.Rhistory

recovery_rate = median(recovery_rate, na.rm = TRUE),
yield_ppm = median(yield_ppm, na.rm = TRUE))
filtered_commodity_country <- filtered_commodity_join %>% filter(GID_0 == country) %>% summarise(geom = st_union(geom))
filtered_commodity_country$commodity <- commodity
filtered_commodity_country$GID_0 <- country
filtered_commodity_country <- filtered_commodity_country %>%
cross_join(filtered_commodity_country_summaryfacility)%>%
cross_join(filtered_commodity_country_summaryamounts)
View(filtered_commodity_country)
print(j)
country <- countries[j]
filtered_commodity_country <- filtered_commodity_join %>% filter(GID_0 == country)
filtered_commodity_country_summaryfacility <- filtered_commodity_country %>%
st_drop_geometry() %>%
group_by(facility_type) %>%
summarise(count = n()) %>%
pivot_wider(names_from = facility_type, values_from = count)
filtered_commodity_country_summaryamounts <- filtered_commodity_country %>%
st_drop_geometry() %>%
summarise(value_tonnes = sum(value_tonnes, na.rm = TRUE),
grade_ppm = median(grade_ppm, na.rm = TRUE),
recovery_rate = median(recovery_rate, na.rm = TRUE),
yield_ppm = median(yield_ppm, na.rm = TRUE))
filtered_commodity_country <- filtered_commodity_join %>% filter(GID_0 == country) %>% summarise(geom = st_union(geom))
filtered_commodity_country$commodity <- commodity
filtered_commodity_country$GID_0 <- country
filtered_commodity_country <- filtered_commodity_country %>%
cross_join(filtered_commodity_country_summaryfacility)%>%
cross_join(filtered_commodity_country_summaryamounts)
country_output_list_shapefile[[j]] <- filtered_commodity_country
View(filtered_commodity_country)
for(j in 1:length(countries)){
print(j)
country <- countries[j]
filtered_commodity_country <- filtered_commodity_join %>% filter(GID_0 == country)
filtered_commodity_country_summaryfacility <- filtered_commodity_country %>%
st_drop_geometry() %>%
group_by(facility_type) %>%
summarise(count = n()) %>%
pivot_wider(names_from = facility_type, values_from = count)
filtered_commodity_country_summaryamounts <- filtered_commodity_country %>%
st_drop_geometry() %>%
summarise(value_tonnes = sum(value_tonnes, na.rm = TRUE),
grade_ppm = median(grade_ppm, na.rm = TRUE),
recovery_rate = median(recovery_rate, na.rm = TRUE),
yield_ppm = median(yield_ppm, na.rm = TRUE))
filtered_commodity_country <- filtered_commodity_join %>% filter(GID_0 == country) %>% summarise(geom = st_union(geom))
filtered_commodity_country$commodity <- commodity
filtered_commodity_country$GID_0 <- country
filtered_commodity_country <- filtered_commodity_country %>%
cross_join(filtered_commodity_country_summaryfacility)%>%
cross_join(filtered_commodity_country_summaryamounts)
country_output_list_shapefile[[j]] <- filtered_commodity_country
}
country_output_list_shapefile_bind <- do.call(bind_rows, country_output_list_shapefile)
View(country_output_list_shapefile_bind)
filtered_commodity <- input_sites_buffer %>%
filter(primary_commodity == commodity |  str_detect(commodities_products, regex(commodity, ignore_case = TRUE)))
View(filtered_commodity)
names(country_output_list_shapefile_bind)
country_output_list_shapefile_bind <- country_output_list_shapefile_bind %>%
select(commodity, GID_0, value_tonnes, grade_ppm, recovery_rate, yield_ppm, everything())
View(country_output_list_shapefile_bind)
commodities_products <- c(commodities, products)
commodities_and_products <- c(commodities, products)
commodities_and_products <- c(commodities, products)
rm(commodities_products)
commodities_and_products
i = 11
print(i)
commodity <- commodities_and_products[i]
# filter data so that we just have records where it is the primary commodity or in the main products
filtered_commodity <- input_sites_buffer %>%
filter(primary_commodity == commodity |  str_detect(commodities_products, regex(commodity, ignore_case = TRUE)))
View(filtered_commodity)
# remove those that have ended production
filtered_commodity <- filtered_commodity %>% filter(is.na(production_end))
# filter commodity production
commodity_values_filter <- commodity_values_max %>%
filter(str_detect(material_name, regex(commodity, ignore_case = TRUE))) %>%
group_by(facility_id) %>%
summarise(value_tonnes = sum(value_tonnes, na.rm = TRUE),
grade_ppm = max(grade_ppm, na.rm = TRUE),
recovery_rate = max(recovery_rate, na.rm = TRUE),
yield_ppm = max(yield_ppm, na.rm = TRUE))%>%
mutate(across(everything(), ~ replace(., . == -Inf, NA)))
View(commodity_values_filter)
# join
filtered_commodity_join <- filtered_commodity %>% left_join(commodity_values_filter)
View(filtered_commodity_join)
View(filtered_commodity_join)
# read in main commodities
commodities <- c("Copper", "Iron", "Coal", "Gold", "Nickel", "Aluminium", "Silver", "Zinc", "Other (poly)-metallic")
products <- c("Cobalt", "Diamonds", "Lead", "Lithium", "Manganese", "Palladium", "Steel", "Tin")
commodities_and_products <- c(commodities, products)
countries <- input_sites_buffer %>% st_drop_geometry() %>% select(GID_0) %>% unique() %>% unlist() %>% unname()
# read in main commodities and Products that we are interested in
commodities <- c("Copper", "Iron", "Coal", "Gold", "Nickel", "Aluminium", "Silver", "Zinc", "Other (poly)-metallic")
products <- c("Cobalt", "Diamonds", "Lead", "Lithium", "Manganese", "Palladium", "Steel", "Tin")
commodities_and_products <- c(commodities, products)
# get commodity/country combinations and create list of shapefiles
output_list <- list()
country_output_list_shapefile_bind
# get commodity/country combinations and create list of shapefiles
output_list <- list()
# loop through
for(i in 1:length(commodities_products)){
print(i)
commodity <- commodities_and_products[i]
# filter data so that we just have records where it is the primary commodity or in the main products
filtered_commodity <- input_sites_buffer %>%
filter(primary_commodity == commodity |  str_detect(commodities_products, regex(commodity, ignore_case = TRUE)))
# remove those that have ended production
filtered_commodity <- filtered_commodity %>% filter(is.na(production_end))
# filter commodity production
commodity_values_filter <- commodity_values_max %>%
filter(str_detect(material_name, regex(commodity, ignore_case = TRUE))) %>%
group_by(facility_id) %>%
summarise(value_tonnes = sum(value_tonnes, na.rm = TRUE),
grade_ppm = max(grade_ppm, na.rm = TRUE),
recovery_rate = max(recovery_rate, na.rm = TRUE),
yield_ppm = max(yield_ppm, na.rm = TRUE))%>%
mutate(across(everything(), ~ replace(., . == -Inf, NA)))
# join
filtered_commodity_join <- filtered_commodity %>% left_join(commodity_values_filter)
# get countries
countries <- filtered_commodity %>% st_drop_geometry() %>% select(GID_0) %>% unique() %>% unlist() %>% unname()
country_output_list_shapefile <- list()
for(j in 1:length(countries)){
print(j)
country <- countries[j]
filtered_commodity_country <- filtered_commodity_join %>% filter(GID_0 == country)
filtered_commodity_country_summaryfacility <- filtered_commodity_country %>%
st_drop_geometry() %>%
group_by(facility_type) %>%
summarise(count = n()) %>%
pivot_wider(names_from = facility_type, values_from = count)
filtered_commodity_country_summaryamounts <- filtered_commodity_country %>%
st_drop_geometry() %>%
summarise(value_tonnes = sum(value_tonnes, na.rm = TRUE),
grade_ppm = median(grade_ppm, na.rm = TRUE),
recovery_rate = median(recovery_rate, na.rm = TRUE),
yield_ppm = median(yield_ppm, na.rm = TRUE))
filtered_commodity_country <- filtered_commodity_join %>% filter(GID_0 == country) %>% summarise(geom = st_union(geom))
filtered_commodity_country$commodity <- commodity
filtered_commodity_country$GID_0 <- country
filtered_commodity_country <- filtered_commodity_country %>%
cross_join(filtered_commodity_country_summaryfacility)%>%
cross_join(filtered_commodity_country_summaryamounts)
country_output_list_shapefile[[j]] <- filtered_commodity_country
}
# bind list together
country_output_list_shapefile_bind <- do.call(bind_rows, country_output_list_shapefile)
# select order of columns
country_output_list_shapefile_bind <- country_output_list_shapefile_bind %>%
select(commodity, GID_0, value_tonnes, grade_ppm, recovery_rate, yield_ppm, everything())
# add to list
output_list[[i]] <- country_output_list_shapefile_bind
}
# loop through
for(i in 1:length(commodities_and_products)){
print(i)
commodity <- commodities_and_products[i]
# filter data so that we just have records where it is the primary commodity or in the main products
filtered_commodity <- input_sites_buffer %>%
filter(primary_commodity == commodity |  str_detect(commodities_products, regex(commodity, ignore_case = TRUE)))
# remove those that have ended production
filtered_commodity <- filtered_commodity %>% filter(is.na(production_end))
# filter commodity production
commodity_values_filter <- commodity_values_max %>%
filter(str_detect(material_name, regex(commodity, ignore_case = TRUE))) %>%
group_by(facility_id) %>%
summarise(value_tonnes = sum(value_tonnes, na.rm = TRUE),
grade_ppm = max(grade_ppm, na.rm = TRUE),
recovery_rate = max(recovery_rate, na.rm = TRUE),
yield_ppm = max(yield_ppm, na.rm = TRUE))%>%
mutate(across(everything(), ~ replace(., . == -Inf, NA)))
# join
filtered_commodity_join <- filtered_commodity %>% left_join(commodity_values_filter)
# get countries
countries <- filtered_commodity %>% st_drop_geometry() %>% select(GID_0) %>% unique() %>% unlist() %>% unname()
country_output_list_shapefile <- list()
for(j in 1:length(countries)){
print(j)
country <- countries[j]
filtered_commodity_country <- filtered_commodity_join %>% filter(GID_0 == country)
filtered_commodity_country_summaryfacility <- filtered_commodity_country %>%
st_drop_geometry() %>%
group_by(facility_type) %>%
summarise(count = n()) %>%
pivot_wider(names_from = facility_type, values_from = count)
filtered_commodity_country_summaryamounts <- filtered_commodity_country %>%
st_drop_geometry() %>%
summarise(value_tonnes = sum(value_tonnes, na.rm = TRUE),
grade_ppm = median(grade_ppm, na.rm = TRUE),
recovery_rate = median(recovery_rate, na.rm = TRUE),
yield_ppm = median(yield_ppm, na.rm = TRUE))
filtered_commodity_country <- filtered_commodity_join %>% filter(GID_0 == country) %>% summarise(geom = st_union(geom))
filtered_commodity_country$commodity <- commodity
filtered_commodity_country$GID_0 <- country
filtered_commodity_country <- filtered_commodity_country %>%
cross_join(filtered_commodity_country_summaryfacility)%>%
cross_join(filtered_commodity_country_summaryamounts)
country_output_list_shapefile[[j]] <- filtered_commodity_country
}
# bind list together
country_output_list_shapefile_bind <- do.call(bind_rows, country_output_list_shapefile)
# select order of columns
country_output_list_shapefile_bind <- country_output_list_shapefile_bind %>%
select(commodity, GID_0, value_tonnes, grade_ppm, recovery_rate, yield_ppm, everything())
# add to list
output_list[[i]] <- country_output_list_shapefile_bind
}
# bind all together
output_sf <- do.call(bind_rows, output_list)
View(output_sf)
# write to file
st_write(output_sf, new_geopackage_file, layer = "Mines_Country_Commodity_Shapefile", driver = "GPKG", delete_layer = TRUE)
# read in data
input_locations <- st_read(new_geopackage_file, layer = "Mines_Country_Commodity_Shapefile")
# read in rasters for main extract
brisk_raster_files <- list.files(paste0(github_filepath, "BRiSK/Prep/Rasters"),
pattern = "*.tif$",
full.names = TRUE)
# readin the biome rasters
biome_raster_files <- list.files(paste0(github_filepath, "BRiSK/Prep/Rasters/IUCN_Typology_Biomes"),
pattern = "*.tif$",
full.names = TRUE)
# read in rasters
brisk_rasters <- brisk_raster_files %>%
lapply(rast) %>%
print()
biome_rasters <- biome_raster_files %>%
lapply(rast) %>%
print()
# stack
brisk_stack <- rast(brisk_rasters)
names(brisk_stack)
biome_stack <- rast(biome_rasters)
names(biome_stack)
# extract
brisk_extract <- exact_extract(brisk_stack, input_locations, c('max', 'mean', 'quantile', 'sum'), quantiles = c(0.8),
progress = TRUE, append_cols = ID_cols, full_colnames = TRUE)
# set ID cols
input_locations <- input_locations %>%
rownames_to_column()
names(input_locations)
ID_cols <- c("rowname", "commodity", "GID_0")
# read in rasters for main extract
brisk_raster_files <- list.files(paste0(github_filepath, "BRiSK/Prep/Rasters"),
pattern = "*.tif$",
full.names = TRUE)
# readin the biome rasters
biome_raster_files <- list.files(paste0(github_filepath, "BRiSK/Prep/Rasters/IUCN_Typology_Biomes"),
pattern = "*.tif$",
full.names = TRUE)
# read in rasters
brisk_rasters <- brisk_raster_files %>%
lapply(rast) %>%
print()
biome_rasters <- biome_raster_files %>%
lapply(rast) %>%
print()
# stack
brisk_stack <- rast(brisk_rasters)
names(brisk_stack)
biome_stack <- rast(biome_rasters)
names(biome_stack)
# extract
brisk_extract <- exact_extract(brisk_stack, input_locations, c('max', 'mean', 'quantile', 'sum'), quantiles = c(0.8),
progress = TRUE, append_cols = ID_cols, full_colnames = TRUE)
input_locations
View(input_locations)
input_locations <- input_locations[st_geometry_type(input_locations) %in% c("POLYGON", "MULTIPOLYGON"), ]
# read in data
input_locations <- st_read(new_geopackage_file, layer = "Mines_Country_Commodity_Shapefile")
input_locations <- input_locations[st_geometry_type(input_locations) %in% c("POLYGON", "MULTIPOLYGON"), ]
# set ID cols
input_locations <- input_locations %>%
rownames_to_column()
ID_cols <- c("rowname", "commodity", "GID_0")
# read in rasters for main extract
brisk_raster_files <- list.files(paste0(github_filepath, "BRiSK/Prep/Rasters"),
pattern = "*.tif$",
full.names = TRUE)
# readin the biome rasters
biome_raster_files <- list.files(paste0(github_filepath, "BRiSK/Prep/Rasters/IUCN_Typology_Biomes"),
pattern = "*.tif$",
full.names = TRUE)
# read in rasters
brisk_rasters <- brisk_raster_files %>%
lapply(rast) %>%
print()
biome_rasters <- biome_raster_files %>%
lapply(rast) %>%
print()
# stack
brisk_stack <- rast(brisk_rasters)
names(brisk_stack)
biome_stack <- rast(biome_rasters)
names(biome_stack)
# extract
brisk_extract <- exact_extract(brisk_stack, input_locations, c('max', 'mean', 'quantile', 'sum'), quantiles = c(0.8),
progress = TRUE, append_cols = ID_cols, full_colnames = TRUE)
View(brisk_extract)
coverage_biomes <- exact_extract(biome_stack, input_locations, 'sum', quantiles = c(0.8),
progress = TRUE, append_cols = ID_cols, full_colnames = TRUE)
potential_biomes <- coverage_biomes %>%
pivot_longer(-c(ID_cols)) %>%
filter(value > 0)
potential_biomes_all <- potential_biomes %>%
ungroup() %>%
select(-value) %>%
unique() %>%
group_by_at(ID_cols) %>%
mutate(Biomes = paste0(name, collapse = "|")) %>%
select(all_of(ID_cols), Biomes) %>%
unique()
potential_biomes_max <- potential_biomes %>%
group_by_at(c(ID_cols,"name")) %>%
summarise(total = sum(value)) %>%
slice_max(total, with_ties = FALSE) %>%
select(-total) %>%
rename(Main_Biome = name) %>%
left_join(potential_biomes_all)
# join back
brisk_extract <- potential_biomes_max %>% left_join(brisk_extract)
View(potential_biomes_max)
View(coverage_biomes)
potential_biomes <- coverage_biomes %>%
pivot_longer(-c(ID_cols)) %>%
filter(value > 0)
coverage_biomes <- exact_extract(biome_stack, input_locations, 'sum', quantiles = c(0.8),
progress = TRUE, append_cols = ID_cols, full_colnames = TRUE)
# sumarise biomes
names(coverage_biomes) <- gsub("sum.", "", names(coverage_biomes))
potential_biomes <- coverage_biomes %>%
pivot_longer(-c(ID_cols)) %>%
filter(value > 0)
potential_biomes_all <- potential_biomes %>%
ungroup() %>%
select(-value) %>%
unique() %>%
group_by_at(ID_cols) %>%
mutate(Biomes = paste0(name, collapse = "|")) %>%
select(all_of(ID_cols), Biomes) %>%
unique()
potential_biomes_max <- potential_biomes %>%
group_by_at(c(ID_cols,"name")) %>%
summarise(total = sum(value)) %>%
slice_max(total, with_ties = FALSE) %>%
select(-total) %>%
rename(Main_Biome = name) %>%
left_join(potential_biomes_all)
# join back
brisk_extract <- potential_biomes_max %>% left_join(brisk_extract)
View(brisk_extract)
# extract
brisk_extract <- exact_extract(brisk_stack, input_locations, c('max', 'mean', 'quantile', 'sum'), quantiles = c(0.8),
progress = TRUE, append_cols = ID_cols, full_colnames = TRUE)
coverage_biomes <- exact_extract(biome_stack, input_locations, 'sum', quantiles = c(0.8),
progress = TRUE, append_cols = ID_cols, full_colnames = TRUE)
# sumarise biomes
names(coverage_biomes) <- gsub("sum.", "", names(coverage_biomes))
View(coverage_biomes)
potential_biomes <- coverage_biomes %>%
pivot_longer(-c(ID_cols)) %>%
filter(value > 0)
potential_biomes_all <- potential_biomes %>%
ungroup() %>%
select(-value) %>%
unique() %>%
group_by_at(ID_cols) %>%
mutate(Biomes = paste0(name, collapse = "|")) %>%
select(all_of(ID_cols), Biomes) %>%
unique()
potential_biomes_max <- potential_biomes %>%
group_by_at(c(ID_cols,"name")) %>%
summarise(total = sum(value)) %>%
slice_max(total, with_ties = FALSE) %>%
select(-total) %>%
rename(Main_Biome = name) %>%
left_join(potential_biomes_all)
# join back
brisk_extract <- potential_biomes_max %>% left_join(brisk_extract)
View(brisk_extract)
# Convert results to data frames if needed
output_results <- input_locations %>% left_join(brisk_extract)
output_results_df <- output_results %>% st_drop_geometry()
write_csv(output_results_df, paste0(path, "Mined_Commodities_Regional_BRiSK_Extract.csv"), na = "")
st_write(output_sf, new_geopackage_file, layer = "Mined_Commodities_Regional_BRiSK_Extract", driver = "GPKG", delete_layer = TRUE)
# calculate category scores
# classify based on mean
reclass_category <- function(x) {ifelse(x <= 1.5, 1,
ifelse(x <= 2.5, 2,
ifelse(x <= 3.5, 3,
ifelse(x <= 4.5, 4,
ifelse(x > 4.5, 5, NA)))))}
output_results_df <- output_results_df %>%
mutate(CATEGORY_Area_Importance_Frameworks = reclass_category(mean.Framework_PAs_KBA),
CATEGORY_Threatened_Species = reclass_category(mean.SpeciesCR)) %>%
mutate(CATEGORY_STAR_T = reclass_category(mean.STARtCategory),
CATEGORY_STAR_R = reclass_category(mean.STARrCategory),
CATEGORY_STAR_T_Amphibians = reclass_category(mean.STARtAmphibiansCategory),
CATEGORY_STAR_R_Amphibians = reclass_category(mean.STARrAmphibiansCategory),
CATEGORY_STAR_Marine = reclass_category(mean.STARmarineCategory)) %>%
mutate(CATEGORY_STAR_Overall = pmax(CATEGORY_STAR_T,  CATEGORY_STAR_Marine, na.rm = TRUE))%>%
mutate(CATEGORY_STAR_Overall= replace_na(CATEGORY_STAR_Overall, 1))%>%
mutate(CATEGORY_STAR_T= replace_na(CATEGORY_STAR_T, 1))%>%
mutate(CATEGORY_STAR_R= replace_na(CATEGORY_STAR_R, 1))%>%
mutate(CATEGORY_STAR_Marine= replace_na(CATEGORY_STAR_Marine, 1))%>%
mutate(CATEGORY_Marine_Habitats = reclass_category(mean.MarineHabitats)) %>%
mutate(CATEGORY_Marine_Habitats = replace_na(CATEGORY_Marine_Habitats, 1))%>%
mutate(CATEGORY_SBTN = reclass_category(mean.NaturalLands_Reclass)) %>%
mutate(CATEGORY_connectivity = reclass_category(mean.MammalConnectivity)) %>%
mutate(CATEGORY_intactness = reclass_category(mean.EcoregionIntactness)) %>%
mutate(CATEGORY_FLII = reclass_category(mean.FLII)) %>%
mutate(CATEGORY_river_fragmentation = reclass_category(mean.RiverFragmentation)) %>%
mutate(CATEGORY_cropland_expansion = reclass_category(mean.CroplandExpansion)) %>%
mutate(CATEGORY_Water_bws =  reclass_category(mean.Water_bws),
CATEGORY_Water_bws_future =  reclass_category(mean.Water_bws_future),
CATEGORY_Water_bwd =  reclass_category(mean.Water_bwd),
CATEGORY_Water_bwd_future =  reclass_category(mean.Water_bwd_future),
CATEGORY_Water_rfr =  reclass_category(mean.Water_rfr),
CATEGORY_Water_cfr =  reclass_category(mean.Water_cfr),
CATEGORY_Water_drr =  reclass_category(mean.Water_drr),
CATEGORY_Water_cep =  reclass_category(mean.Water_cep),
CATEGORY_Water_pgp =  reclass_category(mean.Water_pgp),
CATEGORY_Water_FQT =  reclass_category(mean.Water_FQT))%>%
mutate(CATEGORY_Forest_Loss = reclass_category(mean.ForestLoss_Reclass)) %>%
mutate(CATEGORY_Overall_CNA = reclass_category(mean.CriticalNaturalAssets))%>%
mutate(CATEGORY_Local_CNA = reclass_category(mean.LocalCNA)) %>%
mutate(CATEGORY_Global_CNA = reclass_category(mean.GlobalCNA)) %>%
rename(EBSA_Area_km2 = sum.EBSA_Prop,
IMMA_Area_km2 = sum.IMMA_Prop,
corals_km2 = sum.CoralReefs_Prop,
seagrass_km2 = sum.Seagrass_Prop,
mangroves_km2 = sum.Mangroves_Prop,
tidal_wetland_km2 = sum.TidalWetlands_Prop,
cold_water_corals_km2 = sum.ColdWaterCorals_Prop,
seamounts_km2 = sum.Seamounts_Prop)%>%
mutate(across(ends_with("_km2"), ~ . * 25))
# select the columns that are needed to go with BRiSK outputs
column_names <- fread("C:/Users/JoeTurner/Documents/GitHub/BRiSK/Prep/Tables/BRiSK_Column_Names_Region_v2.csv", na.strings = "")
columns_to_select <- column_names %>% select(Region_Names) %>% drop_na() %>% unlist() %>% unname()
renanmed_names <- column_names %>% select(Region_Names,Renamed_Names) %>% drop_na() %>% select(Renamed_Names) %>% unlist() %>% unname()
github_filepath
# select the columns that are needed to go with BRiSK outputs
column_names <- fread(paste0(github_filepath, "/BRiSK/Prep/Tables/BRiSK_Column_Names_Region_v2.csv"), na.strings = "")
columns_to_select <- column_names %>% select(Region_Names) %>% drop_na() %>% unlist() %>% unname()
renanmed_names <- column_names %>% select(Region_Names,Renamed_Names) %>% drop_na() %>% select(Renamed_Names) %>% unlist() %>% unname()
# rename columns
output_results_df_select <- output_results_df %>%
select(crop_country, country, crop, all_of(columns_to_select)) %>%
rename(GID_0 = country)
names(st_drop_geometry(input_locations))
# rename columns
output_results_df_select <- output_results_df %>%
select(names(st_drop_geometry(input_locations)), all_of(columns_to_select)) %>%
rename(GID_0 = country)
# select the columns that are needed to go with BRiSK outputs
column_names <- fread(paste0(github_filepath, "/BRiSK/Prep/Tables/BRiSK_Column_Names_Region_v2.csv"), na.strings = "")
columns_to_select <- column_names %>% select(Region_Names) %>% drop_na() %>% unlist() %>% unname()
renanmed_names <- column_names %>% select(Region_Names,Renamed_Names) %>% drop_na() %>% select(Renamed_Names) %>% unlist() %>% unname()
columns_to_select
names(st_drop_geometry(input_locations))
# rename columns
output_results_df_select <- output_results_df %>%
select(names(st_drop_geometry(input_locations)), all_of(columns_to_select))
View(output_results_df_select)
output_results_df_renamed <- output_results_df_select
names(output_results_df_renamed) <- c(names(st_drop_geometry(input_locations)), renanmed_names)
# write to file
write_csv(output_results_df_select, "C:/Users/JoeTurner/Documents/GitHub/BRiSK/Regional_Outputs/FinePrintMines_RegionalBRiSK_extracts_20241116.csv", na = "")
write_csv(output_results_df_renamed, "C:/Users/JoeTurner/Documents/GitHub/BRiSK/Regional_Outputs/FinePrintMines_RegionalBRiSK_extracts_renamed_20241116.csv", na = "")
View(output_results_df_select)
setwd("C:/Users/JoeTurner/Documents/GitHub/cv")
# Knit the HTML version
rmarkdown::render("cv_turner.rmd",
params = list(pdf_mode = FALSE),
output_file = "cv.html")
# Knit the PDF version to temporary html location
tmp_html_cv_loc <- fs::file_temp(ext = ".html")
rmarkdown::render("cv_turner.rmd",
params = list(pdf_mode = TRUE),
output_file = tmp_html_cv_loc)
# Convert to PDF using Pagedown
pagedown::find_chrome()
pagedown::chrome_print(input = "C:/temp/Rtmpo77fFw/file9f4c41c24084.html",
output = "cv_turner.pdf")
pagedown::chrome_print(input = "C:/Users/JoeTurner/Documents/GitHub/cv/cv_turner.html",
output = "cv_turner.pdf")
setwd("C:/Users/JoeTurner/Documents/GitHub/cv")
# Knit the HTML version
rmarkdown::render("cv_turner.rmd",
params = list(pdf_mode = FALSE),
output_file = "cv.html")
# Knit the PDF version to temporary html location
tmp_html_cv_loc <- fs::file_temp(ext = ".html")
rmarkdown::render("cv_turner.rmd",
params = list(pdf_mode = TRUE),
output_file = tmp_html_cv_loc)
setwd("C:/Users/JoeTurner/Documents/GitHub/cv")
# Knit the HTML version
rmarkdown::render("cv_turner.rmd",
params = list(pdf_mode = FALSE),
output_file = "cv.html")
# Knit the PDF version to temporary html location
tmp_html_cv_loc <- fs::file_temp(ext = ".html")
rmarkdown::render("cv_turner.rmd",
params = list(pdf_mode = TRUE),
output_file = tmp_html_cv_loc)