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eesys-public
BIA-Resources
Commits
89ed967c
Commit
89ed967c
authored
Oct 21, 2019
by
Weigert, Andreas
Browse files
changed file paths for data load and write functions according to the new file structure
parent
8ba69660
Changes
11
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Tutorial_Scripts/Case_AnnualPowerConsumption/BIA_T05_data_preparation.Rmd
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89ed967c
...
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@@ -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")
```
...
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@@ -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
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Tutorial_Scripts/Case_AnnualPowerConsumption/BIA_T05_data_preparation_exercise.Rmd
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89ed967c
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@@ -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
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Tutorial_Scripts/Case_ElectricVehicles/BIA_T11-12_Case_Emobility.Rmd
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89ed967c
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@@ -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)
```
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Tutorial_Scripts/Case_EnergyRetailAnalytics/BIA_T08_Clustering.Rmd
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89ed967c
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@@ -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)
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Tutorial_Scripts/Case_EnergyRetailAnalytics/BIA_T08_Clustering_exercise.Rmd
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89ed967c
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@@ -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)
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Tutorial_Scripts/Case_EnergyRetailAnalytics/BIA_T09_Classification.Rmd
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89ed967c
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@@ -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
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89ed967c
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@@ -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
View file @
89ed967c
...
...
@@ -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
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89ed967c
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@@ -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)
```
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Tutorial_Scripts/RIntro/BIA_T02_RIntro2.Rmd
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89ed967c
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@@ -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
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@@ -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$Hh_ID != 8899,], file="../
../
output/cleaned_shower_data.csv")
```
After cleaning data we have stored the data to the folder "output".
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Tutorial_Scripts/RIntro/BIA_T03_RIntro3.Rmd
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89ed967c
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@@ -7,7 +7,7 @@ editor_options:
This file is part of the lecture Business Intelligence & Analytics (EESYS-BIA-M), Information Systems and Energy Efficient Systems, University of Bamberg.
```{r Solution for Cooldown exercise}
Shower <- read.csv2("../data/Shower_data.csv")
Shower <- read.csv2("../
../
data/Shower_data.csv")
summary(Shower)
Shower$group <- as.factor(Shower$group)
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@@ -20,7 +20,7 @@ tapply(Shower_more_than_ten$Volume, Shower_more_than_ten$group, mean, na.rm=T)
```{r Functions}
source(file = "../
R/
functions/pot.R")
source(file = "../functions/pot.R")
pot(2,3)
```
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@@ -83,7 +83,7 @@ fruits_weight <- mutate(fruits_weight, fruits_per_pound = round(fruits_per_kg *
select(arrange(fruits_weight, desc(fruits_per_pound)), fruit, fruits_per_pound)
# Task 7
source(file = "../
R/
functions/pot.R")
source(file = "../functions/pot.R")
mutate(fruits_weight, fruits_per_kg_pot = pot(fruits_per_kg))
```
...
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@@ -131,7 +131,7 @@ left_join(fruits, recipe)
```{r Illustrating analytical results by plotting techniques}
# Load and prepare data
Shower <- read.csv2("../data/Shower_data.csv")
Shower <- read.csv2("../
../
data/Shower_data.csv")
Shower$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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