project_timelines_generic.Rmd

---
title: "MHK_Timeline_General_Timeframe"
output:
  html_document:
    self_contained: false
    mathjax: null
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


Using [MHK Project Timeline Input - Google Sheets](https://docs.google.com/spreadsheets/d/1HC5hXyi2RQSHevnV7rvyk748U5-X3iUw70ewHEfrHm0/edit#gid=793817660)


```{r}
library(dplyr)
#library(htmltools)
#library(htmlwidgets)
#library(jsonlite)
#library(plotly)
#library(ggplot2)
#library(ggiraph)
#library(RColorBrewer)

#get the data
csv_key <- "1HC5hXyi2RQSHevnV7rvyk748U5-X3iUw70ewHEfrHm0"
csv_url <- glue::glue("https://docs.google.com/spreadsheets/d/{csv_key}/gviz/tq?tqx=out:csv&sheet=0")
d <- readr::read_csv(csv_url) %>% 
  select(-starts_with("X"))
```



Data Cleaning

```{r}
#sort data by permit type
d$permit_type <- factor(d$permit_type, levels = c("Notice of Intent/Preliminary Permit Application", 'Draft Pilot License App', 'Final Pilot License App', "Draft License App", "Final License App", 'Environmental Assessment', 'Settlement Agreement', "Permit Issued"))

d$technology_type <- factor(d$technology_type, levels = c('Riverine Energy', 'Tidal Energy', 'Wave Energy'))


#data cleanup
d_gen <- d %>% 
  filter(!is.na(date_beg)) %>% 
  tidyr::pivot_longer(c(date_beg, date_end), names_to = "proj_ev", values_to = "proj_ev_date") %>%
  mutate(
    proj_ev_date = as.Date(proj_ev_date, format = "%m/%d/%Y")) %>%
  arrange(project_number, project_name) %>%
  select(project_name, project_number, proj_ev, proj_ev_date, technology_type)
  

#data cleanup
d_permits <- d %>% 
  filter(!is.na(permit_type)) %>% 
  rename(., proj_ev = permit_type,
  proj_ev_date = license_date) %>%
  mutate(
    proj_ev_date = as.Date(proj_ev_date, format = "%m/%d/%Y")) %>%
  arrange(proj_ev, project_number, project_name, proj_ev_date) %>% 
  select(project_name, project_number, proj_ev, proj_ev_date, technology_type)

#combine into single dataset
d_all <- bind_rows(d_gen, d_permits)

d_all_w<- d_all %>%
  tidyr::pivot_wider(names_from = proj_ev, values_from = proj_ev_date) %>%
  relocate(date_end, .after = last_col())

#TO DO N SWANSON: calculate typical time intervals between project events/permits
# How should the two WET environmental assessments with different dates be handled?
# Are the settlement agreement and environmental assessment date markers important for calculating typical permitting time?


```