library(readxl)
library(lubridate)
library(plyr)
library(dplyr)
library(tidyr)
Natural gas combine cycle plant generation depends upon market demand . Attemp has been made to calculate the number of days plant was supplying power to grid.It is a first attempt for a comparitive study with counting wind generating days
G13<-read_excel("GOC-2013.xlsx")
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in DL8767 / R8767C116: got 'pppp'
## New names:
## * `` -> ...113
## * `` -> ...114
## * `` -> ...115
## * `` -> ...116
G13$Date <- as.Date(G13$Date, format = "%Y-%m-%d") # Changing to Date format
#colnames(G13)[colnames(G13)=="DATE"] <- "Date"
##11$Date <- as.Date(G12$Date, format = "%Y-%m-%d") # Changing to Date format
Dz_13Total<-G13 %>% group_by(Date) %>% summarize(Total_2013_Gen=sum(`TOTAL`),n=n())
Dz_13Goreway<-G13 %>% group_by(Date) %>% summarize(Goreway_2013_Total=sum(`SITHE GOREWAY`),n=n())
Dz_13Halton<-G13 %>% group_by(Date) %>% summarize(Halton_2013_Total=sum(HALTONHILLS),n=n())
Dz_13Portland<-G13 %>% group_by(Date) %>% summarize(Portlands_2013_Total=sum(PORTLANDS),n=n())
Dz_13Greenfeild<-G13 %>% group_by(Date) %>% summarize(GEC_2013_Total=sum(`GREENFIELD ENERGY CENTRE`),n=n())
Dz_13Brighton<-G13 %>% group_by(Date) %>% summarize(Brigton_2013_Total=sum(`BRIGHTON BEACH`),n=n())
Dz_13GB<-cbind(Dz_13Total[,-3],Dz_13Goreway[,2],Dz_13Halton[,2],Dz_13Portland[,2],Dz_13Greenfeild[,2],Dz_13Brighton[,2])
Dz_13Goreway
## # A tibble: 366 x 3
## Date Goreway_2013_Total n
## <date> <dbl> <int>
## 1 2013-01-01 0 24
## 2 2013-01-02 11716 24
## 3 2013-01-03 14626 24
## 4 2013-01-04 10032 24
## 5 2013-01-05 4083 24
## 6 2013-01-06 4760 24
## 7 2013-01-07 14990 24
## 8 2013-01-08 9167 24
## 9 2013-01-09 3136 24
## 10 2013-01-10 3933 24
## # ... with 356 more rows
any(is.na(Dz_13GB))
## [1] TRUE
sum(is.na(Dz_13GB))
## [1] 7
colSums(is.na(Dz_13GB))
## Date Total_2013_Gen Goreway_2013_Total
## 1 1 1
## Halton_2013_Total Portlands_2013_Total GEC_2013_Total
## 1 1 1
## Brigton_2013_Total
## 1
nrow(Dz_13GB)
## [1] 366
Dz_13GB<-na.omit(Dz_13GB)
any(is.na(Dz_13GB))
## [1] FALSE
nrow(Dz_13GB)
## [1] 365
head(Dz_13GB)
## Date Total_2013_Gen Goreway_2013_Total Halton_2013_Total
## 1 2013-01-01 413181 0 0
## 2 2013-01-02 473505 11716 11691
## 3 2013-01-03 489156 14626 9094
## 4 2013-01-04 465213 10032 3370
## 5 2013-01-05 420954 4083 5370
## 6 2013-01-06 419761 4760 2903
## Portlands_2013_Total GEC_2013_Total Brigton_2013_Total
## 1 0 4708 0
## 2 7542 14465 0
## 3 8191 13579 0
## 4 0 4273 0
## 5 0 0 0
## 6 4368 0 0
any(is.na(Dz_13Goreway))
## [1] TRUE
sum(is.na(Dz_13Goreway))
## [1] 2
colSums(is.na(Dz_13Goreway))
## Date Goreway_2013_Total n
## 1 1 0
nrow(Dz_13Goreway)
## [1] 366
Dz_13Goreway<-na.omit(Dz_13Goreway)
any(is.na(Dz_13Goreway))
## [1] FALSE
nrow(Dz_13Goreway)
## [1] 365
head(Dz_13Goreway)
## # A tibble: 6 x 3
## Date Goreway_2013_Total n
## <date> <dbl> <int>
## 1 2013-01-01 0 24
## 2 2013-01-02 11716 24
## 3 2013-01-03 14626 24
## 4 2013-01-04 10032 24
## 5 2013-01-05 4083 24
## 6 2013-01-06 4760 24
Goreway_U_Status_13<-cut(Dz_13Goreway$Goreway_2013_Total,breaks = c(0,100,50000),labels = c(0,1))
Goreway_U_Status_13<-cbind(Dz_13Goreway,Goreway_U_Status_13)
#Goreway_U_Status_13
any(is.na(Goreway_U_Status_13))
## [1] TRUE
Goreway_U_Status_13[is.na(Goreway_U_Status_13)]=0
head(Goreway_U_Status_13)
## Date Goreway_2013_Total n Goreway_U_Status_13
## 1 2013-01-01 0 24 0
## 2 2013-01-02 11716 24 1
## 3 2013-01-03 14626 24 1
## 4 2013-01-04 10032 24 1
## 5 2013-01-05 4083 24 1
## 6 2013-01-06 4760 24 1
##aa_G_13<-count(Goreway_U_Status_13,"Goreway_U_Status_13")
aa_G_13<-Goreway_U_Status_13 %>% count(Goreway_U_Status_13)
names(aa_G_13)[1]="Running_Status"
names(aa_G_13)[2]="Goreway_Run"
aa_G_13
## # A tibble: 2 x 2
## Running_Status Goreway_Run
## <fct> <int>
## 1 0 186
## 2 1 179
any(is.na(Dz_13Halton))
## [1] TRUE
sum(is.na(Dz_13Halton))
## [1] 2
colSums(is.na(Dz_13Halton))
## Date Halton_2013_Total n
## 1 1 0
nrow(Dz_13Halton)
## [1] 366
Dz_13Halton<-na.omit(Dz_13Halton)
any(is.na(Dz_13Halton))
## [1] FALSE
nrow(Dz_13Halton)
## [1] 365
head(Dz_13Halton)
## # A tibble: 6 x 3
## Date Halton_2013_Total n
## <date> <dbl> <int>
## 1 2013-01-01 0 24
## 2 2013-01-02 11691 24
## 3 2013-01-03 9094 24
## 4 2013-01-04 3370 24
## 5 2013-01-05 5370 24
## 6 2013-01-06 2903 24
Halton_U_Status_13<-cut(Dz_13Halton$Halton_2013_Total,breaks = c(0,100,50000),labels = c(0,1))
Halton_U_Status_13<-cbind(Dz_13Halton,Halton_U_Status_13)
Halton_U_Status_13[is.na(Halton_U_Status_13)]=0
#Halton_U_Status_13
#aa_H_10<-count(Halton_U_Status_10,"Halton_U_Status_10")
aa_H_13<-Halton_U_Status_13 %>% count(Halton_U_Status_13)
names(aa_H_13)[1]="Running_Status"
names(aa_H_13)[2]="Halton_Run"
aa_H_13
## # A tibble: 2 x 2
## Running_Status Halton_Run
## <fct> <int>
## 1 0 105
## 2 1 260
any(is.na(Dz_13Portland))
## [1] TRUE
sum(is.na(Dz_13Portland))
## [1] 2
colSums(is.na(Dz_13Portland))
## Date Portlands_2013_Total n
## 1 1 0
nrow(Dz_13Portland)
## [1] 366
Dz_13Portland<-na.omit(Dz_13Portland)
any(is.na(Dz_13Portland))
## [1] FALSE
nrow(Dz_13Portland)
## [1] 365
head(Dz_13Portland)
## # A tibble: 6 x 3
## Date Portlands_2013_Total n
## <date> <dbl> <int>
## 1 2013-01-01 0 24
## 2 2013-01-02 7542 24
## 3 2013-01-03 8191 24
## 4 2013-01-04 0 24
## 5 2013-01-05 0 24
## 6 2013-01-06 4368 24
Portlands_U_Status_13<-cut(Dz_13Portland$Portlands_2013_Total,breaks = c(0,100,50000),labels = c(0,1))
Portlands_U_Status_13<-cbind(Dz_13Portland,Portlands_U_Status_13)
Portlands_U_Status_13[is.na(Portlands_U_Status_13)]=0
#Portlands_U_Status_13
#aa_P_10<-count(Portlands_U_Status_10,"Portlands_U_Status_10")
aa_P_13<-Portlands_U_Status_13 %>% count(Portlands_U_Status_13)
names(aa_P_13)[1]="Running_Status"
names(aa_P_13)[2]="Portland_Run"
aa_P_13
## # A tibble: 2 x 2
## Running_Status Portland_Run
## <fct> <int>
## 1 0 166
## 2 1 199
any(is.na(Dz_13Greenfeild))
## [1] TRUE
sum(is.na(Dz_13Greenfeild))
## [1] 2
colSums(is.na(Dz_13Greenfeild))
## Date GEC_2013_Total n
## 1 1 0
nrow(Dz_13Greenfeild)
## [1] 366
Dz_13Greenfeild<-na.omit(Dz_13Greenfeild)
any(is.na(Dz_13Greenfeild))
## [1] FALSE
nrow(Dz_13Greenfeild)
## [1] 365
head(Dz_13Greenfeild)
## # A tibble: 6 x 3
## Date GEC_2013_Total n
## <date> <dbl> <int>
## 1 2013-01-01 4708 24
## 2 2013-01-02 14465 24
## 3 2013-01-03 13579 24
## 4 2013-01-04 4273 24
## 5 2013-01-05 0 24
## 6 2013-01-06 0 24
Greenfeild_U_Status_13<-cut(Dz_13Greenfeild$GEC_2013_Total,breaks = c(0,100,50000),labels = c(0,1))
Greenfeild_U_Status_13<-cbind(Dz_13Greenfeild,Greenfeild_U_Status_13)
Greenfeild_U_Status_13[is.na(Greenfeild_U_Status_13)]= 0
#Greenfeild_U_Status_13
#aa_GEC_10<-count(Greenfeild_U_Status_10,"Greenfeild_U_Status_10")
aa_GEC_13<-Greenfeild_U_Status_13 %>% count(Greenfeild_U_Status_13)
names(aa_GEC_13)[1]="Running_Status"
names(aa_GEC_13)[2]="Greenfeild_Run"
aa_GEC_13
## # A tibble: 2 x 2
## Running_Status Greenfeild_Run
## <fct> <int>
## 1 0 168
## 2 1 197
any(is.na(Dz_13Brighton))
## [1] TRUE
sum(is.na(Dz_13Brighton))
## [1] 2
colSums(is.na(Dz_13Brighton))
## Date Brigton_2013_Total n
## 1 1 0
nrow(Dz_13Brighton)
## [1] 366
Dz_13Brighton<-na.omit(Dz_13Brighton)
any(is.na(Dz_13Brighton))
## [1] FALSE
nrow(Dz_13Brighton)
## [1] 365
head(Dz_13Brighton)
## # A tibble: 6 x 3
## Date Brigton_2013_Total n
## <date> <dbl> <int>
## 1 2013-01-01 0 24
## 2 2013-01-02 0 24
## 3 2013-01-03 0 24
## 4 2013-01-04 0 24
## 5 2013-01-05 0 24
## 6 2013-01-06 0 24
Brighton_U_Status_13<-cut(Dz_13Brighton$Brigton_2013_Total,breaks = c(0,100,50000),labels = c(0,1))
Brighton_U_Status_13<-cbind(Dz_13Brighton,Brighton_U_Status_13)
Brighton_U_Status_13[is.na(Brighton_U_Status_13)]=0
#Brighton_U_Status_13
#aa_BB_10<-count(Brighton_U_Status_10,"Brighton_U_Status_10")
aa_BB_13<-Brighton_U_Status_13 %>% count(Brighton_U_Status_13)
names(aa_BB_13)[1]="Running_Status"
names(aa_BB_13)[2]="Brighton_Run"
aa_BB_13
## # A tibble: 2 x 2
## Running_Status Brighton_Run
## <fct> <int>
## 1 0 327
## 2 1 38
U_R_Status_13<-cbind(aa_G_13,aa_H_13[,2],aa_P_13[,2],aa_GEC_13[,2],aa_BB_13[,2])
names(U_R_Status_13)[2]="Goreway"
names(U_R_Status_13)[3]="Halton"
names(U_R_Status_13)[4]="Portlands"
names(U_R_Status_13)[5]="Greenfield"
names(U_R_Status_13)[6]="Brighton"
Barplot
count<-as.matrix(U_R_Status_13[,-1])
barplot(count)
library(ggplot2)
Plant_Name <-rep(c("Goreway", "Halton", "Portlands", "Greenfield", "Brighton"), 2)
No_Run<-c(U_R_Status_13[1,2],U_R_Status_13[1,3],U_R_Status_13[1,4],U_R_Status_13[1,5],U_R_Status_13[1,6])
Run<-c(U_R_Status_13[2,2],U_R_Status_13[2,3],U_R_Status_13[2,4],U_R_Status_13[2,5],U_R_Status_13[2,6])
Days <-c(No_Run, Run)
Run_type <-c(rep("No_Run", 5), rep("Run",5))
mydata <-data.frame(Plant_Name, Days)
mydata
## Plant_Name Days
## 1 Goreway 186
## 2 Halton 105
## 3 Portlands 166
## 4 Greenfield 168
## 5 Brighton 327
## 6 Goreway 179
## 7 Halton 260
## 8 Portlands 199
## 9 Greenfield 197
## 10 Brighton 38
p <-ggplot(mydata, aes(Plant_Name, Days))
p +geom_bar(stat= "identity",aes(fill=Run_type),position="dodge")+xlab("Plants Name")+ylab("Number of Days")+theme_bw()
ggplot(data=mydata, aes(x=Plant_Name, y=Days, fill=Run_type)) +
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=Days), vjust=1.6, color="white",
position = position_dodge(0.9), size=3.5)+
scale_fill_brewer(palette="Paired")+
theme_minimal()