Streamflow_Parse.Rmd

---
title: "R Notebook"
output: html_notebook
---

```{r}
setwd("/Users/nicholasdepsky/Dropbox/Berkeley_tings/Spring 2018/ER 290A/ER290A-finalproject")
df <- read.csv("NYuba_GoodYears_CFS_raw.csv", check.names = F)
library(lubridate)
library(dplyr)
library(ggplot2)
library(magrittr)
date_fix <- function(x, year=1950){
  d <- mdy(x)
  m <- year(d) %% 100
  year(d) <- ifelse(m > year %% 100, 1900+m, 2000+m)
  as.Date(d)
}

df$Day <- date_fix(df$Day)
day_df <- aggregate(CFS ~ Day, df, mean) %>% set_colnames(c("Date", "CFS"))
day_df$CMS <- day_df$CFS*0.028316847
day_df$CFS <- NULL

#day_df <- read.csv("NYuba_Inflow_Day.csv", check.names = F)
#day_df$Date <- date_fix(day_df$Date)
month_df <- day_df %>% mutate(ymd = paste(month(Date),"01",year(Date),sep = "/"))
month_df$ymd <- date_fix(month_df$ymd)
month_df <- aggregate(CMS ~ ymd, month_df, mean) %>% set_colnames(c("Date", "CMS"))
#month_df$CMS <- month_df$CMS*0.7
write.csv(day_df, file = "NYuba_Inflow_Day.csv", row.names = F)
write.csv(month_df, file = "NYuba_Inflow_Month.csv", row.names = F)
```

```{r}
ggplot(day_df, aes(x = Day, y = CFS)) + geom_line() + ggtitle("Daily Average Streamflow [CFS]") + labs(x = "Date")
```

```{r}
ggplot(month_df, aes(x = ymd, y = CFS)) + geom_line() + ggtitle("Monthly Average Streamflow [CFS]") + labs(x = "Date")
```