R_script_lab.Rmd

---
title: "Data Manipulation"
author: "HMF"
date: "9/9/2020"
output: html_document
---

```{r setup, include=FALSE}
list.of.packages <- c("dplyr", "tidyr", "zoo", "ggplot2", "xfun", "cowplot")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = '.')
```

## R Markdown

```{r, warning=FALSE, message=FALSE}
library(dplyr)
library(tidyr)
library(zoo)
library(ggplot2)
```

# Read in data

We are using data from the Lamprey River sensor record (LMP 73)
To facilitate the processing, this is a subset of the full record

```{r}
lmp_subset <- read.csv("LMP_subset_sensor.csv") 
head(lmp_subset)

```

pipes: %>%  cntl shift m 
df_cleaned <- df %>%  #df_cleaned gets df which gets the filter of a dataframe  
              filter(...) %>%  #which gets the select of that dataframe
              select(...)
# Clean and reshape

```{r}
#remove columns with grab sample data
lmp_clean <- lmp_subset %>% 
      select(DATETIME, Site, TempC, Conductivity, SpConductivity, pH, ODOPerCent, ODOMGL, TurbidityFNU, Q, NO3_corrected, FDOM_corrected_QSU,ABS254_SUNA,Year,Month,Day, Quarter) 
# or %>%  select(-RECORD, -Nitrate_mg)
     
head(lmp_clean)

```

## group_by and summarize

generate summary tables of your dataframes

```{r}
mean_month <- lmp_clean %>% 
      group_by(Month)  %>% # creates a semi-hidden attribute of your dataframe
      summarise(meanNO3 = mean(NO3_corrected), num_obs = n())

head(mean_month)# returns NA. why? combines years also

mean_month <- lmp_clean %>% 
      group_by(Year, Month)  %>% # creates a semi-hidden attribute of your dataframe
      summarise(meanNO3 = mean(NO3_corrected, na.rm = TRUE), num_obs = n())

head(mean_month)

#in what month were nitrate concentrations and discharge highest
month_max <- lmp_clean %>% 
      group_by(Year, Month) %>% 
      summarise(NO3_max = max(NO3_corrected, na.rm = TRUE), Q_max = max(Q, na.rm=TRUE)) %>% 
      arrange(-NO3_max) #arrange dataframe in decreasing order

head(month_max)

```


Generate new column with values based on another column
What year had the highest summer concentration?

```{r}
# generate a season column based on quarter
lmp_clean$Season <- ifelse(lmp_clean$Quarter == "1", "Winter", 
                           ifelse(lmp_clean$Quarter == "2", "Spring", 
                                  ifelse(lmp_clean$Quarter == "3", "Summer", "Fall")))

tail(lmp_clean)
summer_max <- lmp_clean %>% 
      filter(Season == "Summer") %>% 
      group_by(Year) %>% 
      summarise(NO3_max = max(NO3_corrected, na.rm = TRUE)) 
head(summer_max)

#simple ifelse() statement: 
#qualify high and low discharge based on mean

lmp_clean$Qqual <- ifelse(lmp_clean$Q < 8000, "lower", "higher")

head(lmp_clean)

```

Calculate moving average using dplyr pipeline

```{r}
daily_lmp <- lmp_clean %>%
  mutate(NO3_01da = rollapply(NO3_corrected, width = 96, FUN = mean, partial = 48), # daily
         NO3_07da = rollapply(NO3_corrected, width = 672, FUN = mean, partial = 48), # weekly
         NO3_30da = rollapply(NO3_corrected, width = 2880, FUN = mean, partial = 48)) # monthly
tail(daily_lmp)

```

Plot time series

```{r}
plot(x=daily_lmp$NO3_corrected, xlab="Time", ylab="NO3 (mg/L)", col="deepskyblue", type="l") 

plot(x=daily_lmp$NO3_01da, xlab="Time", ylab="NO3 (mg/L)", col="deepskyblue", type="l", main="Daily moving average") 

plot(x=daily_lmp$NO3_07da, xlab="Time", ylab="NO3 (mg/L)", col="darkgreen", type="l", main="Weekly moving average")

plot(x=daily_lmp$NO3_30da, xlab="Time", ylab="NO3 (mg/L)", col="black", type="l", main="Monthly moving average")

```

Plot using ggplot2

Use CQ data from WRR paper to illustrate ggplot
```{r}
lmpcq <- read.csv("LMP_30win_cq.csv")

head(lmpcq)


```

```{r}
ggplot(lmpcq, aes(y=NO3cq, x=NO3Q_rvalue )) +geom_point(col = 'blue')


```


```{r}
ggplot(lmpcq, aes(y=NO3cq, x=NO3Q_rvalue )) +geom_point(col = 'blue') + 
  scale_x_continuous(limits= c(-1,1)) + xlab(expression(italic(r) ~"r value")) +
  ylab(expression("NO"[3]^~{} ~ "CQ"))


```

```{r}
ggplot(lmpcq, aes(y=NO3cq, x=NO3Q_rvalue, shape=as.factor(Year), col=Season )) + geom_point(size=3) + 
  scale_x_continuous(limits= c(-1,1)) + xlab(expression(italic(r) ~" value")) +
  ylab(expression("NO"[3]^~{} ~ "CQ"))  + scale_shape_manual(values = c("2012" = 4,"2013" =21, "2014" = 22, "2015" = 23, "2016" = 24, "2017" = 25)) + scale_color_manual(values= c("Fall" = "chocolate4", "Spring" = "ForestGreen", "Summer" = "goldenrod2", "Winter" = "darkblue")) 
  



```

Change them to customize look. 
```{r}
library(cowplot)
ggplot(lmpcq, aes(y=NO3cq, x=NO3Q_rvalue, shape=as.factor(Year), col=Season )) + geom_point(size=3) + 
  scale_x_continuous(limits= c(-1,1)) + xlab(expression(italic(r) ~" value")) +
  ylab(expression("NO"[3]^~{} ~ "CQ"))  + scale_shape_manual(values = c("2012" = 4,"2013" =21, "2014" = 22, "2015" = 23, "2016" = 24, "2017" = 25)) + scale_color_manual(values= c("Fall" = "chocolate4", "Spring" = "ForestGreen", "Summer" = "goldenrod2", "Winter" = "darkblue")) + theme_cowplot() + theme(legend.position = "none")
  
```
```{r}
ggplot(lmpcq, aes(y=NO3cq, x=NO3Q_rvalue, shape=as.factor(Year), col=Season )) + geom_point(size=3) + 
  scale_x_continuous(limits= c(-1,1)) + xlab(expression(italic(r) ~" value")) +
  ylab(expression("NO"[3]^~{} ~ "CQ"))  + scale_shape_manual(values = c("2012" = 4,"2013" =21, "2014" = 22, "2015" = 23, "2016" = 24, "2017" = 25)) + scale_color_manual(values= c("Fall" = "chocolate4", "Spring" = "ForestGreen", "Summer" = "goldenrod2", "Winter" = "darkblue")) + theme_cowplot() + theme(legend.position = "none") + annotate("rect", ymin=-0.2, ymax=0.2, xmin=-Inf, xmax=Inf, alpha=0.1) + annotate("rect", xmin=-0.3, xmax=0.3, ymin=-Inf, ymax=Inf, alpha=0.1, col="blue", fill="blue") + annotate("text", label="A", x=-1,y=2)

```