knitr::opts_chunk$set(message = FALSE, warning = FALSE)
library(tidyverse)
library(tidymodels)
library(recipes)
library(rvest)
library(stringr)
library(lubridate)
library(revgeo)
library(jsonlite)
library(httr)
library(kableExtra)
library(FinCal)
library(gridExtra)
library(grid)

seda_db <- read_rds("data_v2\\seda-db.rds") %>%
        mutate (
                Repayment.Per = n.period(r = 0.05/12, pv = Investment, fv = 0, pmt = -BGN.Savings/12)/12
        ) %>%
        mutate(
                Financing = if_else(Repayment.Per > 10, "Bankable", "Not Bankable")
        ) %>%
        mutate(Financing = as_factor(Financing)) %>%
        mutate(Rating = as_factor(Rating)) %>%
        mutate(Potential.Rating = as_factor(Potential.Rating)) %>%
        mutate(Area.Class = case_when(
                Area < 1000 ~ "less than 1 000",
                Area >= 1000 && Area < 5000 ~ "between 1 000 and 5 000",
                Area >=5000 ~ "more than 5 000"
        )
        ) %>%
        mutate(Building.Type1 = case_when(
                Building.Type1 == "Детско заведение (градини и ясли)" ~ "Kindergarten",
                Building.Type1 == "Образователни (училища, колежи и университети)" ~ "Educational",
                Building.Type1 == "Сгради за административно обслужване" ~ "Administrative",
                Building.Type1 == "Транспорт (гари, пристанища и летища)" ~ "Transport",
                Building.Type1 == "Библиотеки и читалища" ~ "Libraries",
                Building.Type1 == "Магазини за продажба на дребно иедро (супермаркети и молове)" ~ "Supermarkets",
                Building.Type1 == "Спортни зали и съоръжения" ~ "Sports",
                Building.Type1 == "Хотели и ресторанти" ~ "Hotels and restaurants",
                Building.Type1 == "Здравеопазване (болници, поликлиники и др.)" ~ "Hospitals",
                Building.Type1 == "Театри,кина,опери,худож.галерии" ~ "Cinemas",
                Building.Type1 == "Жилищна - етажна собственост (блок и/или кооперация) с ниско, средно и високо застрояване" ~ "Residential",
                Building.Type1 == "Други, вкл. старч. домове, за сираци и изоставени деца, общ-тия, автосервизи" ~ "Other",
                Building.Type1 == "Производствена сграда" ~ "Industry",
                Building.Type1 == "Еднофамилни къщи" ~ "Houses",
                Building.Type1 == "Сграда  обследвана от физ. лице" ~ "Self-audited"
        )
        ) %>%
        mutate(
                Area.Class = as_factor(Area.Class),
                Building.Type1 = as_factor(Building.Type1)
        ) %>%
        mutate(
                Financing = if_else(Repayment.Per < 10, "Bankable", "Not Bankable")
        ) %>%
        mutate(Financing = as_factor(Financing))

seda_db        

seda_db %>%
        group_by(region) %>%
        summarise(n = n())

seda_db %>%
        summarize(
                Investment = sum(Investment, na.rm = TRUE)/1000000,
                Savings = sum(Energy.Savings, na.rm = TRUE)/1000000,
                Area = sum(Area, na.rm = TRUE)/1000000
        )

seda_db %>%
        filter(Repayment.Per < 50) %>%
        ggplot(aes(Repayment.Per)) +
        geom_histogram() +
        geom_vline(aes(xintercept = 10), color = "red", linetype = 1, size = 3) + 
        labs(y = "Projects Count") + 
        theme_bw()

seda_db %>%
        group_by(Financing) %>%
        filter(!is.na(Financing)) %>%
        summarise(
                Projects = n(),
                Invesments = sum(Investment)/1.96,
                t.CO2 = round(sum(CO2.Savings, na.rm = TRUE))
        ) %>%
        kable(format.args = list(big.mark = " ")) %>%
        kable_styling(full_width = F) %>%
        row_spec(1, bold = T, color = "white", background = "red") %>%
        row_spec(2, bold = T, color = "white", background = "green")

grant_help <- c(0.1, 0.20, 0.30, 0.40)

notb <- seda_db %>%
        filter(Financing == "Not Bankable") %>%
        summarise(
                NotBankable_Invesments = sum(Investment)/1.96,
                NotBankable_BGN.Savings = sum(BGN.Savings)/1.96
        )

new_rep <- function(grant_perc, inv, saving){
        inv = inv - grant_perc*inv
        n.period(
                r = 0.05/12, 
                pv = inv, 
                fv = 0, 
                pmt = -saving/12
        )/12
}

tibble(
        Grant.Support = percent(grant_help, accuracy = 1),
        Repayment.Per = map_dbl(grant_help, 
                                new_rep,
                                notb$NotBankable_Invesments, 
                                notb$NotBankable_BGN.Savings
        ) %>%
                round(1)
) %>%
        kable() %>%
        kable_styling(full_width = F) %>%
        row_spec(3, bold = T, color = "white", background = "green")

seda_db %>%
        select(Building.Type1, Investment, BGN.Savings, Area, Payback) %>%
        filter(Payback < 10) %>%
        
        ggplot() +
        geom_point(aes(x = BGN.Savings, y = Investment), alpha = 0.4) +
        geom_smooth(method = lm, aes(x = BGN.Savings, y = Investment)) +
        #geom_hline(aes(yintercept = 10), color = "red", linetype = 2) +
        facet_wrap(~Building.Type1) +
        scale_y_continuous(labels=function(x) format(x, big.mark = " ", scientific = FALSE)) +
        scale_x_continuous(labels=function(x) format(x, big.mark = " ", scientific = TRUE)) +
        labs(y = "BGN.Investment")

estim <- seda_db %>%
        filter(
                !is.na(Investment) | !is.na(BGN.Savings)
        ) %>%
        group_by(Building.Type1, Rating, Area.Class) %>%
        nest(Investment, BGN.Savings) %>%
        mutate(
                fit = map(data, ~lm(BGN.Savings ~ Investment, data = .x)),
                tidied = map(fit, tidy)
        ) %>% 
        unnest(tidied) %>%
        filter(
                p.value < 0.05,
                term == "Investment"
        ) %>%
        arrange(
                desc(estimate)
        )

gems <- estim %>%
        select(-std.error, -term, -statistic, -p.value) %>%
        mutate(Value = "gem") %>%
        mutate(Estimate = round(estimate, 3)) %>%
        select(-estimate) %>%
        head(5)


dregs <- estim %>%
        select(-std.error, -term, -statistic, -p.value) %>%
        mutate(Value = "dregs") %>%
        mutate(Estimate = round(estimate, 3)) %>%
        select(-estimate) %>%
        tail(5)

bind_rows(gems, dregs) %>%
        kable() %>%
        kable_styling(full_width = F) %>%
        row_spec(1:5, bold = T, color = "white", background = "gold") %>%
        row_spec(6:10, bold = T, color = "white", background = "grey")

---
title: "Certified buildings BG Market - EDA"
output: html_notebook
---

```{r setup}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
library(tidyverse)
library(tidymodels)
library(recipes)
library(rvest)
library(stringr)
library(lubridate)
library(revgeo)
library(jsonlite)
library(httr)
library(kableExtra)
library(FinCal)
library(gridExtra)
library(grid)
```

# Load and transform //a bit

```{r}
seda_db <- read_rds("data_v2\\seda-db.rds") %>%
        mutate (
                Repayment.Per = n.period(r = 0.05/12, pv = Investment, fv = 0, pmt = -BGN.Savings/12)/12
        ) %>%
        mutate(
                Financing = if_else(Repayment.Per > 10, "Bankable", "Not Bankable")
        ) %>%
        mutate(Financing = as_factor(Financing)) %>%
        mutate(Rating = as_factor(Rating)) %>%
        mutate(Potential.Rating = as_factor(Potential.Rating)) %>%
        mutate(Area.Class = case_when(
                Area < 1000 ~ "less than 1 000",
                Area >= 1000 && Area < 5000 ~ "between 1 000 and 5 000",
                Area >=5000 ~ "more than 5 000"
        )
        ) %>%
        mutate(Building.Type1 = case_when(
                Building.Type1 == "Детско заведение (градини и ясли)" ~ "Kindergarten",
                Building.Type1 == "Образователни (училища, колежи и университети)" ~ "Educational",
                Building.Type1 == "Сгради за административно обслужване" ~ "Administrative",
                Building.Type1 == "Транспорт (гари, пристанища и летища)" ~ "Transport",
                Building.Type1 == "Библиотеки и читалища" ~ "Libraries",
                Building.Type1 == "Магазини за продажба на дребно иедро (супермаркети и молове)" ~ "Supermarkets",
                Building.Type1 == "Спортни зали и съоръжения" ~ "Sports",
                Building.Type1 == "Хотели и ресторанти" ~ "Hotels and restaurants",
                Building.Type1 == "Здравеопазване (болници, поликлиники и др.)" ~ "Hospitals",
                Building.Type1 == "Театри,кина,опери,худож.галерии" ~ "Cinemas",
                Building.Type1 == "Жилищна - етажна собственост (блок и/или кооперация) с ниско, средно и високо застрояване" ~ "Residential",
                Building.Type1 == "Други, вкл. старч. домове, за сираци и изоставени деца, общ-тия, автосервизи" ~ "Other",
                Building.Type1 == "Производствена сграда" ~ "Industry",
                Building.Type1 == "Еднофамилни къщи" ~ "Houses",
                Building.Type1 == "Сграда  обследвана от физ. лице" ~ "Self-audited"
        )
        ) %>%
        mutate(
                Area.Class = as_factor(Area.Class),
                Building.Type1 = as_factor(Building.Type1)
        ) %>%
        mutate(
                Financing = if_else(Repayment.Per < 10, "Bankable", "Not Bankable")
        ) %>%
        mutate(Financing = as_factor(Financing))

seda_db        
```

# Group by development regions

```{r}
seda_db %>%
        group_by(region) %>%
        summarise(n = n())
```

# Investments; Savings; Area

```{r}
seda_db %>%
        summarize(
                Investment = sum(Investment, na.rm = TRUE)/1000000,
                Savings = sum(Energy.Savings, na.rm = TRUE)/1000000,
                Area = sum(Area, na.rm = TRUE)/1000000
        )
```

# Repayment period histogram

```{r message=FALSE, warning=FALSE}
seda_db %>%
        filter(Repayment.Per < 50) %>%
        ggplot(aes(Repayment.Per)) +
        geom_histogram() +
        geom_vline(aes(xintercept = 10), color = "red", linetype = 1, size = 3) + 
        labs(y = "Projects Count") + 
        theme_bw()
```

# Repayment period table view 

```{r message=FALSE, warning=FALSE}
seda_db %>%
        group_by(Financing) %>%
        filter(!is.na(Financing)) %>%
        summarise(
                Projects = n(),
                Invesments = sum(Investment)/1.96,
                t.CO2 = round(sum(CO2.Savings, na.rm = TRUE))
        ) %>%
        kable(format.args = list(big.mark = " ")) %>%
        kable_styling(full_width = F) %>%
        row_spec(1, bold = T, color = "white", background = "red") %>%
        row_spec(2, bold = T, color = "white", background = "green")
```

# Grant/Fin. instrument

```{r}
grant_help <- c(0.1, 0.20, 0.30, 0.40)

notb <- seda_db %>%
        filter(Financing == "Not Bankable") %>%
        summarise(
                NotBankable_Invesments = sum(Investment)/1.96,
                NotBankable_BGN.Savings = sum(BGN.Savings)/1.96
        )

new_rep <- function(grant_perc, inv, saving){
        inv = inv - grant_perc*inv
        n.period(
                r = 0.05/12, 
                pv = inv, 
                fv = 0, 
                pmt = -saving/12
        )/12
}

tibble(
        Grant.Support = percent(grant_help, accuracy = 1),
        Repayment.Per = map_dbl(grant_help, 
                                new_rep,
                                notb$NotBankable_Invesments, 
                                notb$NotBankable_BGN.Savings
        ) %>%
                round(1)
) %>%
        kable() %>%
        kable_styling(full_width = F) %>%
        row_spec(3, bold = T, color = "white", background = "green")
```

## Investment ~ Saving by type

```{r}
seda_db %>%
        select(Building.Type1, Investment, BGN.Savings, Area, Payback) %>%
        filter(Payback < 10) %>%
        
        ggplot() +
        geom_point(aes(x = BGN.Savings, y = Investment), alpha = 0.4) +
        geom_smooth(method = lm, aes(x = BGN.Savings, y = Investment)) +
        #geom_hline(aes(yintercept = 10), color = "red", linetype = 2) +
        facet_wrap(~Building.Type1) +
        scale_y_continuous(labels=function(x) format(x, big.mark = " ", scientific = FALSE)) +
        scale_x_continuous(labels=function(x) format(x, big.mark = " ", scientific = TRUE)) +
        labs(y = "BGN.Investment")
```

# Estimating: Type > Rating > Area and sorting 

```{r}
estim <- seda_db %>%
        filter(
                !is.na(Investment) | !is.na(BGN.Savings)
        ) %>%
        group_by(Building.Type1, Rating, Area.Class) %>%
        nest(Investment, BGN.Savings) %>%
        mutate(
                fit = map(data, ~lm(BGN.Savings ~ Investment, data = .x)),
                tidied = map(fit, tidy)
        ) %>% 
        unnest(tidied) %>%
        filter(
                p.value < 0.05,
                term == "Investment"
        ) %>%
        arrange(
                desc(estimate)
        )

gems <- estim %>%
        select(-std.error, -term, -statistic, -p.value) %>%
        mutate(Value = "gem") %>%
        mutate(Estimate = round(estimate, 3)) %>%
        select(-estimate) %>%
        head(5)


dregs <- estim %>%
        select(-std.error, -term, -statistic, -p.value) %>%
        mutate(Value = "dregs") %>%
        mutate(Estimate = round(estimate, 3)) %>%
        select(-estimate) %>%
        tail(5)

bind_rows(gems, dregs) %>%
        kable() %>%
        kable_styling(full_width = F) %>%
        row_spec(1:5, bold = T, color = "white", background = "gold") %>%
        row_spec(6:10, bold = T, color = "white", background = "grey")
```


