library(tidyverse)
library(lubridate)
library(rvest)
library(ggplot2)
library(scales)
library(dplyr)
library(plyr)
library(data.table)Acquisition and Introduction of the Data
# Temporary file for "Planning"
planning_temp=tempfile(fileext=".xlsx")
# Temporary file for "Production"
production_temp=tempfile(fileext=".xlsx")
# Temporary file for "Consumption"
consumption_temp=tempfile(fileext=".xlsx")
# Downloading file from repository to the "Planning" temp
download.file("https://raw.githubusercontent.com/pjournal/mef03g-Kar-R-sizlar/master/4-year-planning.csv?raw=true",destfile=planning_temp,mode='wb')
# Downloading file from repository to the "Production" temp
download.file("https://raw.githubusercontent.com/pjournal/mef03g-Kar-R-sizlar/master/4-year-production.csv?raw=true",destfile=production_temp,mode='wb')
# Downloading file from repository to the "Consumption" temp
download.file("https://raw.githubusercontent.com/pjournal/mef03g-Kar-R-sizlar/master/4-year-consumption.csv?raw=true",destfile=consumption_temp,mode='wb')
# Reading the csv files.
planning_raw_data=read.csv(planning_temp,skip=1)
production_raw_data=read.csv(production_temp,skip=1)
consumption_raw_data=read.csv(consumption_temp,skip=1)
# Removing the temp files
file.remove(planning_temp)## [1] TRUEfile.remove(production_temp)## [1] TRUEfile.remove(consumption_temp)## [1] TRUEPreparation and Introduction of Planning Data
- 4 Year Planning Data: This data includes 33599 rows and 15 variables for the planned pruduction between January 1st, 2016 and October 31st, 2019
# Proper Column Names
colnames(planning_raw_data) <- c('tarih', 'saat', 'toplam_mwh', 'dogal_gaz', 'ruzgar', 'linyit', 'tas_komur', 'ithal_komur', 'fuel_oil', 'jeotermal', 'barajli', 'nafta', 'biyokutle', 'akarsu', 'diger')
# Proper Data Format
planning_raw_data[,3:15] <- as.data.frame(lapply(planning_raw_data[,3:15], function(x) as.numeric(gsub(",", ".", gsub("\\.", "", x)))))
planning_raw_data$tarih <-as.character(planning_raw_data$tarih)
planning_raw_data$saat <-as.character(planning_raw_data$saat)
head(planning_raw_data, 5)## tarih saat toplam_mwh dogal_gaz ruzgar linyit tas_komur
## 1 01.01.2016 01:00 18735.09 5471.14 168.48 4139.53 736
## 2 01.01.2016 02:00 17662.05 5182.14 168.44 4139.53 736
## 3 01.01.2016 03:00 17059.91 5146.13 159.71 4139.53 736
## 4 01.01.2016 04:00 16903.33 4990.13 149.83 4139.53 736
## 5 01.01.2016 05:00 16845.48 4941.13 138.38 4139.53 736
## ithal_komur fuel_oil jeotermal barajli nafta biyokutle akarsu diger
## 1 4587 115 104.1 3166 5 0 122.84 120
## 2 4470 115 104.1 2499 5 0 122.84 120
## 3 4470 115 104.1 1943 5 0 121.44 120
## 4 4470 115 104.1 1943 5 0 130.74 120
## 5 4470 115 104.1 1943 5 0 133.34 120Preparation and Introduction of Production Data
- 4 Year Production Data: This data includes 33597 rows and 18 variables for the actual pruduction between January 1st, 2016 and October 31st, 2019
# Defining Column Names
colnames(production_raw_data) <- c('tarih', 'saat', 'toplam_mwh', 'dogal_gaz', 'barajli', 'linyit', 'akarsu', 'ithal_komur', 'ruzgar', 'gunes', 'fuel_oil', 'jeotermal', 'asfaltit_komur', 'tas_komur', 'biyokutle', 'nafta', 'lng', 'uluslararasi')
# Proper Data Format
production_raw_data[,3:18] <- as.data.frame(lapply(production_raw_data[,3:18], function(x) as.numeric(gsub(",", ".", gsub("\\.", "", x)))))
production_raw_data$tarih <-as.character(production_raw_data$tarih)
production_raw_data$saat <-as.character(production_raw_data$saat)
head(production_raw_data, 5)## tarih saat toplam_mwh dogal_gaz barajli linyit akarsu ithal_komur
## 1 01.01.2016 01:00 24491.99 6430.37 3604.49 4997.36 546.70 5269.60
## 2 01.01.2016 02:00 23109.42 5814.91 2865.77 5018.45 559.95 5165.70
## 3 01.01.2016 03:00 22037.52 5668.09 2156.75 4984.45 551.79 5148.64
## 4 01.01.2016 04:00 21527.40 5811.11 1665.71 4942.55 521.65 5123.66
## 5 01.01.2016 05:00 21560.19 5899.45 1809.77 4949.23 591.53 4917.75
## ruzgar gunes fuel_oil jeotermal asfaltit_komur tas_komur biyokutle
## 1 2277.69 0 174.3 485.05 92.73 451.5 162.20
## 2 2278.95 0 176.1 485.97 128.06 455.5 160.06
## 3 2123.17 0 175.1 479.14 134.68 455.5 160.21
## 4 2057.80 0 177.2 466.16 136.89 462.5 162.17
## 5 1987.09 0 178.0 478.42 134.68 451.5 162.77
## nafta lng uluslararasi
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0Preparation and Introduction of Consumption Data
- 4 Year Consumpiton Data: This data includes 33575 rows and 3 variables for actual consumption between January 1st, 2016 and October 31st, 2019
# Defining Column Names
colnames(consumption_raw_data) <- c('tarih', 'saat', 'tuketim_miktari_mwh')
# Proper Data Format
consumption_raw_data <- cbind(consumption_raw_data, consumption_raw_data)
consumption_raw_data[,3:6] <- as.data.frame(lapply(consumption_raw_data[,3:6], function(x) as.numeric(gsub(",", ".", gsub("\\.", "", x)))))## Warning in FUN(X[[i]], ...): NAs introduced by coercionconsumption_raw_data <- consumption_raw_data[, 1:3]
consumption_raw_data$tarih <-as.character(consumption_raw_data$tarih)
consumption_raw_data$saat <-as.character(consumption_raw_data$saat)
head(consumption_raw_data, 5)## tarih saat tuketim_miktari_mwh
## 1 01.01.2016 01:00 24991.82
## 2 01.01.2016 02:00 23532.61
## 3 01.01.2016 03:00 22464.78
## 4 01.01.2016 04:00 22002.91
## 5 01.01.2016 05:00 21957.084 Year Planning Analysis: January 2016 - October 2019
- The percentage of planning of energy sources was calculated.
planning_source_distribution<-planning_raw_data%>%
summarise(toplam_mwh=sum(toplam_mwh),dogal_gaz=sum(dogal_gaz),ruzgar=sum(ruzgar),linyit=sum(linyit),tas_komur=sum(tas_komur),ithal_komur=sum(ithal_komur),fuel_oil=sum(fuel_oil), jeotermal=sum(jeotermal), barajli=sum(barajli), nafta=sum(nafta), biyokutle=sum(biyokutle),akarsu=sum(akarsu),diger=sum(diger))%>%
transmute(dogal_gaz_ratio=dogal_gaz/toplam_mwh*100, ruzgar_ratio=ruzgar/toplam_mwh*100, linyit_ratio=linyit/toplam_mwh*100, tas_komur_ratio=tas_komur/toplam_mwh*100, ithal_komur_ratio=ithal_komur/toplam_mwh*100, fuel_oil_ratio=fuel_oil/toplam_mwh*100, jeotermal_ratio=jeotermal/toplam_mwh*100, barajli_ratio=barajli/toplam_mwh*100, nafta_ratio=nafta/toplam_mwh*100, biyokutle_ratio=biyokutle/toplam_mwh*100, akarsu_ratio=akarsu/toplam_mwh*100,diger_ratio=diger/toplam_mwh*100)
planning_source_distribution## dogal_gaz_ratio ruzgar_ratio linyit_ratio tas_komur_ratio
## 1 30.57317 6.324945 13.76322 1.495145
## ithal_komur_ratio fuel_oil_ratio jeotermal_ratio barajli_ratio
## 1 18.7245 0.3351872 1.980785 18.96739
## nafta_ratio biyokutle_ratio akarsu_ratio diger_ratio
## 1 0.006768052 0.5881827 6.332958 0.8858297- Distribution of the percentage of planning of energy sources is rearranged before visualization.
Planning_Distribution<- t(planning_source_distribution)
colnames(Planning_Distribution)[1] <- "Oran"
Planning_Distribution<-as.data.frame(Planning_Distribution)
Planning_Distribution <- Planning_Distribution %>%
rownames_to_column(var = "Kaynaklar")
Planning_Distribution %>%
arrange(desc(Oran))## Kaynaklar Oran
## 1 dogal_gaz_ratio 30.573171207
## 2 barajli_ratio 18.967390786
## 3 ithal_komur_ratio 18.724497742
## 4 linyit_ratio 13.763221919
## 5 akarsu_ratio 6.332957557
## 6 ruzgar_ratio 6.324944516
## 7 jeotermal_ratio 1.980785003
## 8 tas_komur_ratio 1.495144766
## 9 diger_ratio 0.885829680
## 10 biyokutle_ratio 0.588182655
## 11 fuel_oil_ratio 0.335187182
## 12 nafta_ratio 0.006768052- Distribution of the percentage of planning of energy sources is shown on the pie chart.
ggplot(Planning_Distribution, aes(x="", y=Oran, fill=factor(Kaynaklar))) + geom_bar(stat="identity", width=1) + coord_polar("y", start=0) +geom_text(aes(label = paste0(round(Oran),"%")), position = position_stack(vjust = 0.5)) + labs(x = NULL, y = NULL, fill = NULL, title = "Planning:General Graphics Distribution by Source") + theme_classic() + theme(axis.line = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(hjust = 0.5, color = "#666666"))
Creating Monthly Sums for Planning
jan2016_planning_1 <- planning_raw_data %>% filter(grepl("01.2016", tarih))
jan_daily_2016_planning <- ddply(jan2016_planning_1,"tarih",numcolwise(sum))
January_2016_Planning <- colSums(Filter(is.numeric, jan2016_planning_1))
jan_monthly_2016_planning <- as.data.frame(January_2016_Planning)
feb2016_planning_1 <- planning_raw_data %>% filter(grepl("02.2016", tarih))
feb_daily_2016_planning <- ddply(feb2016_planning_1,"tarih",numcolwise(sum))
February_2016_Planning <- colSums(Filter(is.numeric, feb2016_planning_1))
feb_monthly_2016_planning <- as.data.frame(February_2016_Planning)
mar2016_planning_1 <- planning_raw_data %>% filter(grepl("03.2016", tarih))
mar_daily_2016_planning <- ddply(jan2016_planning_1,"tarih",numcolwise(sum))
March_2016_Planning <- colSums(Filter(is.numeric, mar2016_planning_1))
mar_monthly_2016_planning <- as.data.frame(March_2016_Planning)
apr2016_planning_1 <- planning_raw_data %>% filter(grepl("04.2016", tarih))
apr_daily_2016_planning <- ddply(apr2016_planning_1,"tarih",numcolwise(sum))
April_2016_Planning <- colSums(Filter(is.numeric, apr2016_planning_1))
apr_monthly_2016_planning <- as.data.frame(April_2016_Planning)
may2016_planning_1 <- planning_raw_data %>% filter(grepl("05.2016", tarih))
may_daily_2016_planning <- ddply(may2016_planning_1,"tarih",numcolwise(sum))
May_2016_Planning <- colSums(Filter(is.numeric, may2016_planning_1))
may_monthly_2016_planning <- as.data.frame(May_2016_Planning)
jun2016_planning_1 <- planning_raw_data %>% filter(grepl("06.2016", tarih))
jun_daily_2016_planning <- ddply(jun2016_planning_1,"tarih",numcolwise(sum))
June_2016_Planning <- colSums(Filter(is.numeric, jun2016_planning_1))
jun_monthly_2016_planning <- as.data.frame(June_2016_Planning)
jul2016_planning_1 <- planning_raw_data %>% filter(grepl("07.2016", tarih))
jul_daily_2016_planning <- ddply(jul2016_planning_1,"tarih",numcolwise(sum))
July_2016_Planning <- colSums(Filter(is.numeric, jul2016_planning_1))
jul_monthly_2016_planning <- as.data.frame(July_2016_Planning)
aug2016_planning_1 <- planning_raw_data %>% filter(grepl("08.2016", tarih))
aug_daily_2016_planning <- ddply(aug2016_planning_1,"tarih",numcolwise(sum))
August_2016_Planning <- colSums(Filter(is.numeric, aug2016_planning_1))
aug_monthly_2016_planning <- as.data.frame(August_2016_Planning)
sep2016_planning_1 <- planning_raw_data %>% filter(grepl("09.2016", tarih))
sep_daily_2016_planning <- ddply(sep2016_planning_1,"tarih",numcolwise(sum))
September_2016_Planning <- colSums(Filter(is.numeric, sep2016_planning_1))
sep_monthly_2016_planning <- as.data.frame(September_2016_Planning)
oct2016_planning_1 <- planning_raw_data %>% filter(grepl("10.2016", tarih))
oct_daily_2016_planning <- ddply(oct2016_planning_1,"tarih",numcolwise(sum))
October_2016_Planning <- colSums(Filter(is.numeric, oct2016_planning_1))
oct_monthly_2016_planning <- as.data.frame(October_2016_Planning)
nov2016_planning_1 <- planning_raw_data %>% filter(grepl("11.2016", tarih))
nov_daily_2016_planning <- ddply(nov2016_planning_1,"tarih",numcolwise(sum))
November_2016_Planning <- colSums(Filter(is.numeric, nov2016_planning_1))
nov_monthly_2016_planning <- as.data.frame(November_2016_Planning)
dec2016_planning_1 <- planning_raw_data %>% filter(grepl("12.2016", tarih))
dec_daily_2016_planning <- ddply(dec2016_planning_1,"tarih",numcolwise(sum))
December_2016_Planning <- colSums(Filter(is.numeric, dec2016_planning_1))
dec_monthly_2016_planning <- as.data.frame(December_2016_Planning)
Planning_2016_Monthly <- cbind(jan_monthly_2016_planning, feb_monthly_2016_planning, mar_monthly_2016_planning, apr_monthly_2016_planning, may_monthly_2016_planning, jun_monthly_2016_planning, jul_monthly_2016_planning, aug_monthly_2016_planning, sep_monthly_2016_planning, oct_monthly_2016_planning, nov_monthly_2016_planning, dec_monthly_2016_planning)
jan2017_planning_1 <- planning_raw_data %>% filter(grepl("01.2017", tarih))
jan_daily_2017_planning <- ddply(jan2017_planning_1,"tarih",numcolwise(sum))
January_2017_Planning <- colSums(Filter(is.numeric, jan2017_planning_1))
jan_monthly_2017_planning <- as.data.frame(January_2017_Planning)
feb2017_planning_1 <- planning_raw_data %>% filter(grepl("02.2017", tarih))
feb_daily_2017_planning <- ddply(feb2017_planning_1,"tarih",numcolwise(sum))
February_2017_Planning <- colSums(Filter(is.numeric, feb2017_planning_1))
feb_monthly_2017_planning <- as.data.frame(February_2017_Planning)
mar2017_planning_1 <- planning_raw_data %>% filter(grepl("03.2017", tarih))
mar_daily_2017_planning <- ddply(jan2017_planning_1,"tarih",numcolwise(sum))
March_2017_Planning <- colSums(Filter(is.numeric, mar2017_planning_1))
mar_monthly_2017_planning <- as.data.frame(March_2017_Planning)
apr2017_planning_1 <- planning_raw_data %>% filter(grepl("04.2017", tarih))
apr_daily_2017_planning <- ddply(apr2017_planning_1,"tarih",numcolwise(sum))
April_2017_Planning <- colSums(Filter(is.numeric, apr2017_planning_1))
apr_monthly_2017_planning <- as.data.frame(April_2017_Planning)
may2017_planning_1 <- planning_raw_data %>% filter(grepl("05.2017", tarih))
may_daily_2017_planning <- ddply(may2017_planning_1,"tarih",numcolwise(sum))
May_2017_Planning <- colSums(Filter(is.numeric, may2017_planning_1))
may_monthly_2017_planning <- as.data.frame(May_2017_Planning)
jun2017_planning_1 <- planning_raw_data %>% filter(grepl("06.2017", tarih))
jun_daily_2017_planning <- ddply(jun2017_planning_1,"tarih",numcolwise(sum))
June_2017_Planning <- colSums(Filter(is.numeric, jun2017_planning_1))
jun_monthly_2017_planning <- as.data.frame(June_2017_Planning)
jul2017_planning_1 <- planning_raw_data %>% filter(grepl("07.2017", tarih))
jul_daily_2017_planning <- ddply(jul2017_planning_1,"tarih",numcolwise(sum))
July_2017_Planning <- colSums(Filter(is.numeric, jul2017_planning_1))
jul_monthly_2017_planning <- as.data.frame(July_2017_Planning)
aug2017_planning_1 <- planning_raw_data %>% filter(grepl("08.2017", tarih))
aug_daily_2017_planning <- ddply(aug2017_planning_1,"tarih",numcolwise(sum))
August_2017_Planning <- colSums(Filter(is.numeric, aug2017_planning_1))
aug_monthly_2017_planning <- as.data.frame(August_2017_Planning)
sep2017_planning_1 <- planning_raw_data %>% filter(grepl("09.2017", tarih))
sep_daily_2017_planning <- ddply(sep2017_planning_1,"tarih",numcolwise(sum))
September_2017_Planning <- colSums(Filter(is.numeric, sep2017_planning_1))
sep_monthly_2017_planning <- as.data.frame(September_2017_Planning)
oct2017_planning_1 <- planning_raw_data %>% filter(grepl("10.2017", tarih))
oct_daily_2017_planning <- ddply(oct2017_planning_1,"tarih",numcolwise(sum))
October_2017_Planning <- colSums(Filter(is.numeric, oct2017_planning_1))
oct_monthly_2017_planning <- as.data.frame(October_2017_Planning)
nov2017_planning_1 <- planning_raw_data %>% filter(grepl("11.2017", tarih))
nov_daily_2017_planning <- ddply(nov2017_planning_1,"tarih",numcolwise(sum))
November_2017_Planning <- colSums(Filter(is.numeric, nov2017_planning_1))
nov_monthly_2017_planning <- as.data.frame(November_2017_Planning)
dec2017_planning_1 <- planning_raw_data %>% filter(grepl("12.2017", tarih))
dec_daily_2017_planning <- ddply(dec2017_planning_1,"tarih",numcolwise(sum))
December_2017_Planning <- colSums(Filter(is.numeric, dec2017_planning_1))
dec_monthly_2017_planning <- as.data.frame(December_2017_Planning)
Planning_2017_Monthly <- cbind(jan_monthly_2017_planning, feb_monthly_2017_planning, mar_monthly_2017_planning, apr_monthly_2017_planning, may_monthly_2017_planning, jun_monthly_2017_planning, jul_monthly_2017_planning, aug_monthly_2017_planning, sep_monthly_2017_planning, oct_monthly_2017_planning, nov_monthly_2017_planning, dec_monthly_2017_planning)
jan2018_planning_1 <- planning_raw_data %>% filter(grepl("01.2018", tarih))
jan_daily_2018_planning <- ddply(jan2018_planning_1,"tarih",numcolwise(sum))
January_2018_Planning <- colSums(Filter(is.numeric, jan2018_planning_1))
jan_monthly_2018_planning <- as.data.frame(January_2018_Planning)
feb2018_planning_1 <- planning_raw_data %>% filter(grepl("02.2018", tarih))
feb_daily_2018_planning <- ddply(feb2018_planning_1,"tarih",numcolwise(sum))
February_2018_Planning <- colSums(Filter(is.numeric, feb2018_planning_1))
feb_monthly_2018_planning <- as.data.frame(February_2018_Planning)
mar2018_planning_1 <- planning_raw_data %>% filter(grepl("03.2018", tarih))
mar_daily_2018_planning <- ddply(jan2018_planning_1,"tarih",numcolwise(sum))
March_2018_Planning <- colSums(Filter(is.numeric, mar2018_planning_1))
mar_monthly_2018_planning <- as.data.frame(March_2018_Planning)
apr2018_planning_1 <- planning_raw_data %>% filter(grepl("04.2018", tarih))
apr_daily_2018_planning <- ddply(apr2018_planning_1,"tarih",numcolwise(sum))
April_2018_Planning <- colSums(Filter(is.numeric, apr2018_planning_1))
apr_monthly_2018_planning <- as.data.frame(April_2018_Planning)
may2018_planning_1 <- planning_raw_data %>% filter(grepl("05.2018", tarih))
may_daily_2018_planning <- ddply(may2018_planning_1,"tarih",numcolwise(sum))
May_2018_Planning <- colSums(Filter(is.numeric, may2018_planning_1))
may_monthly_2018_planning <- as.data.frame(May_2018_Planning)
jun2018_planning_1 <- planning_raw_data %>% filter(grepl("06.2018", tarih))
jun_daily_2018_planning <- ddply(jun2018_planning_1,"tarih",numcolwise(sum))
June_2018_Planning <- colSums(Filter(is.numeric, jun2018_planning_1))
jun_monthly_2018_planning <- as.data.frame(June_2018_Planning)
jul2018_planning_1 <- planning_raw_data %>% filter(grepl("07.2018", tarih))
jul_daily_2018_planning <- ddply(jul2018_planning_1,"tarih",numcolwise(sum))
July_2018_Planning <- colSums(Filter(is.numeric, jul2018_planning_1))
jul_monthly_2018_planning <- as.data.frame(July_2018_Planning)
aug2018_planning_1 <- planning_raw_data %>% filter(grepl("08.2018", tarih))
aug_daily_2018_planning <- ddply(aug2018_planning_1,"tarih",numcolwise(sum))
August_2018_Planning <- colSums(Filter(is.numeric, aug2018_planning_1))
aug_monthly_2018_planning <- as.data.frame(August_2018_Planning)
sep2018_planning_1 <- planning_raw_data %>% filter(grepl("09.2018", tarih))
sep_daily_2018_planning <- ddply(sep2018_planning_1,"tarih",numcolwise(sum))
September_2018_Planning <- colSums(Filter(is.numeric, sep2018_planning_1))
sep_monthly_2018_planning <- as.data.frame(September_2018_Planning)
oct2018_planning_1 <- planning_raw_data %>% filter(grepl("10.2018", tarih))
oct_daily_2018_planning <- ddply(oct2018_planning_1,"tarih",numcolwise(sum))
October_2018_Planning <- colSums(Filter(is.numeric, oct2018_planning_1))
oct_monthly_2018_planning <- as.data.frame(October_2018_Planning)
nov2018_planning_1 <- planning_raw_data %>% filter(grepl("11.2018", tarih))
nov_daily_2018_planning <- ddply(nov2018_planning_1,"tarih",numcolwise(sum))
November_2018_Planning <- colSums(Filter(is.numeric, nov2018_planning_1))
nov_monthly_2018_planning <- as.data.frame(November_2018_Planning)
dec2018_planning_1 <- planning_raw_data %>% filter(grepl("12.2018", tarih))
dec_daily_2018_planning <- ddply(dec2018_planning_1,"tarih",numcolwise(sum))
December_2018_Planning <- colSums(Filter(is.numeric, dec2018_planning_1))
dec_monthly_2018_planning <- as.data.frame(December_2018_Planning)
Planning_2018_Monthly <- cbind(jan_monthly_2018_planning, feb_monthly_2018_planning, mar_monthly_2018_planning, apr_monthly_2018_planning, may_monthly_2018_planning, jun_monthly_2018_planning, jul_monthly_2018_planning, aug_monthly_2018_planning, sep_monthly_2018_planning, oct_monthly_2018_planning, nov_monthly_2018_planning, dec_monthly_2018_planning)
jan2019_planning_1 <- planning_raw_data %>% filter(grepl("01.2019", tarih))
jan_daily_2019_planning <- ddply(jan2019_planning_1,"tarih",numcolwise(sum))
January_2019_Planning <- colSums(Filter(is.numeric, jan2019_planning_1))
jan_monthly_2019_planning <- as.data.frame(January_2019_Planning)
feb2019_planning_1 <- planning_raw_data %>% filter(grepl("02.2019", tarih))
feb_daily_2019_planning <- ddply(feb2019_planning_1,"tarih",numcolwise(sum))
February_2019_Planning <- colSums(Filter(is.numeric, feb2019_planning_1))
feb_monthly_2019_planning <- as.data.frame(February_2019_Planning)
mar2019_planning_1 <- planning_raw_data %>% filter(grepl("03.2019", tarih))
mar_daily_2019_planning <- ddply(jan2019_planning_1,"tarih",numcolwise(sum))
March_2019_Planning <- colSums(Filter(is.numeric, mar2019_planning_1))
mar_monthly_2019_planning <- as.data.frame(March_2019_Planning)
apr2019_planning_1 <- planning_raw_data %>% filter(grepl("04.2019", tarih))
apr_daily_2019_planning <- ddply(apr2019_planning_1,"tarih",numcolwise(sum))
April_2019_Planning <- colSums(Filter(is.numeric, apr2019_planning_1))
apr_monthly_2019_planning <- as.data.frame(April_2019_Planning)
may2019_planning_1 <- planning_raw_data %>% filter(grepl("05.2019", tarih))
may_daily_2019_planning <- ddply(may2019_planning_1,"tarih",numcolwise(sum))
May_2019_Planning <- colSums(Filter(is.numeric, may2019_planning_1))
may_monthly_2019_planning <- as.data.frame(May_2019_Planning)
jun2019_planning_1 <- planning_raw_data %>% filter(grepl("06.2019", tarih))
jun_daily_2019_planning <- ddply(jun2019_planning_1,"tarih",numcolwise(sum))
June_2019_Planning <- colSums(Filter(is.numeric, jun2019_planning_1))
jun_monthly_2019_planning <- as.data.frame(June_2019_Planning)
jul2019_planning_1 <- planning_raw_data %>% filter(grepl("07.2019", tarih))
jul_daily_2019_planning <- ddply(jul2019_planning_1,"tarih",numcolwise(sum))
July_2019_Planning <- colSums(Filter(is.numeric, jul2019_planning_1))
jul_monthly_2019_planning <- as.data.frame(July_2019_Planning)
aug2019_planning_1 <- planning_raw_data %>% filter(grepl("08.2019", tarih))
aug_daily_2019_planning <- ddply(aug2019_planning_1,"tarih",numcolwise(sum))
August_2019_Planning <- colSums(Filter(is.numeric, aug2019_planning_1))
aug_monthly_2019_planning <- as.data.frame(August_2019_Planning)
sep2019_planning_1 <- planning_raw_data %>% filter(grepl("09.2019", tarih))
sep_daily_2019_planning <- ddply(sep2019_planning_1,"tarih",numcolwise(sum))
September_2019_Planning <- colSums(Filter(is.numeric, sep2019_planning_1))
sep_monthly_2019_planning <- as.data.frame(September_2019_Planning)
oct2019_planning_1 <- planning_raw_data %>% filter(grepl("10.2019", tarih))
oct_daily_2019_planning <- ddply(oct2019_planning_1,"tarih",numcolwise(sum))
October_2019_Planning <- colSums(Filter(is.numeric, oct2019_planning_1))
oct_monthly_2019_planning <- as.data.frame(October_2019_Planning)
Planning_2019_Monthly <- cbind(jan_monthly_2019_planning, feb_monthly_2019_planning, mar_monthly_2019_planning, apr_monthly_2019_planning, may_monthly_2019_planning, jun_monthly_2019_planning, jul_monthly_2019_planning, aug_monthly_2019_planning, sep_monthly_2019_planning, oct_monthly_2019_planning)
Planning_Monthly <- cbind(Planning_2016_Monthly, Planning_2017_Monthly, Planning_2018_Monthly, Planning_2019_Monthly)
Planning_Monthly <- rownames_to_column(Planning_Monthly, var = "Sources")
head(Planning_Monthly, 5)## Sources January_2016_Planning February_2016_Planning
## 1 toplam_mwh 18385705.14 16370337.6
## 2 dogal_gaz 6956584.34 5751946.6
## 3 ruzgar 97156.52 125460.9
## 4 linyit 2785761.43 2562704.7
## 5 tas_komur 553672.00 530251.0
## March_2016_Planning April_2016_Planning May_2016_Planning
## 1 18588230.5 18356082.7 19877152.8
## 2 5427182.0 6086039.2 5080386.8
## 3 618063.5 487208.6 705477.8
## 4 2419856.9 1794657.9 2189355.6
## 5 630380.0 554156.0 575697.0
## June_2016_Planning July_2016_Planning August_2016_Planning
## 1 20746099.8 21956748 23643419.4
## 2 6095654.4 7078596 8823670.8
## 3 949855.3 1543691 1486075.8
## 4 2393558.4 2352105 2787876.3
## 5 581260.0 526286 562867.7
## September_2016_Planning October_2016_Planning November_2016_Planning
## 1 19316867 19853251.2 20416596
## 2 6239807 7198373.4 7963266
## 3 1113072 936597.2 1075415
## 4 2605904 2786209.9 2944602
## 5 486432 559910.0 600124
## December_2016_Planning January_2017_Planning February_2017_Planning
## 1 22035101 22896558 20395074
## 2 6404150 7150544 8071274
## 3 1293260 1355388 1264481
## 4 3453476 3091752 2550816
## 5 468259 592147 541018
## March_2017_Planning April_2017_Planning May_2017_Planning
## 1 21348230 19887163.2 20584699
## 2 7900885 7002375.4 7195991
## 3 1119777 851122.8 1118693
## 4 2519660 2368677.4 2226712
## 5 505246 445155.0 379878
## June_2017_Planning July_2017_Planning August_2017_Planning
## 1 19991227.5 25123552 25252276
## 2 7495669.3 10800321 10429291
## 3 868201.3 1710937 1924167
## 4 2703464.9 3150178 2897620
## 5 381940.0 336994 358882
## September_2017_Planning October_2017_Planning November_2017_Planning
## 1 21908345 21326374 21463008.0
## 2 9562382 9976147 10598539.9
## 3 950810 1131337 901615.4
## 4 2818234 2818423 2792746.3
## 5 350164 398836 362860.0
## December_2017_Planning January_2018_Planning February_2018_Planning
## 1 23167497 23437853 20874237
## 2 8535583 8754923 7060268
## 3 1767750 1585354 1586399
## 4 2886354 2960360 3173565
## 5 407124 323369 60690
## March_2018_Planning April_2018_Planning May_2018_Planning
## 1 22269120 20986523 21234273
## 2 5849357 5950954 5176295
## 3 2068717 1184040 1219668
## 4 3398514 3257538 3425836
## 5 46464 56400 67757
## June_2018_Planning July_2018_Planning August_2018_Planning
## 1 21044604 26014706 24621906
## 2 5612888 9815131 7266234
## 3 1227075 1445300 2347874
## 4 3364555 3480049 3435488
## 5 65191 71089 71124
## September_2018_Planning October_2018_Planning November_2018_Planning
## 1 22433107 20928922 20998067
## 2 8319682 7432901 6104561
## 3 1697927 1450966 1943503
## 4 3256088 3548988 3298925
## 5 67285 48226 61687
## December_2018_Planning January_2019_Planning February_2019_Planning
## 1 22817734 23835183.3 20747893.2
## 2 6138570 5013137.7 3029595.2
## 3 1594620 2193021.3 1848907.6
## 4 3368290 3167313.8 2951192.9
## 5 90547 83890.6 146868.7
## March_2019_Planning April_2019_Planning May_2019_Planning
## 1 21886745.2 20997373 21912778
## 2 3785709.1 2298879 1526804
## 3 2031214.9 1465802 1109466
## 4 3242018.5 3002714 3021061
## 5 197587.3 216933 209230
## June_2019_Planning July_2019_Planning August_2019_Planning
## 1 21333372.4 25309947.4 24320486
## 2 2293347.6 5394795.1 4688801
## 3 1664576.9 1957964.5 2638033
## 4 3020739.0 3687543.9 3580506
## 5 214822.6 466350.3 364447
## September_2019_Planning October_2019_Planning
## 1 22181033 20909598.5
## 2 4388389 4166874.4
## 3 1958124 1254835.6
## 4 3536561 3725816.5
## 5 148682 93336.4Creating Monthly Sums for Production
jan2016_production_1 <- production_raw_data %>% filter(grepl("01.2016", tarih))
jan_daily_2016_production <- ddply(jan2016_production_1,"tarih",numcolwise(sum))
January_2016_Production <- colSums(Filter(is.numeric, jan2016_production_1))
jan_monthly_2016_production <- as.data.frame(January_2016_Production)
feb2016_production_1 <- production_raw_data %>% filter(grepl("02.2016", tarih))
feb_daily_2016_production <- ddply(feb2016_production_1,"tarih",numcolwise(sum))
February_2016_Production <- colSums(Filter(is.numeric, feb2016_production_1))
feb_monthly_2016_production <- as.data.frame(February_2016_Production)
mar2016_production_1 <- production_raw_data %>% filter(grepl("03.2016", tarih))
mar_daily_2016_production <- ddply(jan2016_production_1,"tarih",numcolwise(sum))
March_2016_Production <- colSums(Filter(is.numeric, mar2016_production_1))
mar_monthly_2016_production <- as.data.frame(March_2016_Production)
apr2016_production_1 <- production_raw_data %>% filter(grepl("04.2016", tarih))
apr_daily_2016_production <- ddply(apr2016_production_1,"tarih",numcolwise(sum))
April_2016_Production <- colSums(Filter(is.numeric, apr2016_production_1))
apr_monthly_2016_production <- as.data.frame(April_2016_Production)
may2016_production_1 <- production_raw_data %>% filter(grepl("05.2016", tarih))
may_daily_2016_production <- ddply(may2016_production_1,"tarih",numcolwise(sum))
May_2016_Production <- colSums(Filter(is.numeric, may2016_production_1))
may_monthly_2016_production <- as.data.frame(May_2016_Production)
jun2016_production_1 <- production_raw_data %>% filter(grepl("06.2016", tarih))
jun_daily_2016_production <- ddply(jun2016_production_1,"tarih",numcolwise(sum))
June_2016_Production <- colSums(Filter(is.numeric, jun2016_production_1))
jun_monthly_2016_production <- as.data.frame(June_2016_Production)
jul2016_production_1 <- production_raw_data %>% filter(grepl("07.2016", tarih))
jul_daily_2016_production <- ddply(jul2016_production_1,"tarih",numcolwise(sum))
July_2016_Production <- colSums(Filter(is.numeric, jul2016_production_1))
jul_monthly_2016_production <- as.data.frame(July_2016_Production)
aug2016_production_1 <- production_raw_data %>% filter(grepl("08.2016", tarih))
aug_daily_2016_production <- ddply(aug2016_production_1,"tarih",numcolwise(sum))
August_2016_Production <- colSums(Filter(is.numeric, aug2016_production_1))
aug_monthly_2016_production <- as.data.frame(August_2016_Production)
sep2016_production_1 <- production_raw_data %>% filter(grepl("09.2016", tarih))
sep_daily_2016_production <- ddply(sep2016_production_1,"tarih",numcolwise(sum))
September_2016_Production <- colSums(Filter(is.numeric, sep2016_production_1))
sep_monthly_2016_production <- as.data.frame(September_2016_Production)
oct2016_production_1 <- production_raw_data %>% filter(grepl("10.2016", tarih))
oct_daily_2016_production <- ddply(oct2016_production_1,"tarih",numcolwise(sum))
October_2016_Production <- colSums(Filter(is.numeric, oct2016_production_1))
oct_monthly_2016_production <- as.data.frame(October_2016_Production)
nov2016_production_1 <- production_raw_data %>% filter(grepl("11.2016", tarih))
nov_daily_2016_production <- ddply(nov2016_production_1,"tarih",numcolwise(sum))
November_2016_Production <- colSums(Filter(is.numeric, nov2016_production_1))
nov_monthly_2016_production <- as.data.frame(November_2016_Production)
dec2016_production_1 <- production_raw_data %>% filter(grepl("12.2016", tarih))
dec_daily_2016_production <- ddply(dec2016_production_1,"tarih",numcolwise(sum))
December_2016_Production <- colSums(Filter(is.numeric, dec2016_production_1))
dec_monthly_2016_production <- as.data.frame(December_2016_Production)
Production_2016_Monthly <- cbind(jan_monthly_2016_production, feb_monthly_2016_production, mar_monthly_2016_production, apr_monthly_2016_production, may_monthly_2016_production, jun_monthly_2016_production, jul_monthly_2016_production, aug_monthly_2016_production, sep_monthly_2016_production, oct_monthly_2016_production, nov_monthly_2016_production, dec_monthly_2016_production)
jan2017_production_1 <- production_raw_data %>% filter(grepl("01.2017", tarih))
jan_daily_2017_production <- ddply(jan2017_production_1,"tarih",numcolwise(sum))
January_2017_Production <- colSums(Filter(is.numeric, jan2017_production_1))
jan_monthly_2017_production <- as.data.frame(January_2017_Production)
feb2017_production_1 <- production_raw_data %>% filter(grepl("02.2017", tarih))
feb_daily_2017_production <- ddply(feb2017_production_1,"tarih",numcolwise(sum))
February_2017_Production <- colSums(Filter(is.numeric, feb2017_production_1))
feb_monthly_2017_production <- as.data.frame(February_2017_Production)
mar2017_production_1 <- production_raw_data %>% filter(grepl("03.2017", tarih))
mar_daily_2017_production <- ddply(jan2017_production_1,"tarih",numcolwise(sum))
March_2017_Production <- colSums(Filter(is.numeric, mar2017_production_1))
mar_monthly_2017_production <- as.data.frame(March_2017_Production)
apr2017_production_1 <- production_raw_data %>% filter(grepl("04.2017", tarih))
apr_daily_2017_production <- ddply(apr2017_production_1,"tarih",numcolwise(sum))
April_2017_Production <- colSums(Filter(is.numeric, apr2017_production_1))
apr_monthly_2017_production <- as.data.frame(April_2017_Production)
may2017_production_1 <- production_raw_data %>% filter(grepl("05.2017", tarih))
may_daily_2017_production <- ddply(may2017_production_1,"tarih",numcolwise(sum))
May_2017_Production <- colSums(Filter(is.numeric, may2017_production_1))
may_monthly_2017_production <- as.data.frame(May_2017_Production)
jun2017_production_1 <- production_raw_data %>% filter(grepl("06.2017", tarih))
jun_daily_2017_production <- ddply(jun2017_production_1,"tarih",numcolwise(sum))
June_2017_Production <- colSums(Filter(is.numeric, jun2017_production_1))
jun_monthly_2017_production <- as.data.frame(June_2017_Production)
jul2017_production_1 <- production_raw_data %>% filter(grepl("07.2017", tarih))
jul_daily_2017_production <- ddply(jul2017_production_1,"tarih",numcolwise(sum))
July_2017_Production <- colSums(Filter(is.numeric, jul2017_production_1))
jul_monthly_2017_production <- as.data.frame(July_2017_Production)
aug2017_production_1 <- production_raw_data %>% filter(grepl("08.2017", tarih))
aug_daily_2017_production <- ddply(aug2017_production_1,"tarih",numcolwise(sum))
August_2017_Production <- colSums(Filter(is.numeric, aug2017_production_1))
aug_monthly_2017_production <- as.data.frame(August_2017_Production)
sep2017_production_1 <- production_raw_data %>% filter(grepl("09.2017", tarih))
sep_daily_2017_production <- ddply(sep2017_production_1,"tarih",numcolwise(sum))
September_2017_Production <- colSums(Filter(is.numeric, sep2017_production_1))
sep_monthly_2017_production <- as.data.frame(September_2017_Production)
oct2017_production_1 <- production_raw_data %>% filter(grepl("10.2017", tarih))
oct_daily_2017_production <- ddply(oct2017_production_1,"tarih",numcolwise(sum))
October_2017_Production <- colSums(Filter(is.numeric, oct2017_production_1))
oct_monthly_2017_production <- as.data.frame(October_2017_Production)
nov2017_production_1 <- production_raw_data %>% filter(grepl("11.2017", tarih))
nov_daily_2017_production <- ddply(nov2017_production_1,"tarih",numcolwise(sum))
November_2017_Production <- colSums(Filter(is.numeric, nov2017_production_1))
nov_monthly_2017_production <- as.data.frame(November_2017_Production)
dec2017_production_1 <- production_raw_data %>% filter(grepl("12.2017", tarih))
dec_daily_2017_production <- ddply(dec2017_production_1,"tarih",numcolwise(sum))
December_2017_Production <- colSums(Filter(is.numeric, dec2017_production_1))
dec_monthly_2017_production <- as.data.frame(December_2017_Production)
Production_2017_Monthly <- cbind(jan_monthly_2017_production, feb_monthly_2017_production, mar_monthly_2017_production, apr_monthly_2017_production, may_monthly_2017_production, jun_monthly_2017_production, jul_monthly_2017_production, aug_monthly_2017_production, sep_monthly_2017_production, oct_monthly_2017_production, nov_monthly_2017_production, dec_monthly_2017_production)
jan2018_production_1 <- production_raw_data %>% filter(grepl("01.2018", tarih))
jan_daily_2018_production <- ddply(jan2018_production_1,"tarih",numcolwise(sum))
January_2018_Production <- colSums(Filter(is.numeric, jan2018_production_1))
jan_monthly_2018_production <- as.data.frame(January_2018_Production)
feb2018_production_1 <- production_raw_data %>% filter(grepl("02.2018", tarih))
feb_daily_2018_production <- ddply(feb2018_production_1,"tarih",numcolwise(sum))
February_2018_Production <- colSums(Filter(is.numeric, feb2018_production_1))
feb_monthly_2018_production <- as.data.frame(February_2018_Production)
mar2018_production_1 <- production_raw_data %>% filter(grepl("03.2018", tarih))
mar_daily_2018_production <- ddply(jan2018_production_1,"tarih",numcolwise(sum))
March_2018_Production <- colSums(Filter(is.numeric, mar2018_production_1))
mar_monthly_2018_production <- as.data.frame(March_2018_Production)
apr2018_production_1 <- production_raw_data %>% filter(grepl("04.2018", tarih))
apr_daily_2018_production <- ddply(apr2018_production_1,"tarih",numcolwise(sum))
April_2018_Production <- colSums(Filter(is.numeric, apr2018_production_1))
apr_monthly_2018_production <- as.data.frame(April_2018_Production)
may2018_production_1 <- production_raw_data %>% filter(grepl("05.2018", tarih))
may_daily_2018_production <- ddply(may2018_production_1,"tarih",numcolwise(sum))
May_2018_Production <- colSums(Filter(is.numeric, may2018_production_1))
may_monthly_2018_production <- as.data.frame(May_2018_Production)
jun2018_production_1 <- production_raw_data %>% filter(grepl("06.2018", tarih))
jun_daily_2018_production <- ddply(jun2018_production_1,"tarih",numcolwise(sum))
June_2018_Production <- colSums(Filter(is.numeric, jun2018_production_1))
jun_monthly_2018_production <- as.data.frame(June_2018_Production)
jul2018_production_1 <- production_raw_data %>% filter(grepl("07.2018", tarih))
jul_daily_2018_production <- ddply(jul2018_production_1,"tarih",numcolwise(sum))
July_2018_Production <- colSums(Filter(is.numeric, jul2018_production_1))
jul_monthly_2018_production <- as.data.frame(July_2018_Production)
aug2018_production_1 <- production_raw_data %>% filter(grepl("08.2018", tarih))
aug_daily_2018_production <- ddply(aug2018_production_1,"tarih",numcolwise(sum))
August_2018_Production <- colSums(Filter(is.numeric, aug2018_production_1))
aug_monthly_2018_production <- as.data.frame(August_2018_Production)
sep2018_production_1 <- production_raw_data %>% filter(grepl("09.2018", tarih))
sep_daily_2018_production <- ddply(sep2018_production_1,"tarih",numcolwise(sum))
September_2018_Production <- colSums(Filter(is.numeric, sep2018_production_1))
sep_monthly_2018_production <- as.data.frame(September_2018_Production)
oct2018_production_1 <- production_raw_data %>% filter(grepl("10.2018", tarih))
oct_daily_2018_production <- ddply(oct2018_production_1,"tarih",numcolwise(sum))
October_2018_Production <- colSums(Filter(is.numeric, oct2018_production_1))
oct_monthly_2018_production <- as.data.frame(October_2018_Production)
nov2018_production_1 <- production_raw_data %>% filter(grepl("11.2018", tarih))
nov_daily_2018_production <- ddply(nov2018_production_1,"tarih",numcolwise(sum))
November_2018_Production <- colSums(Filter(is.numeric, nov2018_production_1))
nov_monthly_2018_production <- as.data.frame(November_2018_Production)
dec2018_production_1 <- production_raw_data %>% filter(grepl("12.2018", tarih))
dec_daily_2018_production <- ddply(dec2018_production_1,"tarih",numcolwise(sum))
December_2018_Production <- colSums(Filter(is.numeric, dec2018_production_1))
dec_monthly_2018_production <- as.data.frame(December_2018_Production)
Production_2018_Monthly <- cbind(jan_monthly_2018_production, feb_monthly_2018_production, mar_monthly_2018_production, apr_monthly_2018_production, may_monthly_2018_production, jun_monthly_2018_production, jul_monthly_2018_production, aug_monthly_2018_production, sep_monthly_2018_production, oct_monthly_2018_production, nov_monthly_2018_production, dec_monthly_2018_production)
jan2019_production_1 <- production_raw_data %>% filter(grepl("01.2019", tarih))
jan_daily_2019_production <- ddply(jan2019_production_1,"tarih",numcolwise(sum))
January_2019_Production <- colSums(Filter(is.numeric, jan2019_production_1))
jan_monthly_2019_production <- as.data.frame(January_2019_Production)
feb2019_production_1 <- production_raw_data %>% filter(grepl("02.2019", tarih))
feb_daily_2019_production <- ddply(feb2019_production_1,"tarih",numcolwise(sum))
February_2019_Production <- colSums(Filter(is.numeric, feb2019_production_1))
feb_monthly_2019_production <- as.data.frame(February_2019_Production)
mar2019_production_1 <- production_raw_data %>% filter(grepl("03.2019", tarih))
mar_daily_2019_production <- ddply(jan2019_production_1,"tarih",numcolwise(sum))
March_2019_Production <- colSums(Filter(is.numeric, mar2019_production_1))
mar_monthly_2019_production <- as.data.frame(March_2019_Production)
apr2019_production_1 <- production_raw_data %>% filter(grepl("04.2019", tarih))
apr_daily_2019_production <- ddply(apr2019_production_1,"tarih",numcolwise(sum))
April_2019_Production <- colSums(Filter(is.numeric, apr2019_production_1))
apr_monthly_2019_production <- as.data.frame(April_2019_Production)
may2019_production_1 <- production_raw_data %>% filter(grepl("05.2019", tarih))
may_daily_2019_production <- ddply(may2019_production_1,"tarih",numcolwise(sum))
May_2019_Production <- colSums(Filter(is.numeric, may2019_production_1))
may_monthly_2019_production <- as.data.frame(May_2019_Production)
jun2019_production_1 <- production_raw_data %>% filter(grepl("06.2019", tarih))
jun_daily_2019_production <- ddply(jun2019_production_1,"tarih",numcolwise(sum))
June_2019_Production <- colSums(Filter(is.numeric, jun2019_production_1))
jun_monthly_2019_production <- as.data.frame(June_2019_Production)
jul2019_production_1 <- production_raw_data %>% filter(grepl("07.2019", tarih))
jul_daily_2019_production <- ddply(jul2019_production_1,"tarih",numcolwise(sum))
July_2019_Production <- colSums(Filter(is.numeric, jul2019_production_1))
jul_monthly_2019_production <- as.data.frame(July_2019_Production)
aug2019_production_1 <- production_raw_data %>% filter(grepl("08.2019", tarih))
aug_daily_2019_production <- ddply(aug2019_production_1,"tarih",numcolwise(sum))
August_2019_Production <- colSums(Filter(is.numeric, aug2019_production_1))
aug_monthly_2019_production <- as.data.frame(August_2019_Production)
sep2019_production_1 <- production_raw_data %>% filter(grepl("09.2019", tarih))
sep_daily_2019_production <- ddply(sep2019_production_1,"tarih",numcolwise(sum))
September_2019_Production <- colSums(Filter(is.numeric, sep2019_production_1))
sep_monthly_2019_production <- as.data.frame(September_2019_Production)
oct2019_production_1 <- production_raw_data %>% filter(grepl("10.2019", tarih))
oct_daily_2019_production <- ddply(oct2019_production_1,"tarih",numcolwise(sum))
October_2019_Production <- colSums(Filter(is.numeric, oct2019_production_1))
oct_monthly_2019_production <- as.data.frame(October_2019_Production)
Production_2019_Monthly <- cbind(jan_monthly_2019_production, feb_monthly_2019_production, mar_monthly_2019_production, apr_monthly_2019_production, may_monthly_2019_production, jun_monthly_2019_production, jul_monthly_2019_production, aug_monthly_2019_production, sep_monthly_2019_production, oct_monthly_2019_production)
Production_Monthly <- cbind(Production_2016_Monthly, Production_2017_Monthly, Production_2018_Monthly, Production_2019_Monthly)
Production_Monthly <- rownames_to_column(Production_Monthly, var = "Sources")
head(Production_Monthly, 5)## Sources January_2016_Production February_2016_Production
## 1 toplam_mwh 23155072 20639380
## 2 dogal_gaz 8093942 6851969
## 3 barajli 4249679 3283233
## 4 linyit 3401925 3112894
## 5 akarsu 1271297 1815672
## March_2016_Production April_2016_Production May_2016_Production
## 1 21718680 20962513 21523232
## 2 6278424 6733069 6033318
## 3 4260896 4288169 4077205
## 4 3066094 2359898 2775400
## 5 2480136 2680860 2578332
## June_2016_Production July_2016_Production August_2016_Production
## 1 22565419 23822010 25665544.7
## 2 7220660 7791337 9391728.4
## 3 4369125 4781437 4727174.2
## 4 3089750 2999672 3375908.5
## 5 1988281 1364404 864327.5
## September_2016_Production October_2016_Production
## 1 20796022.7 21544666.4
## 2 6736868.4 7652795.8
## 3 3345712.7 3143770.6
## 4 3075347.6 3396936.2
## 5 811553.3 842419.5
## November_2016_Production December_2016_Production
## 1 22337628.5 24534293.9
## 2 8409508.3 7144162.9
## 3 3020372.1 5184177.9
## 4 3597847.9 3616656.1
## 5 701170.6 814256.2
## January_2017_Production February_2017_Production March_2017_Production
## 1 24947364.9 22428514.8 23476834
## 2 8016219.3 8272837.9 8151647
## 3 4666700.5 3336895.0 3614625
## 4 3579836.5 3148954.5 3113307
## 5 951509.6 917218.3 2065849
## April_2017_Production May_2017_Production June_2017_Production
## 1 22049564 22891991 22292781
## 2 7308624 7455195 7823828
## 3 4234511 4254923 3168411
## 4 2896861 2725475 3262895
## 5 2895314 3113303 2097080
## July_2017_Production August_2017_Production September_2017_Production
## 1 27693424 27488692.2 23720681.4
## 2 11101426 10754008.7 9940236.4
## 3 3640201 4080740.9 3190453.6
## 4 3897502 3487902.6 3441187.1
## 5 1080191 733469.4 588883.7
## October_2017_Production November_2017_Production
## 1 23269572 23975113.4
## 2 10302066 11127388.2
## 3 1617151 1523110.1
## 4 3426994 3422955.9
## 5 732781 689576.6
## December_2017_Production January_2018_Production
## 1 25620726 25867412
## 2 8947506 8997292
## 3 3830230 3081555
## 4 3563265 3611223
## 5 1100089 1440039
## February_2018_Production March_2018_Production April_2018_Production
## 1 22780047 24123981 22724977
## 2 7602700 6400983 6449432
## 3 2116045 3477068 3881656
## 4 3511818 3781637 3629942
## 5 1467235 2773322 2444078
## May_2018_Production June_2018_Production July_2018_Production
## 1 22966980 22791619 28184206
## 2 5719984 6188257 10536514
## 3 3928364 3688506 4659082
## 4 3790457 3716589 3861405
## 5 2549600 1970266 1177233
## August_2018_Production September_2018_Production October_2018_Production
## 1 26572192 24153563.0 22631786.7
## 2 7741403 8731743.6 7972260.2
## 3 4608852 2941657.8 1719619.2
## 4 3805940 3602346.4 3964013.1
## 5 961037 716960.4 713009.1
## November_2018_Production December_2018_Production
## 1 23263984.7 25109988
## 2 6835145.3 6909485
## 3 2793663.8 3989790
## 4 3753052.6 3792709
## 5 784830.1 1833282
## January_2019_Production February_2019_Production March_2019_Production
## 1 25316677 22586095 23742549
## 2 5402002 3564382 4255045
## 3 5457724 5032233 5067975
## 4 3561409 3403311 3752697
## 5 2186762 2133259 2635838
## April_2019_Production May_2019_Production June_2019_Production
## 1 22560958 23533417 22982158
## 2 3118247 2929235 2983887
## 3 6472982 7638109 7184776
## 4 3488619 3596315 3562551
## 5 3496262 3950646 2416971
## July_2019_Production August_2019_Production September_2019_Production
## 1 27345793 26371678 24070913.5
## 2 6004182 5251413 4964063.0
## 3 6122945 5816563 4744346.5
## 4 4472509 4310660 3995839.9
## 5 1506393 1131778 937791.3
## October_2019_Production
## 1 22830884.2
## 2 4786532.3
## 3 4300453.0
## 4 4174461.7
## 5 807494.3Creating Monthly Sums for Consumption
jan2016_consumption_1 <- consumption_raw_data %>% filter(grepl("01.2016", tarih))
jan_daily_2016_consumption <- ddply(jan2016_consumption_1,"tarih",numcolwise(sum))
January_2016_Consumption <- colSums(Filter(is.numeric, jan2016_consumption_1))
jan_monthly_2016_consumption <- as.data.frame(January_2016_Consumption)
feb2016_consumption_1 <- consumption_raw_data %>% filter(grepl("02.2016", tarih))
feb_daily_2016_consumption <- ddply(feb2016_consumption_1,"tarih",numcolwise(sum))
February_2016_Consumption <- colSums(Filter(is.numeric, feb2016_consumption_1))
feb_monthly_2016_consumption <- as.data.frame(February_2016_Consumption)
mar2016_consumption_1 <- consumption_raw_data %>% filter(grepl("03.2016", tarih))
mar_daily_2016_consumption <- ddply(jan2016_consumption_1,"tarih",numcolwise(sum))
March_2016_Consumption <- colSums(Filter(is.numeric, mar2016_consumption_1))
mar_monthly_2016_consumption <- as.data.frame(March_2016_Consumption)
apr2016_consumption_1 <- consumption_raw_data %>% filter(grepl("04.2016", tarih))
apr_daily_2016_consumption <- ddply(apr2016_consumption_1,"tarih",numcolwise(sum))
April_2016_Consumption <- colSums(Filter(is.numeric, apr2016_consumption_1))
apr_monthly_2016_consumption <- as.data.frame(April_2016_Consumption)
may2016_consumption_1 <- consumption_raw_data %>% filter(grepl("05.2016", tarih))
may_daily_2016_consumption <- ddply(may2016_consumption_1,"tarih",numcolwise(sum))
May_2016_Consumption <- colSums(Filter(is.numeric, may2016_consumption_1))
may_monthly_2016_consumption <- as.data.frame(May_2016_Consumption)
jun2016_consumption_1 <- consumption_raw_data %>% filter(grepl("06.2016", tarih))
jun_daily_2016_consumption <- ddply(jun2016_consumption_1,"tarih",numcolwise(sum))
June_2016_Consumption <- colSums(Filter(is.numeric, jun2016_consumption_1))
jun_monthly_2016_consumption <- as.data.frame(June_2016_Consumption)
jul2016_consumption_1 <- consumption_raw_data %>% filter(grepl("07.2016", tarih))
jul_daily_2016_consumption <- ddply(jul2016_consumption_1,"tarih",numcolwise(sum))
July_2016_Consumption <- colSums(Filter(is.numeric, jul2016_consumption_1))
jul_monthly_2016_consumption <- as.data.frame(July_2016_Consumption)
aug2016_consumption_1 <- consumption_raw_data %>% filter(grepl("08.2016", tarih))
aug_daily_2016_consumption <- ddply(aug2016_consumption_1,"tarih",numcolwise(sum))
August_2016_Consumption <- colSums(Filter(is.numeric, aug2016_consumption_1))
aug_monthly_2016_consumption <- as.data.frame(August_2016_Consumption)
sep2016_consumption_1 <- consumption_raw_data %>% filter(grepl("09.2016", tarih))
sep_daily_2016_consumption <- ddply(sep2016_consumption_1,"tarih",numcolwise(sum))
September_2016_Consumption <- colSums(Filter(is.numeric, sep2016_consumption_1))
sep_monthly_2016_consumption <- as.data.frame(September_2016_Consumption)
oct2016_consumption_1 <- consumption_raw_data %>% filter(grepl("10.2016", tarih))
oct_daily_2016_consumption <- ddply(oct2016_consumption_1,"tarih",numcolwise(sum))
October_2016_Consumption <- colSums(Filter(is.numeric, oct2016_consumption_1))
oct_monthly_2016_consumption <- as.data.frame(October_2016_Consumption)
nov2016_consumption_1 <- consumption_raw_data %>% filter(grepl("11.2016", tarih))
nov_daily_2016_consumption <- ddply(nov2016_consumption_1,"tarih",numcolwise(sum))
November_2016_Consumption <- colSums(Filter(is.numeric, nov2016_consumption_1))
nov_monthly_2016_consumption <- as.data.frame(November_2016_Consumption)
dec2016_consumption_1 <- consumption_raw_data %>% filter(grepl("12.2016", tarih))
dec_daily_2016_consumption <- ddply(dec2016_consumption_1,"tarih",numcolwise(sum))
December_2016_Consumption <- colSums(Filter(is.numeric, dec2016_consumption_1))
dec_monthly_2016_consumption <- as.data.frame(December_2016_Consumption)
Consumption_2016_Monthly <- cbind(jan_monthly_2016_consumption, feb_monthly_2016_consumption, mar_monthly_2016_consumption, apr_monthly_2016_consumption, may_monthly_2016_consumption, jun_monthly_2016_consumption, jul_monthly_2016_consumption, aug_monthly_2016_consumption, sep_monthly_2016_consumption, oct_monthly_2016_consumption, nov_monthly_2016_consumption, dec_monthly_2016_consumption)
jan2017_consumption_1 <- consumption_raw_data %>% filter(grepl("01.2017", tarih))
jan_daily_2017_consumption <- ddply(jan2017_consumption_1,"tarih",numcolwise(sum))
January_2017_Consumption <- colSums(Filter(is.numeric, jan2017_consumption_1))
jan_monthly_2017_consumption <- as.data.frame(January_2017_Consumption)
feb2017_consumption_1 <- consumption_raw_data %>% filter(grepl("02.2017", tarih))
feb_daily_2017_consumption <- ddply(feb2017_consumption_1,"tarih",numcolwise(sum))
February_2017_Consumption <- colSums(Filter(is.numeric, feb2017_consumption_1))
feb_monthly_2017_consumption <- as.data.frame(February_2017_Consumption)
mar2017_consumption_1 <- consumption_raw_data %>% filter(grepl("03.2017", tarih))
mar_daily_2017_consumption <- ddply(jan2017_consumption_1,"tarih",numcolwise(sum))
March_2017_Consumption <- colSums(Filter(is.numeric, mar2017_consumption_1))
mar_monthly_2017_consumption <- as.data.frame(March_2017_Consumption)
apr2017_consumption_1 <- consumption_raw_data %>% filter(grepl("04.2017", tarih))
apr_daily_2017_consumption <- ddply(apr2017_consumption_1,"tarih",numcolwise(sum))
April_2017_Consumption <- colSums(Filter(is.numeric, apr2017_consumption_1))
apr_monthly_2017_consumption <- as.data.frame(April_2017_Consumption)
may2017_consumption_1 <- consumption_raw_data %>% filter(grepl("05.2017", tarih))
may_daily_2017_consumption <- ddply(may2017_consumption_1,"tarih",numcolwise(sum))
May_2017_Consumption <- colSums(Filter(is.numeric, may2017_consumption_1))
may_monthly_2017_consumption <- as.data.frame(May_2017_Consumption)
jun2017_consumption_1 <- consumption_raw_data %>% filter(grepl("06.2017", tarih))
jun_daily_2017_consumption <- ddply(jun2017_consumption_1,"tarih",numcolwise(sum))
June_2017_Consumption <- colSums(Filter(is.numeric, jun2017_consumption_1))
jun_monthly_2017_consumption <- as.data.frame(June_2017_Consumption)
jul2017_consumption_1 <- consumption_raw_data %>% filter(grepl("07.2017", tarih))
jul_daily_2017_consumption <- ddply(jul2017_consumption_1,"tarih",numcolwise(sum))
July_2017_Consumption <- colSums(Filter(is.numeric, jul2017_consumption_1))
jul_monthly_2017_consumption <- as.data.frame(July_2017_Consumption)
aug2017_consumption_1 <- consumption_raw_data %>% filter(grepl("08.2017", tarih))
aug_daily_2017_consumption <- ddply(aug2017_consumption_1,"tarih",numcolwise(sum))
August_2017_Consumption <- colSums(Filter(is.numeric, aug2017_consumption_1))
aug_monthly_2017_consumption <- as.data.frame(August_2017_Consumption)
sep2017_consumption_1 <- consumption_raw_data %>% filter(grepl("09.2017", tarih))
sep_daily_2017_consumption <- ddply(sep2017_consumption_1,"tarih",numcolwise(sum))
September_2017_Consumption <- colSums(Filter(is.numeric, sep2017_consumption_1))
sep_monthly_2017_consumption <- as.data.frame(September_2017_Consumption)
oct2017_consumption_1 <- consumption_raw_data %>% filter(grepl("10.2017", tarih))
oct_daily_2017_consumption <- ddply(oct2017_consumption_1,"tarih",numcolwise(sum))
October_2017_Consumption <- colSums(Filter(is.numeric, oct2017_consumption_1))
oct_monthly_2017_consumption <- as.data.frame(October_2017_Consumption)
nov2017_consumption_1 <- consumption_raw_data %>% filter(grepl("11.2017", tarih))
nov_daily_2017_consumption <- ddply(nov2017_consumption_1,"tarih",numcolwise(sum))
November_2017_Consumption <- colSums(Filter(is.numeric, nov2017_consumption_1))
nov_monthly_2017_consumption <- as.data.frame(November_2017_Consumption)
dec2017_consumption_1 <- consumption_raw_data %>% filter(grepl("12.2017", tarih))
dec_daily_2017_consumption <- ddply(dec2017_consumption_1,"tarih",numcolwise(sum))
December_2017_Consumption <- colSums(Filter(is.numeric, dec2017_consumption_1))
dec_monthly_2017_consumption <- as.data.frame(December_2017_Consumption)
Consumption_2017_Monthly <- cbind(jan_monthly_2017_consumption, feb_monthly_2017_consumption, mar_monthly_2017_consumption, apr_monthly_2017_consumption, may_monthly_2017_consumption, jun_monthly_2017_consumption, jul_monthly_2017_consumption, aug_monthly_2017_consumption, sep_monthly_2017_consumption, oct_monthly_2017_consumption, nov_monthly_2017_consumption, dec_monthly_2017_consumption)
jan2018_consumption_1 <- consumption_raw_data %>% filter(grepl("01.2018", tarih))
jan_daily_2018_consumption <- ddply(jan2018_consumption_1,"tarih",numcolwise(sum))
January_2018_Consumption <- colSums(Filter(is.numeric, jan2018_consumption_1))
jan_monthly_2018_consumption <- as.data.frame(January_2018_Consumption)
feb2018_consumption_1 <- consumption_raw_data %>% filter(grepl("02.2018", tarih))
feb_daily_2018_consumption <- ddply(feb2018_consumption_1,"tarih",numcolwise(sum))
February_2018_Consumption <- colSums(Filter(is.numeric, feb2018_consumption_1))
feb_monthly_2018_consumption <- as.data.frame(February_2018_Consumption)
mar2018_consumption_1 <- consumption_raw_data %>% filter(grepl("03.2018", tarih))
mar_daily_2018_consumption <- ddply(jan2018_consumption_1,"tarih",numcolwise(sum))
March_2018_Consumption <- colSums(Filter(is.numeric, mar2018_consumption_1))
mar_monthly_2018_consumption <- as.data.frame(March_2018_Consumption)
apr2018_consumption_1 <- consumption_raw_data %>% filter(grepl("04.2018", tarih))
apr_daily_2018_consumption <- ddply(apr2018_consumption_1,"tarih",numcolwise(sum))
April_2018_Consumption <- colSums(Filter(is.numeric, apr2018_consumption_1))
apr_monthly_2018_consumption <- as.data.frame(April_2018_Consumption)
may2018_consumption_1 <- consumption_raw_data %>% filter(grepl("05.2018", tarih))
may_daily_2018_consumption <- ddply(may2018_consumption_1,"tarih",numcolwise(sum))
May_2018_Consumption <- colSums(Filter(is.numeric, may2018_consumption_1))
may_monthly_2018_consumption <- as.data.frame(May_2018_Consumption)
jun2018_consumption_1 <- consumption_raw_data %>% filter(grepl("06.2018", tarih))
jun_daily_2018_consumption <- ddply(jun2018_consumption_1,"tarih",numcolwise(sum))
June_2018_Consumption <- colSums(Filter(is.numeric, jun2018_consumption_1))
jun_monthly_2018_consumption <- as.data.frame(June_2018_Consumption)
jul2018_consumption_1 <- consumption_raw_data %>% filter(grepl("07.2018", tarih))
jul_daily_2018_consumption <- ddply(jul2018_consumption_1,"tarih",numcolwise(sum))
July_2018_Consumption <- colSums(Filter(is.numeric, jul2018_consumption_1))
jul_monthly_2018_consumption <- as.data.frame(July_2018_Consumption)
aug2018_consumption_1 <- consumption_raw_data %>% filter(grepl("08.2018", tarih))
aug_daily_2018_consumption <- ddply(aug2018_consumption_1,"tarih",numcolwise(sum))
August_2018_Consumption <- colSums(Filter(is.numeric, aug2018_consumption_1))
aug_monthly_2018_consumption <- as.data.frame(August_2018_Consumption)
sep2018_consumption_1 <- consumption_raw_data %>% filter(grepl("09.2018", tarih))
sep_daily_2018_consumption <- ddply(sep2018_consumption_1,"tarih",numcolwise(sum))
September_2018_Consumption <- colSums(Filter(is.numeric, sep2018_consumption_1))
sep_monthly_2018_consumption <- as.data.frame(September_2018_Consumption)
oct2018_consumption_1 <- consumption_raw_data %>% filter(grepl("10.2018", tarih))
oct_daily_2018_consumption <- ddply(oct2018_consumption_1,"tarih",numcolwise(sum))
October_2018_Consumption <- colSums(Filter(is.numeric, oct2018_consumption_1))
oct_monthly_2018_consumption <- as.data.frame(October_2018_Consumption)
nov2018_consumption_1 <- consumption_raw_data %>% filter(grepl("11.2018", tarih))
nov_daily_2018_consumption <- ddply(nov2018_consumption_1,"tarih",numcolwise(sum))
November_2018_Consumption <- colSums(Filter(is.numeric, nov2018_consumption_1))
nov_monthly_2018_consumption <- as.data.frame(November_2018_Consumption)
dec2018_consumption_1 <- consumption_raw_data %>% filter(grepl("12.2018", tarih))
dec_daily_2018_consumption <- ddply(dec2018_consumption_1,"tarih",numcolwise(sum))
December_2018_Consumption <- colSums(Filter(is.numeric, dec2018_consumption_1))
dec_monthly_2018_consumption <- as.data.frame(December_2018_Consumption)
Consumption_2018_Monthly <- cbind(jan_monthly_2018_consumption, feb_monthly_2018_consumption, mar_monthly_2018_consumption, apr_monthly_2018_consumption, may_monthly_2018_consumption, jun_monthly_2018_consumption, jul_monthly_2018_consumption, aug_monthly_2018_consumption, sep_monthly_2018_consumption, oct_monthly_2018_consumption, nov_monthly_2018_consumption, dec_monthly_2018_consumption)
jan2019_consumption_1 <- consumption_raw_data %>% filter(grepl("01.2019", tarih))
jan_daily_2019_consumption <- ddply(jan2019_consumption_1,"tarih",numcolwise(sum))
January_2019_Consumption <- colSums(Filter(is.numeric, jan2019_consumption_1))
jan_monthly_2019_consumption <- as.data.frame(January_2019_Consumption)
feb2019_consumption_1 <- consumption_raw_data %>% filter(grepl("02.2019", tarih))
feb_daily_2019_consumption <- ddply(feb2019_consumption_1,"tarih",numcolwise(sum))
February_2019_Consumption <- colSums(Filter(is.numeric, feb2019_consumption_1))
feb_monthly_2019_consumption <- as.data.frame(February_2019_Consumption)
mar2019_consumption_1 <- consumption_raw_data %>% filter(grepl("03.2019", tarih))
mar_daily_2019_consumption <- ddply(jan2019_consumption_1,"tarih",numcolwise(sum))
March_2019_Consumption <- colSums(Filter(is.numeric, mar2019_consumption_1))
mar_monthly_2019_consumption <- as.data.frame(March_2019_Consumption)
apr2019_consumption_1 <- consumption_raw_data %>% filter(grepl("04.2019", tarih))
apr_daily_2019_consumption <- ddply(apr2019_consumption_1,"tarih",numcolwise(sum))
April_2019_Consumption <- colSums(Filter(is.numeric, apr2019_consumption_1))
apr_monthly_2019_consumption <- as.data.frame(April_2019_Consumption)
may2019_consumption_1 <- consumption_raw_data %>% filter(grepl("05.2019", tarih))
may_daily_2019_consumption <- ddply(may2019_consumption_1,"tarih",numcolwise(sum))
May_2019_Consumption <- colSums(Filter(is.numeric, may2019_consumption_1))
may_monthly_2019_consumption <- as.data.frame(May_2019_Consumption)
jun2019_consumption_1 <- consumption_raw_data %>% filter(grepl("06.2019", tarih))
jun_daily_2019_consumption <- ddply(jun2019_consumption_1,"tarih",numcolwise(sum))
June_2019_Consumption <- colSums(Filter(is.numeric, jun2019_consumption_1))
jun_monthly_2019_consumption <- as.data.frame(June_2019_Consumption)
jul2019_consumption_1 <- consumption_raw_data %>% filter(grepl("07.2019", tarih))
jul_daily_2019_consumption <- ddply(jul2019_consumption_1,"tarih",numcolwise(sum))
July_2019_Consumption <- colSums(Filter(is.numeric, jul2019_consumption_1))
jul_monthly_2019_consumption <- as.data.frame(July_2019_Consumption)
aug2019_consumption_1 <- consumption_raw_data %>% filter(grepl("08.2019", tarih))
aug_daily_2019_consumption <- ddply(aug2019_consumption_1,"tarih",numcolwise(sum))
August_2019_Consumption <- colSums(Filter(is.numeric, aug2019_consumption_1))
aug_monthly_2019_consumption <- as.data.frame(August_2019_Consumption)
sep2019_consumption_1 <- consumption_raw_data %>% filter(grepl("09.2019", tarih))
sep_daily_2019_consumption <- ddply(sep2019_consumption_1,"tarih",numcolwise(sum))
September_2019_Consumption <- colSums(Filter(is.numeric, sep2019_consumption_1))
sep_monthly_2019_consumption <- as.data.frame(September_2019_Consumption)
oct2019_consumption_1 <- consumption_raw_data %>% filter(grepl("10.2019", tarih))
oct_daily_2019_consumption <- ddply(oct2019_consumption_1,"tarih",numcolwise(sum))
October_2019_Consumption <- colSums(Filter(is.numeric, oct2019_consumption_1))
oct_monthly_2019_consumption <- as.data.frame(October_2019_Consumption)
Consumption_2019_Monthly <- cbind(jan_monthly_2019_consumption, feb_monthly_2019_consumption, mar_monthly_2019_consumption, apr_monthly_2019_consumption, may_monthly_2019_consumption, jun_monthly_2019_consumption, jul_monthly_2019_consumption, aug_monthly_2019_consumption, sep_monthly_2019_consumption, oct_monthly_2019_consumption)
Consumption_Monthly <- cbind(Consumption_2016_Monthly, Consumption_2017_Monthly, Consumption_2018_Monthly, Consumption_2019_Monthly)
Consumption_Monthly <- rownames_to_column(Consumption_Monthly, var = "Sources")
head(Consumption_Monthly, 10)## Sources January_2016_Consumption February_2016_Consumption
## 1 tuketim_miktari_mwh 23704522 21153165
## March_2016_Consumption April_2016_Consumption May_2016_Consumption
## 1 21535377 21301824 21900302
## June_2016_Consumption July_2016_Consumption August_2016_Consumption
## 1 23051249 24369823 26268794
## September_2016_Consumption October_2016_Consumption
## 1 21233349 21850083
## November_2016_Consumption December_2016_Consumption
## 1 22683005 25161281
## January_2017_Consumption February_2017_Consumption
## 1 25102545 22451554
## March_2017_Consumption April_2017_Consumption May_2017_Consumption
## 1 23586499 21953360 22853967
## June_2017_Consumption July_2017_Consumption August_2017_Consumption
## 1 22303718 27776318 27522377
## September_2017_Consumption October_2017_Consumption
## 1 23807834 23161713
## November_2017_Consumption December_2017_Consumption
## 1 23860824 25594468
## January_2018_Consumption February_2018_Consumption
## 1 25929477 22844496
## March_2018_Consumption April_2018_Consumption May_2018_Consumption
## 1 24145065 22785833 23195356
## June_2018_Consumption July_2018_Consumption August_2018_Consumption
## 1 23005314 28265839 26637203
## September_2018_Consumption October_2018_Consumption
## 1 24212352 22667044
## November_2018_Consumption December_2018_Consumption
## 1 23335700 25147675
## January_2019_Consumption February_2019_Consumption
## 1 25368790 22630356
## March_2019_Consumption April_2019_Consumption May_2019_Consumption
## 1 23793919 22611101 23586529
## June_2019_Consumption July_2019_Consumption August_2019_Consumption
## 1 23034664 27380774 26421503
## September_2019_Consumption October_2019_Consumption
## 1 24125766 22890236colnames(Planning_Monthly) <- c('Sources', 'January_16', 'February_16', 'March_16', 'April_16', 'May_16', 'June_16', 'July_16', 'August_16', 'September_16', 'October_16', 'November_16', 'December_16', 'January_17', 'February_17', 'March_17', 'April_17', 'May_17', 'June_17', 'July_17', 'August_17', 'September_17', 'October_17', 'November_17', 'December_17', 'January_18', 'February_18', 'March_18', 'April_18', 'May_18', 'June_18', 'July_18', 'August_18', 'September_18', 'October_18', 'November_18', 'December_18', 'January_19', 'February_19', 'March_19', 'April_19', 'May_19', 'June_19', 'July_19', 'August_19', 'September_19', 'October_19')
colnames(Production_Monthly) <- c('Sources', 'January_16', 'February_16', 'March_16', 'April_16', 'May_16', 'June_16', 'July_16', 'August_16', 'September_16', 'October_16', 'November_16', 'December_16', 'January_17', 'February_17', 'March_17', 'April_17', 'May_17', 'June_17', 'July_17', 'August_17', 'September_17', 'October_17', 'November_17', 'December_17', 'January_18', 'February_18', 'March_18', 'April_18', 'May_18', 'June_18', 'July_18', 'August_18', 'September_18', 'October_18', 'November_18', 'December_18', 'January_19', 'February_19', 'March_19', 'April_19', 'May_19', 'June_19', 'July_19', 'August_19', 'September_19', 'October_19')
colnames(Consumption_Monthly) <- c('Sources', 'January_16', 'February_16', 'March_16', 'April_16', 'May_16', 'June_16', 'July_16', 'August_16', 'September_16', 'October_16', 'November_16', 'December_16', 'January_17', 'February_17', 'March_17', 'April_17', 'May_17', 'June_17', 'July_17', 'August_17', 'September_17', 'October_17', 'November_17', 'December_17', 'January_18', 'February_18', 'March_18', 'April_18', 'May_18', 'June_18', 'July_18', 'August_18', 'September_18', 'October_18', 'November_18', 'December_18', 'January_19', 'February_19', 'March_19', 'April_19', 'May_19', 'June_19', 'July_19', 'August_19', 'September_19', 'October_19')4 Year Production Analysis: January 2016 - October 2019
- The percentage of production of energy sources was calculated.
production_yuzde <- round((colSums(Filter(is.numeric, production_raw_data))/sum(production_raw_data$toplam_mwh))*100, digits=2)
production_yuzde2 <- as.data.frame(production_yuzde)
production_yuzde2 <- rownames_to_column(production_yuzde2, var = "kaynaklar")
production_yuzde2 <- production_yuzde2 %>% slice(2:max(nrow(.))) %>% filter(kaynaklar !='toplam_mwh')
tail(production_yuzde2, 7)## kaynaklar production_yuzde
## 9 jeotermal 2.11
## 10 asfaltit_komur 0.87
## 11 tas_komur 1.00
## 12 biyokutle 0.76
## 13 nafta 0.00
## 14 lng 0.00
## 15 uluslararasi -0.15- Distribution of the percentage of production of energy sources is shown on the pie chart.
# Create Data
x <- c(330882954.42, 188612868.09, 160974973.37, 75212059.65, 208964232.22, 71168183.03, 233696.49, 5509051.71, 23037800.33, 9467418.70, 10905822.78, 8257603.48, 0.00, 49534.99)
labels <- c("dogal_gaz", "barajli", "linyit", "akarsu", "ithal_komur", "ruzgar", "gunes", "fuel_oil", "jeotermal", "asfaltit_komur","tas_komur", "biyokutle", "nafta", "lng" )
pct <- round((x/1091631578.02)*100, digits=2)
lbls <- paste(labels, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie(x,labels = lbls, col=rainbow(length(lbls)),main="Ratio of Production Resources", cex=0.4)
4 Year Consumption Analysis: January 2016 - October 2019
Hourly consumption data was previously edited on a monthly basis, now we will first change the column headings to follow the consumption from the first month to the last month and then visualize.
colnames(Consumption_Monthly) <- c('Sources', '2016_01', '2016_02', '2016_03', '2016_04', '2016_05', '2016_06', '2016_07', '2016_08', '2016_09', '2016_10', '2016_11', '2016_12', '2017_01', '2017_02', '2017_03', '2017_04', '2017_05', '2017_06', '2017_07', '2017_08', '2017_09', '2017_10', '2017_11', '2017_12', '2018_01', '2018_02', '2018_03', '2018_04', '2018_05', '2018_06', '2018_07', '2018_08', '2018_09', '2018_10', '2018_11', '2018_12', '2019_01', '2019_02', '2019_03', '2019_04', '2019_05', '2019_06', '2019_07', '2019_08', '2019_09','2019_10')
cons_plot<- gather(Consumption_Monthly, key = "months", value = "tuketim_miktari_mwh")
cons_plot<- slice(cons_plot,2:47)
cons_plot## months tuketim_miktari_mwh
## 1 2016_01 23704521.96
## 2 2016_02 21153164.88
## 3 2016_03 21535376.75
## 4 2016_04 21301823.68
## 5 2016_05 21900302.38
## 6 2016_06 23051248.67
## 7 2016_07 24369823.16
## 8 2016_08 26268794.29
## 9 2016_09 21233349.41
## 10 2016_10 21850083.46
## 11 2016_11 22683004.9
## 12 2016_12 25161281.04
## 13 2017_01 25102545.03
## 14 2017_02 22451554.33
## 15 2017_03 23586498.72
## 16 2017_04 21953359.53
## 17 2017_05 22853967.13
## 18 2017_06 22303718.02
## 19 2017_07 27776317.83
## 20 2017_08 27522377.03
## 21 2017_09 23807834.45
## 22 2017_10 23161713.2
## 23 2017_11 23860824.05
## 24 2017_12 25594467.72
## 25 2018_01 25929476.87
## 26 2018_02 22844495.53
## 27 2018_03 24145064.93
## 28 2018_04 22785832.63
## 29 2018_05 23195355.53
## 30 2018_06 23005314.26
## 31 2018_07 28265838.77
## 32 2018_08 26637202.76
## 33 2018_09 24212352.27
## 34 2018_10 22667043.67
## 35 2018_11 23335700.06
## 36 2018_12 25147674.77
## 37 2019_01 25368789.54
## 38 2019_02 22630356.02
## 39 2019_03 23793919.1
## 40 2019_04 22611100.88
## 41 2019_05 23586528.82
## 42 2019_06 23034663.55
## 43 2019_07 27380774.5
## 44 2019_08 26421503.29
## 45 2019_09 24125765.67
## 46 2019_10 22890236.13ggplot(cons_plot, aes(x=cons_plot$months, y=cons_plot$tuketim_miktari_mwh)) +
geom_bar(stat = "identity", width=0.2)
As you can see from the plot, there is a seasonal effect on consumption: In the summer, consumption increases every year!
We now reinstate column headings for further analysis.
colnames(Consumption_Monthly) <- c('Sources','January_16', 'February_16', 'March_16', 'April_16', 'May_16', 'June_16', 'July_16', 'August_16', 'September_16', 'October_16', 'November_16', 'December_16',
'January_17', 'February_17', 'March_17', 'April_17', 'May_17', 'June_17', 'July_17', 'August_17', 'September_17', 'October_17', 'November_17', 'December_17',
'January_18', 'February_18', 'March_18', 'April_18', 'May_18', 'June_18', 'July_18', 'August_18', 'September_18', 'October_18', 'November_18', 'December_18',
'January_19', 'February_19', 'March_19', 'April_19', 'May_19', 'June_19', 'July_19', 'August_19', 'September_19', 'October_19')Comparison of Planning and Actual Production
When the data obtained since 2016 are analyzed, it is seen that the actual production is above the planned production in the whole process.
While there are significant differences between the two values in the first 4 months of 2016, it is observed that these differences have decreased in the continuation of the process.
Comparison of Total Planned Production (MWh) and Total Actual Production (MWh)
Plan_MT <- subset(Planning_Monthly, Sources=="toplam_mwh")
Plan_MT <- as.data.frame(transpose(Plan_MT))
Prod_MT <- subset(Production_Monthly, Sources=="toplam_mwh")
Prod_MT <- as.data.frame(transpose(Prod_MT))
CompPlanProd <- cbind(Plan_MT, Prod_MT)
colnames(CompPlanProd) <- c('Planned_Production', 'Actual_Production')
CompPlanProd <- CompPlanProd[-1,]
CompPlanProd$Dates <- c('16_01', '16_02', '16_03', '16_04', '16_05', '16_06', '16_07', '16_08', '16_09', '16_10', '16_11', '16_12', '17_01', '17_02', '17_03', '17_04', '17_05', '17_06', '17_07', '17_08', '17_09', '17_10', '17_11', '17_12', '18_01', '18_02', '18_03', '18_04', '18_05', '18_06', '18_07', '18_08', '18_09', '18_10', '18_11', '18_12', '19_01', '19_02', '19_03', '19_04', '19_05', '19_06', '19_07', '19_08', '19_09', '19_10')
CompPlanProd$Planned_Production <-as.numeric(CompPlanProd$Planned_Production)
CompPlanProd$Actual_Production <-as.numeric(CompPlanProd$Actual_Production)
head(CompPlanProd, 5)## Planned_Production Actual_Production Dates
## 2 18385705 23155072 16_01
## 3 16370338 20639380 16_02
## 4 18588231 21718680 16_03
## 5 18356083 20962513 16_04
## 6 19877153 21523232 16_05library(ggplot2)
library(reshape2)##
## Attaching package: 'reshape2'## The following objects are masked from 'package:data.table':
##
## dcast, melt## The following object is masked from 'package:tidyr':
##
## smithsG_CompPlanProd <- melt(CompPlanProd, id.vars="Dates")
ggplot(G_CompPlanProd, aes(value/1000000, Dates, col=variable)) +
geom_point() +
stat_smooth() +
geom_line( color="grey") +
labs(title="Comparison of Planned and Actual Productions",
subtitle="Production in MWh (and x1M)",
x="Production (MWh) (x1M)",
y="Dates",
col="Planned vs Actual") ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
CompPlanProd_2<-data.frame(CompPlanProd)
CompPlanProd_2$Difference <- (CompPlanProd_2$Actual_Production - CompPlanProd_2$Planned_Production)/1000000
CompPlanProdDiff <- CompPlanProd_2 %>% select(Dates, Difference)
CompPlanProdDiff <- CompPlanProdDiff %>%
mutate(mycolor = ifelse(CompPlanProdDiff$Difference>0, "type1", "type2"))
ggplot(CompPlanProdDiff, aes(x=CompPlanProdDiff$Dates, y=CompPlanProdDiff$Difference)) +
geom_segment( aes(x=CompPlanProdDiff$Dates, xend=CompPlanProdDiff$Dates, y=0, yend=CompPlanProdDiff$Difference, color=mycolor), size=1.3, alpha=0.9) +
theme_light() +
theme(
legend.position = "none",
panel.border = element_blank(),
) +
xlab("Dates") +
ylab("Actual Production - Planned Production (MWh) (x1M)")
Comparison of Planned Production (MWh) and Actual Production (MWh) for Sources
Production_Monthly_Trp <- as.data.frame(transpose(Production_Monthly))
colnames(Production_Monthly_Trp) <- c('toplam_mwh', 'dogal_gaz', 'barajli', 'linyit', 'akarsu', 'ithal_komur', 'ruzgar', 'gunes', 'fuel_oil', 'jeotermal', 'asfaltit_komur', 'tas_komur', 'biyokutle', 'nafta', 'lng', 'uluslararasi')
Production_Monthly_Trp[] <- lapply(Production_Monthly_Trp, function(x) as.numeric(as.character(x)))
Production_Monthly_Trp$Dates <- c('Sources', '16_01', '16_02', '16_03', '16_04', '16_05', '16_06', '16_07', '16_08', '16_09', '16_10', '16_11', '16_12', '17_01', '17_02', '17_03', '17_04', '17_05', '17_06', '17_07', '17_08', '17_09', '17_10', '17_11', '17_12', '18_01', '18_02', '18_03', '18_04', '18_05', '18_06', '18_07', '18_08', '18_09', '18_10', '18_11', '18_12', '19_01', '19_02', '19_03', '19_04', '19_05', '19_06', '19_07', '19_08', '19_09', '19_10')
Production_Monthly_Trp <- Production_Monthly_Trp[-1,]
Production_Monthly_Trp <- Production_Monthly_Trp[,-1]
head(Production_Monthly_Trp,5)## dogal_gaz barajli linyit akarsu ithal_komur ruzgar gunes fuel_oil
## 2 8093942 4249679 3401925 1271297 3728569 1256556.7 0 153842.2
## 3 6851969 3283233 3112894 1815672 3441797 1136751.9 0 131466.9
## 4 6278424 4260896 3066094 2480136 3252120 1248335.7 0 155338.7
## 5 6733069 4288169 2359898 2680860 2999844 816274.5 0 159601.7
## 6 6033318 4077205 2775400 2578332 4035879 923452.2 0 156074.2
## jeotermal asfaltit_komur tas_komur biyokutle nafta lng uluslararasi
## 2 351444.9 271455.7 249304.5 120657.8 0 6398.32 0
## 3 327393.8 223096.4 191865.0 117012.6 0 6227.85 0
## 4 357303.4 253712.2 232404.0 127375.7 0 6539.90 0
## 5 332710.6 226864.7 236477.0 122834.8 0 5909.81 0
## 6 347485.5 252689.2 211254.5 125607.5 0 6533.56 0
## Dates
## 2 16_01
## 3 16_02
## 4 16_03
## 5 16_04
## 6 16_05Planning_Monthly_Trp <- as.data.frame(transpose(Planning_Monthly))
colnames(Planning_Monthly_Trp) <- c('toplam_mwh', 'dogal_gaz', 'ruzgar', 'linyit', 'tas_komur', 'ithal_komur', 'fuel_oil', 'jeotermal', 'barajli', 'nafta', 'biyokutle', 'akarsu', 'diger')
Planning_Monthly_Trp[] <- lapply(Planning_Monthly_Trp, function(x) as.numeric(as.character(x)))
Planning_Monthly_Trp$Dates <- c('Sources', '16_01', '16_02', '16_03', '16_04', '16_05', '16_06', '16_07', '16_08', '16_09', '16_10', '16_11', '16_12', '17_01', '17_02', '17_03', '17_04', '17_05', '17_06', '17_07', '17_08', '17_09', '17_10', '17_11', '17_12', '18_01', '18_02', '18_03', '18_04', '18_05', '18_06', '18_07', '18_08', '18_09', '18_10', '18_11', '18_12', '19_01', '19_02', '19_03', '19_04', '19_05', '19_06', '19_07', '19_08', '19_09', '19_10')
Planning_Monthly_Trp <- Planning_Monthly_Trp[-1,]
Planning_Monthly_Trp <- Planning_Monthly_Trp[,-1]
head(Planning_Monthly_Trp,5)## dogal_gaz ruzgar linyit tas_komur ithal_komur fuel_oil jeotermal
## 2 6956584 97156.52 2785761 553672 3326572 415328.6 45185.60
## 3 5751947 125460.91 2562705 530251 3121218 522803.0 23687.55
## 4 5427182 618063.48 2419857 630380 2930062 266379.0 195261.82
## 5 6086039 487208.62 1794658 554156 2599052 92350.7 194474.33
## 6 5080387 705477.80 2189356 575697 3544970 90112.0 288480.61
## barajli nafta biyokutle akarsu diger Dates
## 2 3770299 3456.0 267.00 189367.4 242055.7 16_01
## 3 3146464 3124.3 1811.90 354320.6 226544.1 16_02
## 4 4574145 3393.7 31748.90 1251710.1 240048.0 16_03
## 5 4780549 3200.2 38345.52 1515080.7 210968.4 16_04
## 6 4940352 3263.0 56677.03 2168194.0 234186.8 16_05PlanProdSources <- as.data.frame(c(1:46))
PlanProdSources$Dates <- Planning_Monthly_Trp$Dates
PlanProdSources$Barajli <- (Production_Monthly_Trp$barajli - Planning_Monthly_Trp$barajli)
PlanProdSources$Linyit <- (Production_Monthly_Trp$linyit - Planning_Monthly_Trp$linyit)
PlanProdSources$Akarsu <- (Production_Monthly_Trp$akarsu - Planning_Monthly_Trp$akarsu)
PlanProdSources$Ithal_Komur <- (Production_Monthly_Trp$ithal_komur - Planning_Monthly_Trp$ithal_komur)
PlanProdSources$Ruzgar <- (Production_Monthly_Trp$ruzgar - Planning_Monthly_Trp$ruzgar)
PlanProdSources$Fuel_Oil <- (Production_Monthly_Trp$fuel_oil - Planning_Monthly_Trp$fuel_oil)
PlanProdSources$Jeotermal <- (Production_Monthly_Trp$jeotermal - Planning_Monthly_Trp$jeotermal)
PlanProdSources$Taskomur <- (Production_Monthly_Trp$tas_komur - Planning_Monthly_Trp$tas_komur)
PlanProdSources$Biyokutle <- (Production_Monthly_Trp$biyokutle - Planning_Monthly_Trp$biyokutle)
PlanProdSources <- PlanProdSources[,-1]
head(PlanProdSources,5)## Dates Barajli Linyit Akarsu Ithal_Komur Ruzgar Fuel_Oil
## 1 16_01 479380.2 616163.6 1081929.4 401996.9 1159400.2 -261486.43
## 2 16_02 136768.4 550189.7 1461351.4 320578.6 1011291.0 -391336.07
## 3 16_03 -313248.2 646237.0 1228425.6 322058.1 630272.2 -111040.33
## 4 16_04 -492380.7 565239.7 1165779.3 400791.9 329065.9 67250.98
## 5 16_05 -863147.0 586044.8 410138.1 490908.7 217974.4 65962.16
## Jeotermal Taskomur Biyokutle
## 1 306259.34 -304367.5 120390.76
## 2 303706.25 -338386.0 115200.66
## 3 162041.57 -397976.0 95626.83
## 4 138236.29 -317679.0 84489.25
## 5 59004.85 -364442.5 68930.46When the differences between actual production and planned production values are analyzed by source; It is seen that there are no significant differences between the two values in Geothermal, Biomass, Fuel Oil sources, but there are differences between plan and production in sources such as Dams, Stream and Lignite.
library(ggplot2)
library(reshape2)
G_PlanProdSources <- melt(PlanProdSources, id.vars="Dates")
colnames(G_PlanProdSources) <- c('Dates', 'Source', 'Prod_Plan_Difference')
ggplot(data = G_PlanProdSources, aes(x = Dates , y = Prod_Plan_Difference/1000000, group = 1)) +
geom_line() +
facet_wrap(~ Source) +
labs(subtitle = "Difference Between Actual Production and Planned Production by Sources", y="Actual Production - Planned Production (MWh) (x1M)", x="Dates")
Comparison of Production and Consumption
When real production and consumption values are compared, it is seen that consumption values are higher than production values. In the 46-month period from the beginning of 2016 to October 2019, it was found that only 6 months (listed below) Production value was higher than Consumption value and consumption was higher than production in all remaining months;
- March 2016
- April 2017
- May 2017
- October 2017
- November 2017
- December 2017
Con_MT <- subset(Consumption_Monthly, Sources=="tuketim_miktari_mwh")
Con_MT <- as.data.frame(transpose(Con_MT))
CompProdCon <- cbind(Prod_MT, Con_MT)
colnames(CompProdCon) <- c('Actual_Production', 'Consumption')
CompProdCon <- CompProdCon[-1,]
CompProdCon$Dates <- c('16_01', '16_02', '16_03', '16_04', '16_05', '16_06', '16_07', '16_08', '16_09', '16_10', '16_11', '16_12', '17_01', '17_02', '17_03', '17_04', '17_05', '17_06', '17_07', '17_08', '17_09', '17_10', '17_11', '17_12', '18_01', '18_02', '18_03', '18_04', '18_05', '18_06', '18_07', '18_08', '18_09', '18_10', '18_11', '18_12', '19_01', '19_02', '19_03', '19_04', '19_05', '19_06', '19_07', '19_08', '19_09', '19_10')
CompProdCon$Actual_Production <-as.numeric(CompProdCon$Actual_Production)
CompProdCon$Consumption <-as.numeric(CompProdCon$Consumption)
head(CompProdCon,5)## Actual_Production Consumption Dates
## 2 23155072 23704522 16_01
## 3 20639380 21153165 16_02
## 4 21718680 21535377 16_03
## 5 20962513 21301824 16_04
## 6 21523232 21900302 16_05library(ggplot2)
library(reshape2)
G_CompProdCon <- melt(CompProdCon, id.vars="Dates")
ggplot(G_CompProdCon, aes(value/1000000, Dates, col=variable)) +
geom_point() +
stat_smooth() +
geom_line( color="grey") +
labs(title="Comparison of Actual Production and Comsumption",
subtitle="in MWh (and x1M)",
x="MWh (x1M)",
y="Dates",
col="Production vs Consumption") ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
CompProdCon_2<-data.frame(CompProdCon)
CompProdCon_2$Difference <- (CompProdCon_2$Actual_Production - CompProdCon_2$Consumption)/1000000
CompProdConDiff <- CompProdCon_2 %>% select(Dates, Difference)
head(CompProdConDiff, 5)## Dates Difference
## 2 16_01 -0.5494502
## 3 16_02 -0.5137849
## 4 16_03 0.1833031
## 5 16_04 -0.3393110
## 6 16_05 -0.3770707CompProdConDiff <- CompProdConDiff %>%
mutate(mycolor = ifelse(CompProdConDiff$Difference>0, "type1", "type2"))
ggplot(CompProdConDiff, aes(x=CompProdConDiff$Dates, y=CompProdConDiff$Difference)) +
geom_segment( aes(x=CompProdConDiff$Dates, xend=CompProdConDiff$Dates, y=0, yend=CompProdConDiff$Difference, color=mycolor), size=1.3, alpha=0.9) +
theme_light() +
theme(
legend.position = "none",
panel.border = element_blank(),
) +
xlab("Dates") +
ylab("Production - Consumption (MWh) (x1M)")
Ratio of Renewable Energy Resources within 4 Years of Production
- The amount of energy produced in 4 years was calculated.
production_toplam <- colSums(Filter(is.numeric, production_raw_data))
print(production_toplam)## toplam_mwh dogal_gaz barajli linyit akarsu
## 1091631578.02 330882954.42 188612868.09 160974973.37 75212059.65
## ithal_komur ruzgar gunes fuel_oil jeotermal
## 208964232.22 71168183.03 233696.49 5509051.71 23037800.33
## asfaltit_komur tas_komur biyokutle nafta lng
## 9467418.70 10905822.78 8257603.48 0.00 49534.99
## uluslararasi
## -1644621.24- Energy amounts calculated according to sources were converted into data frames.
production_mwh <- colSums(Filter(is.numeric, production_raw_data))
production_mwh2 <- as.data.frame(production_mwh)
production_mwh2 <- rownames_to_column(production_mwh2, var = "kaynaklar")
production_mwh2 <- production_mwh2 %>% slice(2:max(nrow(.))) %>% filter(kaynaklar !='toplam_mwh')
tail(production_mwh2, 5)## kaynaklar production_mwh
## 11 tas_komur 10905822.78
## 12 biyokutle 8257603.48
## 13 nafta 0.00
## 14 lng 49534.99
## 15 uluslararasi -1644621.24- In the last 4 years, the most production was obtained from natural gas (dogal_gaz), imported coal (ithal_komur) and dammed hydro (barajli). Only the negative value (about -1.6 million mwh) was obtained from the importExport (uluslararasi) source.
library(ggplot2)
theme_set(theme_bw())
options(scipen=999)
# Draw plot
ggplot(production_mwh2, aes(x=kaynaklar, y=production_mwh)) +
geom_bar(stat="identity", width=.5, fill="tomato3") +
labs(title="Total Production Amount by Years (mwh)",
subtitle="Amount of Production from All Sources",
y = "Total Sources mwh",
x = "Sources",
caption="source: epiaÅŸ") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
- The production amount of wind and solar energy as a renewable energy source is shown in a separate table.
renewable_energy <- colSums(Filter(is.numeric, production_raw_data))
renewable_energy <- as.data.frame(renewable_energy)
renewable_energy <- rownames_to_column(renewable_energy, var = "kaynaklar")
renewable_energy <- subset(renewable_energy, kaynaklar=="ruzgar" | kaynaklar=="gunes" | kaynaklar=="toplam_mwh")
print(renewable_energy)## kaynaklar renewable_energy
## 1 toplam_mwh 1091631578.0
## 7 ruzgar 71168183.0
## 8 gunes 233696.5solar_stat <- as.data.frame(Production_Monthly_Trp$ruzgar)
solar_stat$Dates <- c('16_01', '16_02', '16_03', '16_04', '16_05', '16_06', '16_07', '16_08', '16_09', '16_10', '16_11', '16_12', '17_01', '17_02', '17_03', '17_04', '17_05', '17_06', '17_07', '17_08', '17_09', '17_10', '17_11', '17_12', '18_01', '18_02', '18_03', '18_04', '18_05', '18_06', '18_07', '18_08', '18_09', '18_10', '18_11', '18_12', '19_01', '19_02', '19_03', '19_04', '19_05', '19_06', '19_07', '19_08', '19_09', '19_10')
ggplot(solar_stat, aes(x=solar_stat$Dates, y=solar_stat$`Production_Monthly_Trp$ruzgar`)) +
geom_bar(stat = "identity", width=0.2) +
labs(subtitle = "Production by Years", y="Solar (MWh)", x="Dates")
sun_stat <- as.data.frame(Production_Monthly_Trp$gunes)
sun_stat$Dates <- c('16_01', '16_02', '16_03', '16_04', '16_05', '16_06', '16_07', '16_08', '16_09', '16_10', '16_11', '16_12', '17_01', '17_02', '17_03', '17_04', '17_05', '17_06', '17_07', '17_08', '17_09', '17_10', '17_11', '17_12', '18_01', '18_02', '18_03', '18_04', '18_05', '18_06', '18_07', '18_08', '18_09', '18_10', '18_11', '18_12', '19_01', '19_02', '19_03', '19_04', '19_05', '19_06', '19_07', '19_08', '19_09', '19_10')
ggplot(sun_stat, aes(x=sun_stat$Dates, y=sun_stat$`Production_Monthly_Trp$gunes`)) +
geom_bar(stat = "identity", width=0.2) +
labs(subtitle = "Production by Years", y="Sun (MWh)", x="Dates")
- The percentage of wind and solar energy in the total amount of energy is shown on a separate pie chart.
# Create Data
x <- c(71168183.03, 233696.49)
labels <- c("ruzgar", "gunes")
pct <- round((x/1091631578.0)*100, digits = 2)
lbls <- paste(labels, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie(x,labels = lbls, col=rainbow(length(lbls)),main="Ratio of Renewable Energy Resources Production", cex=0.5)
- The amount of production of natural gas and fossil fuels is shown in a separate table.
fossil_naturalgas <- colSums(Filter(is.numeric, production_raw_data))
fossil_naturalgas <- as.data.frame(fossil_naturalgas)
fossil_naturalgas <- rownames_to_column(fossil_naturalgas, var = "kaynaklar")
fossil_naturalgas <- subset(fossil_naturalgas, kaynaklar=="toplam_mwh" | kaynaklar=="dogal_gaz" | kaynaklar=="fuel_oil" | kaynaklar=="linyit" | kaynaklar=="ithal_komur" | kaynaklar=="asfaltit_komur" | kaynaklar=="tas_komur")
print(fossil_naturalgas)## kaynaklar fossil_naturalgas
## 1 toplam_mwh 1091631578
## 2 dogal_gaz 330882954
## 4 linyit 160974973
## 6 ithal_komur 208964232
## 9 fuel_oil 5509052
## 11 asfaltit_komur 9467419
## 12 tas_komur 10905823Ratio of Fuel and Natural Gas Resources within 4 Years of Production
- The percentage of natural gas and fossil fuels in the total energy amount is shown in a separate pie chart.
# Create Data
x <- c(330882954, 160974973, 208964232, 5509052, 9467419, 10905823)
labels <- c("dogal_gaz", "linyit", "ithal_komur", "fuel_oil", "asfaltit_komur", "tas_komur")
pct <- round((x/1091631578.0)*100, digits = 2)
lbls <- paste(labels, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie(x,labels = lbls, col=rainbow(length(lbls)),main="Ratio of Fossil Fuel and Natural Gas Production", cex=0.4)
Conclusion
Consumption, usually exceeds the value of production (42/48 months) and in this case it is seen that Turkey is dependent on foreign energy in order to close this gap.
Analysis show that the actual production is above the planned production in the whole process. This shows that the energy demand always exceeds the plan due to different variables.
It is seen that renewable energy sources (especially solar energy) are quite low in total production and these resources have not developed sufficiently yet.