brrav0 · Sefimenko · Jan 26, 2016 · Jan 26, 2016 · Jan 26, 2016 · Jan 26, 2016
diff --git a/README.md b/README.md
diff --git a/RTE - clean data 2012-2014 15-1-16.csv b/RTE - clean data 2012-2014 15-1-16.csv
diff --git a/RTEAnalytics-Alex-01.Rmd b/RTEAnalytics-Alex-01.Rmd
@@ -0,0 +1,74 @@
+---
+title: "Exercise Set 2: A $300 Billion Strategy"
+author: "Alex, Goajun, Sergey, Bastien"
+output: html_document
+---
+
+<br>
+
+```{r echo=FALSE, eval=TRUE, comment=NA, warning=FALSE,error=FALSE, message=FALSE, prompt=FALSE}
+#load packages from helpers.R
+  source("helpers.R")
+```
+
+###Background
+We have performed analysis of electricity data provided by the French Distribution Network (RTE)
+
+####Analysis
+1. Comparison between supply and demand
+2. Evolution of energy mix
+3. Correlation between time of the day and solar energy production 
+4. Correlation between time of the day and wind energy
+5. Correlation between supply and demand vs. import/export
+6. Correlation between consumption and weather
+
+####Source
+
+Open data: https://www.data.gouv.fr/fr/datasets/electricite-consommation-production-co2-et-echanges/
+
+```{r eval=TRUE, echo=FALSE, comment=NA, warning=FALSE, message=FALSE,results='asis',fig.align='center', fig=TRUE}
+DataSet<-read.csv("RTE - clean data 2012-2014 15-1-16.csv", header=TRUE)
+# create vectors with year, month, and day
+DataSet$Year = Year(as.Date(DataSet$Date,format='%m/%d/%Y'))
+DataSet$Month = Month(as.Date(DataSet$Date,format='%m/%d/%Y'))
+DataSet$Day = Day(as.Date(DataSet$Date,format='%m/%d/%Y'))
+#Hour = Hour(as.Date(DataSet$Date,format='%m/%d/%Y'))
+```
+The file has the following structure. It has `r nrow(DataSet)` rows and `r ncol(DataSet)` columns.
+
+###Part I: Comparison between supply and demand
+`r n<-16`
+I am a `r n`J
+
+
+
+###Part II: Evolution of energy mix
+
+
+```{r, echo=FALSE, message=FALSE, prompt=FALSE, results='asis'}
+energyMixYear <-  group_by(DataSet, Year) %>% summarise(Fuel = sum(Fuel/4/1000), Coal = sum(Coal/4/1000), Gas = sum(Gas/4/1000),Nuclear = sum(Nuclear/4/1000), Wind = sum(Wind/4/1000), Solar = sum(Solar/4/1000), Hydro = sum(Hydro/4/1000), Pumping = sum(Pumping/4/1000), Bioenergy = sum(Bioenergy/4/1000))
+
+#conversion to String required to use vector as x-axis in Google Charts
+energyMixYear$Year=as.character(energyMixYear$Year)
+
+#excluded pumping for now
+print(gvisSteppedAreaChart(energyMixYear, xvar = "Year", yvar = c("Fuel", "Coal", "Gas","Nuclear", "Wind", "Solar", "Hydro", "Bioenergy"), options=list(isStacked=TRUE,width = 1000, height = 500, vAxis="{format:'#,###GWh'}")), 'chart')
+```
+
+####??
+
+###Part III: Correlation between time of the day and solar energy production
+
+####??
+
+###Part IV: Correlation between time of the day and wind energy (test Sergey)
+
+####??
+
+###Part V: Correlation between supply and demand vs. import/export (test Gaojun)
+
+####??
+
+###Part VI: Correlation between consumption and weather
+
+
diff --git a/RTEAnalytics.Rmd → RTEAnalytics-Bastien-01.Rmd b/RTEAnalytics.Rmd → RTEAnalytics-Bastien-01.Rmd
@@ -1,9 +1,16 @@
 ---
-Author: "Alex, Goajun, Sergey, Bastien"
+title: "Exercise Set 2: A $300 Billion Strategy"
+author: "Alex, Goajun, Sergey, Bastien"
+output: html_document
 ---
 
 <br>
 
+```{r echo=FALSE, eval=TRUE, comment=NA, warning=FALSE,error=FALSE, message=FALSE, prompt=FALSE}
+#load packages from helpers.R
+  source("helpers.R")
+```
+
 ###Background
 We have performed analysis of electricity data provided by the French Distribution Network (RTE)
 
@@ -21,7 +28,15 @@ Open data: https://www.data.gouv.fr/fr/datasets/electricite-consommation-product
 
 ```{r eval=TRUE, echo=FALSE, comment=NA, warning=FALSE, message=FALSE,results='asis',fig.align='center', fig=TRUE}
 DataSet<-read.csv("RTE - clean data 2012-2014 15-1-16.csv", header=TRUE)
+<<<<<<< HEAD
 Consumption<-DataSet$Consumption
+=======
+# create vectors with year, month, and day
+Year = Year(as.Date(DataSet$Date,format='%m/%d/%Y'))
+Month = Month(as.Date(DataSet$Date,format='%m/%d/%Y'))
+Day = Day(as.Date(DataSet$Date,format='%m/%d/%Y'))
+#Hour = Hour(as.Date(DataSet$Date,format='%m/%d/%Y'))
+>>>>>>> 1fa45162ed8f37f96c10ebb5f95a527bbf849b5d
 ```
 The file has the following structure. It has `r nrow(DataSet)` rows and `r ncol(DataSet)` columns.<br>
 
@@ -35,6 +50,16 @@ I am a `r n`J
 
 ###Part II: Evolution of energy mix
 
+
+```{r, echo=FALSE, message=FALSE, prompt=FALSE, results='asis'}
+list_of_sources=colnames(DataSet[9:16])
+#will try to use list_of_sources as argument to generate graphs
+
+energyMixYear <-  group_by(DataSet, Year) %>% summarise(Wind = sum(Wind), Coal = sum(Coal))
+
+#plot(gvisSteppedAreaChart(energyMixYear, yvar = c("Wind", "Coal"), options=list(isStacked=TRUE)))
+```
+
 ####??
 
 ###Part III: Correlation between time of the day and solar energy production
@@ -51,8 +76,4 @@ I am a `r n`J
 
 ###Part VI: Correlation between consumption and weather
 
-Test for Alex
-=======
-#test de push by ALex
 
-Test for Bastien 4:35pm
diff --git a/RTEAnalytics-Sergey-01.Rmd b/RTEAnalytics-Sergey-01.Rmd
@@ -0,0 +1,133 @@
+---
+title: "RTEAnalytics-Sergey-01.Rmd"
+author: "Sergey Efimenko"
+date: "29 Jan 2016"
+output: html_document
+---
+
+This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
+
+When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
+
+
+
+
+
+
+
+
+##################
+
+Seasonality test for wind power generation.
+
+
+```{r, echo=FALSE}
+library("stringr")
+library("googleVis")
+DataSet<-read.csv("RTE - clean data 2012-2014 15-1-16.csv", sep =",", header=TRUE)
+month_data = sapply(1:length(DataSet$Date), function(i) ifelse(str_length(DataSet$Date[i]) > 6, as.numeric(str_split(DataSet$Date[i], "/")[[1]][1]), NA))
+DataSet$month_data = month_data
+
+season_data = sapply(month_data, function(i){
+  if (i %in% c(11,12,1,2)) res = 1 #"Winter"
+  if (i %in% c(3,4,5)) res = 2 #"Spring"
+  if (i %in% c(6,7,8)) res = 3 #"Summer"
+  if (i %in% c(9,10)) res = 4 #"Fall"
+  res
+})
+DataSet$season_data = season_data # CAN CREATE DUMMIES IF NEEDED! JUST ADD NEW COLUMNS
+```
+
+
+# check this
+table(DataSet$season_data)
+
+# Make sure the regression data only has numeric variables (and dummies). the lm input is a data.frame
+# Make sure the regression data only has numeric variables (and dummies). the lm input is a data.frame
+```{r, echo=FALSE}
+regression_data = data.frame(
+  Cons = as.numeric(DataSet$Consumption), 
+  Fuel = suppressWarnings(as.numeric(DataSet$Fuel)), # I folled google advice
+  Coal = as.numeric(DataSet$Coal),
+  Gas = as.numeric(DataSet$Gas),
+  Nuclear = as.numeric(DataSet$Nuclear),
+  Wind = as.numeric(DataSet$Wind),
+  Solar = as.numeric(DataSet$Solar),
+  Hydro = as.numeric(DataSet$Hydro),
+  Pumping = as.numeric(DataSet$Pumping),
+  Bio = as.numeric(DataSet$Bioenergy),
+  Phys = as.numeric(DataSet$Physical.delivery),
+  CO2 = as.numeric(DataSet$CO2.emission),
+  Trade.UK = as.numeric(DataSet$Trade.with.UK),
+  Trade.ES = as.numeric(DataSet$Trade.with.Spain),
+  Trade.IT = as.numeric(DataSet$Trade.with.Italy),
+  Trade.SW = as.numeric(DataSet$Trade.with.Switzerland),
+  Trade.DE_BG = as.numeric(DataSet$Trade.with.Germany...Belgium),
+  Winter.d = as.numeric(ifelse(DataSet$season_data == 1, 1, 0)),
+  Spring.d = as.numeric(ifelse(DataSet$season_data == 2, 1, 0)),
+  Summer.d = as.numeric(ifelse(DataSet$season_data == 3, 1, 0)),
+  Fall.d = as.numeric(ifelse(DataSet$season_data == 4, 1, 0)),
+  Morning.d = as.numeric(ifelse(DataSet$Time %in% c("6:00", "12:00"), 1, 0)),
+  Noon.d = as.numeric(ifelse(DataSet$Time %in% c("12:00", "18:00"), 1, 0)),
+  Evening.d = as.numeric(ifelse(DataSet$Time %in% c("18:00", "0:00"), 1, 0)),
+  Night.d = as.numeric(ifelse(DataSet$Time %in% c("00:30", "6:00"), 1, 0)),
+  #We dont need the following parameters for correlation tables
+  Monthdata = as.numeric(DataSet$month_data),
+  Time = as.numeric(DataSet$Time),
+  season_data = DataSet$season_data
+
+)
+```
+
+
+m1<-gvisTable(regression_data,options=list(showRowNumber=TRUE,width=1920, height=min(400,27*(nrow(regression_data)+1)),allowHTML=TRUE,page='disable'))
+print(m1,'chart')
+
+```
+
+#Graf correlation matrix
+library(corrplot)
+corrplot(cor(regression_data[1:25]), method = "color", type="upper", order="original", tl.col="black", tl.srt=70)
+
+
+#Table correlation matrix
+View(cor(regression_data[1:25]))
+
+
+#Regression1: consumption vc season
+regression_formula_Cons.Seas = as.formula("Cons ~ Winter.d + Spring.d + Summer.d")
+Regression.Cons.Seas = lm(regression_formula_Cons.Seas, regression_data)
+Regression.Cons.Seas$coefficients
+
+
+#Regression2: CO2 vc Gen.Mix
+regression_formula_CO2 = as.formula("CO2 ~ Fuel + Coal + Gas + Nuclear -1")
+Regression.CO2 = lm(regression_formula_CO2, regression_data)
+Regression.CO2$coefficients
+summary(Regression.CO2)
+
+
+#Regression3: Wind vc Season
+According to corretalition table there is a high correlation between wind farm output and season.
+Here are the results of regression analysis with dummy variables:
+```{r, echo=FALSE}
+regression_formula_Wind = as.formula("Wind ~ Winter.d + Spring.d + Summer.d")
+Regression.Wind = lm(regression_formula_Wind, regression_data)
+Summary.3 <- summary(Regression.Wind)
+Fall_coef.3 <-round(coef(summary(Regression.Wind))["(Intercept)","Estimate"])
+Summer_coef.3 <-round(coef(summary(Regression.Wind))["Summer.d","Estimate"])
+Spring_coef.3 <-round(coef(summary(Regression.Wind))["Spring.d","Estimate"])
+Winter_coef.3 <-round(coef(summary(Regression.Wind))["Winter.d","Estimate"])
+```
+$Wind = `r Fall_coef.3`*Fall + `r Summer_coef.3`*Summer +`r Spring_coef.3`*Spring +`r Winter_coef.3`*Winter$
+
+
+
+
+#Regression4: Solar vc Season
+regression_formula_Solar = as.formula("Solar ~ Winter.d + Spring.d + Summer.d")
+Regression.Solar = lm(regression_formula_Solar, regression_data)
+Regression.Solar$coefficients
+(Regression.Solar)
+
+
diff --git a/RTEAnalytics.html → RTEAnalytics-Sergey-01.html b/RTEAnalytics.html → RTEAnalytics-Sergey-01.html
diff --git a/RTEAnalytics.Rproj b/RTEAnalytics.Rproj
diff --git a/helpers.R b/helpers.R
@@ -0,0 +1,18 @@
+# list of packages required to run RTEAnalytics.Rmd
+
+get_libraries <- function(filenames_list) { 
+  lapply(filenames_list,function(thelibrary){    
+    if (do.call(require,list(thelibrary)) == FALSE) 
+      do.call(install.packages,list(thelibrary)) 
+    do.call(library,list(thelibrary))
+  })
+}
+
+libraries_used=c("corrplot","stringr","gtools","foreign","reshape2","digest","timeDate","devtools","knitr","graphics",
+                 "grDevices","xtable","sqldf","stargazer","TTR","quantmod","shiny",
+                 "Hmisc","vegan","fpc","GPArotation","FactoMineR","cluster",
+                 "psych","stringr","googleVis", "png","ggplot2","googleVis", "gridExtra","RcppArmadillo","xts","DescTools", "dplyr")
+
+get_libraries(libraries_used)
+
+options(stringsAsFactors=FALSE)