of residents"
adults <- as.integer(ifelse(customers$residents.numAdult=="5 oder mehr",
......
......@@ -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$residents.numAdult=="5 oder mehr",
......
......@@ -16,16 +16,16 @@ library(lubridate)
```{r read the files with basic R functions}
nl_mailsSend <- read.csv2("../data/newsletter/newsletterData_mailsSend.csv",
nl_mailsSend <- read.csv2("../../data/newsletter/newsletterData_mailsSend.csv",
encoding = "UTF-8", # this is usually not needed
stringsAsFactors = F # R formats columns with text as factor,
# this is not meaningful in our case
)
nl_clicks <- read.csv2("../data/newsletter/newsletterData_clicks.csv",
nl_clicks <- read.csv2("../../data/newsletter/newsletterData_clicks.csv",
encoding = "UTF-8", stringsAsFactors = F)
nl_links <- read.csv2("../data/newsletter/newsletterData_links.csv",
nl_links <- read.csv2("../../data/newsletter/newsletterData_links.csv",
encoding = "UTF-8", stringsAsFactors = F)
nl_opens <- read.csv2("../data/newsletter/newsletterData_opens.csv",
nl_opens <- read.csv2("../../data/newsletter/newsletterData_opens.csv",
encoding = "UTF-8", stringsAsFactors = F)
```
......
......@@ -12,7 +12,7 @@ This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M)
# Task 2
# Read data. Remember the relative path
Shower <- read.csv2("../data/Shower_data.csv")
Shower <- read.csv2("../../data/Shower_data.csv")
?read.csv #help pages for format options of the data file
......@@ -84,10 +84,10 @@ quantile(Shower_clean$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".
......
......@@ -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")
......
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