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palmer_carbonate_qc.Rmd
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---
title: "palmer_carbonate_qc"
author: "Kate Morkeski"
date: "r Sys.Date()"
output: html_document
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Load libraries and set path
```{r}
library(tidyverse)
library(readxl)
library(lubridate)
library(here)
library(egg)
here("palmer-carb-qc")
```
## Read in 2020 data
```{r}
pal2020 <- read_excel('LMG20-01_carbonate_summary.xlsx')
pal2020 <- pal2020 %>%
rename(DIC_umolkg = "DIC_avg _umolkg-1",
TA_umolkg = "TA_umolkg",
Salinity_psu = "Salinity_to_use")
pal2020 <- pal2020 %>%
mutate(type = case_when(Depth != "underway" ~ "Niskin",
TRUE ~ Depth)) %>%
mutate(depth = case_when(type == "underway" ~ "0",
TRUE ~ Depth))
pal2020$depth <- as.numeric(pal2020$depth)
```
## Read in 2023 data
```{r}
pal2023 <- read_csv('LMG23-01_carbonate_summary.csv')
pal2023 <- pal2023 %>%
rename(Salinity_psu = "Salinity_primary") %>%
mutate(Date = mdy(Date))
pal2023 <- pal2023 %>%
mutate(type = case_when(Depth != "UW" ~ "Niskin",
Depth == "UW" ~ "underway"
)) %>%
mutate(depth = case_when(Depth == "UW" ~ "0",
TRUE ~ Depth))
pal2023$depth <- as.numeric(pal2023$depth)
```
## Read in 2024 data
```{r}
pal2024 <- read_csv('LMG24-01_carbonate_summary.csv')
pal2024 <- pal2024 %>%
rename(Salinity_psu = "Salinity_primary") %>%
mutate(Date = mdy(Date))
pal2024 <- pal2024 %>%
mutate(type = case_when(Depth != "underway" ~ "Niskin",
Depth == "underway" ~ "underway"
)) %>%
mutate(depth = case_when(Depth == "underway" ~ "0",
TRUE ~ Depth))
pal2024$depth <- as.numeric(pal2024$depth)
```
## Plot data
All samples
```{r}
plot1 <- ggplot(pal2020, aes(x= Salinity_psu, y = DIC_umolkg, color = type))+ geom_point()+labs(title = "LMG20-01")+theme(legend.position="none")
plot2 <- ggplot(pal2020, aes(x= Salinity_psu, y = TA_umolkg, color = type))+ geom_point()+theme(legend.position="top")
#ggplot(pal2020, aes(x= TA_umolkg, y = DIC_umolkg, color = type))+ geom_point()+labs(title = "LMG20-01")
plot3 <- ggplot(pal2020, aes(x= DIC_umolkg, y = -depth, color = type))+ geom_point()+theme(legend.position="none")
plot4 <- ggplot(pal2020, aes(x= TA_umolkg, y = -depth, color = type))+ geom_point()+theme(legend.position="none")
ggarrange(plot1, plot2, plot3, plot4, nrow = 2)
ggsave("LMG20-01.png")
plot1 <- ggplot(pal2023, aes(x= Salinity_psu, y = DIC_umolkg, color = type))+ geom_point()+labs(title = "LMG23-01")+theme(legend.position="none")
plot2 <- ggplot(pal2023, aes(x= Salinity_psu, y = TA_umolkg, color = type))+ geom_point()+theme(legend.position="top")
#ggplot(pal2023, aes(x= TA_umolkg, y = DIC_umolkg, color = type))+ geom_point()+labs(title = "LMG23-01")
plot3 <- ggplot(pal2023, aes(x= DIC_umolkg, y = -depth, color = type))+ geom_point()+theme(legend.position="none")
plot4 <- ggplot(pal2023, aes(x= TA_umolkg, y = -depth, color = type))+ geom_point()+theme(legend.position="none")
ggarrange(plot1, plot2, plot3, plot4, nrow = 2)
ggsave("LMG23-01.png")
plot1 <- ggplot(pal2024, aes(x= Salinity_psu, y = DIC_umolkg, color = type))+ geom_point()+labs(title = "LMG24-01")+theme(legend.position="none")
plot2 <- ggplot(pal2024, aes(x= Salinity_psu, y = TA_umolkg, color = type))+ geom_point()+theme(legend.position="top")
#ggplot(pal2024, aes(x= TA_umolkg, y = DIC_umolkg, color = type))+ geom_point()+labs(title = "LMG24-01")
plot3 <- ggplot(pal2024, aes(x= DIC_umolkg, y = -depth, color = type))+ geom_point()+theme(legend.position="none")
plot4 <- ggplot(pal2024, aes(x= TA_umolkg, y = -depth, color = type))+ geom_point()+theme(legend.position="none")
ggarrange(plot1, plot2, plot3, plot4, nrow = 2)
ggsave("LMG24-01.png")
```
Niskin samples
```{r}
ggplot(subset(pal2020, type == "Niskin"), aes(x= DIC_umolkg, y = -depth, color = Salinity_psu))+ geom_point() + scale_color_gradient(low="yellow", high="red")
ggplot(subset(pal2020, type == "Niskin"), aes(x= TA_umolkg, y = -depth, color = Salinity_psu))+ geom_point() + scale_color_gradient(low="yellow", high="red")
ggplot(subset(pal2020, type == "Niskin"), aes(x= Salinity_psu, y = DIC_umolkg, color = Event)) + geom_point() + scale_color_gradient(low="yellow", high="red")
ggplot(subset(pal2020, type == "Niskin"), aes(x= Salinity_psu, y = TA_umolkg, color = Event)) + geom_point() + scale_color_gradient(low="yellow", high="red")
ggplot(subset(pal2020, type == "Niskin"), aes(x= TA_umolkg, y = DIC_umolkg, color = Event)) + geom_point() + scale_color_gradient(low="yellow", high="red")
```
Surface Niskins
```{r}
# plot Niskin dic and ta vs depth, colored by event number
ggplot(subset(pal2020, type == "Niskin" & depth < 100), aes(x= DIC_umolkg, y = -depth, color = Event))+ geom_point() + scale_color_gradient(low="yellow", high="red")
ggplot(subset(pal2020, type == "Niskin" & depth < 100), aes(x= TA_umolkg, y = -depth, color = Event))+ geom_point() + scale_color_gradient(low="yellow", high="red")
# plot Niskin dic and ta vs event number
# note large differences between some DIC replicates
ggplot(subset(pal2020, type == "Niskin" ), aes(x= Event, y = DIC_umolkg, color = depth))+ geom_point() + scale_color_gradient(low="orange", high="red")
ggplot(subset(pal2020, type == "Niskin" ), aes(x= Event, y = TA_umolkg, color = depth))+ geom_point() + scale_color_gradient(low="orange", high="red")
```
Underway samples
```{r}
ggplot(subset(pal2020, type == "underway"), aes(x= Salinity_psu, y = DIC_umolkg, color = Event))+ geom_point()
ggplot(subset(pal2020, type == "underway"), aes(x= Station, y = DIC_umolkg))+ geom_point()
+ scale_color_gradient(low="aquamarine1", high="aquamarine4")
ggplot(subset(pal2020, type == "underway"), aes(x= Salinity_psu, y = TA_umolkg, color = Event))+ geom_point() + scale_color_gradient(low="aquamarine1", high="aquamarine4")
```
## Read in, clean, and plot historical data
```{r}
# run knb-lter-pal.44.8.r downloaded from the EDI Data Portal
# temporal coverage 1993-2019
source("knb-lter-pal.44.8.r")
historical <- dt1
historical <- historical %>%
mutate(DIC1 = replace(DIC1, DIC1 <0, NA)) %>% ## to replace -9.0
mutate(Alkalinity2 = replace(Alkalinity2, Alkalinity2 <0, NA)) %>% ## to replace -999
mutate(Salinity = replace(Salinity, Salinity < 0, NA)) ## to replace -999
# examine outlying values
lowalk <- subset(historical, Alkalinity1 < 2000 | Alkalinity2 < 2000) # note one observation Alkalinity.1 = 10 umol/kg
lowdic <- subset(historical, DIC1 < 1800 | DIC2 < 1800)
lowsal <- subset(historical, Salinity < 1)
# prepare for plotting
# rename columns to match pal2020
historical <- historical %>%
rename(Salinity_psu = Salinity,
DIC_umolkg = DIC1,
TA_umolkg = Alkalinity1) %>%
mutate(type = "historical")
historical <- historical %>%
mutate(year = year(DatetimeGMT))
historical$year <- as.factor(historical$year)
# eliminate some oddly low values from 1996
# one low alkalinity of 10, two samples with DIC of 1212 and 1225 umol/kg
historical <- historical %>%
filter (TA_umolkg > 10) %>%
filter (DIC_umolkg > 1500)
histund <- historical %>% filter(BottleNumber == "UW")
histNiskin <- historical %>% filter(BottleNumber != "UW")
```
```{r}
# plot dic and ta
# note: plotting only DIC1, and Alkalinity1, not DIC2 and Alkalinity2
ggplot(historical, aes(x= Salinity_psu, y = DIC_umolkg, color = -Depth))+ geom_point()
ggplot(historical, aes(x= Salinity_psu, y = TA_umolkg, color = -Depth))+ geom_point()
ggplot(historical, aes(x= DIC_umolkg, y = -Depth, color = Salinity_psu))+ geom_point()
ggplot(historical, aes(x= TA_umolkg, y = -Depth, color = Salinity_psu))+ geom_point()
ggplot(historical, aes(x= DIC_umolkg, y = -Depth, color = year))+ geom_point()
ggplot(historical, aes(x= TA_umolkg, y = -Depth, color = year))+ geom_point()
ggplot(historical, aes(x= Salinity_psu, y = DIC_umolkg, color = year))+ geom_point()+scale_fill_discrete(year)
ggplot(historical, aes(x= Salinity_psu, y = TA_umolkg, color = year))+ geom_point()
ggplot(histund, aes(x= Salinity_psu, y = DIC_umolkg, color = year))+ geom_point()+scale_fill_discrete(year)
ggplot(histund, aes(x= NominalStation, y = DIC_umolkg, color = year))+ geom_point()+scale_fill_discrete(year)
ggplot(histund, aes(x= year, y = DIC_umolkg, color = year))+ geom_point()+scale_fill_discrete(year)
ggplot(histNiskin, aes(x= DIC_umolkg, y = -Depth, color = Salinity_psu))+ geom_point()
ggplot(histNiskin, aes(x= TA_umolkg, y = -Depth, color = Salinity_psu))+ geom_point()
```
## Plot 2020 and historical data
```{r}
# plot historical and 2020 dic and ta vs salinity, colored by type (historical, Niskin 2020, underway 2020)
ggplot(historical, aes(x= Salinity_psu, y = DIC_umolkg, color = type))+ geom_point() +geom_point(data = pal2020 )
ggplot(historical, aes(x= Salinity_psu, y = TA_umolkg, color = type))+ geom_point() +geom_point(data = pal2020 )
```
# Examine replicates
```{r}
replicates <- read_excel("LMG20-01_carbonate_replicates.xlsx")
ggplot(replicates, aes(x= DIC_avg_umolkg, y = DIC_diff_replicates, color = Depth))+ geom_point()
ggplot(replicates, aes(x= TA_umolkg, y = TA_diff_replicates, color = Depth))+ geom_point()
```