Visualizing Images.Rmd

---
title: "Visualizing Series"
output: html_document
---

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

```{r}
library(shiny)
library(tidyverse)
library(magrittr)
```

# Create a function that plots a time series

```{r}
# Set correct path
path = "~/Documents/University/WessexWater/wessexwater/"
setwd(path)
# Load Data
load("../datFlow.Rda")
load("../repFlow.Rda")
```

```{r}
# Night start & Night end
night_start = 9    # 2 am
night_end   = 13   # 3 am
# Compute MNF and ADF
mnf = datFlow %>% filter((night_start <= tod) & (tod <= night_end)) %>% group_by(id, date) %>% summarise(mnf=min(y)) %>% ungroup()
adf = datFlow %>% filter((night_start > tod) | (tod > night_end)) %>% group_by(id, date) %>% summarise(adf=mean(y)) %>% ungroup()
# Put mnf and adf together
mnf_adf = mnf %>% left_join(adf, by=c("id", "date"), suffix=c("_mnf", "_adf"))
# Put into long format
mnf_adf = mnf_adf %>% gather("key", "y", -id, -date)
```

```{r}
alljobs = repFlow %>% 
  select(-Region, -Area, -Area_reference) %>% 
  gather("key", "value", -id) %>% 
  na.omit
```


```{r}
# plot minimum night flow for a given series
series_id = "AW008"
# Specify date range
start_date_plot = "2017-04-01"
end_date_plot = '2017-07-01'
plot = mnf_adf %>% 
  filter(id == series_id) %>% 
  ggplot() + 
    geom_line(aes(x=as.Date(date), y=y, color=key)) +
    theme_bw() + 
    labs(x="Date", y="Flow")

# Grab start jobs and add them to the plot
job_starts = alljobs %>% filter(id == series_id) %>% filter(str_detect(key, "Start")) 
job_ends = alljobs %>% filter(id == series_id) %>% filter(str_detect(key, "End")) 
if(nrow(job_starts)){
 plot <- plot + geom_vline(xintercept = as.Date(job_starts$value), lty=2)
}
if(nrow(job_ends)){
 plot <- plot + geom_vline(xintercept = as.Date(job_ends$value), lty=1)
}
plot + 
  scale_x_date(limits=c(as.Date(start_date_plot), as.Date(end_date_plot)))
```


```{r}
plot_series = function(series_id, date_from, date_to){
  # MNF and ADF plot
  plot = mnf_adf %>% 
        filter(id == series_id, date >= date_from, date <= date_to) %>% 
        ggplot() + 
          geom_line(aes(x=as.Date(date), y=y, color=key)) + 
          theme_bw() + 
          labs(x="Date", y="Flow")
  # Plot end and start jobs
  job_starts = alljobs %>% 
    filter(id == series_id) %>% 
    filter(str_detect(key, "Start")) %>% 
    filter(value >= date_from, value <= date_to)
  job_ends = alljobs %>% 
    filter(id == series_id) %>% 
    filter(str_detect(key, "End")) %>% 
    filter(value >= date_from, value <= date_to)
  if(nrow(job_starts)){
   plot <- plot + geom_vline(xintercept = as.Date(job_starts$value), lty=2)
  }
  if(nrow(job_ends)){
   plot <- plot + geom_vline(xintercept = as.Date(job_ends$value), lty=1)
  }
  # Keep only dates requested (scale_x_date not needed probably)
  plot = plot + scale_x_date(limits=c(as.Date(date_from), as.Date(date_to)))
  return(plot)
}
```