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Dat2_obs_boxplots.R
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### create data boxplots (with Bartlein and Cleator for overlapping sites)
# create maps of Bartlein sites with/without inversions as well as maps with data available for each variable
# Created by Laia Comas-Bru in September 2020
# Last modified: February 2021Note: MI_converted_SD not calculated (as is it is using MI)
#### LOAD OBSERVATIONS AND ORGANISE DATA ####
#don't have MI_SD (added a col with NAs in the CSV source file till that's fixed.)
bart <- read.csv(paste (dataobspath,"bartlein_converted.csv",sep="")) %>%
mutate (INVERSION = as.factor(INVERSION), MAT_max = MAT + MAT_SD , MTCO_max = MTCO + MTCO_SD,
MTWA_max = MTWA + MTWA_SD, MAP_max = MAP + MAP_SD, MI_max = MI_converted,# + MI_converted_SD,
GDD5_max = GDD5 + GDD5_SD, MI_max = MI_converted ,#+ MI_converted_SD,
MAT_min = MAT - MAT_SD, MTCO_min = MTCO - MTCO_SD, MTWA_min = MTWA - MTWA_SD,
MAP_min = MAP - MAP_SD, MI_min = MI_converted,# - MI_converted_SD,
GDD5_min = GDD5 - GDD5_SD)
#numb of sites with no inv
# bart %>% filter (INVERSION == "NO") %>% select (SAMPLE_ID) %>% dim(.)
#map with/without inversions
p1 <- mp + geom_point(data = bart %>% arrange(desc(INVERSION)), aes (x = LON, y = LAT, color = as.factor(INVERSION),
shape = as.factor(INVERSION),size = ordered(INVERSION)), alpha = 0.6, stroke = 1,
show.legend = T, position = "jitter") +
theme_bw() + # apply bw them
labs(title = "Bartlein sites (raw)", color = "Inversion", shape = "Inversion", size = "Inversion") +
theme(legend.position = c(0.063, 0.18), legend.box = "horizontal") +
theme(legend.title = element_text(colour = "black", size = 8, face = "bold" )) + # legend title
theme(legend.text = element_text(colour = "black", size = 8)) + # legend labels
theme(legend.background = element_rect(fill = "white",size = 0.4,linetype = "solid",colour = "black"))
ggsave(plot=p1,file=paste(plotpath,"dat_boxplots_maps/map_Bartlein_inversions.pdf", sep=""),width=10,height=4.5)
#ggsave(plot=p1,file=paste(plotpath,"dat_boxplots_maps/map_Bartlein_inversions.jpg", sep=""),width=10,height=4.5)
#load all data and arrange to common variables
bart_max <- bart %>% filter (INVERSION == "NO") %>%
mutate (REF = "bartlein_max") %>%
dplyr::select(LAT,LON,MAT_max,MTCO_max,MTWA_max,MAP_max,MI_max,GDD5_max,REF) %>%
dplyr::rename (MAT = MAT_max, MTCO = MTCO_max, MTWA = MTWA_max, MAP = MAP_max, MI = MI_max, GDD5 = GDD5_max)
bart_min <- bart %>% filter (INVERSION == "NO") %>%
mutate (REF = "bartlein_min") %>%
dplyr::select(LAT,LON,MAT_min,MTCO_min,MTWA_min,MAP_min,MI_min,GDD5_min,REF) %>%
dplyr::rename (MAT = MAT_min, MTCO = MTCO_min, MTWA = MTWA_min, MAP = MAP_min, MI = MI_min, GDD5 = GDD5_min)
bart <- bart %>% filter (INVERSION == "NO") %>%
mutate (REF = "bartlein") %>%
dplyr::select(LAT,LON,MAT,MTCO,MTWA,MAP,MI_converted,GDD5,REF) %>%
dplyr::rename (MI = MI_converted)
clea <- read.csv(file.path(dataobspath, "cleator244.csv"),na.strings = "NA",strip.white = TRUE) %>%
mutate (REF = "cleator") %>% dplyr::select(LAT,LON,MAT,MTCO,MTWA,MAP,MI,GDD5,REF)
clea_max <- read.csv(file.path(dataobspath, "cleator244.csv"),na.strings = "NA",strip.white = TRUE) %>%
mutate (REF = "cleator_max", MAT_max = MAT + MAT_SD , MTCO_max = MTCO + MTCO_SD, MTWA_max = MTWA + MTWA_SD,
MAP_max = MAP + MAP_SD, MI_max = MI + MI_SD, GDD5_max = GDD5 + GDD5_SD, MI_max = MI + MI_SD) %>%
dplyr::select(LAT,LON,MAT_max,MTCO_max,MTWA_max,MAP_max,MI_max,GDD5_max,REF) %>%
dplyr::rename (MAT = MAT_max, MTCO = MTCO_max, MTWA = MTWA_max, MAP = MAP_max, MI = MI_max, GDD5 = GDD5_max)
clea_min <- read.csv(file.path(dataobspath, "cleator244.csv"),na.strings = "NA",strip.white = TRUE) %>%
mutate (REF = "cleator_min", MAT_min = MAT - MAT_SD, MTCO_min = MTCO - MTCO_SD, MTWA_min = MTWA - MTWA_SD,
MAP_min = MAP - MAP_SD, MI_min = MI - MI_SD, GDD5_min = GDD5 - GDD5_SD) %>%
dplyr::select(LAT,LON,MAT_min,MTCO_min,MTWA_min,MAP_min,MI_min,GDD5_min,REF) %>%
dplyr::rename (MAT = MAT_min, MTCO = MTCO_min, MTWA = MTWA_min, MAP = MAP_min, MI = MI_min, GDD5 = GDD5_min)
prenti <-read.csv(file.path(dataobspath, "prentice.csv"), strip.white = TRUE) %>%
mutate (REF = "prentice", MTCO = NA, GDD5 = NA, MAP = NA, MTWA = NA) %>%
dplyr::select(LAT,LON,MAT,MTCO,MTWA,MAP,MI,GDD5,REF)
# merge the datasets together
data_obs <- rbind(prenti, clea, clea_min, clea_max, bart, bart_min, bart_max) %>%
`colnames<-`(c("lat", "lon", "tas_anom", "mtco_anom", "mtwa_anom", "pre_anom","mi_anom", "gdd5_anom", "ref"))
data_obs %>% write.csv(., file.path(dataobspath, "data_obs_all_wf.csv"), na = "NA", row.names = FALSE)
data_BarPre <- rbind(prenti, bart)
data_BarPre %>% write.csv(., file.path(dataobspath, "data_obs_BarPre.csv"), na = "NA", row.names = FALSE)
data_Cle <- clea
data_Cle %>% write.csv(., file.path(dataobspath, "data_obs_Clea.csv"), na = "NA", row.names = FALSE)
## load and save a data_obs_no_filter (without Inversion filtering)
bart <- read.csv(paste (dataobspath,"bartlein_converted.csv",sep="")) %>%
mutate (INVERSION = as.factor(INVERSION)) %>% mutate (REF = "bartlein") %>%
dplyr::select(LAT,LON,MAT,MTCO,MTWA,MAP,MI_converted,GDD5,REF) %>%
dplyr::rename (MI = MI_converted)
data_obs_all_nofilt <- rbind(prenti, clea, bart) %>%
`colnames<-`(c("lat", "lon", "tas_anom", "mtco_anom", "mtwa_anom", "pre_anom","mi_anom", "gdd5_anom", "ref"))
data_obs_all_nofilt %>% write.csv(., file.path(dataobspath, "data_obs_all_nofilt.csv"), na = "NA", row.names = FALSE)
rm(ls="prenti","clea", "bart")
# load Bartlein's gridded data
variab_name_ls <- c('mat', 'mtco', 'mtwa', 'map', 'alpha' ,'gdd5')
for (variab_name in variab_name_ls){
ncfname <- paste (dataobspath, "raw_data/",variab_name,"_delta_21ka_ALL_grid_2x2.nc",sep="")
ncin <- nc_open(ncfname)
x <- ncvar_get(ncin, paste(variab_name,"_anm_mean", sep="")) %>%
`colnames<-` (c(ncin[["dim"]][["lat"]][["vals"]])) %>%
`rownames<-`(c(ncin[["dim"]][["lon"]][["vals"]])) %>%
reshape2::melt(., na.rm=F, value.name = variab_name) %>%
dplyr::rename (lon = Var1, lat=Var2)
if (variab_name ==variab_name_ls[1]) {
Bart_grid_raw <- x
} else {
Bart_grid_raw <- cbind(Bart_grid_raw, x[,3])
colnames(Bart_grid_raw)[ncol(Bart_grid_raw)]<-variab_name
}
}
names(Bart_grid_raw) <- toupper(names(Bart_grid_raw))
ggsave(plot(Bart_grid_raw[,c(-1,-2)]) , file=paste(plotpath, "dat_boxplots_maps/scatter_Bart_grid_data_raw_sites.jpg", sep = ""),width = 11.69,height = 8.27)
ggsave(plot(data_BarPre[,c(-1,-2,-9)]), file=paste(plotpath, "dat_boxplots_maps/scatter_Bart_Pre_point_data_raw_sites.jpg", sep = ""),width = 11.69,height = 8.27)
#### PLOT MAPS WITH raw SITES (one per variable) ####
var_ls <- c("MAT","MTCO","MTWA","MAP","MI","GDD5")
for (variab in var_ls){
p1 <- mp + geom_point(data = data_BarPre %>% dplyr::select(LAT,LON,variab,REF) %>%
na.omit(), aes (x = LON, y = LAT, color = as.factor(REF)),
alpha = 1, stroke = 1, show.legend = F, size = 0.3) +
theme_bw() +
labs(title = paste("Raw data: ",variab,sep=""), color = "Data source") +
theme(legend.position = c(0.095, 0.24), legend.box = "horizontal") +
theme(legend.title = element_text(colour = "black", size = 8, face = "bold" )) +
theme(legend.text = element_text(colour = "black", size = 8)) +
theme(legend.background = element_rect(fill = "white",size = 0.4,linetype = "solid",colour = "black"))
p2 <- mp + geom_point(data = data_Cle %>% dplyr::select(LAT,LON,variab,REF) %>%
na.omit(), aes (x = LON, y = LAT, color = as.factor(REF)),
alpha = 1, stroke = 1, show.legend = F, size = 0.1) +
theme_bw() +
labs(title = paste("Raw data: ",variab,sep=""), color = "Data source") +
theme(legend.position = c(0.095, 0.24),legend.box = "horizontal") +
theme(legend.title = element_text(colour = "black",size = 8,face = "bold")) +
theme(legend.text = element_text(colour = "black", size = 8)) +
theme(legend.background = element_rect(fill = "white",size = 0.4,linetype = "solid",colour = "black"))
fig <- ggarrange(p1, p2,
labels = c("BP", "CL"),
ncol = 1, nrow = 2)
fig
ggsave(fig,file=paste(plotpath, "dat_boxplots_maps/map_raw_sites_",variab,".jpg", sep = ""),width = 11.69,height = 8.27)
#ggsave(fig,file=paste(plotpath, "dat_boxplots_maps/map_raw_sites_",variab,".pdf", sep = ""),width = 11.69,height = 8.27)
rm(ls="p1","p2","fig")
}
#dev.off()
#### SELECT OVERLAPPING SITES BETWEEN BARTLEIN GRIDS AND CLEATOR ####
# 1. load gridcells from Bartlein's gridded data
# 2. identify how many Bart/Prent sites are in each gridcell
# 3. filter Cleator data to just that spread of data
ncfname <- paste (getwd(), "/input_data_obs/raw_data/mat_delta_21ka_ALL_grid_2x2.nc", sep="")
ncin <- nc_open(ncfname)
lat <- ncin[["dim"]][["lat"]][["vals"]]
lon <- ncin[["dim"]][["lon"]][["vals"]]
rm(ls="ncfname","ncin")
grid <- expand.grid(lon = lon, lat = lat)
grid$lat_min <- grid$lat - mean(diff(lat)) / 2
grid$lat_max <- grid$lat + mean(diff(lat)) / 2
grid$lon_min <- grid$lon - mean(diff(lon)) / 2
grid$lon_max <- grid$lon + mean(diff(lon)) / 2
grid$count_n <- NA
for (n in 1:dim(grid)[1]) {
newx <-
data_BarPre %>% filter (
data_BarPre$LAT >= grid$lat_min [n] &
data_BarPre$LAT < grid$lat_max[n] &
data_BarPre$LON >= grid$lon_min[n] &
data_BarPre$LON < grid$lon_max[n]
)
if (dim(newx)[1] == 0) {
grid$count_n[n] = NA
} else {
grid$count_n[n] <- dim(newx)[1] # how many data points per gridcell?
}
if (n == 1) {
x_temp <- newx [, 3:8] %>% summarise_if(is.numeric, mean, na.rm = T)
} else {
x_temp[n,] <- newx [, 3:8] %>% summarise_if(is.numeric, mean, na.rm = T)
}
}
grid_BartPren <- cbind (grid, x_temp) %>% filter (grid$count_n >= 0) %>%
mutate (REF = "BartPren")
rm(ls="n","x_temp","newx","grid")
# select grid lat/lons for which we have BArt/Pren data and filter Cleator's to that geographical range (with averaged values for all variables)
grid <- grid_BartPren [, 1:6] %>% mutate (count_n = NA)
for (n in 1:dim(grid)[1]) {
newx <-
data_Cle %>% filter (
data_Cle$LAT >= grid$lat_min [n] &
data_Cle$LAT < grid$lat_max[n] &
data_Cle$LON >= grid$lon_min[n] &
data_Cle$LON < grid$lon_max[n]
)
if (dim(newx)[1] == 0) {
grid$count_n[n] = NA
} else {
grid$count_n[n] <- dim(newx)[1] # how many data points per gridcell?
}
if (n == 1) {
x_temp <-
newx [, 3:8] %>% summarise_if(is.numeric, mean, na.rm = T)
} else {
x_temp[n,] <-
newx [, 3:8] %>% summarise_if(is.numeric, mean, na.rm = T)
}
}
grid_Cle <- cbind (grid, x_temp) %>% filter (grid$count_n >= 0) %>%
mutate (REF = "Cleator")
ggsave(plot(grid_Cle[,8:13]) , file=paste(plotpath, "dat_boxplots_maps/scatter_Clea_selected_grid_sites.jpg", sep = ""),width = 11.69,height = 8.27)
ggsave(plot(data_Cle[,3:8]) , file=paste(plotpath, "dat_boxplots_maps/scatter_Clea_all_grid_sites.jpg", sep = ""),width = 11.69,height = 8.27)
rm(ls="n","x_temp","newx","grid")
# produce maps with the location of overlapping sites (purpose: checking that everything is alright)
# cairo_pdf(paste(plotpath, Sys.Date(), '_sites_overlap_grids_per_variable.pdf', sep = ""),
# width = 11.69,height = 8.27,onefile = T)
for (variab in var_ls){
data_plot <- grid_BartPren %>%
dplyr::select(lat,lon,variab,count_n,REF) %>%
na.omit() %>%
semi_join(.,grid_Cle %>% dplyr::select(lat,lon,variab,count_n,REF)%>% na.omit(),
by =c("lon","lat"))
p <- mp +
geom_point(data = data_plot ,aes (x = lon,y = lat),alpha = 1, #,color = as.factor(count_n) # removed this from aes
stroke = 0.5,show.legend = T,size = 0.5, shape = 23,
color= "darkorchid4", fill="darkorchid1") +
theme_bw() +
theme(axis.text.x = element_text(size = rel(0.6)),
axis.text.y = element_text(size = rel(0.6)),
axis.title.y = element_text(size = rel(0.6)),
axis.title.x = element_text(size = rel(0.6)),
plot.title = element_text(size = 8, face = "bold"))+
labs(title = var)#,color = "counts (FYI, data averaged)")
assign(paste(variab,"_plot", sep=""),p)
rm(ls="p","data_plot")
}
fig <- ggarrange(MAT_plot, MTCO_plot, MTWA_plot, MAP_plot, MI_plot, GDD5_plot,
#labels = c("A", "B", "C", "D","E", "F"),
ncol = 3, nrow = 2)
fig
ggsave(fig,file=paste(plotpath, "dat_boxplots_maps/map_overlap_sites.jpg", sep=""),width = 11.69,height = 4)
#ggsave(fig,file=paste(plotpath, "dat_boxplots_maps/map_overlap_sites.pdf", sep = ""),width = 11.69,height = 4)
# dev.off()
#save Cle_overlap dataset for scoring (to compare with Cl_all)
grid_Cle %>% mutate (REF = "cleator") %>%
dplyr::select (lat, lon, MAT, MTCO, MTWA, MAP, MI, GDD5, REF) %>%
`colnames<-`(c("lat", "lon", "tas_anom", "mtco_anom", "mtwa_anom", "pre_anom","mi_anom", "gdd5_anom", "ref")) %>%
write.csv(., file.path(dataobspath, "data_obs_Clea_overlap.csv"), na = "NA", row.names = FALSE)
#### BOXPLOTS WITH LATITUDINAL BANDS ####
# Group the data by latitudinal bands
brkpnt <- seq(-80, 80, by = 20)
startpnt <- brkpnt[1:length(brkpnt) - 1]
endpnt <- brkpnt[2:length(brkpnt)]
brk_lab <- paste(startpnt, '° to ', endpnt, '°', sep = '')
## comparisons for gridded overlapping data sources
# can't apply semijoin here as not all data points have the same site locations
dtBP <- grid_BartPren %>% mutate (source = "BP")
dtCL <- grid_Cle %>% mutate (source = "CL")
dtCL_all <- data_Cle %>% mutate (source = "CL_all") %>% dplyr::rename (lat= LAT, lon = LON)
dtBP$lat_band <- cut(dtBP$lat, breaks = brkpnt,labels = brk_lab)
dtCL$lat_band <- cut(dtCL$lat, breaks = brkpnt,labels = brk_lab)
dtCL_all$lat_band <- cut(dtCL_all$lat, breaks = brkpnt,labels = brk_lab)
rm(ls="brkpnt","startpnt","endpnt","brk_lab")
# Do for each variable with overlapping sites.
cairo_pdf(paste(plotpath, "dat_boxplots_maps/boxplot_20deg_latbands_pervar.pdf", sep = ""),onefile = T,width = 8.27 ,height = 11.69)
plotlist = list()
for (variab in var_ls){
# prepare dataframe with chosen variable and no NAs and rename variable to VAR
data_ls1 <- dtBP %>% dplyr::select(lat,lon,variab,lat_band,source) %>% na.omit()
data_ls2 <- dtCL %>% dplyr::select(lat,lon,variab,lat_band,source) %>% na.omit()
data_ls1 <- semi_join(data_ls1,data_ls2, by =c("lon","lat"))# select the intersect between the two dataframes
data_ls2 <- semi_join(data_ls2,data_ls1, by =c("lon","lat"))# select the intersect between the two dataframes
data_ls3<- dtCL_all %>% dplyr::select(lat,lon,variab,lat_band,source) %>% na.omit()
dtcomb <- rbind(data_ls1, data_ls2, data_ls3)
dtcomb$source <- factor(dtcomb$source)
yrange <- range(dtcomb[,3])
(ymin <- yrange[1])
(ymax <- yrange[2] + diff(yrange) / 10)
counts <- ddply(dtcomb, .(dtcomb$lat_band, dtcomb$source), nrow)
names(counts) <- c("lat_band", "source", "no_sample")
dtsum1 <- subset(counts, source == 'BP')
dtsum2 <- subset(counts, source == 'CL')
dtsum3 <- subset(counts, source == 'CL_all')
assign(paste(variab,"_dataplot", sep=""),dtcomb)
p <- ggplot(data = get(paste(variab,"_dataplot", sep="")),
aes(x = lat_band, y = get(paste(variab,"_dataplot", sep=""))[,variab],
fill = source)) +
# stat_boxplot(geom = 'errorbar') +
geom_boxplot(outlier.alpha = 0.7, width = 0.8,position = position_dodge2(preserve = "single")) +
theme_bw() +
xlab('Latitude band') +
ylab(variab) +
scale_fill_manual(
name = 'source',
breaks = c('BP', 'CL', 'CL_all'),
labels = c(expression(bold('BP')),
expression(bold('CL')),
expression(bold('CL all'))),
values = c('orange', 'steelblue4', 'cyan3')) +
annotate(geom = 'text',x = dtsum2$lat_band,y = yrange[2] + diff(yrange) / 10,label = dtsum2$no_sample,
col = 'steelblue4',fontface = 2,size = 4) +
annotate(geom = 'text',x = dtsum3$lat_band,y = yrange[2] + diff(yrange) / 20,label = dtsum3$no_sample,
col = 'cyan3',fontface = 2,size = 4) +
theme(axis.title = element_text(size = 12, face = 'bold'),
panel.grid = element_blank(),
axis.text = element_text(colour = 'black', size = 10),# face = 'bold'),
legend.position = c(0.01, 0.01),
legend.justification = c(0, 0), # Set the "anchoring point" of the legend (bottom-left is 0,0; top-right is 1,1)
legend.text.align = 0,
legend.background = element_rect(),
legend.title = element_blank(),
legend.text = element_text(colour = 'black', size = 11)#, face = 'bold')
) +
geom_hline(yintercept = 0, linetype="dotted", color = "grey60", size=1) +
coord_flip()
print(p)
#rm(ls="p","data_ls1","data_ls2","data_ls3","yrange","ymin","ymax","counts","dtsum1","dtsum2","dtsum3")
ggsave(p,file=paste(plotpath, "dat_boxplots_maps/data_boxplot_",variab,".jpg", sep=""),width = 6.5,height = 8.27)
}
#### REGIONAL BOXPLOTS ####
# Use Kageyama 2020 (cp-2019-169) regions:
# ### definition of the regions: latitude range, longitude range
# 'Globe':[(-90,90,'cc'),(-180,180,'cc')],
# Tropics':[(-30,30,'cc'),(-180,180,'cc')],
# NAtlEurope':[(30,50,'cc'),(-45,45,'cc')],
# NorthAtlantic':[(30,50,'cc'),(-60,-10,'cc')],
# Europe':[(35,70,'cc'),(-10,60,'cc')],
# WesternEurope':[(35,70,'cc'),(-10,30,'cc')],
# NWAmerica':[(20,50,'cc'),(-125,-105,'cc')],
# NEAmerica':[(20,50,'cc'),(-105,-50,'cc')],
# Africa':[(-35,35,'cc'),(-10,50,'cc'),],
# WestAfrica':[(5,30,'cc'),(-17,30,'cc'),],
# NAmerica':[(20,50,'cc'),(-140,-60,'cc'),],
# SHextratropics':[(-90,-30,'cc'),(-180,180,'cc')],
# NHextratropics':[(30,90,'cc'),(-180,180,'cc')],
# NTropics':[(0,30,'cc'),(-180,180,'cc')],
# ExtratropicalAsia':[(30,75,'cc'),(60,135,'cc')],
# TropicalAsia':[(8,30,'cc'),(60,120,'cc')],
# TropicalAmericas':[(-30,30,'cc'),(-120,-35,'cc')],
# define region boundaries and names
dtBP <-
dtBP %>% mutate(region = ifelse(
lon >= -140 & lon <= -60 & lat >= 20 & lat <= 50,"N America",
ifelse( lon >= -10 & lon <= 30 & lat >= 35 & lat <= 70,"W Europe",
ifelse(lon >= 50 & lon <= 150 & lat >= 35 & lat <= 75,"Extratropical Asia",
ifelse(lon >= -120 & lon <= -60 & lat >= -30 & lat <= 30,"Tropical Americas",
ifelse(lon >= -10 & lon <= 50 & lat >= -35 & lat <= 35, "Africa",
"other"))))))
dtCL <-
dtCL %>% mutate(region = ifelse(
lon >= -140 & lon <= -60 & lat >= 20 & lat <= 50,"N America",
ifelse( lon >= -10 & lon <= 30 & lat >= 35 & lat <= 70,"W Europe",
ifelse(lon >= 50 & lon <= 150 & lat >= 35 & lat <= 75,"Extratropical Asia",
ifelse(lon >= -120 & lon <= -60 & lat >= -30 & lat <= 30,"Tropical Americas",
ifelse(lon >= -10 & lon <= 50 & lat >= -35 & lat <= 35, "Africa",
"other"))))))
dtCL_all <-
dtCL_all %>% mutate(region = ifelse(
lon >= -140 & lon <= -60 & lat >= 20 & lat <= 50,"N America",
ifelse( lon >= -10 & lon <= 30 & lat >= 35 & lat <= 70,"W Europe",
ifelse(lon >= 50 & lon <= 150 & lat >= 35 & lat <= 75,"Extratropical Asia",
ifelse(lon >= -120 & lon <= -60 & lat >= -30 & lat <= 30,"Tropical Americas",
ifelse(lon >= -10 & lon <= 50 & lat >= -35 & lat <= 35, "Africa",
"other"))))))
cairo_pdf(paste(plotpath, "dat_boxplots_maps/boxplot_regions.pdf", sep = ""),onefile = T,width = 8.27 ,height = 11.69)
for (variab in var_ls){
# prepare dataframe with chosen variable and no NAs and rename variable to VAR
data_ls1 <- dtBP %>% filter(region != "other") %>% dplyr::select(lat,lon,variab,region,source) %>% na.omit()
data_ls2 <- dtCL %>% filter(region != "other") %>% dplyr::select(lat,lon,variab,region,source) %>% na.omit()
data_ls1 <- semi_join(data_ls1,data_ls2, by =c("lon","lat"))# select the intersect between the two dataframes
data_ls2 <- semi_join(data_ls2,data_ls1, by =c("lon","lat"))# select the intersect between the two dataframes
data_ls3<- dtCL_all %>% filter(region != "other") %>% dplyr::select(lat,lon,variab,region,source) %>% na.omit(data_ls3)
dtcomb <- rbind(data_ls1, data_ls2, data_ls3) %>% mutate(source = as.factor(source))
yrange <- range(dtcomb[,3])
ymin <- yrange[1]
ymax <- yrange[2] + diff(yrange) / 10
counts <- ddply(dtcomb, .(dtcomb$region, dtcomb$source), nrow)
names(counts) <- c("region", "source", "no_sample")
dtsum1 <- subset(counts, source == 'BP')
dtsum2 <- subset(counts, source == 'CL')
dtsum3 <- subset(counts, source == 'CL_all')
pcomb <- ggplot(data = dtcomb, aes(x = region, y = dtcomb[,3], fill = source)) +
stat_boxplot(geom = 'errorbar') +
geom_boxplot(outlier.alpha = 0.7, width = 0.8, position = position_dodge2(preserve = "single")) +
theme_bw() +
xlab('Region') +
ylab(variab) +
scale_fill_manual(
name = 'source',
breaks = c('CL', 'BP', 'CL_all'),
labels = c(expression(bold('Cleator')), expression(bold('Bartlein+Prentice')),
expression(bold('Cleator all'))),#expression(bold('Bartlein all'))),
values = c('cyan3', 'steelblue4', 'orange'))+#,'red')) +
annotate(geom = 'text',x = dtsum1$region,y = yrange[2] + diff(yrange) / 10,label = dtsum1$no_sample,
col = 'black',fontface = 2,size = 4) +
annotate(geom = 'text',x = dtsum3$region,y = yrange[2] + diff(yrange) / 20,label = dtsum3$no_sample,
col = 'orange',fontface = 2,size = 4) +
theme(axis.title = element_text(size = 12, face = 'bold'),
panel.grid = element_blank(),
axis.text = element_text(colour = 'black', size = 10),
legend.position = c(0.01, 0.01),
legend.justification = c(0, 0),
legend.text.align = 0,
legend.background = element_rect(),
legend.title = element_blank(),
legend.text = element_text(colour = 'black', size = 11)#, face = 'bold')
) +
geom_hline(yintercept = 0, linetype="dotted", color = "grey60", size=1) +
coord_flip()
print(pcomb)
rm(ls="pcomb","data_ls1","data_ls2","yrange","ymin","ymax","counts","dtsum1","dtsum2")
ggsave(p,file=paste(plotpath, "dat_boxplots_maps/data_boxplot_region_",variab,".jpg", sep=""),width = 6.5,height = 8.27)
}
#### SCATTERPLOTS ####
# rearrange
dBP_g <- gather (dtBP, key="variable",value="value", MAT, MTCO, MTWA, MAP, MI, GDD5)
dCL_g <- gather (dtCL, key="variable",value="value", MAT, MTCO, MTWA, MAP, MI, GDD5)
for (variab in var_ls){
# filter by variable
dBP_gat <- dBP_g %>% dplyr::select(lat,lon,variable,value) %>% filter(variable==variab) %>%
dplyr::rename(var_BP = variable, val_BP = value)
dCL_gat <- dCL_g %>% dplyr::select(lat,lon,variable,value) %>% filter(variable==variab) %>%
dplyr::rename(var_CL = variable, val_CL = value)
# keep overlaping lat/long
dBP_gat <- semi_join(dBP_gat,dCL_gat, by =c("lon","lat"))# select the intersect between the two dataframes
dCL_gat <- semi_join(dCL_gat,dBP_gat, by =c("lon","lat"))# select the intersect between the two dataframes
data <- join(dBP_gat, dCL_gat, by = c("lat","lon") , type = "left", match = "all")
# scatterplot
p <- ggplot(na.omit(data), aes(x = val_BP, y = val_CL)) +
geom_point(alpha = 1,color = "darkred", size = 2) +
geom_smooth(method='lm', show.legend = NA,inherit.aes = TRUE,formula = y ~x, se = TRUE,
weight=0.5, color = "darkred", size = 0.5) +
theme_bw()+
labs(title = paste ("",variab,".",sep=""),
x = "Bartlein + Prentice",
y = "Cleator")+
geom_abline(intercept = 0,slope=1, color = "black", linetype="dotted", size=1) +
stat_cor() # Add correlation coefficient
assign(paste(variab,"_plot", sep=""),p)
#print(p)
rm(ls="p","dBP_gat","dCL_gat","data")
}
fig <- ggarrange(MAT_plot, MTCO_plot, MTWA_plot, MAP_plot, MI_plot, GDD5_plot,
labels = c("A", "B", "C", "D","E", "F"),
ncol = 3, nrow = 2)
fig
ggsave(fig,file=paste(plotpath, "dat_boxplots_maps/scatterplots_per_variable.jpg", sep = ""),width = 11.69,height = 8.27)
ggsave(fig,file=paste(plotpath, "dat_boxplots_maps/scatterplots_per_variable.pdf", sep = ""),width = 11.69,height = 8.27)
graphics.off()
# Clear packages
# p_unload(all) # Remove all add-ons
# Clear environment
#rm(list = ls())