Merge pull request #10 from stineb/main

Chapter 6 continuation

R/read_fdk.R

@@ -1,7 +1,7 @@
-read_fdk <- function(site, path){
+read_fdk <- function(site, path, pattern = "DD"){
   print(site)
   filn <- list.files(path = path)
   filn <- filn[which(stringr::str_detect(filn, pattern = site))]
-  filn <- filn[which(stringr::str_detect(filn, pattern = "DD"))]
+  filn <- filn[which(stringr::str_detect(filn, pattern = pattern))]
   readr::read_csv(paste0(path, "/", filn))
 }

analysis/plot_anet_vs_temp.R

@@ -1,38 +1,70 @@
-library(tidyverse)
+library(readr)
+library(dplyr)
+library(ggplot2)
+library(cowplot)
 
 # load Kumarathunge et al. data
 df <- read_csv("~/data/aci-tglob_v1.0/ACi-TGlob_V1.0.csv")
 
-df |> 
-  filter(Dataset == "Tundra USA-AK") |> 
-  group_by(Species) |> 
-  summarise(n = n())
+df <- read_rds("~/Downloads/all_sites_simulation_and_original-data.rds")
 
-# spatial - adaptation
-df |> 
-  filter(
-    Dataset == "Tundra USA-AK" &
-    Ci > 270 & Ci < 280
-  ) |> 
+# good: 5
+gg1 <- df$data_org[[9]] |> 
   ggplot(
     aes(
       x = Tleaf,
-      y = Photo
+      y = Photo,
+      color = Ci,
+      shape = as.factor(Curve_Id)
     )
   ) +
   geom_point()
 
-# seasonal - acclimation
-df |> 
-  filter(
-    Dataset == "Maritime Pine, France" &
-      Ci > 250 & Ci < 300
-  ) |> 
+gg2 <- df$data_org[[9]] |> 
+  filter(Curve_Id == 1) |> 
   ggplot(
     aes(
-      x = Tleaf,
+      x = Ci,
       y = Photo
     )
   ) +
   geom_point()
 
+plot_grid(gg1, gg2, nrow = 2)
+
+ggsave(here::here("fig/example.pdf"), width = 8, height = 6)
+
+# 
+# df |> 
+#   filter(Dataset == "Tundra USA-AK") |> 
+#   group_by(Species) |> 
+#   summarise(n = n())
+# 
+# # spatial - adaptation
+# df |> 
+#   filter(
+#     Dataset == "Tundra USA-AK" &
+#     Ci > 270 & Ci < 280
+#   ) |> 
+#   ggplot(
+#     aes(
+#       x = Tleaf,
+#       y = Photo
+#     )
+#   ) +
+#   geom_point()
+# 
+# # seasonal - acclimation
+# df |> 
+#   filter(
+#     Dataset == "Maritime Pine, France" &
+#       Ci > 250 & Ci < 300
+#   ) |> 
+#   ggplot(
+#     aes(
+#       x = Tleaf,
+#       y = Photo
+#     )
+#   ) +
+#   geom_point()
+# 

analysis/plot_climate_diagrams_biomes.R

@@ -36,7 +36,8 @@ source(here::here("R/read_fdk.R"))
 
 # Reading from file prepared above -----------
 # read from file
-df_sites <- read_rds(here::here("data/df_sites.rds"))
+# df_sites <- read_rds(here::here("data/df_sites.rds"))
+df_sites <- read_csv(here::here("data/fdk_site_info.csv"))
 
 # chose representative sites
 use_sites <- c(
@@ -45,12 +46,15 @@ use_sites <- c(
   "FR-Pue", # Mediterranean Forests, Woodlands & Scrub
   "DE-Hai", # Temperate Broadleaf & Mixed Forests
   "US-Tw1", # Temperate Grasslands, Savannas & Shrublands
-  "AU-How", # Tropical & Subtropical Grasslands, Savannas & Shrublan
-  "MY-PSO", # Tropical moist broadleaved (evergreen) forest
+  "AU-How", # Tropical & Subtropical Grasslands, Savannas & Shrubland
+  # "MY-PSO", # Tropical moist broadleaved (evergreen) forest
+  "BR-Sa3", # Tropical
   "ZM-Mon", # Tropical deciduous forest (xeric woodland)
-  "GL-ZaH"  # Tundra
+  "US-ICh" # Tundra
+  # "GL-ZaH"  # Tundra
 )
 
+
 # subset sites
 df_sites_sub <- df_sites |> 
   filter(sitename %in% use_sites)
@@ -59,8 +63,9 @@ saveRDS(df_sites_sub, file = here::here("data/df_sites_sub.rds"))
 
 # read daily flux data for each site
 df <- df_sites_sub |> 
-  select(sitename, lon, lat, elv, BIOME_NAME) |> 
-  mutate(data = purrr::map(sitename, ~read_fdk(., path = "~/data/FluxDataKit/FLUXDATAKIT_FLUXNET"))) |> 
+  select(sitename, lon, lat, elv) |> 
+  # mutate(data = purrr::map(sitename, ~read_fdk(., path = "~/data/FluxDataKit/FLUXDATAKIT_FLUXNET"))) |> 
+  mutate(data = purrr::map(sitename, ~read_fdk(., path = "~/data/FluxDataKit/v3.1/zenodo_upload/fluxnet/"))) |>
   unnest(data) |> 
   group_by(sitename) |> 
   nest()
@@ -71,7 +76,7 @@ create_diagram_per_site <- function(site, df){
     mutate(year = lubridate::year(TIMESTAMP),
            month = lubridate::month(TIMESTAMP),
            day = lubridate::mday(TIMESTAMP)) |> 
-    select(year, month, day, P_F, TMAX_F_MDS, TMIN_F_MDS, lat, lon, elv, BIOME_NAME) |> 
+    select(year, month, day, P_F, TMAX_F_MDS, TMIN_F_MDS, lat, lon, elv) |> 
     as.data.frame()
 
   filn <- paste0(here::here("book/images/diagram_"),