Analise Reservatório
library(mgcv)Warning: package ‘mgcv’ was built under R version 4.2.2Carregando pacotes exigidos: nlme
Warning: package ‘nlme’ was built under R version 4.2.2This is mgcv 1.8-42. For overview type 'help("mgcv-package")'.library(readr)Warning: package ‘readr’ was built under R version 4.2.2library(tidyverse)Warning: package ‘tidyverse’ was built under R version 4.2.3Warning: package ‘ggplot2’ was built under R version 4.2.3Warning: package ‘tidyr’ was built under R version 4.2.3Warning: package ‘purrr’ was built under R version 4.2.2Warning: package ‘dplyr’ was built under R version 4.2.2Warning: package ‘stringr’ was built under R version 4.2.3Warning: package ‘forcats’ was built under R version 4.2.2Warning: package ‘lubridate’ was built under R version 4.2.2-- Attaching core tidyverse packages ------------------------------------------------ tidyverse 2.0.0 --
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group_by_all, group_by_at, group_by_if, inner_join, left_join, mutate, mutate_all,
mutate_at, mutate_if, relocate, rename, rename_all, rename_at, rename_if, rename_with,
right_join, sample_frac, sample_n, select, select_all, select_at, select_if, semi_join,
slice, slice_head, slice_max, slice_min, slice_sample, slice_tail, summarise,
summarise_all, summarise_at, summarise_if, summarize, summarize_all, summarize_at,
summarize_if, tally, top_frac, top_n, transmute, transmute_all, transmute_at,
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transposelibrary(skimr)Warning: package ‘skimr’ was built under R version 4.2.3Analise da Base de dados
t2 <- fread("reservatorio_base_analise.csv")
|--------------------------------------------------|
|==================================================|
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
t2 %>% str()Classes ‘data.table’ and 'data.frame': 624825 obs. of 78 variables:
$ V1 : int 1 2 3 4 5 6 7 8 9 10 ...
$ ...1 : num 1 2 3 4 5 6 7 8 9 10 ...
$ nom_reservatorio : chr "A. VERMELHA" "ANTA" "B. BONITA" "BALBINA" ...
$ cod_resplanejamento.x : int 18 128 37 277 86 20 118 190 27 14 ...
$ tip_reservatorio.x : chr "Reservat<f3>rio com Usina" "Reservat<f3>rio sem usina" "Reservat<f3>rio com Usina" "Reservat<f3>rio com Usina" ...
$ nom_bacia.x : chr "GRANDE" "PARAIBA DO SUL" "TIETE" "AMAZONAS" ...
$ nom_ree.x : chr "PARANA" "SUDESTE" "PARANA" "MANAUS-AMAPA" ...
$ id_subsistema.x : chr "SE" "SE" "SE" "N" ...
$ nom_subsistema.x : chr "SUDESTE" "SUDESTE" "SUDESTE" "NORTE" ...
$ id_subsistema_jusante : chr NA NA NA NA ...
$ nom_subsistema_jusante : chr NA NA NA NA ...
$ ear_data : IDate, format: "2023-01-01" "2023-01-01" "2023-01-01" "2023-01-01" ...
$ ear_reservatorio_subsistema_proprio_mwmes : num 1817 NA 2227 430 2346 ...
$ ear_reservatorio_subsistema_jusante_mwmes : num NA NA NA NA NA NA NA NA NA NA ...
$ earmax_reservatorio_subsistema_proprio_mwmes: num 4340 NA 2714 786 2991 ...
$ earmax_reservatorio_subsistema_jusante_mwmes: num NA NA NA NA NA NA NA NA NA NA ...
$ ear_reservatorio_percentual : num 41.9 NA 82.1 54.7 78.4 ...
$ ear_total_mwmes : num 1817 0 2227 430 2346 ...
$ ear_maxima_total_mwmes : num 4340 0 2714 786 2991 ...
$ val_contribearbacia : num 0.0572 0 0.2919 0.4667 0.4588 ...
$ val_contribearmaxbacia : num 0.0842 0 0.279 0.3874 0.4445 ...
$ val_contribearsubsistema : num 0.0166 0 0.0203 0.0455 0.136 ...
$ val_contribearmaxsubsistema : num 0.0212 0 0.0133 0.0513 0.1462 ...
$ val_contribearsubsistemajusante : num 0 0 0 0 0 0 0 0 0 0 ...
$ val_contribearmaxsubsistemajusante : num 0 0 0 0 0 0 0 0 0 0 ...
$ val_contribearsin : num 0.0106 0 0.013 0.00251 0.01369 ...
$ val_contribearmaxsin : num 0.01486 0 0.00929 0.00269 0.01024 ...
$ tip_reservatorio.y : chr "RESERVAT<d3>RIO COM USINA" NA "RESERVAT<d3>RIO COM USINA" "RESERVAT<d3>RIO COM USINA" ...
$ cod_resplanejamento.y : int 18 NA 37 277 86 20 NA 190 27 14 ...
$ cod_posto : int 18 NA 237 269 215 22 NA 190 207 14 ...
$ nom_usina : chr "<c1>GUA VERMELHA" NA "BARRA BONITA" "BALBINA" ...
$ ceg : chr "UHE.PH.MG.000041-8.01" NA "UHE.PH.SP.000208-9.01" "UHE.PH.AM.000190-2.01" ...
$ id_subsistema.y : chr "SE" NA "SE" "N" ...
$ nom_subsistema.y : chr "SUDESTE/CENTRO-OESTE" NA "SUDESTE/CENTRO-OESTE" "NORTE" ...
$ nom_bacia.y : chr "GRANDE" NA "TIETE" "AMAZONAS" ...
$ nom_rio : chr "GRANDE" NA "TIET<ca>" "UATUMA" ...
$ nom_ree.y : chr "PARANA" NA "PARANA" "MANAUS-AMAPA" ...
$ dat_entrada : IDate, format: "1978-03-01" NA "1963-02-01" "1989-02-13" ...
$ val_cotamaxima : num 383 NA 452 51 647 ...
$ val_cotaminima : num 373 NA 440 46 617 ...
$ val_volmax : num 11025 NA 3135 20006 4904 ...
$ val_volmin : num 5856 NA 569 9712 2712 ...
$ val_volutiltot : num 5169 NA 2567 10210 2193 ...
$ val_produtibilidadeespecifica : num 0.00882 NA 0.00885 0.00898 0.00922 ...
$ val_produtividade65volutil : num 0.463 NA 0.178 0.208 1.415 ...
$ val_tipoperda : chr "m" NA "m" "m" ...
$ val_perda : num 0.75 NA 0.41 0.3 2.79 1.2 NA 0.53 1.84 0.87 ...
$ val_latitude : num -19.87 NA -22.52 -1.91 -27.78 ...
$ val_longitude : num -50.3 NA -48.5 -59.5 -51.2 ...
$ cod_modalidadeoperacao : chr NA NA NA NA ...
$ nom_subsistema.x.x : chr NA NA NA NA ...
$ nom_estado : chr NA NA NA NA ...
$ nom_tipousina : chr NA NA NA NA ...
$ nom_tipocombustivel : chr NA NA NA NA ...
$ preciptation : num NA NA NA NA NA NA NA NA NA NA ...
$ temperature : num NA NA NA NA NA NA NA NA NA NA ...
$ humidity : num NA NA NA NA NA NA NA NA NA NA ...
$ wind_speed : num NA NA NA NA NA NA NA NA NA NA ...
$ co_ppb : num NA NA NA NA NA NA NA NA NA NA ...
$ no2_ppb : num NA NA NA NA NA NA NA NA NA NA ...
$ o3_ppb : num NA NA NA NA NA NA NA NA NA NA ...
$ pm25_ugm3 : num NA NA NA NA NA NA NA NA NA NA ...
$ so2_ugm3 : num NA NA NA NA NA NA NA NA NA NA ...
$ val_geracao : num NA NA NA NA NA NA NA NA NA NA ...
$ cod_resplanejamento : int 18 NA 37 277 86 20 NA NA 27 14 ...
$ tip_reservatorio : chr "Reservatório com Usina" NA "Reservatório com Usina" "Reservatório com Usina" ...
$ nom_bacia : chr "GRANDE" NA "TIETE" "AMAZONAS" ...
$ nom_ree : chr "PARANA" NA "PARANA" "MANAUS-AMAPA" ...
$ id_subsistema : chr "SE" NA "SE" "N" ...
$ nom_subsistema.y.y : chr "SUDESTE" NA "SUDESTE" "NORTE" ...
$ ena_bruta_res_mwmed : num 1653 NA 187 188 104 ...
$ ena_bruta_res_percentualmlt : num 100 NA 155 227 39 ...
$ ena_armazenavel_res_mwmed : num 1653.1 NA 68.7 187.9 80.3 ...
$ ena_armazenavel_res_percentualmlt : num 100.1 NA 56.7 226.9 30 ...
$ ena_queda_bruta : num 1610 NA 199 192 108 ...
$ mlt_ena : num 1651.7 NA 121.3 82.8 267.7 ...
$ X : int NA NA NA NA NA NA NA NA NA NA ...
$ sum.val_geracao_day. : num NA NA NA NA NA NA NA NA NA NA ...
- attr(*, ".internal.selfref")=<externalptr> Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentdf %>% skim()## estatistica descritivas das colunas
Error in as.Date.numeric(e) : 'origin' deve ser especificadoError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentAnalise de valores faltantes
df %>% mutate(yea=year(ear_data)) %>%# select(yea)%>% unique()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument group_by(yea) %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument summarise(n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument n_unique(nom_reservatorio),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument n_unique(nom_subsistema.x),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument n_unique(nom_estado),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_preciptation=sum(is.na(preciptation))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_temperature=sum(is.na(temperature))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_gera=sum(is.na(val_geracao))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_gera_sub=sum(is.na(sum.val_geracao_day.))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #miss_ena=sum(is.na(ena_bruta_res_mwmed,))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_ear=sum(is.na(ear_reservatorio_subsistema_proprio_mwmes))/n())mutate: new variable 'yea' (integer) with 24 unique values and 0% NA
group_by: one grouping variable (yea)
summarise: now 24 rows and 10 columns, ungroupedError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentdf %>% mutate(yea=year(ear_data)) %>%# select(yea)%>% unique()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument group_by(nom_subsistema.x) %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument summarise(n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument n_unique(nom_reservatorio),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument n_unique(nom_subsistema.x),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument n_unique(nom_estado),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_preciptation=sum(is.na(preciptation))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_temperature=sum(is.na(temperature))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_gera=sum(is.na(val_geracao))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_gera_sub=sum(is.na(sum.val_geracao_day.))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #miss_ena=sum(is.na(ena_bruta_res_mwmed,))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_ear=sum(is.na(ear_reservatorio_subsistema_proprio_mwmes))/n())mutate: new variable 'yea' (integer) with 24 unique values and 0% NA
group_by: one grouping variable (nom_subsistema.x)
summarise: now 4 rows and 10 columns, ungroupedError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentdf %>% mutate(yea=year(ear_data)) %>% drop_na(preciptation,sum.val_geracao_day.,ena_bruta_res_mwmed,ear_reservatorio_subsistema_proprio_mwmes) %>% # select(yea)%>% unique()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument group_by(yea) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument summarise(n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument n_unique(nom_reservatorio),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument n_unique(nom_subsistema.x),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument n_unique(nom_estado),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_preciptation=sum(is.na(preciptation))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_temperature=sum(is.na(temperature))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_gera=sum(is.na(val_geracao))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument miss_gera_sub=sum(is.na(sum.val_geracao_day.))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #miss_ena=sum(is.na(ena_bruta_res_mwmed,))/n(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #miss_ear=sum(is.na(ear_reservatorio_subsistema_proprio_mwmes))/n()Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument )mutate: new variable 'yea' (integer) with 24 unique values and 0% NA
drop_na: removed 388,276 rows (62%), 236,549 rows remaining
group_by: one grouping variable (yea)
summarise: now 19 rows and 9 columns, ungroupedError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentdf1 %>% ggplot(aes(val_geracao,sum.val_geracao_day.,col=nom_subsistema.x))+geom_point() + geom_smooth()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
df1 %>% ggplot(aes(val_geracao,ena_bruta_res_mwmed,col=nom_subsistema.x))+geom_jitter()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
df1 %>% ggplot(aes(val_geracao,ear_reservatorio_subsistema_proprio_mwmes,col=nom_subsistema.x))+geom_jitter()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
df1 %>% ggplot(aes(val_geracao,ear_data,col=nom_subsistema.x))+geom_jitter()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
df1 %>% ggplot(aes(yea,ena_bruta_res_mwmed,col=nom_subsistema.x))+geom_jitter()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
df1 %>% ggplot(aes(yea,ear_reservatorio_subsistema_proprio_mwmes,col=nom_subsistema.x))+geom_jitter()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
df1 %>% ggplot(aes(yea,val_geracao,col=nom_subsistema.x))+geom_jitter()
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
Analise Inferencial
results %>% distinct()
distinct: removed 2 rows (33%), 4 rows remainingError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentresults
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentresults %>% ggplot(aes(x=Generation_Trend,y=subset,col=model,
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument xmax=Generation_Trend+se*1.96,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument xmin=Generation_Trend-se*1.96))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument geom_point()+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument facet_wrap(~`Variavel Respota`,scales="free")+geom_errorbar()+coord_flip()Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
---
title: "Analise Reservatório"
output: html_notebook
---

```{r}
library(mgcv)
library(readr)
library(tidyverse)
library(tidylog)
library(data.table)
library(skimr)

```

# Analise da Base de dados

```{r}
t2 <- fread("reservatorio_base_analise.csv")


```

```{r}

t2 %>% str()

```

```{r}
df<-t2 %>% select(-ends_with("y")) # retirar colunas duplicadas pela união de dados
df %>% skim()## estatistica descritivas das colunas
  
```

Analise de valores faltantes

```{r}
df %>% mutate(yea=year(ear_data)) %>%# select(yea)%>%  unique() 
  group_by(yea) %>%
  summarise(n(),
            n_unique(nom_reservatorio),
            n_unique(nom_subsistema.x),
            n_unique(nom_estado),
            miss_preciptation=sum(is.na(preciptation))/n(),
            miss_temperature=sum(is.na(temperature))/n(),
            miss_gera=sum(is.na(val_geracao))/n(),
            miss_gera_sub=sum(is.na(sum.val_geracao_day.))/n(),
            #miss_ena=sum(is.na(ena_bruta_res_mwmed,))/n(),
            miss_ear=sum(is.na(ear_reservatorio_subsistema_proprio_mwmes))/n())

```

```{r}
df %>% mutate(yea=year(ear_data)) %>%# select(yea)%>%  unique() 
  group_by(nom_subsistema.x) %>%
  summarise(n(),
            n_unique(nom_reservatorio),
            n_unique(nom_subsistema.x),
            n_unique(nom_estado),
            miss_preciptation=sum(is.na(preciptation))/n(),
            miss_temperature=sum(is.na(temperature))/n(),
            miss_gera=sum(is.na(val_geracao))/n(),
            miss_gera_sub=sum(is.na(sum.val_geracao_day.))/n(),
            #miss_ena=sum(is.na(ena_bruta_res_mwmed,))/n(),
            miss_ear=sum(is.na(ear_reservatorio_subsistema_proprio_mwmes))/n())
```

```{r}
df %>% mutate(yea=year(ear_data)) %>% drop_na(preciptation,sum.val_geracao_day.,ena_bruta_res_mwmed,ear_reservatorio_subsistema_proprio_mwmes) %>% # select(yea)%>%  unique()
  group_by(yea) %>% 
  summarise(n(),
            n_unique(nom_reservatorio),
            n_unique(nom_subsistema.x),
            n_unique(nom_estado),
            miss_preciptation=sum(is.na(preciptation))/n(),
            miss_temperature=sum(is.na(temperature))/n(),
            miss_gera=sum(is.na(val_geracao))/n(),
            miss_gera_sub=sum(is.na(sum.val_geracao_day.))/n(),
            #miss_ena=sum(is.na(ena_bruta_res_mwmed,))/n(),
            #miss_ear=sum(is.na(ear_reservatorio_subsistema_proprio_mwmes))/n()
            )
``
```

```{r}
df1<-df %>% mutate(yea=year(ear_data)) %>%
  drop_na(preciptation,sum.val_geracao_day.,ena_bruta_res_mwmed,ear_reservatorio_subsistema_proprio_mwmes) # select(yea)%>%  unique()
  
df1 %>% ggplot(aes(val_geracao,sum.val_geracao_day.,col=nom_subsistema.x))+geom_point() 
```
```{r}
df1 %>% ggplot(aes(val_geracao,ena_bruta_res_mwmed,col=nom_subsistema.x))+geom_jitter() 

```


```{r}
df1 %>% ggplot(aes(val_geracao,ear_reservatorio_subsistema_proprio_mwmes,col=nom_subsistema.x))+geom_jitter() 

```

```{r}
df1 %>% ggplot(aes(val_geracao,ear_data,col=nom_subsistema.x))+geom_jitter() 
```


```{r}
df1 %>% ggplot(aes(yea,ena_bruta_res_mwmed,col=nom_subsistema.x))+geom_jitter() 
```

```{r}
df1 %>% ggplot(aes(yea,ear_reservatorio_subsistema_proprio_mwmes,col=nom_subsistema.x))+geom_jitter() 
```

```{r}
df1 %>% ggplot(aes(yea,val_geracao,col=nom_subsistema.x))+geom_jitter() 
```

## Analise Inferencial

```{r}

results<-data.frame()
results.temp<-data.frame()
#### Modelo Ajustado ena_bruta_res_mwmed#######
#df1$sum.val_geracao_day.
fit.ajusted <- gam(ena_bruta_res_mwmed~ val_geracao+sum.val_geracao_day. +
                       year(ear_data) + as.factor(month(ear_data))+as.factor(weekdays(ear_data))+
                       s(temperature) + s(preciptation) + s(wind_speed) +
                       s(co_ppb), data=df1)     #### Altere o Poluente analisado

##SE
Vcov <- vcov(fit.ajusted, useScale = FALSE)
Std_Errors <- sqrt(diag(Vcov))
se <- Std_Errors[4]
summary(fit.ajusted)
fit.ajusted$coefficients[4]#-3.316e+00
results.temp[1,1]<-"Full_base"
results.temp[1,2]<-fit.ajusted$coefficients[4]
results.temp[1,3]<-"ADJUSTED"
results.temp[1,4]<-"ena_bruta_res_mwmed"
results.temp[1,5] <- se
results.temp[1,6] <- length(df1$nom_reservatorio)

#Modelo Não Ajustado ena_bruta_res_mwmed######
fit.unajusted <- gam(ena_bruta_res_mwmed~ val_geracao+sum.val_geracao_day. +
                       year(ear_data) + as.factor(month(ear_data))+as.factor(weekdays(ear_data)),
                     data=df1)     #### Altere o Poluente analisado

Vcov <- vcov(fit.unajusted, useScale = FALSE)
Std_Errors <- sqrt(diag(Vcov))
se <- Std_Errors[4]

results.temp[2,1]<-"Full_base"
results.temp[2,2]<-fit.unajusted$coefficients[4]
results.temp[2,3]<-"UNADJUSTED"
results.temp[2,4]<-"ena_bruta_res_mwmed"
results.temp[2,5] <- se
results.temp[2,6] <- length(df1$nom_reservatorio)



###### Change here the name

colnames(results.temp)<-c("subset", "Generation_Trend","model","Variavel Respota","SE","n")
results<-rbind(results, results.temp)
results.temp<-data.frame()


#df1$ear_data %>% as_date()->df1$ear_data
#df1$ear_reservatorio_subsistema_proprio_mwmes %>% str



#### Modelo Ajustado ear_reservatorio_subsistema_proprio_mwmes #####
df1$ear_reservatorio_subsistema_proprio_mwmes %>% as.numeric()->df1$ear_reservatorio_subsistema_proprio_mwmes
fit.ajusted <- gam(ear_reservatorio_subsistema_proprio_mwmes~ val_geracao+sum.val_geracao_day. +
                     year(ear_data) + as.factor(month(ear_data))+as.factor(weekdays(ear_data))+
                     s(temperature) + s(preciptation) + s(wind_speed) +
                     s(co_ppb), data=df1)     #### Altere o Poluente analisado

##SE
Vcov <- vcov(fit.ajusted, useScale = FALSE)
Std_Errors <- sqrt(diag(Vcov))
se <- Std_Errors[4]
summary(fit.ajusted)
results.temp<-data.frame()
fit.ajusted$coefficients[4]#-3.316e+00
results.temp[1,1]<-"Full_base"
results.temp[1,2]<-fit.ajusted$coefficients[4]
results.temp[1,3]<-"ADJUSTED"
results.temp[1,4]<-"ear_reservatorio_subsistema_proprio_mwmes"
results.temp[1,5] <- se
results.temp[1,6] <- length(df1$nom_reservatorio)

#Modelo Não Ajustado ear_reservatorio_subsistema_proprio_mwmes#####

fit.unajusted <- gam(ear_reservatorio_subsistema_proprio_mwmes~ val_geracao+sum.val_geracao_day. +
                       year(ear_data) + as.factor(month(ear_data))+as.factor(weekdays(ear_data)),
                     data=df1)     #### Altere o Poluente analisado

Vcov <- vcov(fit.unajusted, useScale = FALSE)
Std_Errors <- sqrt(diag(Vcov))
se <- Std_Errors[4]

results.temp[2,1]<-"Full_base"
results.temp[2,2]<-fit.unajusted$coefficients[4]
results.temp[2,3]<-"UNADJUSTED"
results.temp[2,4]<-"ear_reservatorio_subsistema_proprio_mwmes"
results.temp[2,5] <- se
results.temp[2,6] <- length(df1$nom_reservatorio)

colnames(results.temp)<-c("subset", "Generation_Trend","model","Variavel Respota","SE","n")
results<-rbind(results, results.temp)
results %>% distinct()
```

```{r}
subsistema<-df1$nom_subsistema.x %>% unique()
#df2<-df1$
#subsistema[2:3]
for (i in subsistema) {
  print(i)
  df2<-df1 %>% filter(nom_subsistema.x==i)
  results.temp<-data.frame()
  ##### Modelo Ajustado ena_bruta_res_mwmed#######
  fit.ajusted <- gam(ena_bruta_res_mwmed~ val_geracao+sum.val_geracao_day. +
                       year(ear_data) + as.factor(month(ear_data))+as.factor(weekdays(ear_data))+
                       s(temperature) + s(preciptation) + s(wind_speed) +
                       s(co_ppb), data=df2)     #### Altere o Poluente analisado
  
  ##SE
  Vcov <- vcov(fit.ajusted, useScale = FALSE)
  Std_Errors <- sqrt(diag(Vcov))
  se <- Std_Errors[4]
  #summary(fit.ajusted)
  fit.ajusted$coefficients[4]#-3.316e+00
  results.temp[1,1]<-i
  results.temp[1,2]<-fit.ajusted$coefficients[4]
  results.temp[1,3]<-"ADJUSTED"
  results.temp[1,4]<-"ena_bruta_res_mwmed"
  results.temp[1,5] <- se
  results.temp[1,6] <- length(df2$nom_reservatorio)
  
  #Modelo Não Ajustado ena_bruta_res_mwmed######
  fit.unajusted <- gam(ena_bruta_res_mwmed~ val_geracao+sum.val_geracao_day. +
                         year(ear_data) + as.factor(month(ear_data))+as.factor(weekdays(ear_data)),
                       data=df2)     #### Altere o Poluente analisado
  
  Vcov <- vcov(fit.unajusted, useScale = FALSE)
  Std_Errors <- sqrt(diag(Vcov))
  se <- Std_Errors[4]
  
  results.temp[2,1]<-i
  results.temp[2,2]<-fit.unajusted$coefficients[4]
  results.temp[2,3]<-"UNADJUSTED"
  results.temp[2,4]<-"ena_bruta_res_mwmed"
  results.temp[2,5] <- se
  results.temp[2,6] <- length(df2$nom_reservatorio)
  
  
  
  ###### Change here the name
  
  colnames(results.temp)<-c("subset", "Generation_Trend","model","Variavel Respota","SE","n")
  results<-rbind(results, results.temp)
  results.temp<-data.frame()
  
  
  
  
  #### Modelo Ajustado ear_reservatorio_subsistema_proprio_mwmes #####
  df2$ear_reservatorio_subsistema_proprio_mwmes %>% as.numeric()->df2$ear_reservatorio_subsistema_proprio_mwmes
  fit.ajusted <- gam(ear_reservatorio_subsistema_proprio_mwmes~ val_geracao+sum.val_geracao_day. +
                       year(ear_data) + as.factor(month(ear_data))+as.factor(weekdays(ear_data))+
                       s(temperature) + s(preciptation) + s(wind_speed) +
                       s(co_ppb), data=df2)     #### Altere o Poluente analisado
  
  ##SE
  Vcov <- vcov(fit.ajusted, useScale = FALSE)
  Std_Errors <- sqrt(diag(Vcov))
  se <- Std_Errors[4]
  summary(fit.ajusted)
  results.temp<-data.frame()
  fit.ajusted$coefficients[4]#-3.316e+00
  results.temp[1,1]<-i
  results.temp[1,2]<-fit.ajusted$coefficients[4]
  results.temp[1,3]<-"ADJUSTED"
  results.temp[1,4]<-"ear_reservatorio_subsistema_proprio_mwmes"
  results.temp[1,5] <- se
  results.temp[1,6] <- length(df1$nom_reservatorio)
  
  #Modelo Não Ajustado ear_reservatorio_subsistema_proprio_mwmes#####
  
  fit.unajusted <- gam(ear_reservatorio_subsistema_proprio_mwmes~ val_geracao+sum.val_geracao_day. +
                         year(ear_data) + as.factor(month(ear_data))+as.factor(weekdays(ear_data)),
                       data=df2)     #### Altere o Poluente analisado
  
  Vcov <- vcov(fit.unajusted, useScale = FALSE)
  Std_Errors <- sqrt(diag(Vcov))
  se <- Std_Errors[4]
  
  results.temp[2,1]<-i
  results.temp[2,2]<-fit.unajusted$coefficients[4]
  results.temp[2,3]<-"UNADJUSTED"
  results.temp[2,4]<-"ear_reservatorio_subsistema_proprio_mwmes"
  results.temp[2,5] <- se
  results.temp[2,6] <- length(df1$nom_reservatorio)
  
  colnames(results.temp)<-c("subset", "Generation_Trend","model","Variavel Respota","SE","n")
  results<-rbind(results, results.temp)
}
results
```



```{r}
results %>% ggplot(aes(x=Generation_Trend,y=subset,col=model,
                       xmax=Generation_Trend+se*1.96,
                       xmin=Generation_Trend-se*1.96))+
                     geom_point()+
  facet_wrap(~`Variavel Respota`,scales="free")+geom_errorbar()+coord_flip()
```









