Draft material for SP workshop slides

.gitignore

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 .Ruserdata
 .DS_Store
 *.dcf
+
+sp-slides_files/
+sp-slides_cache/

sp-slides.qmd

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+---
+title: "sp-slides"
+title-block-banner: true
+params:
+ L1_DATA: "./synoptic/data/L1/"
+date: now
+date-format: "YYYY-MM-DD HH:mm:ssZ"
+format: 
+ html:
+ code-fold: true
+editor: visual
+---
+
+## Maps and figures
+
+```{r prelims}
+#| include: false
+
+library(readr)
+library(dplyr)
+library(tidyr)
+library(lubridate)
+library(gt)
+
+library(ggplot2)
+theme_set(theme_bw())
+library(scales)
+library(viridis)
+library(gt)
+
+make_depth <- function(research_name) {
+ depth <- gsub("cm", " cm", substr(research_name, 10, 13))
+ factor(depth, levels = c("5 cm", "10 cm", "15 cm", "30 cm"))
+}
+```
+
+![Synoptic sites](synoptic/docs/synoptic_sites.png)
+
+![Exchange sites](synoptic/docs/exchange-synoptic.png)
+
+![Installation](synoptic/docs/alice_install.jpg)
+![Sensor](synoptic/docs/teros-12-soil-water-potential-sensor-curved.png)
+
+
+## Data prep
+
+```{r data-prep}
+#| include: false
+#| cache: true
+
+variable <- "soil_vwc"
+
+# Get the names of the files we need. Note this assumes that your
+# working directory is the main directory of the L1 data
+files <- list.files(params$L1_DATA,
+ pattern = ".*L1_v1-[0-9]\\.csv$", 
+ recursive = TRUE, full.names = TRUE)
+
+# Helper function to read the files and filter to just the variable we want
+# Note that we set "col_types" to force the "Plot" column to be read as a
+# character; see https://github.com/COMPASS-DOE/data-workflows/issues/186
+f <- function(f, variable) {
+ message("Reading ", basename(f))
+ x <- read_csv(f, col_types = "ccTccccdccii")
+ x <- x[grep(variable, x$research_name),]
+ x %>% 
+ group_by(Site, research_name) %>% 
+ summarise(TIMESTAMP = mean(TIMESTAMP), n = n(), .groups = "drop") %>% 
+ mutate(Date = as.Date(TIMESTAMP)) %>% 
+ select(-TIMESTAMP)
+}
+
+# Read the files and concatenate
+dat <- lapply(files, f, variable)
+dat <- bind_rows(dat)
+
+```
+
+## Observations (M)
+
+```{r table}
+dat %>% 
+ mutate(Year = year(Date)) %>% 
+ group_by(Year, Site) %>% 
+ summarise(n = sum(n, na.rm = TRUE), .groups = "drop") %>% 
+ pivot_wider(names_from = "Year", values_from = "n", values_fill = 0) %>% 
+ arrange(Site) ->
+ dat_table
+
+dat_table %>% 
+ gt::gt() %>% 
+ grand_summary_rows(columns = which(sapply(dat_table, is.numeric)),
+ fmt = ~fmt_number(., scale_by = 1e-6, decimals = 2),
+ fns = list(label = "Totals", id = "totals", fn = "sum")) %>% 
+ fmt_number(scale_by = 1e-6, decimals = 2)
+```
+
+## Observations over time
+
+```{r obs-over-time}
+dat %>%
+ complete(Site, Date, research_name, fill = list(rows = 0)) %>%
+ group_by(Site, Date) %>%
+ summarise(n = sum(n, na.rm = TRUE), .groups = "drop") %>%
+ arrange(Date) %>%
+ group_by(Site) %>%
+ mutate(cum_n = cumsum(n)) ->
+ smry1
+
+p1 <- ggplot(smry1, aes(Date, cum_n, fill = Site)) +
+ geom_area(alpha = 0.8 , linewidth = 0.5, colour = "white") +
+ xlab("Year") + ylab("COMPASS-FME VWC observations") +
+ scale_fill_viridis(discrete = TRUE) +
+ theme(axis.title = element_text(size = 14)) +
+ scale_y_continuous(labels = unit_format(unit = "M", scale = 1e-6))
+print(p1)
+
+dat %>%
+ complete(Site, Date, research_name, fill = list(rows = 0)) %>%
+ group_by(Date, research_name) %>%
+ summarise(n = sum(n, na.rm = TRUE), .groups = "drop") %>%
+ arrange(Date) %>%
+ group_by(research_name) %>%
+ mutate(cum_n = cumsum(n),
+ Depth = make_depth(research_name)) ->
+ smry2
+
+p2 <- ggplot(smry2, aes(Date, cum_n, fill = Depth)) +
+ geom_area(alpha = 0.8 , linewidth = 0.5, colour = "white") +
+ xlab("Year") + ylab("COMPASS-FME VWC observations") +
+ scale_fill_viridis(discrete = TRUE) +
+ theme(axis.title = element_text(size = 14)) +
+ scale_y_continuous(labels = unit_format(unit = "M", scale = 1e-6))
+print(p2)
+```
+
+## TEMPEST TEROS data
+
+```{r teros-prep}
+#| include: false
+#| cache: true
+
+# Focus on TEMPEST control to show some nice time series
+files <- list.files(params$L1_DATA,
+ pattern = "^TMP_C.*L1_v1-[0-9]\\.csv$", 
+ recursive = TRUE, full.names = TRUE)
+
+f <- function(f, variable) {
+ message("Reading ", basename(f))
+ x <- read_csv(f, col_types = "ccTccccdccii")
+ x[grep(variable, x$Instrument), c("TIMESTAMP", "Sensor_ID", "Value", "research_name")]
+}
+
+# Read the files and concatenate
+teros_dat <- lapply(files, f, "TEROS")
+teros_dat <- bind_rows(teros_dat)
+```
+
+```{r two-days}
+teros_dat %>% 
+ filter(as.Date(TIMESTAMP) %in% c(ymd("2021-06-14"), ymd("2021-06-15"))) ->
+ twodays
+
+twodays <- twodays[grep("soil_vwc", twodays$research_name),]
+
+# Two days plot that shows a cool midnight rain event
+twodays %>%
+ mutate(Depth = make_depth(research_name)) %>% 
+ ggplot(aes(TIMESTAMP, Value, color = Depth, group = Sensor_ID)) + 
+ geom_line(na.rm = TRUE) +
+ scale_x_datetime(date_breaks = "1 day") +
+ ylab("Soil VWC") +
+ theme(axis.text.x = element_text(angle = 90)) +
+ ggtitle("Rain event - TEMPEST control plot") +
+ facet_wrap(~Depth)
+```
+
+```{r teros-plot}
+teros_dat %>% 
+ mutate(Date = as.Date(TIMESTAMP)) %>% 
+ group_by(Sensor_ID, research_name, Date) %>% 
+ summarise(Value = mean(Value, na.rm = TRUE), .groups = "drop") %>% 
+ mutate(Depth = make_depth(research_name)) ->
+ full_record
+
+full_record_vwc <- full_record[grep("soil_vwc", full_record$research_name),]
+
+# Plot complete VWC record at TEMPEST control
+ggplot(full_record_vwc, aes(Date, Value, color = Depth, group = Sensor_ID)) + 
+ geom_line(na.rm = TRUE, alpha = 0.5) +
+ ylab("Soil VWC") +
+ ylim(c(0.1, 0.5)) +
+ ggtitle("TEMPEST control plot, all data")
+
+# Plot VWC versus temperature
+full_record %>% 
+ mutate(Month = month(Date)) %>% 
+ select(-Depth) %>% 
+ pivot_wider(names_from = "research_name", values_from = "Value") %>% 
+ ggplot(aes(soil_temp_15cm, soil_vwc_15cm, color = Month)) + 
+ geom_point(size = 0.4, na.rm = TRUE) +
+ ylim(c(0.1, 0.5)) + xlim(c(0,25)) +
+ xlab("Soil 15 cm temperature") + ylab("Soil 15 cm VWC") +
+ ggtitle("TEMPEST control plot, all data")
+```
+