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milotictanja/WMH_datapaper_graphs.R

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Western marsh harrier graphs created for the data paper
Raw
WMH_datapaper_graphs.R
library(dplyr)
library(readr)
library(lubridate)
library(ggplot2)
####Read Groningen data####
WMH_GRONINGEN_movebank_gps_accelero <- read_delim("./data/H_GRONINGEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding in Groningen (the Netherlands).csv",
",", quote = "\"", skip = 0, col_names = TRUE, na = c('','NA'),
locale = readr::locale(encoding = "UTF-8", decimal_mark = ".", grouping_mark = "," ),
col_types = list(`event-id` = col_double(),
visible = col_logical(),
timestamp = col_datetime(format = ""),
`location-long` = col_double(),
`location-lat` = col_double(),
`acceleration-raw-x` = col_double(),
`acceleration-raw-y` = col_double(),
`acceleration-raw-z` = col_double(),
`bar:barometric-pressure` = col_double(),
`eobs:start-timestamp` = col_datetime(),
`external-temperature` = col_double(),
`gps:dop` = col_double(),
`gps:satellite-count` = col_double(),
`gps-time-to-fix` = col_double(),
`gps:vdop` = col_double(),
`ground-speed` = col_double(),
`heading` = col_double(),
`height-above-msl` = col_double(),
`location-error-numerical` = col_double(),
`manually-marked-outlier` = col_logical(),
`start-timestamp` = col_datetime(),
`tilt-x` = col_double(),
`tilt-y` = col_double(),
`tilt-z` = col_double(),
`vertical-error-numerical` = col_double(),
`sensor-type` = col_character(),
`individual-taxon-canonical-name` = col_character(),
`tag-local-identifier` = col_double(),
`individual-local-identifier` = col_double(),
`study-name` = col_character()))
WMH_GRONINGEN_movebank_ref_data <- read_delim("./data/H_GRONINGEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding in Groningen (the Netherlands)-reference-data.csv",
",", quote = "\"", skip = 0, col_names = TRUE, na = c('','NA'),
locale = readr::locale(encoding = "UTF-8", decimal_mark = ".", grouping_mark = "," ),
trim_ws = TRUE, progress = FALSE)
# select GPS data
WMH_GRONINGEN_movebank_gps <- WMH_GRONINGEN_movebank_gps_accelero %>%
filter( !is.na(`location-long`))
# join GPS data and reference data
WMH_GRONINGEN_movebank_gps_join <- left_join(WMH_GRONINGEN_movebank_gps, WMH_GRONINGEN_movebank_ref_data,
by = c("individual-local-identifier" = "animal-id"))
####Read Waterland data####
WMH_WATERLAND_movebank_gps <- read_delim("./data/MH_WATERLAND - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding near the Belgium-Netherlands border.csv",
",", quote = "\"", skip = 0, col_names = TRUE, na = c('','NA'),
locale = readr::locale(encoding = "UTF-8", decimal_mark = ".", grouping_mark = "," ),
col_types = list(`event-id` = col_double(),
visible = col_logical(),
timestamp = col_datetime(format = ""),
`location-long` = col_double(),
`location-lat` = col_double(),
`algorithm-marked-outlier` = col_logical(),
`bar:barometric-height` = col_logical(),
`bar:barometric-pressure` = col_double(),
`compass-heading` = col_logical(),
`external-temperature` = col_double(),
`gps:dop` = col_double(),
`gps:satellite-count` = col_double(),
`gps-time-to-fix` = col_double(),
`gps:vdop` = col_double(),
`ground-speed` = col_double(),
`heading` = col_double(),
`height-above-msl` = col_double(),
`location-error-numerical` = col_double(),
`manually-marked-outlier` = col_logical(),
`vertical-error-numerical` = col_double(),
`sensor-type` = col_character(),
`individual-taxon-canonical-name` = col_character(),
`tag-local-identifier` = col_double(),
`individual-local-identifier` = col_character(),
`study-name` = col_character()))
WMH_WATERLAND_movebank_ref_data <- read_delim("./data/MH_WATERLAND - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding near the Belgium-Netherlands border-reference-data.csv",
",", quote = "\"", skip = 0, col_names = TRUE, na = c('','NA'),
locale = readr::locale(encoding = "UTF-8", decimal_mark = ".", grouping_mark = "," ),
trim_ws = TRUE, progress = FALSE)
# join GPS data and reference data
WMH_WATERLAND_movebank_gps_join <- left_join(WMH_WATERLAND_movebank_gps, WMH_WATERLAND_movebank_ref_data, by = c("individual-local-identifier" = "animal-id"))
#####Read Antwerpen data####
WMH_ANTWERPEN_movebank_gps_accelero <- read_delim("./data/MH_ANTWERPEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding near Antwerp (Belgium).csv" ,
",", quote = "\"", skip = 0, col_names = TRUE, na = c('','NA'),
locale = readr::locale(encoding = "UTF-8", decimal_mark = ".", grouping_mark = "," ),
col_types = list(`event-id` = col_double(),
visible = col_logical(),
timestamp = col_datetime(format = ""),
`location-long` = col_double(),
`location-lat` = col_double(),
`acceleration-raw-x` = col_double(),
`acceleration-raw-y` = col_double(),
`acceleration-raw-z` = col_double(),
`bar:barometric-pressure` = col_double(),
`eobs:start-timestamp` = col_datetime(),
`external-temperature` = col_double(),
`gps:dop` = col_double(),
`gps:satellite-count` = col_double(),
`gps-time-to-fix` = col_double(),
`gps:vdop` = col_double(),
`ground-speed` = col_double(),
`heading` = col_double(),
`height-above-msl` = col_double(),
`location-error-numerical` = col_double(),
`manually-marked-outlier` = col_logical(),
`start-timestamp` = col_datetime(),
`tilt-x` = col_double(),
`tilt-y` = col_double(),
`tilt-z` = col_double(),
`vertical-error-numerical` = col_double(),
`sensor-type` = col_character(),
`individual-taxon-canonical-name` = col_character(),
`tag-local-identifier` = col_double(),
`individual-local-identifier` = col_character(),
`study-name` = col_character()))
WMH_ANTWERPEN_movebank_ref_data <- read_delim("./data/MH_ANTWERPEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding near Antwerp (Belgium)-reference-data.csv",
",", quote = "\"", skip = 0, col_names = TRUE, na = c('','NA'),
locale = readr::locale(encoding = "UTF-8", decimal_mark = ".", grouping_mark = "," ),
trim_ws = TRUE, progress = FALSE)
# select GPS data
WMH_ANTWERPEN_movebank_gps <- WMH_ANTWERPEN_movebank_gps_accelero %>%
filter( !is.na(`location-long`))
# join GPS data and reference data
WMH_ANTWERPEN_movebank_gps_join <- left_join(WMH_ANTWERPEN_movebank_gps, WMH_ANTWERPEN_movebank_ref_data,
by = c("individual-local-identifier" = "animal-id"))
####Graphs####
# select relevant columns
WMH_GRONINGEN_temp <- WMH_GRONINGEN_movebank_gps_join[,c('tag-id', 'height-above-msl', 'location-long', 'location-lat', 'external-temperature',
'timestamp', 'animal-sex', 'animal-nickname', 'animal-life-stage', 'animal-mass')]
WMH_WATERLAND_temp <- WMH_WATERLAND_movebank_gps_join[,c('tag-id', 'height-above-msl', 'location-long', 'location-lat', 'external-temperature',
'timestamp', 'animal-sex', 'animal-nickname', 'animal-life-stage', 'animal-mass')]
WMH_ANTWERPEN_temp <- WMH_ANTWERPEN_movebank_gps_join[,c('tag-id', 'height-above-msl', 'location-long', 'location-lat', 'external-temperature',
'timestamp', 'animal-sex', 'animal-nickname', 'animal-life-stage', 'animal-mass')]
# add dataset name
WMH_GRONINGEN_temp$dataset <- rep("H_GRONINGEN", nrow(WMH_GRONINGEN_temp))
WMH_WATERLAND_temp$dataset <- rep("MH_WATERLAND", nrow(WMH_WATERLAND_temp))
WMH_ANTWERPEN_temp$dataset <- rep("MH_ANTWERPEN",nrow(WMH_ANTWERPEN_temp))
# combine datasets
WMH_all <- WMH_GRONINGEN_temp %>%
bind_rows(WMH_WATERLAND_temp) %>%
bind_rows(WMH_ANTWERPEN_temp)
WMH_all$date <- date(WMH_all$timestamp)
# Figure 3 - graph with number of tracking effort per individual
WMH_all$`animal-nickname` <- as.factor(WMH_all$`animal-nickname`)
WMH_all_TE <- WMH_all %>%
group_by(date, `animal-nickname`, dataset) %>%
count()
WMH_all_TE$nickname <- factor(WMH_all_TE$`animal-nickname`, levels = c("Job", "Kjell", "Roelof", "William", "Almut", "Ben", "Jozef",
"Mia", "Peter", "Raymond","Walter", "Lilla", "Lillo", "Zuzanna"))
WMH_all_TE$dataset <- factor(WMH_all_TE$dataset, levels = c("H_GRONINGEN", "MH_WATERLAND", "MH_ANTWERPEN"))
TE_plot <- ggplot(data = WMH_all_TE) +
geom_line(aes(x = date, y = n, colour = factor(dataset)), size = 1) +
facet_wrap(~ nickname, scales = "free") +
theme_minimal() +
scale_y_continuous(name = "Tracking effort (observations/day)", expand = c(0,0), limits = c(0, NA), labels = scales::comma, breaks = scales::pretty_breaks()) +
scale_x_date(name = "Date", breaks = scales::pretty_breaks(4), labels = scales::date_format("%Y-%m")) +
scale_colour_manual(name = "Dataset", values = c("H_GRONINGEN" = "#9ccb86", "MH_WATERLAND" = "#e9e29c", "MH_ANTWERPEN" = "#eeb479"),
breaks = c("H_GRONINGEN", "MH_WATERLAND", "MH_ANTWERPEN")) +
theme(axis.text = element_text(size = 8),
axis.title = element_text(face = "bold", size = 18),
legend.text = element_text(size = 14),
legend.title = element_text(face = "bold", size = 18),
axis.line.x = element_line(size = 0.5),
axis.line.y = element_line(size = 0.5),
axis.ticks = element_line(size = 0.5),
strip.text = element_text(size = 14, hjust = 0),
legend.position = c(0.85, 0.075),
plot.margin = unit(c(0.5, 1, 0.5, 0.5),"cm"))
TE_plot
ggsave("fig3_trackingEffort.png", TE_plot)
# Figure 4 - graph with number of fixes/year/dataset
WMH_all$year <- year(WMH_all$timestamp)
WMH_all_fixes <- WMH_all %>%
group_by(year, dataset) %>%
count()
WMH_all_fixes <- WMH_all_fixes %>%
group_by((year)) %>%
arrange(year, desc(dataset)) %>%
mutate(lab_ypos = cumsum(n) - (0.5 * n) + 3000)
fixes_plot <- ggplot(WMH_all_fixes, aes(x = year, y = n)) +
geom_col(aes(fill = dataset)) +
geom_text(aes(y = lab_ypos, x = year, label = scales::comma(n), group = dataset), size = 4, color = "black") +
theme_minimal() +
theme(axis.line.y = element_blank()) +
scale_fill_manual(name = "Dataset", values = c("H_GRONINGEN" = "#9ccb86", "MH_WATERLAND" = "#e9e29c", "MH_ANTWERPEN" = "#eeb479"),
breaks = c("H_GRONINGEN", "MH_WATERLAND", "MH_ANTWERPEN")) +
scale_x_continuous(name = "Year", breaks = c(2012:2019)) +
scale_y_continuous(name = "Number of fixes", limits = c(0, 350000), breaks = c(0, 100000, 200000, 300000),
expand = c(0,0), labels = scales::comma) +
theme(axis.text = element_text(size = 14),
axis.title = element_text(face = "bold", size = 18),
legend.text = element_text(size = 14),
legend.title = element_text(face = "bold", size = 18),
axis.line.x = element_line(size = 0.5))
fixes_plot
ggsave("fig4_fixes.png", fixes_plot)
#### Figure 5 - graph with accelerometer data####
# select accelerometer data
WMH_GRONINGEN_movebank_accelero <- WMH_GRONINGEN_movebank_gps_accelero %>%
filter(!is.na(`acceleration-raw-x`))
WMH_ANTWERPEN_movebank_accelero <- WMH_ANTWERPEN_movebank_gps_accelero %>%
filter(!is.na(`acceleration-raw-x`))
# make dataset for plotting
WMH_GRONINGEN_temp2 <- WMH_GRONINGEN_movebank_accelero[, c('event-id', 'timestamp', 'acceleration-raw-x', 'acceleration-raw-y',
'acceleration-raw-z', 'eobs:start-timestamp', 'start-timestamp',
'tilt-x', 'tilt-y', 'tilt-z')]
WMH_ANTWERPEN_temp2 <- WMH_ANTWERPEN_movebank_accelero[, c('event-id', 'timestamp', 'acceleration-raw-x', 'acceleration-raw-y',
'acceleration-raw-z', 'eobs:start-timestamp', 'start-timestamp',
'tilt-x', 'tilt-y', 'tilt-z')]
WMH_GRONINGEN_temp2$dataset <- rep("H_GRONINGEN", nrow(WMH_GRONINGEN_temp2))
WMH_ANTWERPEN_temp2$dataset <- rep("MH_ANTWERPEN",nrow(WMH_ANTWERPEN_temp2))
WMH_all_accelero <- WMH_GRONINGEN_temp2 %>%
bind_rows(WMH_ANTWERPEN_temp2)
WMH_all_accelero$year <- year(WMH_all_accelero$timestamp)
WMH_all_accelero_plot <- WMH_all_accelero %>%
group_by(year, dataset) %>%
count()
WMH_all_accelero_plot <- WMH_all_accelero_plot %>%
group_by((year)) %>%
arrange(year, desc(dataset)) %>%
mutate(lab_ypos = cumsum(n) - (0.5 * n) + 10000)
accelero_plot <- ggplot(WMH_all_accelero_plot, aes(x = year, y = n)) +
geom_col(aes(fill = dataset)) +
geom_text(aes(y = lab_ypos, x = year, label = scales::comma(n), group = dataset), size = 4, color = "black") +
theme_minimal() +
theme(axis.line.y = element_blank()) +
scale_fill_manual(name = "Dataset", values = c("H_GRONINGEN" = "#9ccb86", "MH_ANTWERPEN" = "#eeb479"),
breaks = c("H_GRONINGEN", "MH_ANTWERPEN")) +
scale_x_continuous(name = "Year", breaks = c(2012:2019)) +
scale_y_continuous(name = "Acceleration records (n)", limits = c(0, 1000000), breaks = c(0, 250000, 500000, 750000),
expand = c(0,0), labels = scales::comma) +
theme(axis.text = element_text(size = 14),
axis.title = element_text(face = "bold", size = 18),
legend.text = element_text(size = 14),
legend.title = element_text(face = "bold", size = 18),
axis.line.x = element_line(size = 0.5))
accelero_plot
ggsave("fig5_accelero.png", accelero_plot)
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