-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathlme_extractions.R
285 lines (248 loc) · 11.7 KB
/
lme_extractions.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
# This script extracts and plots OBIS records inside Large Marine Ecosystems (LME)
# Written by E. Montes
# Aug 19, 2019
library(robis)
library(rgdal) # for `ogrInfo()` and `readOGR()`
library(tools) # for `file_path_sans_ext()`
library(dplyr) # for `inner_join()`, `filter()`, `summarise()`, and the pipe operator (%>%)
library(ggplot2) # for `fortify()` and for plotting
library(sp) # for `point.in.polygon()` and `spDists()`
library(tidyr) # for `gather()`
library(readr) # for `write_tsv()`
library(leaflet)
library(lubridate)
# Provide the function fortify.shape(), which puts the shapefile data in the object class data.frame,
# so that it can be used by ggplot2
fortify.shape <- function(x){
x@data$id <- rownames(x@data)
x.f <- fortify(x, region = "id")
x.join <- inner_join(x.f, x@data, by = "id")
}
# extract portions of the data (from the fortified data.frame object) for a smaller domain
subset.shape <- function(x, domain){
x.subset <- filter(x, long > domain[1] &
long < domain[2] &
lat > domain[3] &
lat < domain[4])
x.subset
}
# Plotting the coastline and some animal observations
# Specify the local directory and name of the Natural Earth shapefile (previously downloaded)
# and read its contents (global coastline data)
path.lme.coast <- ("~/lme-extractions/data")
fnam.lme.coast <- "LMEs66.shp"
dat.coast <- readOGR(dsn = path.lme.coast,
layer = file_path_sans_ext(fnam.lme.coast))
# fortify the global data and then extract domain
dat.coast <- fortify.shape(dat.coast) # a 410951x8 dataframe
# Specify the desired LME:
dat.sel_1 <- subset(dat.coast, LME_NUMBER == 15) # S. Brazil. See numbers here: http://lme.edc.uri.edu/index.php/lme-introduction
dat.sel_2 <- subset(dat.coast, LME_NUMBER == 16) # E. Brazil
dat.sel_3 <- subset(dat.coast, LME_NUMBER == 17) # N. Brazil
dat.sel_4 <- subset(dat.coast, LME_NUMBER == 14) # Patagonia
dat.sel_5 <- subset(dat.coast, LME_NUMBER == 13) # Humboldt C.
dat.sel_6 <- subset(dat.coast, LME_NUMBER == 12) # Caribbean
dat.sel_7 <- subset(dat.coast, LME_NUMBER == 11) # P. Ctral A.
dat.sel_8 <- subset(dat.coast, LME_NUMBER == 5) # GoM
dat.sel_9 <- subset(dat.coast, LME_NUMBER == 4) # Gulf of California
dat.sel_10 <- subset(dat.coast, LME_NUMBER == 3) # CCS
dat.sel_11 <- subset(dat.coast, LME_NUMBER == 2) # G. Alaska
dat.sel_12 <- subset(dat.coast, LME_NUMBER == 1) # E. Bearing S.
dat.sel_13 <- subset(dat.coast, LME_NUMBER == 54) # Chukchi S.
dat.sel_14 <- subset(dat.coast, LME_NUMBER == 55) # Beauford S.
dat.sel_15 <- subset(dat.coast, LME_NUMBER == 66) # Canadian Arctic
dat.sel_16 <- subset(dat.coast, LME_NUMBER == 18) # Canadian E. Arctic
dat.sel_17 <- subset(dat.coast, LME_NUMBER == 9) # Labrador S.
dat.sel_18 <- subset(dat.coast, LME_NUMBER == 8) # Scotian Shelf
dat.sel_19 <- subset(dat.coast, LME_NUMBER == 7) # NE US
dat.sel_20 <- subset(dat.coast, LME_NUMBER == 6) # SE US
dat.sel_21 <- subset(dat.coast, LME_NUMBER == 10) # Hawaii
dat.sel_22 <- subset(dat.coast, LME_NUMBER == 63) # Hudson Bay Complex
dat.sel_23 <- subset(dat.coast, LME_NUMBER == 19) # Greenland Sea
dat.sel_24 <- subset(dat.coast, LME_NUMBER == 21) # Norwegian Sea
dat.sel_25 <- subset(dat.coast, LME_NUMBER == 20) # Barents Sea
dat.sel_26 <- subset(dat.coast, LME_NUMBER == 58) # Kara Sea
dat.sel_27 <- subset(dat.coast, LME_NUMBER == 57) # Laptev Sea
dat.sel_28 <- subset(dat.coast, LME_NUMBER == 56) # E. Sibarian Sea
xlims <- c(-150, -25)
ylims <- c(-60, 60)
# World map
mapWorld <- borders(database = "world", colour="gray50", fill="gray50")
# Generate a base map with the coastline:
p0 <- ggplot() + theme(text = element_text(size=18)) +
geom_path(data = dat.coast, aes(x = long, y = lat, group = group),
color = "black", size = 0.25) +
coord_map(projection = "mercator") +
scale_x_continuous(limits = xlims, expand = c(0, 0)) +
scale_y_continuous(limits = ylims, expand = c(0, 0)) +
labs(list(title = "", x = "Longitude", y = "Latitude"))
p0
# highlight LME of interest
p.sel <- p0 +
# geom_path(data = dat.sel_1,
# aes(x = long, y = lat, group = group),
# colour = "goldenrod2", size = 0.75)
# geom_path(data = dat.sel_2,
# aes(x = long, y = lat, group = group),
# colour = "coral3", size = 0.75)
# geom_path(data = dat.sel_3,
# aes(x = long, y = lat, group = group),
# colour = "chocolate4", size = 0.75)
# geom_path(data = dat.sel_4,
# aes(x = long, y = lat, group = group),
# colour = "coral", size = 1)
# geom_path(data = dat.sel_5,
# aes(x = long, y = lat, group = group),
# colour = "chocolate1", size = 0.75)
geom_path(data = dat.sel_6,
aes(x = long, y = lat, group = group),
colour = "chartreuse4", size = 1) +
# geom_path(data = dat.sel_7,
# aes(x = long, y = lat, group = group),
# colour = "chartreuse", size = 0.75)
geom_path(data = dat.sel_8,
aes(x = long, y = lat, group = group),
colour = "coral", size = 0.75)
# geom_path(data = dat.sel_9,
# aes(x = long, y = lat, group = group),
# colour = "cadetblue4", size = 0.75)
# geom_path(data = dat.sel_10,
# aes(x = long, y = lat, group = group),
# colour = "brown3", size = 0.75)
# geom_path(data = dat.sel_11,
# aes(x = long, y = lat, group = group),
# colour = "red", size = 0.75)
# geom_path(data = dat.sel_12,
# aes(x = long, y = lat, group = group),
# colour = "blue", size = 0.75)
# geom_path(data = dat.sel_13,
# aes(x = long, y = lat, group = group),
# colour = "green", size = 0.75)
# geom_path(data = dat.sel_14,
# aes(x = long, y = lat, group = group),
# colour = "red", size = 0.75)
# geom_path(data = dat.sel_15,
# aes(x = long, y = lat, group = group),
# colour = "blue", size = 0.75)
# geom_path(data = dat.sel_16,
# aes(x = long, y = lat, group = group),
# colour = "green", size = 0.75)
# geom_path(data = dat.sel_17,
# aes(x = long, y = lat, group = group),
# colour = "red", size = 0.75)
# geom_path(data = dat.sel_18,
# aes(x = long, y = lat, group = group),
# colour = "blue", size = 0.75) +
# geom_path(data = dat.sel_19,
# aes(x = long, y = lat, group = group),
# colour = "red", size = 0.75)
# geom_path(data = dat.sel_20,
# aes(x = long, y = lat, group = group),
# colour = "blue", size = 0.75)
# geom_path(data = dat.sel_21,
# aes(x = long, y = lat, group = group),
# colour = "red", size = 0.75)
# geom_path(data = dat.sel_22,
# aes(x = long, y = lat, group = group),
# colour = "blue", size = 0.75)
# geom_path(data = dat.sel_23,
# aes(x = long, y = lat, group = group),
# colour = "green", size = 0.75)
# geom_path(data = dat.sel_24,
# aes(x = long, y = lat, group = group),
# colour = "red", size = 0.75)
# geom_path(data = dat.sel_25,
# aes(x = long, y = lat, group = group),
# colour = "blue", size = 0.75)
# geom_path(data = dat.sel_26,
# aes(x = long, y = lat, group = group),
# colour = "green", size = 0.75)
# geom_path(data = dat.sel_27,
# aes(x = long, y = lat, group = group),
# colour = "red", size = 0.75)
# geom_path(data = dat.sel_28,
# aes(x = long, y = lat, group = group),
# colour = "blue", size = 0.75)
p.sel
#######################################################################################################################
#######################################################################################################################
# This section extracts OBIS records of using the "occurrence" function or downloaded data,
# and plots time series or pie charts with distributions of large groups (annelids, molluscs, plants and echinoderms) in the upper 100m
# Written by E. Montes
# Aug 19, 2019
# Set extraction params
# LME codes (from OBIS URL)
# N Brazil=40017; E Brazil=40016; S Brazil=40015; Patagonia=40014; Humboldt=40013; Caribbean=40012;
# P Ctral A=40011; CCS=40003; GoA=40002; NE USA=40007; E Bearing=40001; Canada E Arctic=40018; GoM=40005;
# Chukchi=40054; SE USA=40006; Labrador=40009; Scotian S=40008
area = 40005
depth = 100
mol_code = 51
echi_code = 1806
anne_code = 882
plan_code = 3
## read data
# fileID = list.files(path = "C:/Users/Enrique/obis_extractions/obis_data/obis_e_bra", pattern="*.csv")
# all_records = read_csv(file = fileID)
#
# ## Molluscs (up to 100 m)
# mollusc.100 <- filter(all_records, maximumDepthInMeters <= 100, phylumid == 51)
# mollusc.100 <- mollusc.100[order(mollusc.100$date_start),]
# Directly from OBIS
mollusc.100_2 = occurrence(areaid = area, taxonid = mol_code, enddepth = depth)
## Ehinoderms
# echino.100 <- filter(all_records, maximumDepthInMeters <= 100, phylumid == 1806)
# echino.100 <- echino.100[order(echino.100$date_start),]
# Directly from OBIS
echino.100_2 = occurrence(areaid = area, taxonid = echi_code, enddepth = depth)
## Annelida
# anne.100 <- filter(all_records, maximumDepthInMeters <= 100, phylumid == 882)
# anne.100 <- anne.100[order(anne.100$date_start),]
# Directly from OBIS
anne.100_2 = occurrence(areaid = area, taxonid = anne_code, enddepth = depth)
## Platae
# plant.100 <- filter(all_records, maximumDepthInMeters <= 100, phylumid == 3)
# plant.100 <- plant.100[order(plant.100$date_start),]
# Directly from OBIS
plant.100_2 = occurrence(areaid = area, taxonid = plan_code, enddepth = depth)
## Merge the data frames
#subset data (year and phylum)
sub_mollusc <- data.frame(mollusc.100_2$date_year, mollusc.100_2$phylum)
# rename column headers
names(sub_mollusc)[names(sub_mollusc) == "mollusc.100_2.date_year"] <- "year"
names(sub_mollusc)[names(sub_mollusc) == "mollusc.100_2.phylum"] <- "phylum"
sub_echino <- data.frame(echino.100_2$date_year, echino.100_2$phylum)
names(sub_echino)[names(sub_echino) == "echino.100_2.date_year"] <- "year"
names(sub_echino)[names(sub_echino) == "echino.100_2.phylum"] <- "phylum"
sub_anne <- data.frame(anne.100_2$date_year, anne.100_2$phylum)
names(sub_anne)[names(sub_anne) == "anne.100_2.date_year"] <- "year"
names(sub_anne)[names(sub_anne) == "anne.100_2.phylum"] <- "phylum"
sub_plant <- data.frame(plant.100_2$date_year, plant.100_2$phylum)
names(sub_plant)[names(sub_plant) == "plant.100_2.date_year"] <- "year"
names(sub_plant)[names(sub_plant) == "plant.100_2.phylum"] <- "phylum"
total.100 <- bind_rows(sub_mollusc, sub_echino, sub_anne, sub_plant)
## Plot the data
ts_plot <- ggplot() +
geom_histogram(data = total.100, aes(x = year, fill = phylum), binwidth = 2) +
scale_fill_brewer(palette = "Spectral") +
xlim(c(1960, 2017)) +
theme(axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold")) +
theme(axis.text.x = element_text(size=14, angle=0),
axis.text.y = element_text(size=14, angle=0))
ts_plot
# ggsave(ts_plot, filename = "test.png", device = "png", width = 20, height = 10, dpi=300)
## Plot pie chart
# Group plants in a single group
all_tbl <- table(total.100$phylum)
all_df <- as.data.frame(all_tbl)
p_list = c("Chlorophyta", "Rhodophyta", "Tracheophyta")
rest_list = c("Annelida", "Echinodermata", "Mollusca")
p_idx = match(p_list, rownames(all_tbl))
rest_idx = match(rest_list, rownames(all_tbl))
p_sum = sum(all_df$Freq[p_idx], na.rm = TRUE)
freq_val <- c(all_df$Freq[rest_idx], p_sum)
group_id <- c(c(rest_list), "Plants")
f_tbl <- data.frame(group = group_id, freq = freq_val)
cols <- rainbow(nrow(f_tbl))
groups_pie <- pie(f_tbl$freq, labels = f_tbl$group, col = cols)