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Analysis of existing line-point-intercept data to learn whether fewer samples can return similar results

R 100.00%

bootstrap rarefaction vegetation-measurement methods

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efficient_intercepts's Issues

Typo

Correct spelling of "vegetation"

efficient_intercepts/veg_survey_model.R

Line 22 in 2a8d6e5

    
           #' the vegetaiton data, and the grid points used in the test were chosen to maximize contrasts.

Revise loop function, replace with vectorized function

efficient_intercepts/raw_code_sandbox.R

Lines 168 to 186 in 04666e8

    
           # Create list objects for each grid point and transform objects to species-samples matrices 
        
           for (i in 1:length(grid_points)) { 
        
             # filter spe_mat_df to individual grid points and pivot to a species-samples matrix 
        
             spe_mat_temp_df <- 
        
               data.frame( 
        
                 spe_mat_df %>%  
        
                   filter(grid_point == grid_points[i]) %>%  
        
                   pivot_wider(names_from = key_plant_code, values_from = detected, values_fn = min, values_fill = 0) %>%  
        
                   arrange(transect_point) %>%  
        
                   select(-NV, -grid_point), 
        
                 row.names = 1 
        
               ) 
        
             # store filtered data as list object 
        
             spe_mat_list[[i]] <- 
        
               assign( 
        
                 paste0("gp_", grid_points[i]), 
        
                 spe_mat_temp_df 
        
               ) 
        
           }

Remove mapping tools

We won't be using maps in this report.

efficient_intercepts/raw_code_sandbox.R

Lines 49 to 62 in 78b8692

    
           # GG map, current version GitHub (slow loading) 
        
           if(!requireNamespace("devtools")) install.packages("devtools") 
        
           devtools::install_github("dkahle/ggmap", ref = "tidyup", force = TRUE) 
        
           # mapping 
        
           library("ggmap") 
        
           # API keys 
        
           # ———————————————————————————————————————— 
        
           # API keys are pulled from local resources and are not available in the hosted environment. 
        
           # Users must have API keys for Google Big Query and Google Maps 
        
           # Google Maps API (local file) 
        
           mapKey <- fromJSON(file = "/Users/blarkin/Egnyte/Private/blarkin/ΩMiscellaneous/R_global/R_globalKeys.json")$mapKey 
        
           register_google(key = mapKey)

Ground cover resampling code is incomplete

Complete the ground cover resampling code

The code linked here is a good start, but needs to be modified and finished to continue the analysis
This is a test as I have not done an issue or branch yet

Tasks

efficient_intercepts/raw_code_sandbox.R

Lines 268 to 281 in 0e8592a

    
           #### Ground cover #### 
        
           # —————————————————————————————————— 
        
           grcov_pull_df %>%   
        
             filter(grid_point == 285) %>%  
        
             mutate(detected = 1) %>%  
        
             glimpse() %>%  
        
             group_by(intercept_ground_code) %>%  
        
             summarize(pct = sum(detected) / 2) 
        
           ## use sample_n https://dplyr.tidyverse.org/reference/sample.html 
        
           samp_grcov <- grcov_pull_df[sample(nrow(grcov_pull_df), replace = TRUE, 100), ]

bglarkin / efficient_intercepts Goto Github PK

efficient_intercepts's People

Contributors

Watchers

efficient_intercepts's Issues

Typo

Revise loop function, replace with vectorized function

Remove mapping tools

Ground cover resampling code is incomplete

Complete the ground cover resampling code

Tasks

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	# Create list objects for each grid point and transform objects to species-samples matrices
	for (i in 1:length(grid_points)) {
	# filter spe_mat_df to individual grid points and pivot to a species-samples matrix
	spe_mat_temp_df <-
	data.frame(
	spe_mat_df %>%
	filter(grid_point == grid_points[i]) %>%
	pivot_wider(names_from = key_plant_code, values_from = detected, values_fn = min, values_fill = 0) %>%
	arrange(transect_point) %>%
	select(-NV, -grid_point),
	row.names = 1
	)
	# store filtered data as list object
	spe_mat_list[[i]] <-
	assign(
	paste0("gp_", grid_points[i]),
	spe_mat_temp_df
	)
	}

	# GG map, current version GitHub (slow loading)
	if(!requireNamespace("devtools")) install.packages("devtools")
	devtools::install_github("dkahle/ggmap", ref = "tidyup", force = TRUE)
	# mapping
	library("ggmap")

	# API keys
	# ————————————————————————————————————————
	# API keys are pulled from local resources and are not available in the hosted environment.
	# Users must have API keys for Google Big Query and Google Maps

	# Google Maps API (local file)
	mapKey <- fromJSON(file = "/Users/blarkin/Egnyte/Private/blarkin/ΩMiscellaneous/R_global/R_globalKeys.json")$mapKey
	register_google(key = mapKey)

	#### Ground cover ####
	# ——————————————————————————————————


	grcov_pull_df %>%
	filter(grid_point == 285) %>%
	mutate(detected = 1) %>%
	glimpse() %>%
	group_by(intercept_ground_code) %>%
	summarize(pct = sum(detected) / 2)

	## use sample_n https://dplyr.tidyverse.org/reference/sample.html

	samp_grcov <- grcov_pull_df[sample(nrow(grcov_pull_df), replace = TRUE, 100), ]