rebent_plot.R

library(dplyr)
library(ggplot2)
library(meowR); data(regions)
library(sp)

kelp <- read.csv("../01_clean_raw_data/temporal_data_REBENT_Brittany_NW_France.csv")



#Create a spatial Points Data Frame
pts <- with(kelp, SpatialPoints(cbind(Longitude, Latitude),
                                   proj4string=CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")))
pts <- SpatialPointsDataFrame(pts, kelp)
#plot(pts)


#extract regional info
dataRegions <- over(pts, regions) %>%
  select(ECOREGION, PROVINCE, REALM)

kelp <- cbind(kelp, dataRegions)

#filter data down
kelpPlot <- kelp %>%
  filter(Depth.m > 3) %>%
  group_by(Site, Sample.Year, ECOREGION, Sample.ID) %>%
  dplyr::summarise(kelp_dens = sum(Stipe.Density.num.per.sq.m)) %>%
  ungroup() %>%
  group_by(Site, Sample.Year, ECOREGION) %>%
  dplyr::summarise(kelp_dens = mean(kelp_dens)) %>%
  ungroup() %>%
  group_by(Site) %>%
  dplyr::mutate(npoints = length(Site),
                nYears = length(unique(Sample.Year))) %>%
  ungroup() %>%
  filter(npoints>2)%>%
  filter(nyears>2)

#plot
ggplot(kelpPlot, aes(x=Sample.Year, y = log(kelp_dens+1))) +
  geom_line(mapping=aes(group=Site), color="grey") +
  geom_point(mapping=aes(group=Site), color="grey") +
  stat_smooth(method="lm") +
  theme_bw() +
  facet_wrap(~ECOREGION)



#plot
ggplot(kelpPlot, aes(x=Sample.Year, y = log(kelp_dens+1), color=Site)) +
  geom_line(mapping=aes(group=Site)) +
  geom_point(mapping=aes(group=Site)) +
  stat_smooth(method="lm", fill=NA) +
  theme_bw() +
  facet_wrap(~ECOREGION) +
  ylim(c(0,5))