Now we will calculate log ratio, jaccard, fractal dimension.
rm(list=ls())
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 592983 31.7 1351252 72.2 685968 36.7
Vcells 1086517 8.3 8388608 64.0 1876884 14.4suppressPackageStartupMessages({
library(sf)
library(here)
library(dplyr)
library(tidyr)
library(ggplot2)
})
sf_use_s2(TRUE) # we use WGS84 projected data (it's spherical)data_sf <-
readRDS(here::here("Demo_NewYork/Data/output/1_data_sf_ny.rds")) %>%
select(datasetID, scalingID, samplingPeriodID,
siteID, croppedArea, area,
verbatimIdentification, scientificName,
centroidDecimalLatitude, centroidDecimalLongitude,
startYear, endYear, time_span,
cells_keep, species_keep,cell_sampling_repeats,
verbatimFootprintSRS, footprintSRS,
geometry)#----------------------------------------------------------#
# Calculate grid information -----
#----------------------------------------------------------#
# Custom function to get total and sampled area/cells from datasets
calculate_grid_info <- function(data_sf) {
grid_info <-
full_join(
data_sf %>%
st_drop_geometry() %>%
distinct(datasetID, scalingID, siteID,
cell_sampling_repeats, croppedArea, time_span) %>%
group_by(datasetID, scalingID) %>%
summarise(
Total_Ncells = n_distinct(siteID)),
data_sf %>%
st_drop_geometry() %>%
distinct(datasetID, scalingID, siteID,
cell_sampling_repeats, croppedArea, time_span) %>%
filter(cell_sampling_repeats == 2) %>%
group_by(datasetID, scalingID, time_span) %>%
summarise(
Total_Ncells_samp = n_distinct(siteID),
Total_area_samp = sum(croppedArea, na.rm = TRUE))
)
return(grid_info)
}
#--------------------------------------------------#
# Apply function to compute grid information
atlas_areas <-
calculate_grid_info(data_sf)`summarise()` has grouped output by 'datasetID'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'datasetID', 'scalingID'. You can override
using the `.groups` argument.
Joining with `by = join_by(datasetID, scalingID)`# Checks
kableExtra::kable(atlas_areas)| datasetID | scalingID | Total_Ncells | time_span | Total_Ncells_samp | Total_area_samp |
|---|---|---|---|---|---|
| 6 | 1 | 5335 | 5 | 5319 | 126878.5 |
| 6 | 2 | 1398 | 5 | 1396 | 127118.1 |
| 6 | 4 | 386 | 5 | 384 | 127118.1 |
| 6 | 8 | 113 | 5 | 112 | 127121.8 |
| 6 | 16 | 37 | 5 | 37 | 127146.8 |
| 6 | 32 | 13 | 5 | 13 | 127146.8 |
| 6 | 64 | 5 | 5 | 5 | 127146.8 |
| 6 | 128 | 2 | 5 | 2 | 127146.8 |
desired_levels <-
factor(
c("1", "2", "4", "8", "16", "32", "64", "128"),
ordered = T,
levels = c("1", "2", "4", "8", "16", "32", "64", "128")
)
# Read processed species data & merge with atlas_areas
pres_dat_final <-
readRDS(here::here("Demo_NewYork/Data/output/1_data_filtered_ny.rds")) %>%
left_join(atlas_areas) %>%
mutate(scalingID = factor(scalingID, levels = desired_levels)) %>%
filter(!is.na(verbatimIdentification)) # Remove unsampled cellsJoining with `by = join_by(scalingID)`glimpse(pres_dat_final)Rows: 1,120,865
Columns: 18
$ scalingID <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ siteID <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ samplingPeriodID <dbl> 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, …
$ verbatimIdentification <chr> "Baeolophus bicolor", "Junco hyemalis", "Corv…
$ scientificName <chr> "Baeolophus bicolor", "Junco hyemalis", "Corv…
$ verbatimFootprintSRS <chr> "epsg:26918", "epsg:26918", "epsg:26918", "ep…
$ footprintSRS <chr> "epsg:4326", "epsg:4326", "epsg:4326", "epsg:…
$ croppedArea <dbl> 20.62383, 20.62383, 20.62383, 20.62383, 20.62…
$ areaUnit <chr> "km2", "km2", "km2", "km2", "km2", "km2", "km…
$ centroidDecimalLongitude <dbl> -79.74312, -79.74312, -79.74312, -79.74312, -…
$ centroidDecimalLatitude <dbl> 42.06346, 42.06346, 42.06346, 42.06346, 42.06…
$ startYear <int> 2000, 2000, 2000, 2000, 2000, 1980, 2000, 198…
$ endYear <int> 2005, 2005, 2005, 2005, 2005, 1985, 2005, 198…
$ datasetID <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, …
$ Total_Ncells <int> 5335, 5335, 5335, 5335, 5335, 5335, 5335, 533…
$ time_span <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, …
$ Total_Ncells_samp <int> 5319, 5319, 5319, 5319, 5319, 5319, 5319, 531…
$ Total_area_samp <dbl> 126878.5, 126878.5, 126878.5, 126878.5, 12687…#----------------------------------------------------------#
# Calculate jaccard similarity index -----
#----------------------------------------------------------#
# Custom function to calculate jaccard across sites
jaccard <- function(set1, set2) {
a <- length(base::intersect(set1, set2))
b <- length(setdiff(set2, set1))
c <- length(setdiff(set1, set2))
n_union <- length(base::union(set1,set2))
return(list(jaccard_index = a / (a + b + c),
a = a,
b = b,
c = c,
n_union = n_union))
}
#----------------------------------------------------------#
# Calculate jaccard for all datasets
Jaccard_df <-
pres_dat_final %>%
filter(scalingID == 1) %>%
group_by(datasetID, verbatimIdentification) %>%
mutate(
jaccard_results = list(jaccard(
filter(pick(everything()), samplingPeriodID == 1)$siteID,
filter(pick(everything()), samplingPeriodID == 2)$siteID
))
) %>%
tidyr::unnest_wider(jaccard_results) %>%
group_by(verbatimIdentification, datasetID) %>%
mutate(d = Total_Ncells_samp - n_union) %>%
rename(Jaccard_sim = jaccard_index) %>%
mutate(Jaccard_dissim = 1 - Jaccard_sim) %>%
ungroup() %>%
distinct(datasetID, verbatimIdentification, Jaccard_dissim, Jaccard_sim, a, b, c, d, Total_Ncells_samp)
hist(Jaccard_df$Jaccard_dissim, breaks = 30, main = "Jaccard dissimilarity distribution", xlab = "Jaccard dissimilarity")
hist(log(Jaccard_df$d), breaks = 30, main = "log Number of cells never occupied", xlab = "log Number of cells never occupied")
hist(log(Jaccard_df$a), breaks = 30, main = "log Number of persistances", xlab = "log Number of persistances (cells occupied in both time periods)")
# Merge:
pres_dat_final_v2 <- pres_dat_final %>%
left_join(Jaccard_df)Joining with `by = join_by(verbatimIdentification, datasetID,
Total_Ncells_samp)`head(pres_dat_final_v2) scalingID siteID samplingPeriodID verbatimIdentification
1 1 1 2 Baeolophus bicolor
2 1 1 2 Junco hyemalis
3 1 1 2 Corvus brachyrhynchos
4 1 1 2 Cathartes aura
5 1 1 2 Toxostoma rufum
6 1 1 1 Icterus galbula
scientificName verbatimFootprintSRS footprintSRS croppedArea areaUnit
1 Baeolophus bicolor epsg:26918 epsg:4326 20.62383 km2
2 Junco hyemalis epsg:26918 epsg:4326 20.62383 km2
3 Corvus brachyrhynchos epsg:26918 epsg:4326 20.62383 km2
4 Cathartes aura epsg:26918 epsg:4326 20.62383 km2
5 Toxostoma rufum epsg:26918 epsg:4326 20.62383 km2
6 Icterus galbula epsg:26918 epsg:4326 20.62383 km2
centroidDecimalLongitude centroidDecimalLatitude startYear endYear datasetID
1 -79.74312 42.06346 2000 2005 6
2 -79.74312 42.06346 2000 2005 6
3 -79.74312 42.06346 2000 2005 6
4 -79.74312 42.06346 2000 2005 6
5 -79.74312 42.06346 2000 2005 6
6 -79.74312 42.06346 1980 1985 6
Total_Ncells time_span Total_Ncells_samp Total_area_samp Jaccard_dissim
1 5335 5 5319 126878.5 0.56401597
2 5335 5 5319 126878.5 0.39391086
3 5335 5 5319 126878.5 0.07149777
4 5335 5 5319 126878.5 0.56727452
5 5335 5 5319 126878.5 0.52084419
6 5335 5 5319 126878.5 0.14839259
Jaccard_sim a b c d
1 0.4359840 1420 1700 137 2062
2 0.6060891 1931 892 363 2133
3 0.9285022 4792 204 165 158
4 0.4327255 1756 1912 390 1261
5 0.4791558 1839 497 1502 1481
6 0.8516074 4000 271 426 622#----------------------------------------------------------#
rm(pres_dat_final, Jaccard_df, jaccard, atlas_areas, calculate_grid_info)#----------------------------------------------------------#
# Calculate area of occupancy -----
#----------------------------------------------------------#
occ_data_final <-
pres_dat_final_v2 %>%
ungroup() %>%
distinct(datasetID, samplingPeriodID, scalingID,
verbatimIdentification, siteID, time_span,
.keep_all = TRUE) %>%
group_by(datasetID, samplingPeriodID, scalingID, verbatimIdentification) %>%
# Calculate AOO:
dplyr::summarise(
mean_area = mean(croppedArea, na.rm = TRUE),
AOO = sum(croppedArea, na.rm = TRUE),
occ_Ncells = n_distinct(siteID)) %>%
left_join(pres_dat_final_v2 %>%
mutate(scalingID = as.factor(as.character(scalingID)))) %>%
# Calculate relative Occupancy:
dplyr::mutate(
rel_AOO = AOO / Total_area_samp,
rel_occ_Ncells = occ_Ncells / Total_Ncells_samp) %>% # Prevalence
# Remove duplicated rows:
distinct()`summarise()` has grouped output by 'datasetID', 'samplingPeriodID',
'scalingID'. You can override using the `.groups` argument.
Joining with `by = join_by(datasetID, samplingPeriodID, scalingID,
verbatimIdentification)`#----------------------------------------------------------#
rm(pres_dat_final_v2)
hist(occ_data_final$AOO, breaks = 30, main = "AOO distribution", xlab = "AOO")
#----------------------------------------------------------#
# Create species-level data
species_data <-
occ_data_final %>%
dplyr::select(
-siteID, -croppedArea
) %>%
distinct(datasetID, samplingPeriodID, scalingID, verbatimIdentification,
.keep_all = TRUE)
glimpse(species_data)Rows: 3,728
Columns: 27
Groups: datasetID, samplingPeriodID, scalingID [16]
$ datasetID <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, …
$ samplingPeriodID <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ scalingID <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ verbatimIdentification <chr> "Accipiter cooperii", "Accipiter gentilis", "…
$ mean_area <dbl> 24.80957, 24.70131, 24.66508, 23.82849, 24.91…
$ AOO <dbl> 13645.26106, 10967.38185, 21187.30398, 48157.…
$ occ_Ncells <int> 550, 444, 859, 2021, 129, 5059, 1923, 72, 348…
$ scientificName <chr> "Accipiter cooperii", "Accipiter gentilis", "…
$ verbatimFootprintSRS <chr> "epsg:26918", "epsg:26918", "epsg:26918", "ep…
$ footprintSRS <chr> "epsg:4326", "epsg:4326", "epsg:4326", "epsg:…
$ areaUnit <chr> "km2", "km2", "km2", "km2", "km2", "km2", "km…
$ centroidDecimalLongitude <dbl> -79.44191, -79.21572, -79.67959, -79.68952, -…
$ centroidDecimalLatitude <dbl> 42.07554, 42.30919, 42.02105, 42.15570, 42.30…
$ startYear <int> 1980, 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ endYear <int> 1985, 1985, 1985, 1985, 1985, 1985, 1985, 198…
$ Total_Ncells <int> 5335, 5335, 5335, 5335, 5335, 5335, 5335, 533…
$ time_span <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, …
$ Total_Ncells_samp <int> 5319, 5319, 5319, 5319, 5319, 5319, 5319, 531…
$ Total_area_samp <dbl> 126878.5, 126878.5, 126878.5, 126878.5, 12687…
$ Jaccard_dissim <dbl> 0.88131610, 0.90397805, 0.84927391, 0.6971467…
$ Jaccard_sim <dbl> 0.11868390, 0.09602195, 0.15072609, 0.3028532…
$ a <int> 202, 70, 301, 881, 13, 4837, 1288, 48, 18, 27…
$ b <int> 1152, 285, 1138, 888, 133, 133, 1496, 13, 52,…
$ c <int> 348, 374, 558, 1140, 116, 222, 635, 24, 330, …
$ d <int> 3617, 4590, 3322, 2410, 5057, 127, 1900, 5234…
$ rel_AOO <dbl> 0.1075458567, 0.0864400081, 0.1669888724, 0.3…
$ rel_occ_Ncells <dbl> 0.1034028953, 0.0834743373, 0.1614965219, 0.3…library(purrr)
#----------------------------------------------------------#
# Calculate occupancy-area-relationship -----
#----------------------------------------------------------#
# Custom function to calculate OAR and Fractal dimension
calculate_OAR <- function(species_data) {
datasets <-
unique(species_data$datasetID)
sampling_periods <-
unique(species_data$samplingPeriodID)
sp_dta <-
species_data %>%
select(datasetID, samplingPeriodID, scalingID,
verbatimIdentification,
rel_AOO, rel_occ_Ncells, mean_area, AOO)
# Expand the grid for all combinations of datasetID and samplingPeriodID
species_data_new <-
expand_grid(
datasetID = datasets,
samplingPeriodID = sampling_periods) %>%
inner_join(sp_dta, by = c("datasetID", "samplingPeriodID")) %>%
# Group by identifiers
group_by(datasetID, samplingPeriodID, verbatimIdentification) %>%
# Get available scales where relative occupancy is not saturated (< 1)
summarise(
available_scales = n(),
mean_relAOO = mean(rel_AOO, na.rm = TRUE),
exclude_sp_OAR = if_else(available_scales < 2, 1, 0),
.groups = "drop"
) %>%
# remove those where the range is saturated at the smallest resolution
mutate(
exclude_sp_OAR = if_else(available_scales == 0, 1, exclude_sp_OAR),
mean_relAOO = if_else(available_scales == 0, 1, mean_relAOO)
) %>%
full_join(sp_dta, by = c("datasetID", "samplingPeriodID", "verbatimIdentification")) %>%
filter(
exclude_sp_OAR == 0,
rel_occ_Ncells < 1
) %>%
distinct() %>%
filter_at(vars(scalingID, AOO, mean_area), any_vars(!is.na(.))) %>%
ungroup()
# Fit models using purrr::map
species_data_new_v2 <-
species_data_new %>%
group_by(datasetID, samplingPeriodID, verbatimIdentification) %>%
nest(data = c(scalingID, AOO, mean_area, rel_AOO, rel_occ_Ncells)) %>%
mutate(
coefficients = map(
data,
~ .x %>%
filter(
!is.na(AOO) & AOO > 0,
!is.na(mean_area) & mean_area > 0
) %>%
lm(log(AOO) ~ log(mean_area), data = .) %>%
coef() %>%
{
tibble(
m_AOO_a = .[2],
b_AOO_a = .[1],
D_AOO_a = -2 * .[2] + 2
)
}
)
) %>%
unnest(coefficients) %>%
ungroup() %>%
# Final cleanup of results
select(datasetID, samplingPeriodID, verbatimIdentification, m_AOO_a, b_AOO_a, D_AOO_a) %>%
distinct() %>%
# Merge with the original dataset
full_join(species_data) %>%
filter(scalingID == 1) %>%
select(
datasetID, verbatimIdentification, samplingPeriodID, Total_area_samp,
Total_Ncells, Total_Ncells_samp, AOO, occ_Ncells, rel_occ_Ncells,
rel_AOO, Jaccard_dissim, m_AOO_a, b_AOO_a, D_AOO_a
)
return(species_data_new_v2)
}
#----------------------------------------------------------#
# Apply OAR-function:
species_data_new <-
calculate_OAR(species_data) %>%
distinct(datasetID, samplingPeriodID, verbatimIdentification, .keep_all = TRUE) %>%
mutate(
D_AOO_a = case_when(is.na(D_AOO_a) ~ 2,
.default = D_AOO_a)) %>%
left_join(species_data %>% filter(scalingID == 1)) %>%
# reduce columns
select(
datasetID, verbatimIdentification, samplingPeriodID,
Total_area_samp, Total_Ncells, Total_Ncells_samp,
AOO, occ_Ncells, rel_occ_Ncells, rel_AOO,
Jaccard_dissim, a,b,c,d, D_AOO_a, time_span
) %>%
distinct(datasetID, samplingPeriodID, verbatimIdentification,
.keep_all = TRUE)Joining with `by = join_by(datasetID, samplingPeriodID,
verbatimIdentification)`
Joining with `by = join_by(datasetID, verbatimIdentification, samplingPeriodID,
Total_area_samp, Total_Ncells, Total_Ncells_samp, AOO, occ_Ncells,
rel_occ_Ncells, rel_AOO, Jaccard_dissim)`glimpse(species_data_new)Rows: 466
Columns: 17
$ datasetID <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,…
$ verbatimIdentification <chr> "Accipiter cooperii", "Accipiter gentilis", "Ac…
$ samplingPeriodID <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Total_area_samp <dbl> 126878.5, 126878.5, 126878.5, 126878.5, 126878.…
$ Total_Ncells <int> 5335, 5335, 5335, 5335, 5335, 5335, 5335, 5335,…
$ Total_Ncells_samp <int> 5319, 5319, 5319, 5319, 5319, 5319, 5319, 5319,…
$ AOO <dbl> 13645.26106, 10967.38185, 21187.30398, 48157.36…
$ occ_Ncells <int> 550, 444, 859, 2021, 129, 5059, 1923, 72, 348, …
$ rel_occ_Ncells <dbl> 0.1034028953, 0.0834743373, 0.1614965219, 0.379…
$ rel_AOO <dbl> 0.1075458567, 0.0864400081, 0.1669888724, 0.379…
$ Jaccard_dissim <dbl> 0.88131610, 0.90397805, 0.84927391, 0.69714679,…
$ a <int> 202, 70, 301, 881, 13, 4837, 1288, 48, 18, 27, …
$ b <int> 1152, 285, 1138, 888, 133, 133, 1496, 13, 52, 9…
$ c <int> 348, 374, 558, 1140, 116, 222, 635, 24, 330, 21…
$ d <int> 3617, 4590, 3322, 2410, 5057, 127, 1900, 5234, …
$ D_AOO_a <dbl> 1.537572e+00, 1.473556e+00, 1.647100e+00, 1.637…
$ time_span <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…hist(species_data_new$D_AOO_a, breaks = 30, main = "Fractal dimension distribution", xlab = "Fractal dimension (D)")
#----------------------------------------------------------#
# Calculate log ratio AOO -----
#----------------------------------------------------------#
time_periods <- c(1,2)
# Custom function to transform data to wide-format
transform_to_wide <- function(species_data_new, time_periods = c(1, 2)) {
# Create a list to store wide data for each time period
wide_dfs <- list()
for (i in seq_along(time_periods)) {
wide_dfs[[i]] <- species_data_new %>%
distinct(datasetID, samplingPeriodID, verbatimIdentification, AOO) %>%
group_by(datasetID, samplingPeriodID, verbatimIdentification) %>%
filter(samplingPeriodID == time_periods[i]) %>%
setNames(paste0("samplingPeriodID", i, "_", names(.))) %>%
ungroup() %>%
select(-c(paste0("samplingPeriodID", i, "_samplingPeriodID"))) %>%
dplyr::rename(
verbatimIdentification = paste0("samplingPeriodID", i, "_verbatimIdentification"),
datasetID = paste0("samplingPeriodID", i, "_datasetID")
)
}
# Merge the wide data frames sequentially
sp_dat_wide <- reduce(wide_dfs, full_join, by = c("verbatimIdentification", "datasetID"))
cat("NA counts in wide data after processing:\n")
print(colSums(is.na(sp_dat_wide)))
cat("Preview of wide data:\n")
print(head(sp_dat_wide))
return(sp_dat_wide)
}
#----------------------------------------------------------#
time_between_samples <- species_data %>%
ungroup() %>%
select(datasetID, startYear, endYear) %>% distinct() %>% mutate(n_years = endYear-startYear)
# Apply function:
sp_dat_wide <-
transform_to_wide(species_data_new, time_periods) %>%
na.omit() # drop species lost or gained completelyNA counts in wide data after processing:
datasetID verbatimIdentification samplingPeriodID1_AOO
0 0 0
samplingPeriodID2_AOO
0
Preview of wide data:
# A tibble: 6 × 4
datasetID verbatimIdentification samplingPeriodID1_AOO samplingPeriodID2_AOO
<int> <chr> <dbl> <dbl>
1 6 Accipiter cooperii 13645. 32884.
2 6 Accipiter gentilis 10967. 8758.
3 6 Accipiter striatus 21187. 35376.
4 6 Actitis macularius 48157. 42520.
5 6 Aegolius acadicus 3214. 3626.
6 6 Agelaius phoeniceus 120992. 118744.#----------------------------------------------------------#
# log[(St2 /St1 )/(t2 − t1 + 1)]
# Calculate log ratio of AOO
logRatio <-
sp_dat_wide %>%
left_join(time_between_samples) %>%
group_by(datasetID, verbatimIdentification) %>%
mutate(
log_R2_1 = log(samplingPeriodID2_AOO / samplingPeriodID1_AOO)) %>%
mutate(
log_R2_1_per_year = log_R2_1/ n_years) %>%
select(-samplingPeriodID1_AOO, -samplingPeriodID2_AOO) %>%
unique()Joining with `by = join_by(datasetID)`Warning in left_join(., time_between_samples): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 1 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.hist(logRatio$log_R2_1, breaks = 30, main = "Log ratio of AOO distribution", xlab = "Log ratio of AOO = log(AOO2/AOO1)")
hist(logRatio$log_R2_1_per_year, breaks = 30, main = "Log ratio of AOO per year distribution", xlab = "Log ratio of AOO per year = log(AOO2/AOO1)/duration in years")
# save final predictor table from grids/atlases
big_table <-
full_join(species_data_new, logRatio) %>%
distinct(datasetID, samplingPeriodID, verbatimIdentification,
.keep_all = T) %>%
mutate_if(is.numeric, round, 3)Joining with `by = join_by(datasetID, verbatimIdentification)`Warning in full_join(species_data_new, logRatio): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 1 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.saveRDS(big_table, here("Demo_NewYork/Data/output/A_Big_table_ny.rds"))
glimpse(big_table)Rows: 466
Columns: 22
$ datasetID <dbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,…
$ verbatimIdentification <chr> "Accipiter cooperii", "Accipiter gentilis", "Ac…
$ samplingPeriodID <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Total_area_samp <dbl> 126878.5, 126878.5, 126878.5, 126878.5, 126878.…
$ Total_Ncells <dbl> 5335, 5335, 5335, 5335, 5335, 5335, 5335, 5335,…
$ Total_Ncells_samp <dbl> 5319, 5319, 5319, 5319, 5319, 5319, 5319, 5319,…
$ AOO <dbl> 13645.261, 10967.382, 21187.304, 48157.370, 321…
$ occ_Ncells <dbl> 550, 444, 859, 2021, 129, 5059, 1923, 72, 348, …
$ rel_occ_Ncells <dbl> 0.103, 0.083, 0.161, 0.380, 0.024, 0.951, 0.362…
$ rel_AOO <dbl> 0.108, 0.086, 0.167, 0.380, 0.025, 0.954, 0.370…
$ Jaccard_dissim <dbl> 0.881, 0.904, 0.849, 0.697, 0.950, 0.068, 0.623…
$ a <dbl> 202, 70, 301, 881, 13, 4837, 1288, 48, 18, 27, …
$ b <dbl> 1152, 285, 1138, 888, 133, 133, 1496, 13, 52, 9…
$ c <dbl> 348, 374, 558, 1140, 116, 222, 635, 24, 330, 21…
$ d <dbl> 3617, 4590, 3322, 2410, 5057, 127, 1900, 5234, …
$ D_AOO_a <dbl> 1.538, 1.474, 1.647, 1.638, 1.129, 1.931, 1.630…
$ time_span <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
$ startYear <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980,…
$ endYear <dbl> 1985, 1985, 1985, 1985, 1985, 1985, 1985, 1985,…
$ n_years <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
$ log_R2_1 <dbl> 0.880, -0.225, 0.513, -0.124, 0.121, -0.019, 0.…
$ log_R2_1_per_year <dbl> 0.176, -0.045, 0.103, -0.025, 0.024, -0.004, 0.…skimr::skim(big_table)| Name | big_table |
| Number of rows | 466 |
| Number of columns | 22 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 21 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| verbatimIdentification | 0 | 1 | 9 | 32 | 0 | 233 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| datasetID | 0 | 1 | 6.00 | 0.00 | 6.00 | 6.00 | 6.00 | 6.00 | 6.00 | ▁▁▇▁▁ |
| samplingPeriodID | 0 | 1 | 1.50 | 0.50 | 1.00 | 1.00 | 1.50 | 2.00 | 2.00 | ▇▁▁▁▇ |
| Total_area_samp | 0 | 1 | 126878.54 | 0.00 | 126878.54 | 126878.54 | 126878.54 | 126878.54 | 126878.54 | ▁▁▇▁▁ |
| Total_Ncells | 0 | 1 | 5335.00 | 0.00 | 5335.00 | 5335.00 | 5335.00 | 5335.00 | 5335.00 | ▁▁▇▁▁ |
| Total_Ncells_samp | 0 | 1 | 5319.00 | 0.00 | 5319.00 | 5319.00 | 5319.00 | 5319.00 | 5319.00 | ▁▁▇▁▁ |
| AOO | 0 | 1 | 37447.22 | 41802.67 | 4.98 | 1156.73 | 16974.24 | 73050.82 | 125067.48 | ▇▂▁▂▂ |
| occ_Ncells | 0 | 1 | 1550.62 | 1729.79 | 1.00 | 70.50 | 700.00 | 2993.75 | 5229.00 | ▇▂▁▂▂ |
| rel_occ_Ncells | 0 | 1 | 0.29 | 0.33 | 0.00 | 0.01 | 0.13 | 0.56 | 0.98 | ▇▂▁▂▂ |
| rel_AOO | 0 | 1 | 0.30 | 0.33 | 0.00 | 0.01 | 0.13 | 0.58 | 0.99 | ▇▂▁▂▂ |
| Jaccard_dissim | 0 | 1 | 0.61 | 0.28 | 0.00 | 0.43 | 0.68 | 0.84 | 1.00 | ▃▃▃▇▇ |
| a | 0 | 1 | 1181.49 | 1573.21 | 0.00 | 25.00 | 248.00 | 2012.00 | 5138.00 | ▇▂▁▁▁ |
| b | 0 | 1 | 415.33 | 473.61 | 0.00 | 50.00 | 231.00 | 685.00 | 2999.00 | ▇▂▁▁▁ |
| c | 0 | 1 | 322.92 | 322.93 | 0.00 | 36.00 | 214.00 | 530.00 | 1502.00 | ▇▃▂▁▁ |
| d | 0 | 1 | 3399.26 | 1910.40 | 33.00 | 1474.00 | 4126.00 | 5197.00 | 5318.00 | ▃▂▂▂▇ |
| D_AOO_a | 0 | 1 | 1.32 | 0.55 | 0.00 | 0.90 | 1.41 | 1.82 | 1.99 | ▂▂▃▃▇ |
| time_span | 0 | 1 | 5.00 | 0.00 | 5.00 | 5.00 | 5.00 | 5.00 | 5.00 | ▁▁▇▁▁ |
| startYear | 0 | 1 | 1980.00 | 0.00 | 1980.00 | 1980.00 | 1980.00 | 1980.00 | 1980.00 | ▁▁▇▁▁ |
| endYear | 0 | 1 | 1985.00 | 0.00 | 1985.00 | 1985.00 | 1985.00 | 1985.00 | 1985.00 | ▁▁▇▁▁ |
| n_years | 0 | 1 | 5.00 | 0.00 | 5.00 | 5.00 | 5.00 | 5.00 | 5.00 | ▁▁▇▁▁ |
| log_R2_1 | 0 | 1 | 0.06 | 0.66 | -1.79 | -0.15 | 0.01 | 0.24 | 3.75 | ▁▇▁▁▁ |
| log_R2_1_per_year | 0 | 1 | 0.01 | 0.13 | -0.36 | -0.03 | 0.00 | 0.05 | 0.75 | ▁▇▁▁▁ |