Last updated: 2024-01-08

Checks: 7 0

Knit directory: R_workflowr/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20230501)

The command set.seed(20230501) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 9d4271f

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 9d4271f. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    R_workflowr/.RData
    Ignored:    R_workflowr/.Rproj.user/
    Ignored:    R_workflowr/data/
    Ignored:    R_workflowr/output/pubFiles/
    Ignored:    R_workflowr/output/webGestalt/
    Ignored:    R_workflowr/renv/library/
    Ignored:    R_workflowr/renv/staging/
    Ignored:    R_workflowr/test.phy

Unstaged changes:
    Modified:   R_workflowr/analysis/RICR.Rmd
    Modified:   R_workflowr/output/draft_figs/Myotis_size_life_LQ_upham.pdf
    Modified:   R_workflowr/output/subfigs/phylo_auNG_circ_col_withLine.pdf

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (R_workflowr/analysis/sample_map.Rmd) and HTML (R_workflowr/docs/sample_map.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 8f66d05 docmanny 2023-12-21 clean version of Fig 1, now with LQ
Rmd 3d9fd9c docmanny 2023-12-15 draft sub figs for F1&2
html 3d9fd9c docmanny 2023-12-15 draft sub figs for F1&2
Rmd 0408a6b docmanny 2023-05-09 Figure 1 work
html 5848dd7 Juan M Vazquez (docmanny) 2023-01-31 Adding workflowr
Rmd ca07528 Juan M Vazquez (docmanny) 2023-01-31 wflow_git_commit(".") 
our_genomes = c(
  "Myotis_auriculus",
  "Myotis_californicus",
  "Myotis_occultus",
  "Myotis_lucifugus",
  "Myotis_yumanensis",
  "Myotis_volans",
  "Myotis_velifer",
  "Myotis_evotis",
  "Myotis_thysanodes"
)

species_color = ggsci::pal_d3(palette = "category20")(length(our_genomes)+1) %>% 
  set_names(., c(our_genomes, "Other"))
species_color["Myotis_evotis"] = "#17BECFFF"
species_color["Other"] = "#7F7F7FFF"
sheet_cols <- c(
  'CollectionID' = 'c',
  'Source Info & Permit #' = 'c',
  'Date Captured' = 'D',
  'Date Sampled' = 'D',
  'SampleTimeStart' = 't',
  'SampleTimeEnd' = 't',
  'Collector' = 'c',
  'County' = 'c',
  'State' = 'c',
  'LocationShort' = 'c',
  'LocationFull' = 'c',
  'LocationDetails' = 'c',
  'CoordExact' = 'c',
  'What3Words' = 'c',
  'Lattitude' = 'd',
  'Longitude' = 'd',
  'Altitude (ft)' = 'c',
  'Net #' = 'c',
  'FieldSpeciesID' = 'c',
  'FieldSpeciesMethod' = 'c',
  'ConfirmedSpeciesID' = 'c',
  'ConfirmationMethod' = 'c',
  'Sex' = 'c',
  'Age' = 'c',
  'Reproductive Status' = 'c',
  'Weight (g)' = 'd',
  'Forearm (mm)' = 'd',
  'Ear (mm)' = 'd',
  'Tragus (mm)' = 'd',
  'Tail (mm)' = 'd',
  'Thumb (mm)' = 'd',
  'WNS Status' = 'c',
  'HasAccoustic' = 'c',
  'PunchesCollected' = 'c',
  'Preservation' = 'c',
  'OtherSamples' = 'c',
  'OtherMeasurements' = 'c',
  'Comments' = 'c',
  'AltID' = 'c'
)


# datasheet <- read_sheet("https://docs.google.com/spreadsheets/d/1DJctPKAd9TQOLNnmGXP8t0TPAEe4-CICus9QZ7IYsCU/edit#gid=915133740", col_types = sheet_cols %>% paste0(collapse=""))
# datasheet %>% write_tsv("../data/collection_datasheet_2022-11-15.tsv")
datasheet <- read_tsv("../data/collection_datasheet_2022-11-15.tsv", col_types = sheet_cols %>% paste0(collapse=""))
shape.myoAui <- dir(path="../data/USGS_SpeciesRanges/Myotis_auriculus/", pattern = ".shp$", full.names=T) %>% 
  st_read() %>% 
  mutate(species = "Myotis_auriculus") %>% 
  st_make_valid()
shape.myoCai <- dir(path = "../data/USGS_SpeciesRanges/Myotis_californicus/", pattern = ".shp$", full.names=T) %>%
  st_read() %>% 
  mutate(species = "Myotis_californicus") %>% 
  st_make_valid()
shape.myoOcc <- dir(path = "../data/USGS_SpeciesRanges/Myotis_occultus/", pattern = ".shp$", full.names=T) %>%
  st_read() %>% 
  mutate(species = "Myotis_occultus") %>% 
  st_make_valid()
shape.myoLuc <- dir(path = "../data/USGS_SpeciesRanges/Myotis_lucifugus/", pattern = ".shp$", full.names=T) %>%
  st_read() %>% 
  mutate(species = "Myotis_lucifugus") %>% 
  st_make_valid()
shape.myoYum <- dir(path = "../data/USGS_SpeciesRanges/Myotis_yumanensis/", pattern = ".shp$", full.names=T) %>%
  st_read() %>% 
  mutate(species = "Myotis_yumanensis") %>% 
  st_make_valid()
shape.myoVol <- dir(path = "../data/USGS_SpeciesRanges/Myotis_volans/", pattern = ".shp$", full.names=T) %>%
  st_read() %>% 
  mutate(species = "Myotis_volans") %>% 
  st_make_valid()
shape.myoVel <- dir(path = "../data/USGS_SpeciesRanges/Myotis_velifer/", pattern = ".shp$", full.names=T) %>%
  st_read() %>% 
  mutate(species = "Myotis_velifer") %>% 
  st_make_valid()
shape.myoEvo <- dir(path = "../data/USGS_SpeciesRanges/Myotis_evotis/", pattern = ".shp$", full.names=T) %>%
  st_read() %>% 
  mutate(species = "Myotis_evotis") %>% 
  st_make_valid()
shape.myoThy <- dir(path = "../data/USGS_SpeciesRanges/Myotis_thysanodes/", pattern = ".shp$", full.names=T) %>%
  st_read() %>% 
  mutate(species = "Myotis_thysanodes") %>% 
  st_make_valid()
world <- ne_countries(scale = "medium", returnclass = "sf") %>% 
  st_make_valid()
northAmerica <- ne_countries(scale = "medium", returnclass = "sf", country = c("United States of America", "Mexico", "Canada")) %>% st_make_valid
# class(world)
states <- st_as_sf(map("state", plot = FALSE, fill = TRUE))
west <- subset(states, ID %in% c("california", "arizona", "nevada", "new mexico", "idaho", "oregon", "washington", "montana", "nebraska", "colorado", "utah", "wyoming"))
notwest <- subset(states, !(ID %in% c("california", "arizona", "nevada", "new mexico", "idaho", "oregon", "washington", "montana", "nebraska", "colorado", "utah", "wyoming")))
cal <- subset(states, ID == "california")
calaz <- subset(states, ID %in% c("california", "arizona"))
caaznv <- subset(states, ID %in% c("california", "arizona", "nevada"))
notcalaz <- subset(states, !(ID %in% c("california", "arizona")))
datasheet.known <- datasheet %>% 
  filter(!is.na(Lattitude)) %>% 
  select(species=FieldSpeciesID, Lattitude, Longitude) #%>% 
  # mutate(species = species %>% str_replace_all(" ", "_"))
p.map.collections <- ggplot() + 
  geom_sf(data= west, color = "#ABB0B8", fill = "#6F7378", size = 0.1) +
  geom_spatial_point(data= datasheet.known, aes(group=species, y=Lattitude, x=Longitude, color=species), size=4, position="jitter") + 
  geom_spatial_label_repel(data= datasheet.known, aes(label=species,group=species, y=Lattitude, x=Longitude, color=species), show.legend=F) + 
  scale_color_manual(values = species_color %>% set_names(., names(.) %>% str_replace_all("_", " "))) +
  ggpubr::theme_pubclean() + 
  theme(legend.position = "none")

p.map.collections
Assuming `crs = 4326` in stat_spatial_identity()
Assuming `crs = 4326` in stat_spatial_identity()

Version Author Date
3d9fd9c docmanny 2023-12-15 
p.map.collections.caaznv <- ggplot() + 
  geom_sf(data= caaznv, color = "#ABB0B8", fill = "#6F7378", size = 0.1) +
  geom_spatial_point(data= datasheet.known, aes(group=species, y=Lattitude, x=Longitude, fill=species), size=4, pch=21, position="jitter") + 
  geom_spatial_label_repel(data= datasheet.known, aes(label=species,group=species, y=Lattitude, x=Longitude, fill=species), show.legend=F, color='black', nudge_x=-0.5) + 
  scale_color_manual(values = species_color %>% set_names(., names(.) %>% str_replace_all("_", " "))) +
  scale_fill_manual(values = species_color %>% set_names(., names(.) %>% str_replace_all("_", " "))) +
  ggpubr::theme_pubclean() + 
  theme(legend.position = "none")

p.map.collections.caaznv
Assuming `crs = 4326` in stat_spatial_identity()
Assuming `crs = 4326` in stat_spatial_identity()

Version Author Date
3d9fd9c docmanny 2023-12-15 
shape.myoLuc$geometry <- shape.myoLuc$geometry %>%
  s2::s2_rebuild() %>%
  sf::st_as_sfc()
sf::sf_use_s2(TRUE)
st_intersection(west, shape.myoLuc)
shape.myoLuc2 <- st_transform(shape.myoLuc, "NAD83")
st_crs(shape.myoLuc)
st_crs(shape.myoLuc2)
st_crs(west)
west2 <- st_transform(west, crs="NAD83")
st_crs(west2)
shape.myoLuc3 <- shape.myoLuc
st_crs(shape.myoLuc3) <- "NAD83"
st_intersection(shape.myoLuc3, west2)
p.mapMyo.split <- ggplot() + 
  geom_sf(data= west, color = "#ABB0B8", fill = "#6F7378", size = 0.1) + 
  annotation_spatial(data = shape.myoLuc, aes(group = species, fill=species), alpha = 0.8, color="black") +
  annotation_spatial(data = shape.myoYum, aes(group = species, fill=species), alpha = 0.8, color="black") +
  annotation_spatial(data = shape.myoCai, aes(group = species, fill=species), alpha = 0.8, color="black") +
  annotation_spatial(data = shape.myoVol, aes(group = species, fill=species), alpha = 0.8, color="black") +
  annotation_spatial(data = shape.myoEvo, aes(group = species, fill=species), alpha = 0.8, color="black") +
  annotation_spatial(data = shape.myoThy, aes(group = species, fill=species), alpha = 0.8, color="black") +
  annotation_spatial(data = shape.myoOcc, aes(group = species, fill=species), alpha = 0.8, color="black") +
  annotation_spatial(data = shape.myoVel, aes(group = species, fill=species), alpha = 0.8, color="black") +
  annotation_spatial(data = shape.myoAui, aes(group = species, fill=species), alpha = 0.8, color="black") +
  annotation_spatial(data=notwest, fill="white", color="white") +
  geom_spatial_point(data= datasheet.known, aes(group=species, y=Lattitude, x=Longitude), size=2) + 
  scale_fill_manual("Species", values = species_color) +
  scale_color_manual("Species", values = species_color) +
  guides(fill=guide_none()) + 
  theme_void() +
  facet_wrap(~species) + 
  theme(
    strip.text = element_text(face="bold", size=12, hjust = 0.5)
  )
p.mapMyo.split
Assuming `crs = 4326` in stat_spatial_identity()

Version Author Date
3d9fd9c docmanny 2023-12-15
5848dd7 Juan M Vazquez (docmanny) 2023-01-31 
#%>% plotly::ggplotly()
pic.myoLuc <- image_read("https://www.batcon.org/wp-content/uploads/2020/02/108746-19-e1593640880404.jpg") %>% image_ggplot()
pic.myoYum <- image_read("https://www.batcon.org/wp-content/uploads/2020/02/106605-56-812x600.jpg") %>% image_ggplot()
pic.myoCai <- image_read("https://www.batcon.org/wp-content/uploads/2020/02/106608-56-812x600.jpg") %>% image_ggplot()
pic.myoVol <- image_read("https://www.batcon.org/wp-content/uploads/2020/02/106442-19-812x600.jpg") %>% image_ggplot()
pic.myoEvo <- image_read("https://www.batcon.org/wp-content/uploads/2020/02/Long-eared-Myotis-MYOTIS-EVOTIS-Michael-DurhamMinden-PIctures-e1642201106124-812x600.jpeg") %>% image_ggplot()
pic.myoThy <- image_read("https://www.batcon.org/wp-content/uploads/2020/02/106472-19.jpg") %>% image_ggplot()
pic.myoOcc <- image_read("https://www.batcon.org/wp-content/uploads/2020/02/Arizona-myotis-Rick-and-Nora-Bowers-Alamy-Stock-Photo--812x600.jpeg") %>% image_ggplot()
pic.myoVel <- image_read("https://www.batcon.org/wp-content/uploads/2020/02/106463-19-e1642201625671-812x600.jpg") %>% image_ggplot()
pic.myoAui <- image_read("https://www.batcon.org/wp-content/uploads/2020/02/106475-19-e1593638188243-812x600.jpg") %>% image_ggplot()
pic.myoLuc <- image_read(path = '../data/img/batCon-myoLuc.jpg') %>% image_ggplot()
pic.myoYum <- image_read("../data/img/batCon-myoYum.jpg") %>% image_ggplot()
pic.myoCai <- image_read("../data/img/batCon-myoCai.jpg") %>% image_ggplot()
pic.myoVol <- image_read("../data/img/batCon-myoVol.jpg") %>% image_ggplot()
pic.myoEvo <- image_read("../data/img/batCon-myoEvo-Michael-DurhamMinden-PIctures.jpeg") %>% image_ggplot()
pic.myoThy <- image_read("../data/img/batCon-myoThy.jpg") %>% image_ggplot()
pic.myoOcc <- image_read("../data/img/batCon-myoOcc-Rick-and-Nora-Bowers-Alamy.jpeg") %>% image_ggplot()
pic.myoVel <- image_read("../data/img/batCon-myoVel.jpg") %>% image_ggplot()
pic.myoAui <- image_read("../data/img/batCon-myoAui.jpg") %>% image_ggplot()
p.map.base <- ggplot() + 
  geom_sf(data= west, color = "#ABB0B8", fill = "#6F7378", size = 0.1)

p.map.myoLuc <- p.map.base + 
  annotation_spatial(data = shape.myoLuc, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notwest, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=12, hjust = 0.5), 
        plot.background = element_rect(color="black"), 
        plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis lucifugus") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_lucifugus"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoYum <- p.map.base + 
  annotation_spatial(data = shape.myoYum, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notwest, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=12, hjust = 0.5), 
        plot.background = element_rect(color="black"), 
        plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis yumanensis") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_yumanensis"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoCai <- p.map.base + 
  annotation_spatial(data = shape.myoCai, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notwest, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=12, hjust = 0.5), 
        plot.background = element_rect(color="black"), 
        plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis californicus") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_californicus"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoVol <- p.map.base + 
  annotation_spatial(data = shape.myoVol, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notwest, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=12, hjust = 0.5), 
        plot.background = element_rect(color="black"), 
        plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis volans") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_volans"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoEvo <- p.map.base + 
  annotation_spatial(data = shape.myoEvo, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notwest, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=12, hjust = 0.5), 
        plot.background = element_rect(color="black"), 
        plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis evotis") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_evotis"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoThy <- p.map.base + 
  annotation_spatial(data = shape.myoThy, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notwest, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=12, hjust = 0.5), 
        plot.background = element_rect(color="black"), 
        plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis thysanodes") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_thysanodes"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoOcc <- p.map.base + 
  annotation_spatial(data = shape.myoOcc, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notwest, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=12, hjust = 0.5), 
        plot.background = element_rect(color="black"), 
        plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis occultus") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_occultus"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoVel <- p.map.base + 
  annotation_spatial(data = shape.myoVel, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notwest, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=12, hjust = 0.5), 
        plot.background = element_rect(color="black"), 
        plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis velifer") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_velifer"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoAui <- p.map.base + 
  annotation_spatial(data = shape.myoAui, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notwest, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=12, hjust = 0.5), 
        plot.background = element_rect(color="black"), 
        plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis auriculus") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_auriculus"), aes(group=species, y=Lattitude, x=Longitude), size=2)


p.map.pic.myoLuc <- p.map.myoLuc + inset_element(pic.myoLuc, left = 0.69, bottom = 0.6, right = 1, top = 1)
p.map.pic.myoYum <- p.map.myoYum + inset_element(pic.myoYum, left = 0.69, bottom = 0.6, right = 1, top = 1)
p.map.pic.myoCai <- p.map.myoCai + inset_element(pic.myoCai, left = 0.69, bottom = 0.6, right = 1, top = 1)
p.map.pic.myoVol <- p.map.myoVol + inset_element(pic.myoVol, left = 0.69, bottom = 0.6, right = 1, top = 1)
p.map.pic.myoEvo <- p.map.myoEvo + inset_element(pic.myoEvo, left = 0.69, bottom = 0.6, right = 1, top = 1)
p.map.pic.myoThy <- p.map.myoThy + inset_element(pic.myoThy, left = 0.69, bottom = 0.6, right = 1, top = 1)
p.map.pic.myoOcc <- p.map.myoOcc + inset_element(pic.myoOcc, left = 0.69, bottom = 0.6, right = 1, top = 1)
p.map.pic.myoVel <- p.map.myoVel + inset_element(pic.myoVel, left = 0.69, bottom = 0.6, right = 1, top = 1)
p.map.pic.myoAui <- p.map.myoAui + inset_element(pic.myoAui, left = 0.69, bottom = 0.6, right = 1, top = 1)

p.mapPic <- (p.map.pic.myoLuc | p.map.pic.myoYum | p.map.pic.myoCai)/
  (p.map.pic.myoVol | p.map.pic.myoEvo | p.map.pic.myoThy)/
  (p.map.pic.myoOcc | p.map.pic.myoVel | p.map.pic.myoAui)

p.mapPic

Version Author Date
3d9fd9c docmanny 2023-12-15
5848dd7 Juan M Vazquez (docmanny) 2023-01-31 
p.map.base.calaz <- ggplot() + 
  geom_sf(data= calaz, color = "#ABB0B8", fill = "#6F7378", size = 0.1)

p.map.myoLuc.calaz <- p.map.base.calaz + 
  annotation_spatial(data = shape.myoLuc, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notcalaz, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=10, hjust = 0.5), plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis lucifugus") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_lucifugus"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoYum.calaz <- p.map.base.calaz + 
  annotation_spatial(data = shape.myoYum, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notcalaz, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=10, hjust = 0.5), plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis yumanensis") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_yumanensis"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoCai.calaz <- p.map.base.calaz + 
  annotation_spatial(data = shape.myoCai, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notcalaz, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=10, hjust = 0.5), plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis californicus") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_californicus"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoVol.calaz <- p.map.base.calaz + 
  annotation_spatial(data = shape.myoVol, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notcalaz, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=10, hjust = 0.5), plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis volans") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_volans"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoEvo.calaz <- p.map.base.calaz + 
  annotation_spatial(data = shape.myoEvo, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notcalaz, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=10, hjust = 0.5), plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis evotis") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_evotis"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoThy.calaz <- p.map.base.calaz + 
  annotation_spatial(data = shape.myoThy, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notcalaz, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=10, hjust = 0.5), plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis thysanodes") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_thysanodes"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoOcc.calaz <- p.map.base.calaz + 
  annotation_spatial(data = shape.myoOcc, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notcalaz, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=10, hjust = 0.5), plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis occultus") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_occultus"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoVel.calaz <- p.map.base.calaz + 
  annotation_spatial(data = shape.myoVel, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notcalaz, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=10, hjust = 0.5), plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis velifer") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_velifer"), aes(group=species, y=Lattitude, x=Longitude), size=2)
p.map.myoAui.calaz <- p.map.base.calaz + 
  annotation_spatial(data = shape.myoAui, aes(group = species, fill=species), alpha = 0.8, color="black") + 
  annotation_spatial(data=notcalaz, fill="white", color="white") + 
  scale_fill_manual(values = species_color) + 
  guides(fill=guide_none()) + 
  theme_void() + 
  theme(title = element_text(face="bold", size=10, hjust = 0.5), plot.margin = margin(0,0,0,0, "cm")) + 
  ggtitle("Myotis auriculus") +
  geom_spatial_point(data= datasheet.known %>% filter(species == "Myotis_auriculus"), aes(group=species, y=Lattitude, x=Longitude), size=2)


p.map.pic.myoLuc.calaz <- p.map.myoLuc.calaz + inset_element(pic.myoLuc, left = 0.4, bottom = 0.55, right = 1, top = 0.95)
p.map.pic.myoYum.calaz <- p.map.myoYum.calaz + inset_element(pic.myoYum, left = 0.4, bottom = 0.55, right = 1, top = 0.95)
p.map.pic.myoCai.calaz <- p.map.myoCai.calaz + inset_element(pic.myoCai, left = 0.4, bottom = 0.55, right = 1, top = 0.95)
p.map.pic.myoVol.calaz <- p.map.myoVol.calaz + inset_element(pic.myoVol, left = 0.4, bottom = 0.55, right = 1, top = 0.95)
p.map.pic.myoEvo.calaz <- p.map.myoEvo.calaz + inset_element(pic.myoEvo, left = 0.4, bottom = 0.55, right = 1, top = 0.95)
p.map.pic.myoThy.calaz <- p.map.myoThy.calaz + inset_element(pic.myoThy, left = 0.4, bottom = 0.55, right = 1, top = 0.95)
p.map.pic.myoOcc.calaz <- p.map.myoOcc.calaz + inset_element(pic.myoOcc, left = 0.4, bottom = 0.55, right = 1, top = 0.95)
p.map.pic.myoVel.calaz <- p.map.myoVel.calaz + inset_element(pic.myoVel, left = 0.4, bottom = 0.55, right = 1, top = 0.95)
p.map.pic.myoAui.calaz <- p.map.myoAui.calaz + inset_element(pic.myoAui, left = 0.4, bottom = 0.55, right = 1, top = 0.95)

p.mapPic.calaz <- 
  (p.map.pic.myoLuc.calaz | p.map.pic.myoYum.calaz | p.map.pic.myoCai.calaz)/
  (p.map.pic.myoVol.calaz | p.map.pic.myoEvo.calaz | p.map.pic.myoThy.calaz)/
  (p.map.pic.myoOcc.calaz | p.map.pic.myoVel.calaz | p.map.pic.myoAui.calaz)

p.mapPic.calaz

Version Author Date
3d9fd9c docmanny 2023-12-15
5848dd7 Juan M Vazquez (docmanny) 2023-01-31 

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

time zone: America/La_Paz
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.1.3         magick_2.8.1            ggspatial_1.1.9        
 [4] rnaturalearthdata_0.1.0 rnaturalearth_0.3.4     maps_3.4.1             
 [7] sf_1.0-14               lubridate_1.9.2         forcats_1.0.0          
[10] stringr_1.5.0           dplyr_1.1.3             purrr_1.0.2            
[13] readr_2.1.4             tidyr_1.3.0             tibble_3.2.1           
[16] ggplot2_3.4.3           tidyverse_2.0.0        

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0   farver_2.1.1       fastmap_1.1.1      promises_1.2.1    
 [5] digest_0.6.33      timechange_0.2.0   lifecycle_1.0.3    magrittr_2.0.3    
 [9] compiler_4.3.1     rlang_1.1.1        sass_0.4.7         tools_4.3.1       
[13] utf8_1.2.3         yaml_2.3.7         knitr_1.44         ggsignif_0.6.4    
[17] labeling_0.4.3     bit_4.0.5          sp_2.1-1           classInt_0.4-10   
[21] abind_1.4-5        KernSmooth_2.23-22 workflowr_1.7.1    withr_2.5.0       
[25] grid_4.3.1         fansi_1.0.5        ggpubr_0.6.0       git2r_0.32.0      
[29] e1071_1.7-13       colorspace_2.1-0   scales_1.2.1       cli_3.6.1         
[33] rmarkdown_2.25     crayon_1.5.2       generics_0.1.3     rstudioapi_0.15.0 
[37] httr_1.4.7         tzdb_0.4.0         DBI_1.1.3          cachem_1.0.8      
[41] proxy_0.4-27       parallel_4.3.1     s2_1.1.4           vctrs_0.6.3       
[45] jsonlite_1.8.7     carData_3.0-5      car_3.1-2          hms_1.1.3         
[49] bit64_4.0.5        ggrepel_0.9.4      rstatix_0.7.2      jquerylib_0.1.4   
[53] units_0.8-4        glue_1.6.2         stringi_1.7.12     gtable_0.3.4      
[57] later_1.3.1        munsell_0.5.0      pillar_1.9.0       htmltools_0.5.6   
[61] R6_2.5.1           wk_0.9.0           rprojroot_2.0.3    vroom_1.6.3       
[65] evaluate_0.21      lattice_0.21-8     backports_1.4.1    broom_1.0.5       
[69] ggsci_3.0.0        httpuv_1.6.11      bslib_0.5.1        class_7.3-22      
[73] Rcpp_1.0.11        whisker_0.4.1      xfun_0.40          fs_1.6.3          
[77] pkgconfig_2.0.3