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TMax_viz.Rmd
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---
title: 'Spatial and Temporal Analysis of Reservoir Warming Trends: Assessing Geographic
Gradients, Temporal Dynamics, and Regional Influences'
output:
html_document:
toc: TRUE
toc_depth: 4
toc_float: true
---
```{r setup, include=FALSE, message = FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, echo = FALSE)
library(dplyr)
library(ggplot2)
# read in data
df <- readr::read_csv("output_data/TMax_mblmModelStatistics.csv")
df.stations <- readr::read_csv("VADEQ_stationInfo.csv")
df.trendSites <- openxlsx::read.xlsx("data_original.xlsx", sheet = 2)
df.merged <- openxlsx::read.xlsx("data_original.xlsx", sheet = 3)
```
```{r `derive/prepare plotting data`}
# derive mean warming and incorporate back into df for plotting
all_months_mean <- df %>%
group_by(FDT_STA_ID) %>%
summarize(mean_slope = mean(model_slope, na.rm = TRUE),
.groups = "drop") %>%
mutate(Month = "All Months",
trend= "N/A")
df <- bind_rows(df, all_months_mean)
# populate model slope column with mean slope where Month == "All Months"
df <- df %>%
mutate(model_slope = if_else(Month == "All Months", mean_slope, model_slope))
# set month as factor and join long/lat data
df <- df %>%
mutate(Month = factor(Month, levels = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December", "All Months")),
trend = ifelse(is.na(df$model_pval), "N/A",
ifelse(df$model_pval <= 0.05, "significant", "not significant"))) %>%
left_join(df.stations, join_by("FDT_STA_ID" == "Station_Id"))
df$trend <- factor(df$trend, levels = c("significant", "not significant", "N/A"))
# derive monitoring record length
df <- df %>%
mutate(monitoring_length =
as.numeric(
(lubridate::mdy(df$LAST_SAMPLE_DATE) -
lubridate::mdy(df$FIRST_SAMPLE_DATE))
/365)
)
# derive monitoring span
whatIneed <- df.trendSites[, c(2, 4:5)]
df <- df %>%
left_join(whatIneed, join_by("FDT_STA_ID"))
# derive midpoint
df <- df %>%
mutate(midpoint = (MaxYear + MinYear) / 2)
# derive profile depth
depth.data <- df.merged %>%
select(FDT_STA_ID, MaxDepth.y) %>%
group_by(FDT_STA_ID) %>%
summarize(max.depth = first(MaxDepth.y))
df <- df %>%
left_join(depth.data, join_by("FDT_STA_ID"))
```
## warming vs. lat/long
<br>
```{r `vs lat, by month`, fig.height = 6.5, message = FALSE}
# Faceting plots by month
ggplot(data = df, aes(x = Latitude, y = model_slope)) +
geom_point(aes(shape = trend), alpha = .6, size =2) +
geom_smooth(data = df, aes(x = Latitude, y = model_slope),
method = "lm") +
facet_wrap(~factor(Month), scales = "free_y") +
labs(title = "Monthly Warming Rates vs. Latitude",
x = "Latitude",
y = "T Trend (C/y)") +
scale_shape_manual(values = c(1, 16, 10)) +
theme_minimal() +
theme(legend.position = "top",
panel.border = element_rect(colour = "black", fill=NA, size=.5),
plot.title = element_text(size = 16), # Adjusts plot title size
axis.title = element_text(size = 14), # Adjusts axis titles size
axis.text = element_text(size = 11.5), # Adjusts axis text size
legend.text = element_text(size = 9.5), # Adjusts legend text size
legend.title = element_text(size = 10.5), # Adjusts legend title size
strip.text = element_text(size = 12)) # Adjusts facet label size
```
<br>
```{r `vs long, by month`, fig.height=6.5}
ggplot(data = df, aes(x = Longitude, y = model_slope)) +
geom_point(aes(shape = trend), alpha = .6, size = 2) +
geom_smooth(data = df, aes(x = Longitude, y = model_slope),
method = "lm") +
facet_wrap(~factor(Month), scales = "free_y") +
labs(title = "Monthly Warming Rates vs. Longitude",
x = "Longitude",
y = "T Trend (C/y)") +
scale_shape_manual(values = c(1, 16, 10)) +
theme_minimal() +
theme(legend.position = "top",
panel.border = element_rect(colour = "black", fill=NA, size=.5),
plot.title = element_text(size = 16), # Adjusts plot title size
axis.title = element_text(size = 14), # Adjusts axis titles size
axis.text = element_text(size = 11.5), # Adjusts axis text size
legend.text = element_text(size = 9.5), # Adjusts legend text size
legend.title = element_text(size = 10.5), # Adjusts legend title size
strip.text = element_text(size = 12)) # Adjusts facet label size
```
<br>
## warming vs monitoring span
```{r `vs monitoring span, by month`, fig.height = 6.5}
ggplot() +
geom_point(data = df,
aes(x = monitoring_length, y = model_slope,
shape = trend), alpha = .6, size = 2) +
geom_smooth(data = df, aes(x = monitoring_length, y = model_slope),
method = "lm") +
facet_wrap(~factor(Month), scales = "free_y") +
labs(title = "Warming Rates vs. Monitoring Span",
x = "Monitoring Span (y)",
y = "T Trend (C/y)") +
scale_shape_manual(values = c(1, 16, 10)) +
theme_minimal() +
theme(legend.position = "top",
panel.border = element_rect(colour = "black", fill=NA, size=.5),
plot.title = element_text(size = 16), # Adjusts plot title size
axis.title = element_text(size = 14), # Adjusts axis titles size
axis.text = element_text(size = 11.5), # Adjusts axis text size
legend.text = element_text(size = 9.5), # Adjusts legend text size
legend.title = element_text(size = 10.5), # Adjusts legend title size
strip.text = element_text(size = 12)) # Adjusts facet label size
```
<br>
## warming vs. monitoring start year
```{r `vs start year, by month`, fig.height = 6.5}
ggplot() +
geom_point(data = df,
aes(x = MinYear, y = model_slope,
shape = trend), alpha = .6, size = 2) +
geom_smooth(data = df, aes(x = MinYear, y = model_slope),
method = "lm") +
facet_wrap(~factor(Month), scales = "free_y") +
labs(title = "Warming Rates vs. Initial Monitoring Year",
x = "First Year of Monitoring",
y = "T Trend (C/y)") +
scale_shape_manual(values = c(1, 16, 10)) +
theme_minimal() +
theme(legend.position = "top",
panel.border = element_rect(colour = "black", fill=NA, size=.5),
plot.title = element_text(size = 16), # Adjusts plot title size
axis.title = element_text(size = 14), # Adjusts axis titles size
axis.text = element_text(size = 11.5), # Adjusts axis text size
legend.text = element_text(size = 9.5), # Adjusts legend text size
legend.title = element_text(size = 10.5), # Adjusts legend title size
strip.text = element_text(size = 12)) # Adjusts facet label size
```
<br><br>
## warming vs. midpoint of years
```{r `vs midpoint, by month`, fig.height = 6.5}
ggplot() +
geom_point(data = df,
aes(x = midpoint, y = model_slope,
shape = trend), alpha = .6, size = 2) +
geom_smooth(data = df, aes(x = midpoint, y = model_slope),
method = "lm") +
facet_wrap(~factor(Month), scales = "free_y") +
labs(title = "Warming Rates vs. Monitoring Midpoint",
x = "Monitoring Midpoint",
y = "T Trend (C/y)") +
scale_shape_manual(values = c(1, 16, 10)) +
theme_minimal() +
theme(legend.position = "top",
panel.border = element_rect(colour = "black", fill=NA, size=.5),
plot.title = element_text(size = 16), # Adjusts plot title size
axis.title = element_text(size = 14), # Adjusts axis titles size
axis.text = element_text(size = 11.5), # Adjusts axis text size
legend.text = element_text(size = 9.5), # Adjusts legend text size
legend.title = element_text(size = 10.5), # Adjusts legend title size
strip.text = element_text(size = 12)) # Adjusts facet label size
```
<br><br>
## warming vs level III ecoregions
```{r, message = FALSE, include=FALSE}
library(sf)
# read in ecoregions shapefile
eco.sf <- suppressMessages(st_read("ecoregions_shp"))
# transform primary df to sf object
df.sf <- st_as_sf(df, coords = c("Longitude", "Latitude"), crs = 4326)
# convert to same crs as ecoregions
df.sf <- st_transform(df.sf, crs = st_crs(eco.sf))
# spatial join ecoregions to reservoir data
eco.sf <- eco.sf %>%
select(US_L3CODE, US_L3NAME, geometry)
ecoregions.df <- st_join(df.sf, eco.sf)
unique(ecoregions.df$US_L3NAME)
```
```{r, fig.height=6.5}
# bar charts
p <- ecoregions.df %>%
group_by(US_L3NAME, Month) %>%
summarize(mean_anomaly = mean(model_slope))
ggplot(p, aes(x = US_L3NAME, y = mean_anomaly, fill = US_L3NAME)) +
geom_bar(stat = "identity", position = position_dodge(), alpha = .8) +
facet_wrap(~factor(Month), scales = "free_y") +
labs(title = "Mean Warming Anomaly by Ecoregion and Month",
x = "Ecoregion",
y = "Mean Warming Anomaly",
fill = "Ecoregion") +
theme_minimal() +
theme(legend.position = "top") +
theme(axis.text.x = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=.5),
plot.title = element_text(size = 16), # Adjusts plot title size
axis.title = element_text(size = 14), # Adjusts axis titles size
axis.text = element_text(size = 11.5), # Adjusts axis text size
legend.text = element_text(size = 9.5), # Adjusts legend text size
legend.title = element_text(size = 10.5, face="bold"), # Adjusts legend title size
strip.text = element_text(size = 12)) # Adjusts facet label size
```
<br>
```{r, fig.height=7.5}
# box plots
ggplot(ecoregions.df, aes(x = US_L3NAME, y = model_slope, fill = US_L3NAME)) +
geom_boxplot(alpha = .8) +
facet_wrap(~factor(Month), scales = "fixed") +
labs(title = "Variation in Warming Rates by Month",
x = "Ecoregion",
y = "T Trend (C/y)",
fill = "Ecoregion") +
theme_minimal() +
theme(axis.text.x = element_blank(),
legend.position = "top",
panel.border = element_rect(colour = "black", fill=NA, size=.5),
plot.title = element_text(size = 16), # Adjusts plot title size
axis.title = element_text(size = 14), # Adjusts axis titles size
axis.text = element_text(size = 11.5), # Adjusts axis text size
legend.text = element_text(size = 9.5), # Adjusts legend text size
legend.title = element_text(size = 10.5, face="bold"), # Adjusts legend title size
strip.text = element_text(size = 12)) # Adjusts facet label size
```
<br><br>
## warming vs profile depth
```{r `vs profile depth`, fig.height = 6.5}
ggplot() +
geom_point(data = df,
aes(x = max.depth, y = model_slope,
shape = trend), alpha = .6, size = 2) +
geom_smooth(data = df, aes(x = max.depth, y = model_slope),
method = "lm") +
facet_wrap(~factor(Month), scales = "free_y") +
labs(title = "Warming Rates vs. Max Profile Depth",
x = "Max Profile Depth (m)",
y = "T Trend (C/y)") +
scale_shape_manual(values = c(1, 16, 10)) +
theme_minimal() +
theme(legend.position = "top",
panel.border = element_rect(colour = "black", fill=NA, size=.5),
plot.title = element_text(size = 16), # Adjusts plot title size
axis.title = element_text(size = 14), # Adjusts axis titles size
axis.text = element_text(size = 11.5), # Adjusts axis text size
legend.text = element_text(size = 9.5), # Adjusts legend text size
legend.title = element_text(size = 10.5), # Adjusts legend title size
strip.text = element_text(size = 12)) # Adjusts facet label size
```
<br><br>
## variation in warming rate, by month
```{r `warming variation box plot`}
ggplot(df, aes(x = Month, y = model_slope, fill = Month)) +
geom_boxplot(alpha = .85) +
labs(title = "Variation in Warming Rates by Month",
x = "Month",
y = "T Trend (C/y)",
fill = "Month") +
theme_minimal() +
theme(axis.title.x = element_text(margin = margin(t = 10)),
legend.position = "none")
```
<br><br>
## Roanoke River (station 4AROA038.49)
mblm regressions by month for Roanoke River station, the only monitoring station where data collection began prior to 1970.
<br>
```{r `longest monitored site`}
#Lastly, we have one site where monitoring began before 1970. Could you make plots of anomaly vs. year with mblm regressions for the 6 months (and show the slope and p value on the plot)?
# this code is copied and adapted from the initial dataPrep.rmd file. Refer to that for more thorough annotation
pre70 <- df.merged %>%
filter(FDT_STA_ID == "4AROA038.49")
pre70$Date <- pre70$Date * 86400 # 86400 = seconds in a day.
pre70$Date <- as.POSIXct(pre70$Date, origin = "1899-12-30", tz = "UTC")
pre70 <- pre70 %>%
mutate(MonthNum = lubridate::month(Date),
Month = month.name[MonthNum]) %>%
filter(FDT_STA_ID == "4AROA038.49" ) %>%
filter(MonthNum %in% 5:10)
pre70_summary <- pre70 %>%
group_by(FDT_STA_ID, MonthNum) %>%
summarize(Mean = mean(TMax, na.rm = TRUE),
Min = min(TMax, na.rm = TRUE),
Max = max(TMax, na.rm = TRUE),
Nobs = n(),
.groups = 'drop')
pre70 <- pre70 %>%
left_join(pre70_summary, by = c("FDT_STA_ID", "MonthNum")) %>%
mutate(TAnomaly = TMax - Mean)
pre70$Year <- lubridate::year(pre70$Date)
# remove NA values -- mblm() does not handle NA as lm() does
pre70 <- pre70 %>%
filter(!is.na(TAnomaly))
# create list to store regression results
modelSummaries <- list()
for (month_num in 5:10) {
month_station <- pre70 %>%
filter(MonthNum == month_num)
model <- mblm::mblm(TAnomaly ~ Year, data = month_station)
mod.sum <- mblm::summary.mblm(model)
# store results
modelSummaries[[month.name[month_num]]] <- list(
slope = mod.sum$coefficients[2,1],
MAD = mod.sum$coefficients["Year", "MAD"] ,
pvalue = mod.sum$coefficients["Year", 4]
)
}
#---
pre70_summary <- pre70_summary %>%
mutate( model_slope = NA,
model_MAD = NA,
model_pval = NA)
for (i in 1:nrow(pre70_summary)) {
month = month.name[as.integer(pre70_summary[i, "MonthNum"])]
pre70_summary$model_slope[i] <- modelSummaries[[month]]$slope
pre70_summary$model_MAD[i] <- modelSummaries[[month]]$MAD
pre70_summary$model_pval[i] <- modelSummaries[[month]]$pvalue
}
```
```{r `plot for pre-70 station`}
# refer to UVA statlab page for plotting CI with mblm regression via bootstrapping
# calculate mblm for each month and store intercepts and slopes
mblm_fits <- pre70 %>%
group_by(Month) %>%
do(model = mblm::mblm(TAnomaly ~ Year, data = .)) %>%
summarise(intercept = coef(model)[1],
slope = coef(model)[2],
"p val" = mblm::summary.mblm(model)$coefficients[2,4],
Month = first(Month)) %>%
mutate(Month = factor(Month))
# create df to use for plotting
plottingData <- merge(pre70, mblm_fits, by = "Month")
plottingData <- plottingData %>%
mutate(Month = factor(Month, levels = c("May", "June", "July", "August", "September", "October")))
# create labels and df to use for adding slope/pval to plots
my_labels <- c()
for (month in month.name[5:10]) {
my_labels <- c(my_labels, mblm_fits %>%
filter(Month == month) %>%
summarise(label = paste("Slope=", round(slope, 3), "\np-value=", round(`p val`, 4))) %>%
pull(label))
}
annotation_data <- data.frame(
Month = factor(c("May", "June", "July", "August", "September", "October")),
x = c(1983,1985,1980, 1994, 1981.2, 1982.5),
y = c(0.1, -4.5, 1.4, -4.1, -2.5, -6.2),
label = my_labels)
ggplot(data = plottingData) +
geom_point(aes(x = Year, y = TAnomaly), alpha = .6, size = 2) +
geom_abline(aes(intercept = intercept, slope = slope), color = "blue", linewidth = 1) +
facet_wrap(~factor(Month), scales = "free_y") +
geom_text(data = annotation_data, aes(x = x, y = y, label = label),
size = 2.5,
color = "grey35",
hjust =0,
inherit.aes = FALSE) +
labs(title = "Warming Rates vs. Year, Roanoke River Station 4AROA038.49",
x = "Year",
y = "T Trend (C/y)") +
scale_shape_manual(values = c(1, 16)) +
theme_minimal() +
theme(legend.position = "top",
panel.border = element_rect(colour = "black", fill=NA, size=.5),
plot.title = element_text(size = 16), # Adjusts plot title size
axis.title = element_text(size = 14), # Adjusts axis titles size
axis.text = element_text(size = 11.5), # Adjusts axis text size
legend.text = element_text(size = 9.5), # Adjusts legend text size
legend.title = element_text(size = 10.5), # Adjusts legend title size
strip.text = element_text(size = 12)) # Adjusts facet label size
```
```{r, include = FALSE, eval = FALSE, echo = FALSE}
ggplot(df, aes(x = Month, y = model_slope, fill = Month)) +
geom_boxplot(alpha = .85) +
labs(title = "Variation in Warming Rates by Month",
x = "Month",
y = "T Trend (C/y)",
fill = "Month") +
theme_minimal() +
theme(axis.title.x = element_text(margin = margin(t = 10)),
axis.title = element_text(size = 20),
axis.text = element_text(size = 15),
legend.position = "none")
ggsave("boxplot.jpg")
```