Bias Analysis
Thalia Ballell Carl
2024-04-12
ESVD
###1 ESVD
options(repos = "https://cloud.r-project.org")
install.packages("ggrepel")## Installing package into 'C:/Users/thali/AppData/Local/R/win-library/4.2'
## (as 'lib' is unspecified)## package 'ggrepel' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'ggrepel'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problema al copiar
## C:\Users\thali\AppData\Local\R\win-library\4.2\00LOCK\ggrepel\libs\x64\ggrepel.dll
## a C:\Users\thali\AppData\Local\R\win-library\4.2\ggrepel\libs\x64\ggrepel.dll:
## Permission denied## Warning: restored 'ggrepel'##
## The downloaded binary packages are in
## C:\Users\thali\AppData\Local\Temp\RtmpQf2ffK\downloaded_packagessetwd("C:/Users/thali/OneDrive - Wageningen University & Research/Escritorio/MASTERS/13. 14. 15. Internship/TASKS/4. DUPLICATES AND HOTSPOTS")
data <- read.csv("C:/Users/thali/OneDrive - Wageningen University & Research/Escritorio/MASTERS/13. 14. 15. Internship/TASKS/4. DUPLICATES AND HOTSPOTS/ESVD MASTER 230616.xlsx - ESVD MASTER 230616.csv")
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(maps)
library(mapdata)
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select##1A GEOGRAPHY
# Aggregate data by country
country_counts <- data %>%
group_by(Country_Code) %>%
summarise(count = n())
data_clean <- data[complete.cases(data$Longitude, data$Latitude), ]
# Load map data
world_map <- map_data("world")
# Plot the map with datapoints
ggplot(data = data_clean, aes(x = Longitude, y = Latitude)) +
geom_polygon(data = world_map, aes(x = long, y = lat, group = group), fill = "#f2f2f2", color = "#cccccc", linewidth = 0.5) +
geom_point(alpha = 0.5, size = 0.5, color = "darkgreen") +
scale_alpha(range = c(0.1, 0.7)) +
theme_minimal() +
labs(title = "Geographical Distribution of ESVD Data",
subtitle = "Datapoints",
caption = "Source: ESVD MASTER 230616") +
theme(plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 14, hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10))
# Plot the map with heatmap
ggplot() +
geom_polygon(data = world_map, aes(x = long, y = lat, group = group), fill = "darkgrey", color = "white") +
stat_density_2d(data = data_clean, aes(x = Longitude, y = Latitude, fill = after_stat(level)), geom = "polygon", alpha = 0.5) +
scale_fill_viridis_c(name = "Density") +
theme_minimal() +
labs(title = "Geographical Distribution of ESVD Data",
subtitle = "Density of Values",
fill = "Density",
caption = "Source: ESVD MASTER 230616",
x = "Longitude", y = "Latitude") +
theme(plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 14, hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))
##1B BIOMES
library(dplyr)
library(tidyr)
library(ggplot2)
# Gather the specified columns into a new dataframe
gathered_data <- gather(data, key = "Column", value = "Biomess",
ESVD2.0_Biome_1:ESVD2.0_Biome_10)
# Filter out empty cells
gathered_data <- gathered_data[!is.na(gathered_data$Biomess) & gathered_data$Biomess != "", ]
# Reset row names
rownames(gathered_data) <- NULL
# Select only the "Biome" column
gathered_data <- gathered_data[c('Biomess')]
#FIX ERRORS
gathered_data <- gathered_data %>%
mutate(Biomess = ifelse(Biomess == "Coastal systems biome", "Coastal systems", Biomess))
gathered_data <- gathered_data %>%
separate_rows(Biomess, sep = "; ")
gathered_data <- gathered_data %>%
mutate(Biomess = ifelse(Biomess == "Rivers and Lakes", "Rivers and lakes", Biomess))
gathered_data <- gathered_data %>%
mutate(Biomess = ifelse(Biomess == "Temperate forests and woodland", "Temperate forest and woodland", Biomess))
# Count the frequency of each biome
biome_counts <- table(gathered_data$Biomess)
# Convert the counts to a data frame
biome_counts_df <- as.data.frame(biome_counts)
colnames(biome_counts_df) <- c("Biome", "Frequency")
desired_order <- c("Marine", "Coastal systems", "Inland wetlands", "Rivers and lakes",
"Tropical and subtropical forests", "Temperate forest and woodland", "Cold climate evergreen forest and woodland", "Shrubland and shrubby woodland",
"Rangelands and natural grasslands", "Desert and semi desert", "Polar and alpine systems", "Subterranean systems",
"Intensive land use", "Anthropogenic systems",
"Urban green and blue infrastructure")
biome_counts_df$Biome <- factor(biome_counts_df$Biome, levels = desired_order)
biome_counts_df <- biome_counts_df[order(match(biome_counts_df$Biome, desired_order)), ]
library(RColorBrewer)
library(viridis)## Loading required package: viridisLite##
## Attaching package: 'viridis'## The following object is masked from 'package:maps':
##
## unemplibrary(ggthemes)
custom_palette <- viridis(23)
# Create the pie chart
pie_chart <- ggplot(biome_counts_df, aes(x = "", y = Frequency, fill = Biome)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0.5) +
theme_excel_new() +
scale_fill_manual(values = custom_palette) +
geom_text(aes(label = paste0(round((Frequency/sum(Frequency)) * 100, 1), "%"),
x = 1.7),
position = position_stack(vjust = 0.5),
color = "black", size = 3, fontface = "bold") +
theme(legend.position = "right",
legend.title = element_text(size = 10, face = "bold"),
plot.title = element_text(size = 14, face = "bold"),
plot.subtitle = element_text(size = 12),
plot.caption = element_text(size = 8),
panel.grid = element_blank()) + # Remove grid lines
labs(title = "Biome Distribution",
subtitle = "ESVD2.0_Biome",
caption = "Source: ESVD MASTER 230616")
# Print the modified pie chart
print(pie_chart)
#create radar chart
biome_counts_df <- biome_counts_df %>%
mutate(Biome_Number = as.numeric(factor(Biome, levels = unique(Biome))))
biome_counts_df$Biome_Number <- as.factor(biome_counts_df$Biome_Number)
new_row <- data.frame(Biome = "Other", Frequency = 0, Biome_Number = "16")
biome_counts_df <- rbind(biome_counts_df, new_row)
library(ggplot2)
library(dplyr)
library(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:viridis':
##
## viridis_pallegend_labels <- c("1. Marine", "2. Coastal systems", "3. Inland wetlands", "4. Rivers and lakes","5. Tropical and subtropical forests", "6. Temperate forest and woodland", "7. Cold climate evergreen forest and woodland", "8. Shrubland and shrubby woodland","9. Rangelands and natural grasslands", "10. Desert and semi desert", "11. Polar and alpine systems", "12. Subterranean systems",
"13. Intensive land use", "14. Anthropogenic systems",
"15. Urban green and blue infrastructure", "16. Other")
ggplot(biome_counts_df, aes(x = Biome_Number, y = Frequency, color = Biome)) +
geom_line(size = 1) + # Thicker lines
geom_point(size = 3) + # White-filled points
geom_path() +
coord_polar(start = 0) +
labs(title = "Biome Distribution",
subtitle = "ESVD2.0_Biome",
caption = "Source: ESVD MASTER 230616",
x = NULL, # Remove x-axis label
y = NULL) + # Remove y-axis label
scale_y_continuous(labels = scales::comma) +
scale_color_manual(values = custom_palette, labels = legend_labels) + # Use custom color palette
theme_light() + # Minimal theme
theme(legend.position = "right", # Move legend to the bottom
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), # Adjust x-axis text angle and position
plot.title = element_text(size = 16, face = "bold"), # Larger and bold title
plot.subtitle = element_text(size = 12), # Smaller subtitle
legend.text = element_text(size = 10), # Smaller legend text
legend.key.size = unit(0.5, "cm")) # Smaller legend key size## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## `geom_path()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
##1C ES
gathered_data2 <- gather(data, key = "Column", value = "ESs",
ES_1:ES_17)
gathered_data2 <- gathered_data2[c('ESs')]
head(gathered_data2)## ESs
## 1 Food
## 2 Food
## 3 Opportunities for recreation and tourism
## 4 Opportunities for recreation and tourism
## 5 Food
## 6 Opportunities for recreation and tourismgathered_data2 <- subset(gathered_data2, !is.na(ESs) & ESs != "")
#Correct errors at the ES level
gathered_data2$ES <- NA
gathered_data2 <- gathered_data2 %>%
separate_rows(ESs, sep = "; ")
gathered_data2 <- gathered_data2 %>%
mutate(ESs = ifelse(ESs == "Existence, bequest value", "Existence, bequest values\n", ESs))
gathered_data2 <- gathered_data2 %>%
mutate(ESs = ifelse(ESs == "Existence, bequest value", "Water purification ", ESs))
# Classify ES readings into categories
gathered_data2$ES[gathered_data2$ESs %in% c("Abatement of noise", "Waste treatment", "Water purification")] <- "11. Waste treatment"
gathered_data2$ES[gathered_data2$ESs %in% c("Aesthetic information", "Aesthetic information ", "Attractive landscapes")] <- "18. Aesthetic information"
gathered_data2$ES[gathered_data2$ESs == "Air quality regulation"] <- "7. Air quality regulation"
gathered_data2$ES[gathered_data2$ESs %in% c("Artistic inspiration", "Cultural use", "Inspiration for culture, art and design")] <- "20. Inspiration for culture, art and design"
gathered_data2$ES[gathered_data2$ESs %in% c("Bequest value", "Bequest values", "Existence", "Existence value", "Existence, bequest value", "Existence, bequest values", "Existence, bequest values", "Existence, bequest values")] <- "23. Existence, bequest values"
gathered_data2$ES[gathered_data2$ESs %in% c( "Biodiversity protection", "Maintenance of genetic diversity")] <- "17. Maintenance of genetic diversity"
gathered_data2$ES[gathered_data2$ESs == "Biological control"] <- "15. Biological control"
gathered_data2$ES[gathered_data2$ESs %in% c("Climate regulation", "Climate regulation ")] <- "8. Climate regulation"
gathered_data2$ES[gathered_data2$ESs %in% c("Ecotourism", "Opportunities for recreation ", "Opportunities for recreation and tourism", "Recreation", " Recreation", "Tourism")] <- "19. Opportunities for recreation and tourism"
gathered_data2$ES[gathered_data2$ESs %in% c("Erosion prevention", "Erosion prevention ")] <- "12. Erosion prevention"
gathered_data2$ES[gathered_data2$ESs %in% c("Fish", "Food")] <- "1. Food"
gathered_data2$ES[gathered_data2$ESs %in% c("Flood prevention", "Moderation of extreme events", "Prevention of extreme events [unspecified]")] <- "9. Moderation of extreme events"
gathered_data2$ES[gathered_data2$ESs == "Genetic resources"] <- "4. Genetic resources"
gathered_data2$ES[gathered_data2$ESs %in% c("Information for cognitive development")] <- "22. Information for cognitive development"
gathered_data2$ES[gathered_data2$ESs == "Maintenance of life cycles"] <- "16. Maintenance of life cycles"
gathered_data2$ES[gathered_data2$ESs %in% c("Maintenance of soil fertility", "Nutrient cycling")] <- "13. Maintenance of soil fertility"
gathered_data2$ES[gathered_data2$ESs == "Medicinal resources"] <- "5. Medicinal resources"
gathered_data2$ES[gathered_data2$ESs == "Ornamental resources"] <- "6. Ornamental resources"
gathered_data2$ES[gathered_data2$ESs %in% c("Other raw", "Raw materials", "Raw Materials", "Raw materials ")] <- "3. Raw materials"
gathered_data2$ES[gathered_data2$ESs == "Pollination"] <- "14. Pollination"
gathered_data2$ES[gathered_data2$ESs %in% c("Regulation of water flows", "Regulation of water flows ", "Regulation of water flows")] <- "10. Regulation of water flows"
gathered_data2$ES[gathered_data2$ESs %in% c("Spiritual / Religious use", "Spiritual experience")] <- "21. Spiritual experience"
gathered_data2$ES[gathered_data2$ESs == "Water"] <- "2. Water"
na_rows <- subset(gathered_data2, is.na(ES))
print(na_rows)## # A tibble: 21 × 2
## ESs ES
## <chr> <chr>
## 1 "Existence, bequest values\n" <NA>
## 2 "Existence, bequest values\n" <NA>
## 3 "Existence, bequest values\n" <NA>
## 4 "Existence, bequest values\n" <NA>
## 5 "Existence, bequest values\n" <NA>
## 6 "Existence, bequest values\n" <NA>
## 7 "Existence, bequest values\n" <NA>
## 8 "Existence, bequest values\n" <NA>
## 9 "Existence, bequest values\n" <NA>
## 10 "Existence, bequest values\n" <NA>
## # ℹ 11 more rowsES_counts <- table(gathered_data2$ES)
ES_counts_df <- as.data.frame(ES_counts)
colnames(ES_counts_df) <- c("ES", "Frequency")
ES_counts_df$ES_general <- NA
ES_counts_df$ES_general[ES_counts_df$ES %in% c("1. Food", "2. Water", "3. Raw materials", "4. Genetic resources", "5. Medicinal resources", "6. Ornamental resources")] <- "Provisioning"
ES_counts_df$ES_general[ES_counts_df$ES %in% c("7. Air quality regulation", "8. Climate regulation", "9. Moderation of extreme events", "10. Regulation of water flows", "11. Waste treatment", "12. Erosion prevention", "13. Maintenance of soil fertility", "14. Pollination", "15. Biological control")] <- "Regulating"
ES_counts_df$ES_general[ES_counts_df$ES %in% c("16. Maintenance of life cycles", "17. Maintenance of genetic diversity")] <- "Habitat"
ES_counts_df$ES_general[ES_counts_df$ES %in% c("18. Aesthetic information", "19. Opportunities for recreation and tourism", "20. Inspiration for culture, art and design", "21. Spiritual experience", "22. Information for cognitive development", "23. Existence, bequest values")] <- "Cultural"
desired_order <- c("1. Food", "2. Water", "3. Raw materials", "4. Genetic resources", "5. Medicinal resources", "6. Ornamental resources",
"7. Air quality regulation", "8. Climate regulation", "9. Moderation of extreme events", "10. Regulation of water flows", "11. Waste treatment", "12. Erosion prevention", "13. Maintenance of soil fertility", "14. Pollination", "15. Biological control",
"16. Maintenance of life cycles", "17. Maintenance of genetic diversity",
"18. Aesthetic information", "19. Opportunities for recreation and tourism", "20. Inspiration for culture, art and design", "21. Spiritual experience", "22. Information for cognitive development", "23. Existence, bequest values")
ES_counts_df$ES <- factor(ES_counts_df$ES, levels = desired_order)
#ES Category
install.packages("ggrepel")## Installing package into 'C:/Users/thali/AppData/Local/R/win-library/4.2'
## (as 'lib' is unspecified)## package 'ggrepel' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'ggrepel'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problema al copiar
## C:\Users\thali\AppData\Local\R\win-library\4.2\00LOCK\ggrepel\libs\x64\ggrepel.dll
## a C:\Users\thali\AppData\Local\R\win-library\4.2\ggrepel\libs\x64\ggrepel.dll:
## Permission denied## Warning: restored 'ggrepel'##
## The downloaded binary packages are in
## C:\Users\thali\AppData\Local\Temp\RtmpQf2ffK\downloaded_packageslibrary(ggrepel)
custom_palette <- viridis(23)
library(ggplot2)
ggplot(ES_counts_df, aes(x = "", y = Frequency, fill = ES)) +
geom_bar(stat = "identity", width = 0.5) +
coord_polar("y", start = 0) +
scale_fill_manual(values = custom_palette) +
geom_text(aes(label = paste0(round(Frequency / sum(Frequency) * 100), "%")),
position = position_stack(vjust = 0.5), size = 2.5, fontface = "bold",
colour = "white", check_overlap = TRUE) +
theme(legend.position = "right",
legend.title = element_text(size = 20, face = "bold"),
plot.title = element_text(size = 14, face = "bold"),
plot.subtitle = element_text(size = 12),
plot.caption = element_text(size = 8)) +
labs(title = "Distribution of Ecosystem Services",
fill = "Ecosystem Service",
subtitle = "ES_TEEB",
caption = "Source: ESVD MASTER 230616") +
theme_void() +
theme(legend.position = "right") +
geom_polygon(data = data.frame(x = c(0, rep(NA, 100), 0),
y = c(0, rep(NA, 100), 0)),
aes(x = x, y = y), fill = "white", color = NA)
VSL
##2 VSL
library(readxl)
library(tidyr)
#2A GEOGRAPHY
dataG <- read_excel("C:/Users/thali/OneDrive - Wageningen University & Research/Escritorio/MASTERS/13. 14. 15. Internship/TASKS/4. DUPLICATES AND HOTSPOTS/VSLxlsx.xlsx")
dataG <- separate_rows(dataG, Country, sep = ";")
country_counts <- table(dataG$Country)
country_counts_df <- as.data.frame(country_counts)
colnames(country_counts_df) <- c("Country", "Frequency")
country_counts_df$Country <- sub(",.*", "", country_counts_df$Country)
country_counts_df$Country <- gsub("^\\s+", "", country_counts_df$Country)
country_counts_df$Country <- trimws(country_counts_df$Country, "right")
print(country_counts_df)## Country Frequency
## 1 Algeria 1
## 2 Australia 1
## 3 Bolivia 2
## 4 Cambodia 1
## 5 Cameroon 1
## 6 China 1
## 7 Colombia 1
## 8 Costa Rica 2
## 9 Ecuador 1
## 10 Egypt 1
## 11 El Salvador 1
## 12 Finland 1
## 13 France 1
## 14 French Guiana 1
## 15 Guatemala 2
## 16 Guinea 1
## 17 Guyana 1
## 18 Honduras 1
## 19 Hungary 2
## 20 Ireland 1
## 21 Italy 1
## 22 Kenya 1
## 23 Lao People's Democratic Republic 3
## 24 Mali 1
## 25 Mexico 1
## 26 Myanmar 1
## 27 Nicaragua 1
## 28 Niger 1
## 29 Nigeria 1
## 30 Norway 1
## 31 Pakistan 1
## 32 Panama 1
## 33 Peru 2
## 34 Poland 1
## 35 Portugal 1
## 36 Romania 2
## 37 Slovenia 1
## 38 Somalia 1
## 39 Sri Lanka 1
## 40 Sudan 1
## 41 Suriname 2
## 42 Sweden 1
## 43 Tanzania 2
## 44 Thailand 2
## 45 Turkey 1
## 46 Uganda 1
## 47 United Kingdom of Great Britain & Northern Ireland 2
## 48 United States of America 3
## 49 Venezuela 1
## 50 Vietnam 2
## 51 Australia 18
## 52 Austria 6
## 53 Bangladesh 4
## 54 Belgium 4
## 55 Belize 4
## 56 Benin 1
## 57 Bermuda 1
## 58 Bhutan 1
## 59 Bolivia 1
## 60 Bonaire 3
## 61 Brazil 24
## 62 Bulgaria 1
## 63 Burkina Faso 4
## 64 Cambodia 3
## 65 Cameroon 2
## 66 Canada 7
## 67 China 50
## 68 Colombia 3
## 69 Costa Rica 6
## 70 Côte d'Ivoire 1
## 71 Cuba 2
## 72 Czech Republic 3
## 73 Denmark 5
## 74 Egypt 1
## 75 El Salvador 1
## 76 Ethiopia 5
## 77 Finland 15
## 78 France 14
## 79 Germany 65
## 80 Ghana 6
## 81 Greece 4
## 82 Guatemala 3
## 83 Guyana 1
## 84 Honduras 2
## 85 Hong Kong 3
## 86 India 27
## 87 Indonesia 23
## 88 Iran 6
## 89 Ireland 6
## 90 Israel 3
## 91 Italy 19
## 92 Japan 2
## 93 Jordan 1
## 94 Kenya 2
## 95 Korea 3
## 96 Lao People's Democratic Republic 3
## 97 Lebanon 1
## 98 Liberia 1
## 99 Lithuania 1
## 100 Madagascar 2
## 101 Malaysia 18
## 102 Mali 5
## 103 Mauritania 2
## 104 Mexico 18
## 105 Moldova 1
## 106 Morocco 1
## 107 Mozambique 1
## 108 Myanmar 2
## 109 Nepal 3
## 110 Netherlands 34
## 111 New Zealand 3
## 112 Nigeria 6
## 113 Norway 10
## 114 Pakistan 3
## 115 Panama 4
## 116 Papua New Guinea 1
## 117 Peru 8
## 118 Philippines 6
## 119 Poland 9
## 120 Portugal 14
## 121 Romania 1
## 122 Russian Federation 2
## 123 Saudi Arabia 1
## 124 Senegal 5
## 125 Serbia 1
## 126 Solomon Islands 1
## 127 South Africa 6
## 128 Spain 45
## 129 Sri Lanka 5
## 130 Suriname 2
## 131 Sweden 8
## 132 Switzerland 10
## 133 Taiwan 3
## 134 Tanzania 1
## 135 Thailand 6
## 136 Turkey 9
## 137 Uganda 2
## 138 United Arab Emirates 1
## 139 United Kingdom of Great Britain & Northern Ireland 27
## 140 United States of America 85
## 141 Uruguay 2
## 142 Venezuela 1
## 143 Vietnam 10
## 144 World 14
## 145 Zambia 1
## 146 Zimbabwe 1country_counts_df <- country_counts_df %>%
filter(Country != "World")
country_lookup <- data.frame(
non_standard_name = c("Lao People's Democratic Republic",
"United Kingdom of Great Britain & Northern Ireland",
"United States of America",
"Côte d'Ivoire",
"Russian Federation",
"Solomon Islands"),
standard_name = c("Laos",
"United Kingdom",
"United States of America",
"Ivory Coast",
"Russia",
"Solomon Islands")
)
# Convert non-standard names to standard names
country_counts_df <- left_join(country_counts_df, country_lookup, by = c("Country" = "non_standard_name")) %>%
mutate(Country = ifelse(is.na(standard_name), Country, standard_name))
country_counts_df <- country_counts_df[order(country_counts_df$Frequency, decreasing = TRUE), ]
# Load map data
world_map <- map_data("world")
library(rworldmap)## Loading required package: sp## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
## (status 2 uses the sf package in place of rgdal)## ### Welcome to rworldmap ##### For a short introduction type : vignette('rworldmap')frequency.map <- joinCountryData2Map(country_counts_df, joinCode = "NAME", nameJoinColumn = "Country")## 144 codes from your data successfully matched countries in the map
## 1 codes from your data failed to match with a country code in the map
## 139 codes from the map weren't represented in your datalibrary(ggplot2)
library(scales)
library(sf)## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUElibrary(rnaturalearth)
library(rnaturalearthdata)##
## Attaching package: 'rnaturalearthdata'## The following object is masked from 'package:rnaturalearth':
##
## countries110world <- ne_countries(scale = "medium", returnclass = "sf")
frequency.world <- merge(world, country_counts_df, by.x="admin", by.y="Country")
world_map <- map_data("world")
ggplot(data = frequency.world) +
geom_map(data = world_map, map = world_map,
aes(x = long, y = lat, map_id = region), fill = "lightgrey", color = "white") + # Add world map
geom_sf(aes(fill=Frequency), color = "white") + # Set border color to white
scale_fill_gradient(name = "Density", limits = c(0, max(frequency.world$Frequency)),
low = "lightblue", high = "darkblue") + # Customize the color scale
labs(title = "Geographical Distribution of VSL Data; Density of Studies",
caption = "Source: Valuation Studies List") + # Add a title
theme_minimal() + # Use a minimal theme
theme(plot.title = element_text(hjust = 0.5)) ## Warning in geom_map(data = world_map, map = world_map, aes(x = long, y = lat, :
## Ignoring unknown aesthetics: x and y
#2B BIOME
library(dplyr)
dataB <- read_excel("C:/Users/thali/OneDrive - Wageningen University & Research/Escritorio/MASTERS/13. 14. 15. Internship/TASKS/4. DUPLICATES AND HOTSPOTS/Biomes.xlsx")
dataB <- separate_rows(dataB, Biome, sep = ";")
biomes_counts <- table(dataB$Biome)
biomes_counts_df <- as.data.frame(biomes_counts)
colnames(biomes_counts_df) <- c("Biome", "Frequency")
biomes_counts_df$Biome <- trimws(biomes_counts_df$Biome)
biomes_counts_df <- biomes_counts_df %>%
group_by(Biome) %>%
summarise(Frequency = sum(Frequency))
biomes_counts_df <- biomes_counts_df %>%
mutate(Biome = case_when(
Biome == "Open sea/ocean" ~ "1",
Biome == "Coral reefs" ~ "1",
Biome == "Coastal systems (incl wetlands)" ~ "2",
Biome == "Inland wetlands" ~ "3",
Biome == "Rivers and lakes" ~ "4",
Biome == "Tropical forests" ~ "5",
Biome == "Temperate forests" ~ "6",
Biome == "Woodland & Shrubland" ~ "8",
Biome == "Grass-/Rangeland" ~ "9",
Biome == "Desert" ~ "10",
Biome == "Tundra" ~ "8",
Biome == "High mountain & Polar systems" ~ "11",
Biome == "Inland Un- or Sparsely Vegetated" ~ "12",
Biome == "Cultivated areas" ~ "13",
Biome == "Rangelands and natural grasslands" ~ "9",
Biome == "Desert and semi desert" ~ "10",
Biome == "Polar and alpine systems" ~ "11",
Biome == "Subterranean systems" ~ "12",
Biome == "Intensive land use" ~ "13",
Biome == "Urban Green and Blue Infrastructure" ~ "15",
Biome == "Other" ~ "16",
TRUE ~ Biome
))
new_row1 <- data.frame(Biome = "7", Frequency = 0)
new_row2 <- data.frame(Biome = "14", Frequency = 0)
biomes_counts_df <- rbind(biomes_counts_df, new_row1)
biomes_counts_df <- rbind(biomes_counts_df, new_row2)
biomes_counts_df <- biomes_counts_df %>%
group_by(Biome) %>%
summarize(Frequency = sum(Frequency))
biomes_counts_df <- biomes_counts_df %>%
arrange(as.numeric(as.character(Biome)))
biomes_counts_df$Biome <- factor(biomes_counts_df$Biome)
print(levels(biomes_counts_df$Biome))## [1] "1" "10" "11" "12" "13" "14" "15" "16" "2" "3" "4" "5" "6" "7" "8"
## [16] "9"library(forcats)
biomes_counts_df$Biome <- fct_inorder(biomes_counts_df$Biome)
library(ggplot2)
library(dplyr)
library(scales)
legend_labels <- c("1. Marine", "2. Coastal systems", "3. Inland wetlands", "4. Rivers and lakes",
"5. Tropical and subtropical forests", "6. Temperate forest and woodland", "7. Cold climate evergreen forest and woodland", "8. Shrubland and shrubby woodland",
"9. Rangelands and natural grasslands", "10. Desert and semi desert", "11. Polar and alpine systems", "12. Subterranean systems",
"13. Intensive land use", "14. Anthropogenic systems",
"15. Urban green and blue infrastructure", "16. Other")
# Radar plot with legend
custom_palette <- viridis(23)
ggplot(biomes_counts_df, aes(x = Biome, y = Frequency, color = Biome)) +
geom_line(size = 1) + # Thicker lines
geom_point(size = 3) + # White-filled points
geom_path() +
coord_polar(start = 0) +
labs(title = "Frequency of Biomes",
subtitle = "Unread Studies",
caption = "Source: Valuation Studies List",
x = NULL, # Remove x-axis label
y = NULL) + # Remove y-axis label
scale_y_continuous(labels = scales::comma) +
scale_color_manual(values = custom_palette, labels = legend_labels) + # Use custom color palette
theme_light() + # Minimal theme
theme(legend.position = "right", # Move legend to the bottom
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), # Adjust x-axis text angle and position
plot.title = element_text(size = 16, face = "bold"), # Larger and bold title
plot.subtitle = element_text(size = 12), # Smaller subtitle
legend.text = element_text(size = 10), # Smaller legend text
legend.key.size = unit(0.5, "cm")) # Smaller legend key size## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## `geom_path()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
#2C ES
dataE <- read_excel("C:/Users/thali/OneDrive - Wageningen University & Research/Escritorio/MASTERS/13. 14. 15. Internship/TASKS/4. DUPLICATES AND HOTSPOTS/ES.xlsx")
dataE <- separate_rows(dataE, ES, sep = ";")
ES_counts <- table(dataE$ES)
ES_counts_df <- as.data.frame(ES_counts)
colnames(ES_counts_df) <- c("ES", "Frequency")
ES_counts_df$ES <- trimws(ES_counts_df$ES)
ES_counts_df <- ES_counts_df %>%
group_by(ES) %>%
summarise(Frequency = sum(Frequency))
ES_counts_df$ES[ES_counts_df$ES %in% c("Abatement of noise", "Waste treatment", "Water purification")] <- "11. Waste treatment"
ES_counts_df$ES[ES_counts_df$ES %in% c("Aesthetic information", "Aesthetic information ", "Attractive landscapes")] <- "18. Aesthetic information"
ES_counts_df$ES[ES_counts_df$ES == "Air quality regulation"] <- "7. Air quality regulation"
ES_counts_df$ES[ES_counts_df$ES %in% c("Artistic inspiration", "Cultural use", "Inspiration for culture, art and design")] <- "20. Inspiration for culture, art and design"
ES_counts_df$ES[ES_counts_df$ES %in% c("Bequest value", "Bequest values", "Existence", "Existence value", "Existence, bequest value", "Existence, bequest values", "Existence, bequest values", "Existence, bequest values")] <- "23. Existence, bequest values"
ES_counts_df$ES[ES_counts_df$ES %in% c( "Biodiversity protection", "Maintenance of genetic diversity")] <- "17. Maintenance of genetic diversity"
ES_counts_df$ES[ES_counts_df$ES == "Biological control"] <- "15. Biological control"
ES_counts_df$ES[ES_counts_df$ES %in% c("Climate regulation", "Climate regulation ")] <- "8. Climate regulation"
ES_counts_df$ES[ES_counts_df$ES %in% c("Ecotourism", "Opportunities for recreation ", "Opportunities for recreation and tourism", "Recreation", " Recreation", "Tourism")] <- "19. Opportunities for recreation and tourism"
ES_counts_df$ES[ES_counts_df$ES %in% c("Erosion prevention", "Erosion prevention ")] <- "12. Erosion prevention"
ES_counts_df$ES[ES_counts_df$ES %in% c("Fish", "Food")] <- "1. Food"
ES_counts_df$ES[ES_counts_df$ES %in% c("Flood prevention", "Moderation of extreme events", "Prevention of extreme events [unspecified]")] <- "9. Moderation of extreme events"
ES_counts_df$ES[ES_counts_df$ES == "Genetic resources"] <- "4. Genetic resources"
ES_counts_df$ES[ES_counts_df$ES %in% c("Information for cognitive development")] <- "22. Information for cognitive development"
ES_counts_df$ES[ES_counts_df$ES == "Maintenance of life cycles"] <- "16. Maintenance of life cycles"
ES_counts_df$ES[ES_counts_df$ES %in% c("Maintenance of soil fertility", "Nutrient cycling")] <- "13. Maintenance of soil fertility"
ES_counts_df$ES[ES_counts_df$ES == "Medicinal resources"] <- "5. Medicinal resources"
ES_counts_df$ES[ES_counts_df$ES == "Ornamental resources"] <- "6. Ornamental resources"
ES_counts_df$ES[ES_counts_df$ES %in% c("Other raw", "Raw materials", "Raw Materials", "Raw materials ")] <- "3. Raw materials"
ES_counts_df$ES[ES_counts_df$ES == "Pollination"] <- "14. Pollination"
ES_counts_df$ES[ES_counts_df$ES %in% c("Regulation of water flows", "Regulation of water flows ", "Regulation of water flows")] <- "10. Regulation of water flows"
ES_counts_df$ES[ES_counts_df$ES %in% c("Spiritual / Religious use", "Spiritual experience")] <- "21. Spiritual experience"
ES_counts_df$ES[ES_counts_df$ES == "Water"] <- "2. Water"
ES_counts_df <- ES_counts_df %>%
arrange(ES)
ggplot(ES_counts_df, aes(x = "", y = Frequency, fill = ES)) +
geom_bar(stat = "identity", width = 0.5) +
coord_polar("y", start = 0) +
scale_fill_manual(values = custom_palette) +
geom_text(aes(label = paste0(round(Frequency / sum(Frequency) * 100), "%")),
position = position_stack(vjust = 0.5), size = 3, fontface = "bold",
colour = "white", check_overlap = TRUE) +
theme(legend.position = "right",
legend.title = element_text(size = 20, face = "bold"),
plot.title = element_text(size = 14, face = "bold"),
plot.subtitle = element_text(size = 12),
plot.caption = element_text(size = 8)) +
labs(title = "Distribution of Ecosystem Services",
fill = "Ecosystem Service",
subtitle = "Unread Studies",
caption = "Source: Valuation Studies List") +
theme_void() +
theme(legend.position = "right") +
geom_polygon(data = data.frame(x = c(0, rep(NA, 100), 0),
y = c(0, rep(NA, 100), 0)),
aes(x = x, y = y), fill = "white", color = NA)