R Notebook

# Load necessary libraries
library(dplyr)
library(tidyr)
library(kableExtra)
library(here)
# Add other libraries as needed

# Define the path to your R folder
r_folder_path <- here("R")

# List all R script files in the R folder (including subdirectories)
function_files <- list.files(
  path = r_folder_path,
  pattern = "\\.R$",
  full.names = TRUE,
  recursive = TRUE
)

# Source each function file
invisible(sapply(function_files, function(file) {
  tryCatch(
    source(file, echo = FALSE, print.eval = FALSE),
    error = function(e) {
      message(sprintf("Error sourcing %s: %s", file, e$message))
    }
  )
}))

Load CA Treatment Stand Level Summary Data

fvs.stand.raw = readRDS('/Users/katharynduffy/Library/CloudStorage/GoogleDrive-katharyn@vibrantplanet.net/Shared drives/VP - Resilience Group/Forest_Analytics_Team/FVS/fvs_resp_fxns/sp-raw-data/ca-data/outputs/raw_stand_data-ca.rds')

fvs.stand.raw = fvs.stand.raw%>%
  filter(!MgmtID %in% paste0('FIC', 1:6))%>%
  filter(Year>2024)

# Define the years of interest
years_of_interest <- c(2025, 2029, 2030, 2031, 2035, 2036, 2040, 2045, 2050, 2055, 2060)

all.metrics = data.frame(colnames(fvs.stand.raw)) #peruse possible variables
#Unique removal codes
unique(fvs.stand.raw$RmvCode)

[1] 0 2 1

#Unique Years
unique(fvs.stand.raw$Year)

 [1] 2025 2029 2030 2031 2035 2036 2040 2045 2050 2055 2060 2065

# Call the function
yrs.raw.data <- temporal_coverage_table(fvs.stand.raw, years_of_interest)

# View the resulting presence table
yrs.raw.data%>%
  kbl()%>%
  kable_minimal()

MgmtID	2025	2029	2030	2031	2035	2036	2040	2045	2050	2055	2060
MTIR	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MTUR	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RMGP	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RXGF	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
BASE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
CMCC	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
HCTA	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
CMUR	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
HERB	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MRCC	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MRCT	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MTTH	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
REVA	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RXAI	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RMMA	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RMTF	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE

Remove multiple Stand / Year combos populated by RmvCode:

# Apply the filter_stand_data function
fvs.stand <- filter_duplicate_rmvcodes(fvs.stand.raw)

# QC check
unique(fvs.stand$RmvCode)

[1] 0 2

rm(fvs.stand.raw)

# Call the function
yrs.rmv.data <- temporal_coverage_table(fvs.stand, years_of_interest)

# View the resulting presence table
yrs.rmv.data%>%
  kbl()%>%
  kable_minimal()

MgmtID	2025	2029	2030	2031	2035	2036	2040	2045	2050	2055	2060
MTIR	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MTUR	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RMGP	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RXGF	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
BASE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
CMCC	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
HCTA	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
CMUR	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
HERB	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MRCC	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MRCT	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MTTH	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
REVA	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RXAI	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RMMA	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RMTF	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE

fvs.treated.stands <- apply_trt_thresholds(fvs.stand)
rm(fvs.stand)

# Call the function
yrs.trt.data <- temporal_coverage_table(fvs.treated.stands, years_of_interest)

# View the resulting presence table
yrs.trt.data%>%
  kbl()%>%
  kable_minimal()

MgmtID	2025	2029	2030	2031	2035	2036	2040	2045	2050	2055	2060
MTIR	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MTUR	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RMGP	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RXGF	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
BASE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
CMCC	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
HCTA	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
CMUR	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
HERB	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MRCC	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MRCT	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
MTTH	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
REVA	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RXAI	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RMMA	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE
RMTF	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE

head(fvs.treated.stands)%>%
  kbl()%>%
  kable_minimal()

CaseID	StandID	Year	RmvCode	Age	Tpa	TPrdTpa	BA	SDI	CCF	TopHt	QMD	TCuFt	TPrdTCuFt	MCuFt	TPrdMCuFt	BdFt	TPrdBdFt	RTpa	RTCuFt	RMCuFt	RBdFt	PrdLen	Acc	Mort	MAI	ForTyp	SizeCls	StkCls	Stand_CN	MgmtID	RunTitle	KeywordFile	SamplingWt	Variant	Version	RV	Groups	RunDateTime	Aboveground_Total_Live	Aboveground_Merch_Live	Belowground_Live	Belowground_Dead	Standing_Dead	Forest_Down_Dead_Wood	Forest_Floor	Forest_Shrub_Herb	Total_Stand_Carbon	Total_Removed_Carbon	Carbon_Released_From_Fire	Surface_Litter	Surface_Duff	Surface_lt3	Surface_ge3	Surface_3to6	Surface_6to12	Surface_ge12	Surface_Herb	Surface_Shrub	Surface_Total	Standing_Snag_lt3	Standing_Snag_ge3	Standing_Foliage	Standing_Live_lt3	Standing_Live_ge3	Standing_Total	Total_Biomass	Total_Consumed	Biomass_Removed	Removal_Code	Stratum_1_DBH	Stratum_1_Nom_Ht	Stratum_1_Lg_Ht	Stratum_1_Sm_Ht	Stratum_1_Crown_Base	Stratum_1_Crown_Cover	Stratum_1_SpeciesFVS_1	Stratum_1_SpeciesFVS_2	Stratum_1_SpeciesPLANTS_1	Stratum_1_SpeciesPLANTS_2	Stratum_1_SpeciesFIA_1	Stratum_1_SpeciesFIA_2	Stratum_1_Status_Code	Stratum_2_DBH	Stratum_2_Nom_Ht	Stratum_2_Lg_Ht	Stratum_2_Sm_Ht	Stratum_2_Crown_Base	Stratum_2_Crown_Cover	Stratum_2_SpeciesFVS_1	Stratum_2_SpeciesFVS_2	Stratum_2_SpeciesPLANTS_1	Stratum_2_SpeciesPLANTS_2	Stratum_2_SpeciesFIA_1	Stratum_2_SpeciesFIA_2	Stratum_2_Status_Code	Stratum_3_DBH	Stratum_3_Nom_Ht	Stratum_3_Lg_Ht	Stratum_3_Sm_Ht	Stratum_3_Crown_Base	Stratum_3_Crown_Cover	Stratum_3_SpeciesFVS_1	Stratum_3_SpeciesFVS_2	Stratum_3_SpeciesPLANTS_1	Stratum_3_SpeciesPLANTS_2	Stratum_3_SpeciesFIA_1	Stratum_3_SpeciesFIA_2	Stratum_3_Status_Code	Number_of_Strata	Total_Cover	Structure_Class	RSDI	Hard_snags_class1	Hard_snags_class2	Hard_snags_class3	Hard_snags_class4	Hard_snags_class5	Hard_snags_class6	Hard_snags_total	Soft_snags_class1	Soft_snags_class2	Soft_snags_class3	Soft_snags_class4	Soft_snags_class5	Soft_snags_class6	Soft_snags_total	Hard_soft_snags_total	...1	...16	...32	...34	...78	...98	...125
d2853743-4b60-45b9-9561-9d58a65a9fe5	10002	2025	0	9	70.31014	70.31014	15.55495	34	17	38	6.368859	367.7327	367.7327	56.59509	56.59509	257.4843	257.4843	0	0	0	0	4	5.266905	0.9283861	0	997	2	4	1009	MTIR	MTIR	fe528030-2e70-4121-8fdc-fbbe7c1ee03c	1	CA	FS2023.2	20230518	All_Plots, All_Stands	2024-05-07-14:15:59	4.216443	0.4951076	0.4189228	0.3393207	0.6408244	2.248843	0.3650955	0.3554602	8.584909	0	0	0.8236790	0.1630656	1.223393	3.2742934	2.6682353	0.6060582	0	0.4155434	0.2953770	6.195351	0.0286911	1.2529577	0.4256367	1.470347	7	10	16	0	0	1	6.656566	38	48	29	28	31	OH	--	2TB	--	998	--	2	0	0	0	0	0	0	--	--	--	--	--	--	0	0	0	0	0	0	0	--	--	--	--	--	--	0	1	31	1=SI	6.163643	51.734772	0	0	0	0	0	51.734772	0	0	0	0	0	0	0	51.734772	NA	NA	NA	NA	NA	NA	NA
d2853743-4b60-45b9-9561-9d58a65a9fe5	10002	2029	0	13	69.47730	69.47730	16.17525	35	17	39	6.533415	385.0866	385.0866	76.54671	76.54671	358.0955	358.0955	0	0	0	0	1	5.480518	0.9781715	0	997	2	4	1009	MTIR	MTIR	fe528030-2e70-4121-8fdc-fbbe7c1ee03c	1	CA	FS2023.2	20230518	All_Plots, All_Stands	2024-05-07-14:15:59	4.488533	0.6763937	0.4318150	0.2896062	0.0832323	2.370314	0.3970927	0.3541953	8.414789	0	0	0.8500748	0.2231488	1.090417	3.6502111	2.8971472	0.7530638	0	0.4137444	0.2946461	6.522242	0.0344498	0.1320147	0.4411587	1.606773	7	9	16	0	0	1	6.854605	41	48	30	29	31	OH	--	2TB	--	998	--	2	0	0	0	0	0	0	--	--	--	--	--	--	0	0	0	0	0	0	0	--	--	--	--	--	--	0	1	31	1=SI	6.328206	1.822002	0	0	0	0	0	1.822002	0	0	0	0	0	0	0	1.822002	NA	NA	NA	NA	NA	NA	NA
d2853743-4b60-45b9-9561-9d58a65a9fe5	10002	2030	0	14	69.27037	69.27037	16.32815	35	18	39	6.574021	389.5891	389.5891	107.45572	107.45572	528.1117	528.1117	0	0	0	0	1	5.085802	0.9902851	0	997	2	4	1009	MTIR	MTIR	fe528030-2e70-4121-8fdc-fbbe7c1ee03c	1	CA	FS2023.2	20230518	All_Plots, All_Stands	2024-05-07-14:15:59	4.519055	0.9516329	0.4349737	0.2784372	0.0786167	2.276728	0.4100558	0.3538950	8.351762	0	0	0.8705845	0.2376745	3.515780	1.0376769	0.8206959	0.2169810	0	0.4133173	0.2944726	6.369506	0.0317411	0.1254924	0.4449717	1.567224	7	9	16	0	0	1	6.893935	41	49	31	29	31	OH	--	2TB	--	998	--	2	0	0	0	0	0	0	--	--	--	--	--	--	0	0	0	0	0	0	0	--	--	--	--	--	--	0	1	31	1=SI	6.368267	1.705845	0	0	0	0	0	1.705845	0	0	0	0	0	0	0	1.705845	NA	NA	NA	NA	NA	NA	NA
d2853743-4b60-45b9-9561-9d58a65a9fe5	10002	2031	0	15	69.06400	69.06400	16.47536	36	18	40	6.613446	393.6846	393.6846	114.23206	114.23206	560.5508	560.5508	0	0	0	0	4	5.082666	0.9974985	0	997	2	4	1009	MTIR	MTIR	fe528030-2e70-4121-8fdc-fbbe7c1ee03c	1	CA	FS2023.2	20230518	All_Plots, All_Stands	2024-05-07-14:15:59	4.564504	1.0149332	0.4380045	0.2677520	0.0785624	2.111834	0.4200583	0.3536138	8.234328	0	0	0.8814083	0.2538843	3.232385	0.9912822	0.7830707	0.2082115	0	0.4129174	0.2943102	6.066188	0.0320543	0.1250705	0.4486377	1.560144	7	9	15	0	0	1	6.932452	41	49	31	29	31	OH	--	2TB	--	998	--	2	0	0	0	0	0	0	--	--	--	--	--	--	0	0	0	0	0	0	0	--	--	--	--	--	--	0	1	31	1=SI	6.406497	1.663239	0	0	0	0	0	1.663239	0	0	0	0	0	0	0	1.663239	NA	NA	NA	NA	NA	NA	NA
d2853743-4b60-45b9-9561-9d58a65a9fe5	10002	2035	0	19	68.24440	68.24440	17.07808	37	18	40	6.773644	410.0252	410.0252	159.20885	159.20885	790.8375	790.8375	0	0	0	0	1	5.524703	1.0500964	0	997	2	4	1009	MTIR	MTIR	fe528030-2e70-4121-8fdc-fbbe7c1ee03c	1	CA	FS2023.2	20230518	All_Plots, All_Stands	2024-05-07-14:15:59	4.833869	1.4297450	0.4503874	0.2296645	0.0843967	1.536088	0.4477706	0.3524703	7.934647	0	0	0.8964277	0.3137632	2.248035	0.8241401	0.6468021	0.1773379	0	0.4112912	0.2936495	4.987307	0.0372365	0.1315569	0.4636208	1.695878	8	10	15	0	0	1	7.065926	42	49	33	29	32	OH	--	2TB	--	998	--	2	0	0	0	0	0	0	--	--	--	--	--	--	0	0	0	0	0	0	0	--	--	--	--	--	--	0	1	32	1=SI	6.561871	1.620583	0	0	0	0	0	1.620583	0	0	0	0	0	0	0	1.620583	NA	NA	NA	NA	NA	NA	NA
d2853743-4b60-45b9-9561-9d58a65a9fe5	10002	2036	0	20	68.04076	68.04076	17.23244	37	18	41	6.814363	414.4999	414.4999	173.78842	173.78842	869.8893	869.8893	0	0	0	0	4	5.206530	1.0578144	0	997	2	4	1009	MTIR	MTIR	fe528030-2e70-4121-8fdc-fbbe7c1ee03c	1	CA	FS2023.2	20230518	All_Plots, All_Stands	2024-05-07-14:15:59	4.861170	1.5613950	0.4535350	0.2210992	0.0825141	1.457364	0.4602750	0.3521876	7.888146	0	0	0.9164776	0.3275088	2.123497	0.7912316	0.6190196	0.1722119	0	0.4108890	0.2934861	4.863091	0.0336190	0.1314091	0.4674380	1.648465	8	10	15	0	0	1	7.110052	41	49	33	30	32	OH	--	2TB	--	998	--	2	0	0	0	0	0	0	--	--	--	--	--	--	0	0	0	0	0	0	0	--	--	--	--	--	--	0	1	32	1=SI	6.601368	1.580098	0	0	0	0	0	1.580098	0	0	0	0	0	0	0	1.580098	NA	NA	NA	NA	NA	NA	NA

NA

library(plotly)

# Set a seed for reproducibility
set.seed(123)

# Define the sample size per MgmtID (adjust as needed)
sample_size <- 500  # Sample up to 500 observations per MgmtID

# Sample the data
sampled_data <- fvs.treated.stands %>%
  filter(Year>2025 & Year<2036)%>%
  group_by(MgmtID) %>%
  sample_n(size = min(n(), sample_size), replace = FALSE) %>%
  ungroup()

# Create the plot using the sampled data
qc.plot <- plot_ly(
  data = sampled_data,
  x = ~Year,
  y = ~Stratum_1_Crown_Cover,
  type = 'box',
  color = ~MgmtID
)

# Display the plot
qc.plot

NA

# First plot: BA
p1 <- plot_ly(data = sampled_data, x = ~Year, y = ~BA, type = 'box', color = ~MgmtID) %>%
  layout(title = "BA", yaxis = list(title = "BA"))
p1

# Second plot: Tpa
p2 <- plot_ly(data = sampled_data, x = ~Year, y = ~Tpa, type = 'box', color = ~MgmtID) %>%
  layout(title = "Tpa", yaxis = list(title = "Tpa"))
p2

# Third plot: Stratum_1_Crown_Cover
p3 <- plot_ly(data = sampled_data, x = ~Year, y = ~Stratum_1_Crown_Cover, type = 'box', color = ~MgmtID) %>%
  layout(title = "Stratum_1_Crown_Cover", yaxis = list(title = "Stratum_1_Crown_Cover"))
p3

NA

saveRDS(fvs.treated.stands, here('data','CA-TRT-StandLevel.rds'))