Final_Project
Sydney Mazzanti
2024-04-17
Set working directory
setwd("/EEB 4100/Final Project") #set a working directoryLoad all necessary libraries
#Load possible packages
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.3## Warning: package 'ggplot2' was built under R version 4.3.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(skimr)
library(sf)## Warning: package 'sf' was built under R version 4.3.3## Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUElibrary(leaflet)## Warning: package 'leaflet' was built under R version 4.3.3library(scales)##
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## col_factorlibrary(ggmap)## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## Stadia Maps' Terms of Service: <https://stadiamaps.com/terms-of-service/>
## OpenStreetMap's Tile Usage Policy: <https://operations.osmfoundation.org/policies/tiles/>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.library(readxl)## Warning: package 'readxl' was built under R version 4.3.3library(raster)## Loading required package: sp
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## selectView the dataset
#Dataset into R
ss_obsv <- read_excel("Song_Sparrow_Obsv.xlsx")
print(ss_obsv) # View the dataset## # A tibble: 20 × 21
## `Species Name` `Scientific Name` State Location Coordinates Latitude
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 Song Sparrow Melospiza melodia CT 33 Nancy Drive, … 41.418697,… 41.4
## 2 Song Sparrow Melospiza melodia CT Quarry Road, New… 41.278177,… 41.3
## 3 Song Sparrow Melospiza melodia CT Tarrywile Park, … 41.3754293… 41.4
## 4 Song Sparrow Melospiza melodia CT Hartford 41.7849541… 41.8
## 5 Song Sparrow Melospiza melodia CT Pistol Creek Par… 41.5989289… 41.6
## 6 Song Sparrow Melospiza melodia CT Roaring Brook Na… 41.8412831… 41.8
## 7 Song Sparrow Melospiza melodia CT Milford Point, C… 41.1754303… 41.2
## 8 Song Sparrow Melospiza melodia CT Tulmeadow Farm, … 41.8726165… 41.9
## 9 Song Sparrow Melospiza melodia CT Quarry Road, New… 41.278177,… 41.3
## 10 Song Sparrow Melospiza melodia CT Westmoor Park, H… 41.7836289… 41.8
## 11 Song Sparrow Melospiza melodia AZ Riparian Preserv… 33.3614502… 33.4
## 12 Song Sparrow Melospiza melodia AZ Sweetwater Wetla… 32.278549,… 32.3
## 13 Song Sparrow Melospiza melodia AZ Glendale Recharg… 33.5242237… 33.5
## 14 Song Sparrow Melospiza melodia AZ Gilbert and Chan… 33.2386696… 33.2
## 15 Song Sparrow Melospiza melodia AZ Riparian Preserv… 33.3614502… 33.4
## 16 Song Sparrow Melospiza melodia AZ stakeout Golden-… 33.517422,… 33.5
## 17 Song Sparrow Melospiza melodia AZ Sweetwater Wetla… 32.278549,… 32.3
## 18 Song Sparrow Melospiza melodia AZ Hassayampa River… 33.9333419… 33.9
## 19 Song Sparrow Melospiza melodia AZ Canoa Ranch Cons… 31.7948045… 31.8
## 20 Song Sparrow Melospiza melodia AZ Tres Rios Overba… 33.389402,… 33.4
## # ℹ 15 more variables: Longitude <dbl>, Date <dttm>,
## # `eBird Checklist ID` <chr>, `Macaulay Library Catalog ID` <chr>,
## # `Macaulay Library Page` <chr>, Photographer <chr>, `Age of Bird` <chr>,
## # `Original Photo` <chr>, `Selected Area` <chr>, `Average Color` <lgl>,
## # RGB <chr>, Hexadecimal <chr>, HSL <chr>, Lightness <dbl>,
## # `Color Finder` <chr>str(ss_obsv) # Examine the structure of the data## tibble [20 × 21] (S3: tbl_df/tbl/data.frame)
## $ Species Name : chr [1:20] "Song Sparrow" "Song Sparrow" "Song Sparrow" "Song Sparrow" ...
## $ Scientific Name : chr [1:20] "Melospiza melodia" "Melospiza melodia" "Melospiza melodia" "Melospiza melodia" ...
## $ State : chr [1:20] "CT" "CT" "CT" "CT" ...
## $ Location : chr [1:20] "33 Nancy Drive, Fairfield" "Quarry Road, New Haven" "Tarrywile Park, Fairfield" "Hartford" ...
## $ Coordinates : chr [1:20] "41.418697, -73.442016" "41.278177, -72.742086" "41.3754293, -73.4535956" "41.7849541, -72.7109299" ...
## $ Latitude : num [1:20] 41.4 41.3 41.4 41.8 41.6 ...
## $ Longitude : num [1:20] -73.4 -72.7 -73.5 -72.7 -72.7 ...
## $ Date : POSIXct[1:20], format: "2023-11-22" "2023-12-26" ...
## $ eBird Checklist ID : chr [1:20] "S155003917" "S157150385" "S127679025" "S134921546" ...
## $ Macaulay Library Catalog ID: chr [1:20] "ML611450322" "ML612689641" "ML576007381" "ML562451521" ...
## $ Macaulay Library Page : chr [1:20] "https://macaulaylibrary.org/asset/611450322" "https://macaulaylibrary.org/asset/612689641" "https://macaulaylibrary.org/asset/576007381" "https://macaulaylibrary.org/asset/562451521" ...
## $ Photographer : chr [1:20] "Alton Spencer" "Caleb Villar" "Alton Spencer" "Debbie Lombardo" ...
## $ Age of Bird : chr [1:20] "Adult" "Adult" "Adult" "Adult" ...
## $ Original Photo : chr [1:20] "ML611450322" "ML612689641" "ML576007381" "ML562451521" ...
## $ Selected Area : chr [1:20] "ML611450322_Selected" "ML612689641_Selected" "ML576007381_Selected" "ML562451521_Selected" ...
## $ Average Color : logi [1:20] NA NA NA NA NA NA ...
## $ RGB : chr [1:20] "61, 56, 51" "59, 54, 50" "88, 77, 69" "80, 78, 71" ...
## $ Hexadecimal : chr [1:20] "#3d3833" "#3b3632" "#584d45" "#504e47" ...
## $ HSL : chr [1:20] "30, 9%, 22%" "27, 8%, 21%" "25, 12%, 31%" "47, 6%, 30%" ...
## $ Lightness : num [1:20] 0.22 0.21 0.31 0.3 0.16 0.27 0.14 0.31 0.16 0.16 ...
## $ Color Finder : chr [1:20] "Dune" "Dune" "Liver" "Liiver" ...summary(ss_obsv) # Summarize the data## Species Name Scientific Name State Location
## Length:20 Length:20 Length:20 Length:20
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Coordinates Latitude Longitude
## Length:20 Min. :31.79 Min. :-112.69
## Class :character 1st Qu.:33.36 1st Qu.:-111.73
## Mode :character Median :37.55 Median : -92.23
## Mean :37.30 Mean : -92.33
## 3rd Qu.:41.46 3rd Qu.: -72.83
## Max. :41.87 Max. : -72.71
## Date eBird Checklist ID Macaulay Library Catalog ID
## Min. :2023-01-14 00:00:00 Length:20 Length:20
## 1st Qu.:2023-04-13 06:00:00 Class :character Class :character
## Median :2023-09-25 00:00:00 Mode :character Mode :character
## Mean :2023-08-10 21:36:00
## 3rd Qu.:2023-11-24 18:00:00
## Max. :2023-12-26 00:00:00
## Macaulay Library Page Photographer Age of Bird Original Photo
## Length:20 Length:20 Length:20 Length:20
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Selected Area Average Color RGB Hexadecimal
## Length:20 Mode:logical Length:20 Length:20
## Class :character NA's:20 Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## HSL Lightness Color Finder
## Length:20 Min. :0.1400 Length:20
## Class :character 1st Qu.:0.2175 Class :character
## Mode :character Median :0.3000 Mode :character
## Mean :0.2820
## 3rd Qu.:0.3400
## Max. :0.4500#Includes range in lightness valuesVariables to be used in maps
#Categorizing the lightness values for plummage color from the data set
Black <- ss_obsv %>%
filter(Lightness >= 0.00, Lightness <= 0.20) #filter for lightness values within said range
DarkGray <- ss_obsv %>%
filter(Lightness >= 0.21, Lightness <= 0.40)
Gray <- ss_obsv %>%
filter(Lightness >= 0.41, Lightness <= 0.60)
LightGray <- ss_obsv %>%
filter(Lightness >= 0.61, Lightness <= 0.80)
White <- ss_obsv %>%
filter(Lightness >= 0.81, Lightness <= 1.0)
#Filter dataset by region
ssparrow_CT <- ss_obsv %>% #filter by state
filter(State=="CT")
ssparrow_AZ <- ss_obsv %>%
filter(State=="AZ")
#Circle the states using their coordinates
arizona_center <- c(mean(c(-114.818, -109.045)), mean(c(32.541, 37)))
connecticut_center <- c(mean(c(-73.727775, -71.787115)), mean(c(41.022732, 42.050002)))Histogram of lightness observations
#created a subset of the original data frames to be used to make a histogram of the observations
histo_data <- rbind(
data.frame(State = "CT", Lightness = ssparrow_CT$Lightness),
data.frame(State = "AZ", Lightness = ssparrow_AZ$Lightness)
)
lightness_groups <- c("Black", "DarkGray", "Gray", "LightGray", "White") #Categorize the groups
# Create a function to classify lightness values into groups (5 categories, values range from 0-1.0)
classify_lightness <- function(lightness) {
cut(lightness, breaks = c(0, 0.20, 0.40, 0.60, 0.80, 1.0), labels = lightness_groups, include.lowest = TRUE)
}
# Add the lightness groups to the histo_data variable
histo_data$Lightness_Group <- classify_lightness(histo_data$Lightness)
#Stacked Histogram of Plummage Lightness Observations (x-axis = State, Y-axis = Number of Observations, Fill = Lightness Group)
ggplot(histo_data, aes(x = State)) +
geom_bar(aes(fill = Lightness_Group)) + labs(title = "Observations of Plummage Lightness", x = "State", y = "Number of Observations", fill = "Plummage Lightness") + scale_fill_manual(values = c("Black" = "black", "DarkGray" = "darkgray", "Gray" = "gray"), labels = c("Black (0-20%)", "Dark Gray (21-40%)", "Gray (41-60%)")) + #Labels include lightness range
theme_bw() +
theme(
panel.grid.major.y = element_blank(), #Removed horizontal grid lines
panel.grid.minor.y = element_blank(), #Removed horizontal grid lines
panel.border = element_blank(), # Remove borders
axis.line = element_line(color = "black", size = 0.5), # Added darker axis lines
axis.title = element_text(size = 14), #Font Size
axis.text = element_text(size = 10), #Font Size
axis.ticks = element_line(color = "black")) ## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Location Map
location_map <- leaflet() %>% #Using leaflet to build the map and ESRI National Geographic World Map for the base map
setView(lng = -79, lat = 37.8, zoom = 5) %>%
addTiles() %>%
addProviderTiles(providers$Esri.NatGeoWorldMap) %>%
#Add circles around the areas of interest (Arizona and Connecticut). Changed the radius to highlight the whole states.
addCircles(
lng = arizona_center[1], lat = arizona_center[2], #Uses the center of the states defined above
fillColor = "NA",
radius = 400000,
color = "#FEFE62",
weight = 5
) %>%
addCircles(
lng = connecticut_center[1], lat = connecticut_center[2],
fillColor = "NA",
radius = 80500,
color = "#FEFE62",
weight = 5
) %>%
#Lightness color Observations: Circle markers used for each observation
addCircleMarkers(data = Black, color = "black", fillOpacity = 1, radius = 5, group = "Black: 0-20% Lightness") %>%
addCircleMarkers(data = DarkGray, color = "black", fillColor = "gray", fillOpacity = 1, radius = 5, group = "Dark Gray: 21-40% Lightness") %>% #All outlines done in black to make them more noticable/
addCircleMarkers(data = Gray, color = "black", fillColor = "darkgray", fillOpacity = 1, radius = 5, group = "Gray: 41-60% Lightness") %>% #Darkgray and gray colors were switched for fill color because on the map, gray appeared darker than darkgray.
addCircleMarkers(data = LightGray, color = "black", fillColor = "lightgray", fillOpacity = 1, radius = 5, group = "Light Gray: 61-80% Lightness") %>%
addCircleMarkers(data = White, color = "black", fillColor = "white", fillOpacity = 1, radius = 5, group = "White: 81-100% Lightness") %>%
#Added Layers Control to be able to look at the number of specific observation in each lightness value
addLayersControl(
overlayGroups = c("Black: 0-20% Lightness", "Dark Gray: 21-40% Lightness", "Gray: 41-60% Lightness", "Light Gray: 61-80% Lightness", "White: 81-100% Lightness"),
options = layersControlOptions(collapsed = FALSE)) %>%
#Legend of all the lightness categories along with how they are classified
addLegend(position = "bottomright", title = "Legend",
colors = c("black", "gray", "darkgray", "lightgray", "white"),
labels = c("Black: 0-20% Lightness", "Dark Gray: 21-40% Lightness", "Gray: 41-60% Lightness", "Light Gray: 61-80% Lightness", "White: 81-100% Lightness"),
opacity = 1)## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectivelylocation_mapAverage Annual Precipitation Raster for US
#Precipitation Raster from WorldClim (Prec)
precip_data <- getData("worldclim", var = "prec", res = 10)## Warning in getData("worldclim", var = "prec", res = 10): getData will be removed in a future version of raster
## . Please use the geodata package instead#Clipped the raster to just represent the US (Lower 48 states)
US_border <- extent(-125, -66.5, 24, 49.5)
USprecip_data <- crop(precip_data, US_border)
#Originally showed average temperatures for each month. Found the mean of all the data to get the yearly average
avg_precip <- mean(USprecip_data)
#Precipitation color scale (red = low precipitation, darkblue = high precipitation)
color_precip <- colorNumeric(
palette = c("red", "yellow", "green", "blue", "darkblue"),
domain = c(0, 250) #ranges from 0-250 mm of precipitation
)Precipitation Map
precipitation_map <- leaflet() %>%
setView(lng = -79, lat = 37.8, zoom = 5) %>%
addTiles() %>%
addProviderTiles(providers$Esri.NatGeoWorldMap) %>% #Same as observation map
#Lightness Plummage Observations. Same as the observations map, but removed lightgray and white since there were no observations within those ranges
addCircleMarkers(data = Black, color = "black", fillOpacity = 1, radius = 5, group = "Black: 0-20% Lightness") %>%
addCircleMarkers(data = DarkGray, color = "black", fillColor = "gray", fillOpacity = 1, radius = 5, group = "Dark Gray: 21-40% Lightness") %>%
addCircleMarkers(data = Gray, color = "black", fillColor = "darkgray", fillOpacity = 1, radius = 5, group = "Gray: 41-60% Lightness") %>%
#Added the average precipitation raster and used the color_precip palette to represent it on the map
addRasterImage(avg_precip, colors = color_precip, opacity = 0.4, group = "Precipitation (mm)") %>%
#Added Layers Control to be able to look at the number of specific observation in each lightness value and to remove/add precipitation
addLayersControl(
overlayGroups = c("Black: 0-20% Lightness", "Dark Gray: 21-40% Lightness", "Gray: 41-60% Lightness", "Precipitation (mm)"),
options = layersControlOptions(collapsed = FALSE)) %>%
#Added a legend for the observations
addLegend(position = "bottomright", title = "Legend",
colors = c("black", "gray", "darkgray"),
labels = c("Black: 0-20% Lightness", "Dark Gray: 21-40% Lightness", "Gray: 41-60% Lightness"),
opacity = 1) %>%
#Added a legend for the precipitation colors so viewers are aware of what the colors mean
addLegend(position = "bottomright", title = "Average Annual Precipitation (mm)",
colors = color_precip(seq(0, 250, by = 50)),
labels = seq(0, 250, by = 50),
opacity = .5)## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectivelyprecipitation_mapAverage Annual Temperature Raster for US
#Temperature Raster from WorldClim (using tmean (mean temperatures))
temp_data <- getData("worldclim", var = "tmean", res = 10)## Warning in getData("worldclim", var = "tmean", res = 10): getData will be removed in a future version of raster
## . Please use the geodata package insteadUStemp_data <- crop(temp_data, US_border) #cropped the raster to just show the US
avg_temp_large <- mean(UStemp_data) #Found the yearly average
avg_temp <- avg_temp_large / 10 #Originally units were °C x 10, so I divided the values by 10 to just have the temperature in °C
#Temperature color scale (lightblue = colder, red = warmer)
color_temp <- colorNumeric(
palette = c("lightblue", "blue", "green", "yellow", "red"),
domain = c(0, 25)
)#Temperature Map
temp_map <- leaflet() %>%
setView(lng = -79, lat = 37.8, zoom = 5) %>%
addTiles() %>%
addProviderTiles(providers$Esri.NatGeoWorldMap) %>% #Same basemap as other maps
#Lightness Plummage Observations. Same as the observations map, but removed lightgray and white since there were no observations within those ranges
addCircleMarkers(data = Black, color = "black", fillOpacity = 1, radius = 5, group = "Black: 0-20% Lightness") %>%
addCircleMarkers(data = DarkGray, color = "black", fillColor = "gray", fillOpacity = 1, radius = 5, group = "Dark Gray: 21-40% Lightness") %>%
addCircleMarkers(data = Gray, color = "black", fillColor = "darkgray", fillOpacity = 1, radius = 5, group = "Gray: 41-60% Lightness") %>%
#Added the average annual temperature raster and used the color_temp palette to represent it on the map
addRasterImage(avg_temp, colors = color_temp, opacity = 0.5, group = "Temperature (°C)") %>%
#Layer controls to be able to look at the number of specific observation in each lightness value and to remove/add temperature
addLayersControl(
overlayGroups = c("Black: 0-20% Lightness", "Dark Gray: 21-40% Lightness", "Gray: 41-60% Lightness", "Temperature (°C)"),
options = layersControlOptions(collapsed = FALSE)) %>%
#Added a legend for the observations
addLegend(position = "bottomright", title = "Legend",
colors = c("black", "gray", "darkgray"),
labels = c("Black: 0-20% Lightness", "Dark Gray: 21-40% Lightness", "Gray: 41-60% Lightness"),
opacity = 1) %>%
#Added a legend for the precipitation colors so viewers are aware of what the colors mean (range from 0-25°C)
addLegend(position = "bottomright", title = "Average Annual Temperature (°C)",
colors = color_temp(seq(0, 25, by = 5)),
labels = seq(0, 25, by = 5),
opacity = .5)## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectivelytemp_mapAltitude Raster for US
#Altitude Raster from WorldClim using altitude (alt) variable
altitude_data <- getData("worldclim", var = "alt", res = 10)## Warning in getData("worldclim", var = "alt", res = 10): getData will be removed in a future version of raster
## . Please use the geodata package insteadUSalt <- crop(altitude_data, US_border) #Clipped to US
#Did not need to find the average due to altitude not changing from month to month
#Altitude color scale (lightblue = low altitude, red = high altitude)
color_alt <- colorNumeric(
palette = c("lightblue", "blue", "green", "yellow", "red"),
domain = c(0, 3500)
)Altitude Map
altitude_map <- leaflet() %>%
setView(lng = -79, lat = 37.8, zoom = 5) %>%
addTiles() %>%
addProviderTiles(providers$Esri.NatGeoWorldMap) %>% #Same as other maps
#Lightness Plummage Observations. Same as the observations map, but removed lightgray and white since there were no observations within those ranges
addCircleMarkers(data = Black, color = "black", fillOpacity = 1, radius = 5, group = "Black: 0-20% Lightness") %>%
addCircleMarkers(data = DarkGray, color = "black", fillColor = "gray", fillOpacity = 1, radius = 5, group = "Dark Gray: 21-40% Lightness") %>%
addCircleMarkers(data = Gray, color = "black", fillColor = "darkgray", fillOpacity = 1, radius = 5, group = "Gray: 41-60% Lightness") %>%
#Added the altitude raster and used the color_alt palette to represent it on the map
addRasterImage(USalt, colors = color_alt, opacity = 0.5, group = "Altitude") %>%
#Layer controls to be able to look at the number of specific observation in each lightness value and to remove/add altitude
addLayersControl(
overlayGroups = c("Black: 0-20% Lightness", "Dark Gray: 21-40% Lightness", "Gray: 41-60% Lightness", "Altitude (m)"),
options = layersControlOptions(collapsed = FALSE)) %>%
#Added a legend for the observations
addLegend(position = "bottomright", title = "Legend",
colors = c("black", "gray", "darkgray"),
labels = c("Black: 0-20% Lightness", "Dark Gray: 21-40% Lightness", "Gray: 41-60% Lightness"),
opacity = 1) %>%
#Added a legend for the altitude colors so viewers are aware of what the colors represent (range from 0-3500m)
addLegend(position = "bottomright", title = "Altitude (m)",
colors = color_alt(seq(0, 3500, by = 500)),
labels = seq(0, 3500, by = 500),
opacity = 0.5)## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectivelyaltitude_mapMap including temperature, precipitation, and altitude rasters
Final_Map <- leaflet() %>%
setView(lng = -79, lat = 37.8, zoom = 5) %>%
addTiles() %>%
addProviderTiles(providers$Esri.NatGeoWorldMap) %>% #Same basemap as other maps
#Observations (same as the above maps)
addCircleMarkers(data = Black, color = "black", fillOpacity = 1, radius = 5, group = "Black: 0-20% Lightness") %>%
addCircleMarkers(data = DarkGray, color = "black", fillColor = "gray", fillOpacity = 1, radius = 5, group = "Dark Gray: 21-40% Lightness") %>%
addCircleMarkers(data = Gray, color = "black", fillColor = "darkgray", fillOpacity = 1, radius = 5, group = "Gray: 41-60% Lightness") %>%
#Added each of the climate rasters and their corresponding color palettes
addRasterImage(avg_precip, colors = color_precip, opacity = 0.4, group = "Precipitation (mm)") %>%
addRasterImage(avg_temp, colors = color_temp, opacity = 0.5, group = "Temperature (°C)") %>%
addRasterImage(USalt, colors = color_alt, opacity = 0.5, group = "Altitude (m)") %>%
#Only Included the climate variables to the layers control so viewer can directly see relationships between the 3 in the areas of interest
addLayersControl(
overlayGroups = c("Precipitation (mm)", "Temperature (°C)", "Altitude (m)"),
options = layersControlOptions(collapsed = FALSE)) %>%
#Due to space constraints, only black, dark gray, and gray were included in the legend since there were no observations for light gray or white.
addLegend(position = "topright", title = "Legend",
colors = c("black", "gray", "darkgray"),
labels = c("Black: 0-20% Lightness", "Dark Gray: 21-40% Lightness", "Gray: 41-60% Lightness"),
opacity = 1) %>%
#Legend for Precipitation raster
addLegend(position = "bottomleft", title = "Average Annual Precipitation (mm)",
colors = color_precip(seq(0, 250, by = 50)),
labels = seq(0, 250, by = 50),
opacity = .5) %>%
#Legend for Temperature raster
addLegend(position = "bottomleft", title = "Average Annual Temperature (°C)",
colors = color_temp(seq(0, 25, by = 5)),
labels = seq(0, 25, by = 5),
opacity = .5) %>%
#Legend for Altitude raster
addLegend(position = "bottomright", title = "Altitude (m)",
colors = color_alt(seq(0, 3500, by = 500)),
labels = seq(0, 3500, by = 500),
opacity = 0.5)## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA
## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA
## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively
## Assuming "Longitude" and "Latitude" are longitude and latitude, respectivelyFinal_Map