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04_20210623_GH_LC_catchment_final.R
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04_20210623_GH_LC_catchment_final.R
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# MLFischer 2020-08
# Fischer et al., 2021
# A Phytolith supported Biosphere-Hydrosphere Predictive Model for southern Ethiopia:
# insights into paleoenvironmental changes and human landscape preferences since the Last Glacial Maximum
###
# LC catchment for TS 1 to 3
###
remove(list=ls()) #clean up#
removeTmpFiles(0)
#get some packages
library(raster)
library(rgdal)
library(gbm)
library(dismo)
library(rasterVis)
library(dplyr)
#load shapefiles and data
setwd("../01_data/00_Shapes/")
#area.shp <- readOGR(".",layer = "20190902_masc_studyarea_stage1")
#a.shp <- readOGR(".",layer = "20191024_abaya")
#b.shp <- readOGR(".",layer = "20191024_bahir")
#c.shp <- readOGR(".",layer = "20191024_chamo")
get(load("20201119_masc_studyarea_stage1.RData"))
get(load("20201119__abaya.RData"))
get(load("20201119__bahir.RData"))
get(load("20201119__chamo.RData"))
modistsp_file <- "../01_data/02_MODIS/LandCover_Type_Yearly_500m_v6/LC2/MCD12Q1_LC2_2018_001.tif"
MCD12Q1_2001 <-raster(modistsp_file)
path_2ndlevel<- "../02_data_2nd_level/"
path_3ndlevel<- "../02_data_3nd_brt/"
path_4ndlevel<- "../02_data_4nd_scenarios/"
path_img <- "../00_Results/20200705_GIF"
setwd(path_3ndlevel)
get(load("20200821_gbm_LC_final.RData"))
setwd(path_2ndlevel)
get(load("data_all_pr.RData"))
get(load("data_all.RData"))
get(load("mask_all.RData"))
#################################################################################################
#################################################################################################
#################################################################################################
#################################################################################################
#TIME SLICE 1: MODERN DAY
d_pr <- as.data.frame(data_all_pr,na.rm=F,xy=T)
par(mfrow=c(1,1))
itree <- gbm.perf(gbm_LC, plot.it = F, oobag.curve = FALSE, overlay = TRUE)[1]
gbm_pr <-predict.gbm(gbm_LC , d_pr[c(3:15)] ,type = "response", n.trees=itree) #prediction
d_pr[,16]<- apply(gbm_pr, 1, function(row) which.max(row))
d_pr[which(d_pr[,16]==4),16]<-10
d_pr[which(d_pr[,16]==3),16]<-9
d_pr[which(d_pr[,16]==2),16]<-7
d_pr[which(d_pr[,16]==1),16]<-2
d_pr[which(is.na(d_pr[,4])==T),16]<-NA
r3<- rasterFromXYZ(d_pr[,c(1,2,16)])
r3@crs@projargs<- MCD12Q1_2001@crs@projargs
r3 <- projectRaster(r3,crs= area.shp@proj4string,method="ngb" )
masc_all <- projectRaster(masc_all,crs= area.shp@proj4string,method="ngb" )
data_all_lc <- projectRaster(data_all$lc,crs= area.shp@proj4string,method="ngb" )
r_pr_MD<- r3
r3<-r3*masc_all
r_dif<- r3-data_all_lc
r_dif[which(r_dif[]!=0)]<-1
############################################################################################################
##probability raster for each class:
# ..$ : chr [1:4] "2 Evergreen Broadleaf Forest" "7 Open Shrubland" "9 Savanna" "10 Grassland"
d_pr[,17]<- gbm_pr[,1,]
d_pr[,18]<- gbm_pr[,2,]
d_pr[,19]<- gbm_pr[,3,]
d_pr[,20]<- gbm_pr[,4,]
par(mfrow=c(2,2))
for(i in 17:20){
r4 <- rasterFromXYZ(d_pr[,c(1,2,i)])
r4@crs@projargs <- MCD12Q1_2001@crs@projargs
r4 <- projectRaster(r4,crs= area.shp@proj4string,method="ngb" )
plot(r4)
oldwd<-getwd()
setwd("../00_Results")
writeRaster(r4,paste("04_20200831_MD_pr_", colnames(gbm_pr)[i-16], ".tif",sep=""),overwrite=TRUE)
setwd(oldwd)
}
d_pr[which(d_pr[,17]>0.5),17] <- d_pr[which(d_pr[,17]>0.5),17]+2
d_pr[which(d_pr[,18]>0.5),18] <- d_pr[which(d_pr[,18]>0.5),18]+7
d_pr[which(d_pr[,19]>0.5),19] <- d_pr[which(d_pr[,19]>0.5),19]+9
d_pr[which(d_pr[,20]>0.5),20] <- d_pr[which(d_pr[,20]>0.5),20]+10
d_pr[,21]<- d_pr[,17]+d_pr[,18]+d_pr[,19]+d_pr[,20]
r4 <- rasterFromXYZ(d_pr[,c(1,2,21)])
r4@crs@projargs <- MCD12Q1_2001@crs@projargs
r4 <- projectRaster(r4,crs= area.shp@proj4string,method="ngb" )
par(mfrow=c(1,1))
plot(r4)
hist(d_pr[,21])
############################################################################################################
###
oldwd<-getwd()
setwd(path_4ndlevel)
writeRaster(r3,"04_20201117_PI_pr.tif",overwrite=TRUE)
writeRaster(r_pr_MD,"04_20201117_PI_pr_all.tif",overwrite=TRUE)
writeRaster(r_dif,"04_20201117_PI_pr_dif.tif",overwrite=TRUE)
setwd(oldwd)
#################################################################################################
#################################################################################################
###elevation gradient PI
par(mfrow=c(1,1))
d_pr_l <- d_pr[,c(15,16)]
d_pr_l$srtm[d_pr_l$srtm>2000]<- 3
d_pr_l$srtm[d_pr_l$srtm>1000]<- 2
d_pr_l$srtm[d_pr_l$srtm>5] <- 1
require(ggplot2)
require(dplyr)
#
df <- na.omit(d_pr_l)
df_d <- df %>%
count(srtm, tpi)%>%
group_by(srtm) %>%
mutate(freq = n / sum(n))
df_d$tpi<- as.factor(df_d$tpi)
eg_1 <- ggplot(df_d, aes(x="", y=freq,fill=tpi)) +
geom_bar(stat="identity", width=1, color="white") +
coord_polar("y", start=0)+
theme_void() + facet_wrap(~srtm,ncol = 4)
plot(eg_1)
####elevation gradient MD
d_pr_l <- as.data.frame(stack(data_all$lc, data_all$srtm))
d_pr_l$srtm[d_pr_l$srtm>2000]<- 3
d_pr_l$srtm[d_pr_l$srtm>1000]<- 2
d_pr_l$srtm[d_pr_l$srtm>5] <- 1
require(ggplot2)
require(dplyr)
#
df <- na.omit(d_pr_l)
df_d <- df %>%
count(srtm, lc)%>%
group_by(srtm) %>%
mutate(freq = n / sum(n))
df_d$lc<- as.factor(df_d$lc)
eg_1md <- ggplot(df_d, aes(x="", y=freq,fill=lc)) +
geom_bar(stat="identity", width=1, color="white") +
coord_polar("y", start=0)+
theme_void() + facet_wrap(~srtm,ncol = 4)
plot(eg_1md)
########################################################
####MD new with unmasked dataset
setwd(path_4ndlevel)
r_md_lc <- raster("01_20201116_LC_MD_all.tif")
plot(r_md_lc)
d_pr_l <- as.data.frame(stack(r_md_lc, data_all_pr$srtm))
colnames(d_pr_l)<- c("lc","srtm")
d_pr_l$srtm[d_pr_l$srtm>2000]<- 3
d_pr_l$srtm[d_pr_l$srtm>1000]<- 2
d_pr_l$srtm[d_pr_l$srtm>5] <- 1
df <- na.omit(d_pr_l)
df_d <- df %>%
count(srtm, lc)%>%
group_by(srtm) %>%
mutate(freq = n / sum(n))
df_d$lc<- as.factor(df_d$lc)
eg_1md <- ggplot(df_d, aes(x="", y=freq,fill=lc)) +
geom_bar(stat="identity", width=1, color="white") +
coord_polar("y", start=0)+
theme_void() + facet_wrap(~srtm,ncol = 4)
plot(eg_1md)
#################################################################################################
#################################################################################################
# Time Slice 2a: AHP
# NO CAB
#
df_s <- data.frame(matrix(ncol = 15, nrow = 101))
colnames(df_s) <- c("pre", "a_2","a_7","a_9","a_10", "b_2","b_7","b_9","b_10","c_2","c_7","c_9","c_10")
d_pr_n <- as.data.frame(data_all_pr,na.rm=F,xy=T)
for (i in 1:101){
d_pr <- d_pr_n
j<- i-1
f_pr <- 0.5+ j/100
d_pr[c(3:14)]<- d_pr[c(3:14)]*f_pr
itree <- gbm.perf(gbm_LC, plot.it = F, oobag.curve = FALSE, overlay = TRUE)[1]
gbm_pr <-predict.gbm(gbm_LC , d_pr[c(3:15)] ,type = "response", n.trees=itree) #prediction
#unique(apply(gbm_pr, 1, function(row) which.max(row)))
#unique(colnames(gbm_pr)[apply(gbm_pr, 1, function(row) which.max(row))])
d_pr[,16]<- apply(gbm_pr, 1, function(row) which.max(row))
d_pr[which(d_pr[,16]==4),16]<-10
d_pr[which(d_pr[,16]==3),16]<-9
d_pr[which(d_pr[,16]==2),16]<-7
d_pr[which(d_pr[,16]==1),16]<-2
d_pr[which(is.na(d_pr[,4])==T),16]<-NA
r3<- rasterFromXYZ(d_pr[,c(1,2,16)])
r3@crs@projargs<- MCD12Q1_2001@crs@projargs
r3<-projectRaster(r3,crs= area.shp@proj4string,method="ngb" )
r3_a <- mask(r3,a.shp)
#plot(r3_a)
r3_b <- mask(r3,b.shp)
#plot(r3_b)
r3_c <- mask(r3,c.shp)
#plot(r3_c)
df_s$pre[i]<-f_pr
a_all <-freq(r3_a,value=2)+freq(r3_a,value=7)+freq(r3_a,value=8)+freq(r3_a,value=9)+freq(r3_a,value=10)
b_all <-freq(r3_b,value=2)+freq(r3_b,value=7)+freq(r3_b,value=8)+freq(r3_b,value=9)+freq(r3_b,value=10)
c_all <-freq(r3_c,value=2)+freq(r3_c,value=7)+freq(r3_c,value=8)+freq(r3_c,value=9)+freq(r3_c,value=10)
df_s$a_2[i]<- freq(r3_a,value=2)/a_all
df_s$b_2[i]<- freq(r3_b,value=2)/b_all
df_s$c_2[i]<- freq(r3_c,value=2)/c_all
df_s$a_7[i]<- freq(r3_a,value=7)/a_all
df_s$b_7[i]<- freq(r3_b,value=7)/b_all
df_s$c_7[i]<- freq(r3_c,value=7)/c_all
df_s$a_9[i]<- freq(r3_a,value=9)/a_all
df_s$b_9[i]<- freq(r3_b,value=9)/b_all
df_s$c_9[i]<- freq(r3_c,value=9)/c_all
df_s$a_10[i]<- freq(r3_a,value=10)/a_all
df_s$b_10[i]<- freq(r3_b,value=10)/b_all
df_s$c_10[i]<- freq(r3_c,value=10)/c_all
print(i)
}
olwd<- getwd()
setwd(path_4ndlevel)
save(df_s,file= "20201120_AHP01_noCAB_LC.RData")
#setwd(olwd)
require(ggplot2)
require(reshape2)
d <- melt(df_s, id.vars="pre")
ggplot(d[1:404,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201104_AHP01_noCAB_LC_Abaya",".png"))
ggplot(d[405:808,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201104_AHP01_noCAB_LC_Bahir",".png"))
ggplot(d[809:1212,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201104_AHP01_noCAB_LC_Chamo",".png"))
#################################################################################################
#################################################################################################
# Time Slice 2b: AHP
# CAB Szenario
# Temperature:
#
df_s <- data.frame(matrix(ncol = 13, nrow = 101))
colnames(df_s) <- c("pre", "a_2","a_7","a_9","a_10", "b_2","b_7","b_9","b_10","c_2","c_7","c_9","c_10")
d_pr_n <- as.data.frame(data_all_pr,na.rm=F,xy=T)
d_pr_n$gpm_a <- d_pr_n[c(3)]+d_pr_n[c(4)]+d_pr_n[c(5)]+d_pr_n[c(6)]+d_pr_n[c(7)]+d_pr_n[c(8)]+d_pr_n[c(9)]+d_pr_n[c(10)]+d_pr_n[c(11)]+d_pr_n[c(12)]+d_pr_n[c(13)]+d_pr_n[c(14)]
d_pr_n2<- d_pr_n
d_pr_n2<- d_pr_n
for (i in 1:101){
d_pr_n <- d_pr_n2
j<- i-1
f_pr <- 0.5+ j/100
fac <- 1+ ( d_pr_n[,17]*(f_pr-1)/ (d_pr_n[,8]+d_pr_n[,9]+d_pr_n[,10]+d_pr_n[,11]) )
d_pr_n[,c(8)] <- d_pr_n[,c(8)]* fac[,1]
d_pr_n[,c(9)] <- d_pr_n[,c(9)]* fac[,1]
d_pr_n[,c(10)] <- d_pr_n[,c(10)]* fac[,1]
d_pr_n[,c(11)] <- d_pr_n[,c(11)]* fac[,1]
d_pr <- d_pr_n
itree <- gbm.perf(gbm_LC, plot.it = F, oobag.curve = FALSE, overlay = TRUE)[1]
gbm_pr <-predict.gbm(gbm_LC , d_pr[c(3:15)] ,type = "response", n.trees=itree) #prediction
#unique(apply(gbm_pr, 1, function(row) which.max(row)))
#unique(colnames(gbm_pr)[apply(gbm_pr, 1, function(row) which.max(row))])
d_pr[,16]<- apply(gbm_pr, 1, function(row) which.max(row))
d_pr[which(d_pr[,16]==4),16]<-10
d_pr[which(d_pr[,16]==3),16]<-9
d_pr[which(d_pr[,16]==2),16]<-7
d_pr[which(d_pr[,16]==1),16]<-2
d_pr[which(is.na(d_pr[,4])==T),16]<-NA
r3<- rasterFromXYZ(d_pr[,c(1,2,16)])
r3@crs@projargs<- MCD12Q1_2001@crs@projargs
r3<-projectRaster(r3,crs= area.shp@proj4string,method="ngb" )
plot(r3)
r3_a <- mask(r3,a.shp)
#plot(r3_a)
r3_b <- mask(r3,b.shp)
#plot(r3_b)
r3_c <- mask(r3,c.shp)
#plot(r3_c)
df_s$pre[i]<-f_pr
a_all <-freq(r3_a,value=2)+freq(r3_a,value=7)+freq(r3_a,value=8)+freq(r3_a,value=9)+freq(r3_a,value=10)
b_all <-freq(r3_b,value=2)+freq(r3_b,value=7)+freq(r3_b,value=8)+freq(r3_b,value=9)+freq(r3_b,value=10)
c_all <-freq(r3_c,value=2)+freq(r3_c,value=7)+freq(r3_c,value=8)+freq(r3_c,value=9)+freq(r3_c,value=10)
df_s$a_2[i]<- freq(r3_a,value=2)/a_all
df_s$b_2[i]<- freq(r3_b,value=2)/b_all
df_s$c_2[i]<- freq(r3_c,value=2)/c_all
df_s$a_7[i]<- freq(r3_a,value=7)/a_all
df_s$b_7[i]<- freq(r3_b,value=7)/b_all
df_s$c_7[i]<- freq(r3_c,value=7)/c_all
df_s$a_9[i]<- freq(r3_a,value=9)/a_all
df_s$b_9[i]<- freq(r3_b,value=9)/b_all
df_s$c_9[i]<- freq(r3_c,value=9)/c_all
df_s$a_10[i]<- freq(r3_a,value=10)/a_all
df_s$b_10[i]<- freq(r3_b,value=10)/b_all
df_s$c_10[i]<- freq(r3_c,value=10)/c_all
print(i)
}
olwd<- getwd()
setwd(path_4ndlevel)
save(df_s,file= "20201120_AHP01_CAB_LC.RData")
#setwd(olwd)
require(ggplot2)
require(reshape2)
d <- melt(df_s, id.vars="pre")
ggplot(d[1:404,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201120_AHP01_CAB_LC_Abaya",".png"))
ggplot(d[405:808,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201120_AHP01_CAB_LC_Bahir",".png"))
ggplot(d[809:1212,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201120_AHP01_CAB_LC_Chamo",".png"))
#################################################################################################
#################################################################################################
# Time Slice 3: LGM
# Temperature change based on Loomis et al. 2012, 2017
d_pr <- as.data.frame(data_all_pr,na.rm=F,xy=T)
# Temperature lowering for LGM lower liit based on:
# Loomis 2017 laps rate differenfce of 0.9 over whole Eastern Africa (6.7 LGM, 5.9 PI)
# Loomis 2012 general cooling of 3 to 4°C
d_pr_tl <- d_pr
d_pr_tl$srtm <- 1000*(0.9*(d_pr_tl$srtm/1000) +3)/5.8 + d_pr_tl$srtm
d_pr_tu <- d_pr
d_pr_tu$srtm <- 1000*(0.9*(d_pr_tu$srtm/1000) +4)/5.8 + d_pr_tu$srtm
###kernel densitiy plot
e_md_d <- density(d_pr$srtm ,na.rm=T)
e_tl_d <- density(d_pr_tl$srtm ,na.rm=T)
e_tu_d <- density(d_pr_tu$srtm ,na.rm=T)
par(mfrow=c(1,1))
plot(e_md_d)
lines(e_tl_d,col="blue")
lines(e_tu_d,col="red")
##########
################################################
df_s <- data.frame(matrix(ncol = 16, nrow = 101))
colnames(df_s) <- c("pre", "a_2","a_7","a_9","a_10","a_16", "b_2","b_7","b_9","b_10","b_16","c_2","c_7","c_9","c_10","c_16")
d_pr_n <- d_pr_tl
for (i in 1:101){
d_pr <- d_pr_n
j<- i-1
f_pr <- 0.5+ j/100
d_pr[c(3:14)]<- d_pr[c(3:14)]*f_pr
itree <- gbm.perf(gbm_LC, plot.it = F, oobag.curve = FALSE, overlay = TRUE)[1]
gbm_pr <-predict.gbm(gbm_LC , d_pr[c(3:15)] ,type = "response", n.trees=itree) #prediction
#unique(apply(gbm_pr, 1, function(row) which.max(row)))
#unique(colnames(gbm_pr)[apply(gbm_pr, 1, function(row) which.max(row))])
d_pr[,16]<- apply(gbm_pr, 1, function(row) which.max(row))
#elevation threshold for trees
d_pr[which(d_pr[,15]>3000),16]<-16
d_pr[which(d_pr[,16]==4),16]<-10
d_pr[which(d_pr[,16]==3),16]<-9
d_pr[which(d_pr[,16]==2),16]<-7
d_pr[which(d_pr[,16]==1),16]<-2
d_pr[which(is.na(d_pr[,4])==T),16]<-NA
r3<- rasterFromXYZ(d_pr[,c(1,2,16)])
r3@crs@projargs<- MCD12Q1_2001@crs@projargs
r3<-projectRaster(r3,crs= area.shp@proj4string,method="ngb" )
r3_a <- mask(r3,a.shp)
#plot(r3_a)
r3_b <- mask(r3,b.shp)
#plot(r3_b)
r3_c <- mask(r3,c.shp)
#plot(r3_c)
df_s$pre[i]<-f_pr
a_all <-freq(r3_a,value=2)+freq(r3_a,value=7)+freq(r3_a,value=8)+freq(r3_a,value=9)+freq(r3_a,value=10)+freq(r3_a,value=16)
b_all <-freq(r3_b,value=2)+freq(r3_b,value=7)+freq(r3_b,value=8)+freq(r3_b,value=9)+freq(r3_b,value=10)+freq(r3_b,value=16)
c_all <-freq(r3_c,value=2)+freq(r3_c,value=7)+freq(r3_c,value=8)+freq(r3_c,value=9)+freq(r3_c,value=10)+freq(r3_c,value=16)
df_s$a_2[i]<- freq(r3_a,value=2)/a_all
df_s$b_2[i]<- freq(r3_b,value=2)/b_all
df_s$c_2[i]<- freq(r3_c,value=2)/c_all
df_s$a_7[i]<- freq(r3_a,value=7)/a_all
df_s$b_7[i]<- freq(r3_b,value=7)/b_all
df_s$c_7[i]<- freq(r3_c,value=7)/c_all
df_s$a_9[i]<- freq(r3_a,value=9)/a_all
df_s$b_9[i]<- freq(r3_b,value=9)/b_all
df_s$c_9[i]<- freq(r3_c,value=9)/c_all
df_s$a_10[i]<- freq(r3_a,value=10)/a_all
df_s$b_10[i]<- freq(r3_b,value=10)/b_all
df_s$c_10[i]<- freq(r3_c,value=10)/c_all
df_s$a_16[i]<- freq(r3_a,value=16)/a_all
df_s$b_16[i]<- freq(r3_b,value=16)/b_all
df_s$c_16[i]<- freq(r3_c,value=16)/c_all
#print(paste(i,c_all,df_s$c_10[i],freq(r3_c,value=10)))
print(i)
}
olwd<- getwd()
setwd(path_4ndlevel)
save(df_s,file= "20201104_LGM01_TL_LC.RData")
#setwd(olwd)
require(ggplot2)
require(reshape2)
d <- melt(df_s, id.vars="pre")
ggplot(d[1:505,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201104_LGM01_TL_LC_Abaya",".png"))
ggplot(d[506:1010,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201104_LGM01_TL_LC_Bahir",".png"))
ggplot(d[1011:1515,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201104_LGM01_TL_LC_Chamo",".png"))
#################################################################################################
################################################
################################################
#UPPER TEMPERATURE BOUNDARY
df_s <- data.frame(matrix(ncol = 16, nrow = 101))
colnames(df_s) <- c("pre", "a_2","a_7","a_9","a_10","a_16", "b_2","b_7","b_9","b_10","b_16","c_2","c_7","c_9","c_10","c_16")
d_pr_n <- d_pr_tu
for (i in 1:101){
d_pr <- d_pr_n
j<- i-1
f_pr <- 0.5+ j/100
d_pr[c(3:14)]<- d_pr[c(3:14)]*f_pr
itree <- gbm.perf(gbm_LC, plot.it = F, oobag.curve = FALSE, overlay = TRUE)[1]
gbm_pr <-predict.gbm(gbm_LC , d_pr[c(3:15)] ,type = "response", n.trees=itree) #prediction
#unique(apply(gbm_pr, 1, function(row) which.max(row)))
#unique(colnames(gbm_pr)[apply(gbm_pr, 1, function(row) which.max(row))])
d_pr[,16]<- apply(gbm_pr, 1, function(row) which.max(row))
d_pr[which(d_pr[,15]>3000),16]<-16
d_pr[which(d_pr[,16]==4),16]<-10
d_pr[which(d_pr[,16]==3),16]<-9
d_pr[which(d_pr[,16]==2),16]<-7
d_pr[which(d_pr[,16]==1),16]<-2
d_pr[which(is.na(d_pr[,4])==T),16]<-NA
r3<- rasterFromXYZ(d_pr[,c(1,2,16)])
r3@crs@projargs<- MCD12Q1_2001@crs@projargs
r3<-projectRaster(r3,crs= area.shp@proj4string,method="ngb" )
r3_a <- mask(r3,a.shp)
#plot(r3_a)
r3_b <- mask(r3,b.shp)
#plot(r3_b)
r3_c <- mask(r3,c.shp)
#plot(r3_c)
df_s$pre[i]<-f_pr
a_all <-freq(r3_a,value=2)+freq(r3_a,value=7)+freq(r3_a,value=8)+freq(r3_a,value=9)+freq(r3_a,value=10)+freq(r3_a,value=16)
b_all <-freq(r3_b,value=2)+freq(r3_b,value=7)+freq(r3_b,value=8)+freq(r3_b,value=9)+freq(r3_b,value=10)+freq(r3_b,value=16)
c_all <-freq(r3_c,value=2)+freq(r3_c,value=7)+freq(r3_c,value=8)+freq(r3_c,value=9)+freq(r3_c,value=10)+freq(r3_c,value=16)
df_s$a_2[i]<- freq(r3_a,value=2)/a_all
df_s$b_2[i]<- freq(r3_b,value=2)/b_all
df_s$c_2[i]<- freq(r3_c,value=2)/c_all
df_s$a_7[i]<- freq(r3_a,value=7)/a_all
df_s$b_7[i]<- freq(r3_b,value=7)/b_all
df_s$c_7[i]<- freq(r3_c,value=7)/c_all
df_s$a_9[i]<- freq(r3_a,value=9)/a_all
df_s$b_9[i]<- freq(r3_b,value=9)/b_all
df_s$c_9[i]<- freq(r3_c,value=9)/c_all
df_s$a_10[i]<- freq(r3_a,value=10)/a_all
df_s$b_10[i]<- freq(r3_b,value=10)/b_all
df_s$c_10[i]<- freq(r3_c,value=10)/c_all
df_s$a_16[i]<- freq(r3_a,value=16)/a_all
df_s$b_16[i]<- freq(r3_b,value=16)/b_all
df_s$c_16[i]<- freq(r3_c,value=16)/c_all
print(i)
}
olwd<- getwd()
setwd(path_4ndlevel)
save(df_s,file= "20201104_LGM01_TU_LC.RData")
#setwd(olwd)
require(ggplot2)
require(reshape2)
d <- melt(df_s, id.vars="pre")
ggplot(d[1:505,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201104_LGM01_TL_LC_Abaya",".png"))
ggplot(d[506:1010,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201104_LGM01_TL_LC_Bahir",".png"))
ggplot(d[1011:1515,], aes(pre,value, col=variable)) +
geom_line() +
geom_point()
ggsave(paste("20201104_LGM01_TL_LC_Chamo",".png"))
#################################################################################################